Bio -based and A ppl ied Economics BAE Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6e172 | DOI: 10.36253/bae-13516 Copyright: © 2022 S. Russo, L. Phali, M. Prosperi. Open access, article published by Firenze University Press under CC-BY-4.0 License. Firenze University Press | www.fupress.com/bae Citation: S. Russo, L. Phali, M. Pros- peri (2022). Dealing with endogeneity in risk analysis within the stochastic fron- tier approach in agricultural econom- ics: a scoping review. Bio-based and Applied Economics 11(4): 339-350. doi: 10.36253/bae-13516 Received: August 18, 2022 Accepted: February 12, 2023 Published: May 3, 2023 Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Editor: Simone Cerroni. ORCID SR: 0000-0003-2166-1994 LP: 0000-0002-7531-7577 MP: 0000-0003-0836-766X Dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: A scoping review Simone Russo1,2,*, Lerato Phali3, Maurizio Prosperi1 1 Department of Agriculture, Food, Natural Resources and Engineering (DAFNE), Uni- versity of Foggia, 71122 Foggia, Italy 2 Department of Soil, Plant and Food Science (DISSPA), University of Bari Aldo Moro, 70126 Bari, Italy 3 Discipline of Agricultural Economics, School of Agriculture, Environment, and Earth Sciences, University of KwaZulu-Natal, 3201 Pietermaritzburg, South Africa *Corresponding author. E-mail: simone.russo@unifg.it Abstract. Literature on farm productivity and efficiency was reviewed using a scop- ing review methodology, focusing on studies that have included risk and risk manage- ment tools within the stochastic frontier analysis in agricultural economics. This study contributes to investigating the methods used to account for endogeneity by using a risk-accommodating stochastic frontier approach when analysing farmers’ perfor- mance. Despite the increasing methodologies proposed in the literature, only a few studies have treated endogeneity in farm risk-performance evaluations. According to our findings, it can be concluded that there is a literature gap regarding the adoption of a comprehensive approach capable of dealing with endogeneity when assessing farm performances. Endogeneity and risk issues need to be concurrently addressed to make strides in achieving economic and environmental sustainability. Neglecting endogene- ity in these analyses may lead to biased estimates and thus inappropriate policy recom- mendations failing to boost the productivity and technical efficiency of farmers. Keywords: stochastic frontier analysis, agricultural economics, risk, endogeneity. JEL codes: C18, Q12, D81. HIGHLIGHTS · Scoping review of studies that account for risk in Stochastic Frontier Analysis · We synthesise methodologies dealing with endogeneity in risk-accom- modating SFA · The lack of risk and endogeneity accommodation in analysis yields biased results · Literature gap in SFA dealing with risk and endogeneity in agricultural economics · Risk and endogeneity inclusion may help develop effective agricultural policies http://creativecommons.org/licenses/by/4.0/legalcode 340 Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13516 Simone Russo, Lerato Phali, Maurizio Prosperi 1. INTRODUCTION Agriculture is one of the sectors where risk and uncertainty play a decisive role in production decision- making (Ahsan et al., 1982; Moschini and Hennessy, 2001). It is well-known that since farmers make input use decisions before knowing the true state of nature, they choose the input allocation according to their sub- jective propensity to take a certain level of risk (Ramas- wami, 1992; Cerroni, 2020). While exerting their typical actions, farmers do not aim only to maximize profits but also try to minimize the risk impact on income loss (Just and Pope, 1978, 1979; Antle, 1983; Finger, 2013). The conceptualization of agricultural risk is usually attributed to the length and complexity of the biological production cycle, which exposes farmers to risks such as pests, erratic climatic changes, price fluctuations, and even policy changes (Duong et al., 2019; Komarek et al., 2020). According to Komarek et al. (2020), agricultural risks are classified into production, market, institution- al, personal, and financial risks. Production risks stem from the natural growth processes and are also related to weather and climatic conditions. These are factors beyond the farmer’s control given the stochastic nature of agriculture. Within market risks, there are those asso- ciated with price volatility for both input and output prices, as well as those related to asymmetric informa- tion, international trade, and liberalization processes. Institutional risks are generally associated with abrupt policy and regulation changes, as well as changes in the behaviour of informal institutions that affect transac- tions. Personal risks are farmer-specific and related to health, personal relationships, and well-being, whereas financial risks stem from farm finance factors, credit access, and interest rate payments. Researchers and policymakers have various reasons to be interested in how risk affects farmers’ decision- making and their economic performances. Farm per- formance evaluations are fundamental for policymak- ers and producers to enhance both the economic and environmental sustainability of farming (Farrell, 1957). Moreover, understanding the interrelations between farmers’ behaviour in a risky environment and farm performance is essential to enhance the effectiveness of policy measures (Khanal et al., 2021). For example, while risk-neutral farmers aim to maximize profits by con- sidering only the mean effect of production, risk-averse producers account for both mean and higher moments of their production functions (Antle, 1983). Therefore, risk-averse production decisions differ from risk-neutral ones due to the marginal risk premium, which is the absolute value of the risk effect of input use on output (MacMinn and Holtmann, 1983; Ramaswami, 1992). The marginal risk premium may have a positive or negative sign and indicates whether risk-averse producers use more or less input than risk-neutral ones. Thus, risk- averse farmers use less risk-increasing (and more risk- decreasing) inputs to cope with risk compared to a risk- neutral farmer, who employ the profit-maximizing input vector (Nelson and Loehman, 1987; Ramaswami, 1993). As such, the risk aversion due to the uncertainty of out- comes may result in non-profit-maximizing input use, potentially resulting in lower technical efficiency and productivity (Roll, 2019). By ignoring the risk impact on production, Battese et al. (1997) conclude that estimates of technical efficiency would be skewed. Consequently, neglecting the interrelation between farm performance and risk-averse deviations from efficient behaviour would lead to incorrect policy implications and recom- mendations (Just, 2003). In literature, most productivity and efficiency analy- ses are conducted through the development of produc- tion frontier models. The two commonly used methods in productivity and efficiency analysis are Data Envel- opment Analysis (DEA) and Stochastic Frontier Analy- sis (SFA). Although these two methods have their mer- its, there has been constant debate amongst scholars on which method is better for modelling production tech- nology. A relevant distinction between the two meth- ods is that DEA is deterministic while SFA is stochastic. While in the stochastic frontier model, the individual observations may be affected by random noise, in the deterministic approach the potential noise is neglected, and each variation in data is assumed to influence the firm’s efficiency and the shape of the frontier (Bogetoft and Otto, 2010). Therefore, one of the principal limita- tions of the DEA methodology is that it is not possible to consider the effect of risk on efficiency, which could be confused and interpreted as technical inefficiency. Accordingly, it seems that SFA might be more suitable to model productivity and efficiency in the presence of risk as it is suited to disentangle the inefficiency from the standard statistical error related, for example, to weather events, market volatility, and regulation changes. Stochastic production functions appeared to be a reasonable solution to account for risk in agricultural economics (Chavas et al., 2010). Just and Pope (1978) introduced a production function specification that can distinguish between the marginal effect of inputs on both the mean and variance of output. Then, Antle (1983) expanded this technique to account for the impact of production inputs on higher moments of produc- tion function (i.e., skewness). Later, Battese et al. (1997) extended the model proposed by Just and Pope (1978) to 341Dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: A scoping review Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13516 the stochastic frontier production approach developed originally by Aigner et al. (1977) and Meeusen and van Den Broeck (1977). According to the authors, the sto- chastic frontier production function is more consist- ent with economic theory and reality with regard to the so-called average production function. More recently, Kumbhakar (2002) generalized the approach proposed by the previous authors by estimating a model which includes production risk, technical efficiency, and pro- ducers’ attitude toward risk. Given the inevitable conse- quence of risk effects on producers’ technical efficiency, risk sources have to be incorporated into the stochastic production frontier to realistically account for and pre- dict producers’ technical efficiency (Battese et al., 1997). The primary motivation paving the way for the pre- sent study is that, despite its importance, most of the sci- entific literature on production at the farm level does not account for risk (Just, 2003). Moreover, it is worth men- tioning that one of the central assumptions of the SFA model is that the input variables should be independent of both the error terms (technical efficiency and random error) in the model. It is the general definition of endoge- neity, which refers to the correlation between explanatory variables and the error terms. However, it is essential to note that endogeneity may occur for several reasons. For instance, farmers may adjust their inputs according to observed shocks, which usually are included in the ran- dom error term. Therefore, the correlation between the production inputs and the statistical error term due to the observed shocks would result in endogeneity (Latruffe et al., 2017). In addition, a possible endogeneity issue may arise when farmers, being aware they are inefficient, tend to optimize their input use (Shee and Stefanou, 2014). Finally, other endogeneity sources may occur when farm- ers cope with risk by adopting risk management tools or risk-mitigation practices (Vigani and Kathage, 2019). The model misspecifications due to the presence of endogene- ity leads to erroneous inferences about the assessment of input elasticities and economies of scale, as well as inac- curate and inconsistent estimates of farm technical effi- ciency (Karakaplan and Kutlu, 2017). It is worth noting that endogeneity in SFA is often ignored, which could overstate or even undermine the effects of factors on pro- duction and, thus, results in key strategies or recommen- dations that boost farm performance being left out (Russo et al., 2022). The impact of the inaccuracy and inconsist- ency of results may be highly relevant when risk analysis is performed (Battese et al., 1997). Given the motivations listed above, this paper pre- sents a review of literature that covers agricultural pro- ductivity and efficiency analysis. The particular focus is on studies that have adopted the SFA method with the inclusion of risk. The scoping review method has been adopted for the capability to identify and map out evi- dence and clarify key concepts in agricultural stochastic frontier literature with the inclusion and consideration of risk. Specifically, this article aims to provide insights into how risks and risk mitigation strategies have been factored into SFA. The main contribution of the present research relates to analysing the different methods used to deal with endogeneity while aiming to investigate the risk effects on agricultural production within the SFA approach. It is important to highlight these two issues as when they are not considered in modelling, the biased estimates found after analysis may be used to inform pol- icy. This then leaves room for the ineffectiveness of policy interventions as they would be developed without consid- erations of the complexity of the agricultural production modelling. The exclusion of the effects of risk and risk- mitigation practices on studies that aim to investigate farmers’ decision-making would provide inconsistent and irrelevant production guidelines. This review depicts the gaps that researchers need to fill and methods that can be adopted to ensure valid and consistent results that can be used for policy development aimed at ensuring agricul- tural productivity and efficiency. In the following section, the scoping review meth- odology, eligibility criteria, and selection process of articles are presented. The results section, presents and illustrates insights of the literature analysed. Finally, we discuss the results and provide some conclusions, high- lighting the limitations of the study and future research areas. 2. METHODOLOGY The scoping review method was adopted to conduct the study following the guidelines provided by Tricco et al. (2018) in the Preferred Reporting Items for System- atic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR). A scoping review is a form of knowledge synthesis that systematically searches, selects, and synthesizes existing knowledge to map the key con- cepts, types of evidence, and gaps in research related to a given area or field (Colquhoun et al., 2014). The advantage of the scoping review method is that it helps to summarise the existing knowledge used to develop policy or practical recommendations, as well as to provide practical pathways for future research (Ark- sey and O’Malley, 2005; Piñeiro et al., 2020). Compared to the traditional literature review, the scoping method is more rigorous, transparent, and replicable, includ- ing steps to reduce the subjectivity bias resulting from 342 Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13516 Simone Russo, Lerato Phali, Maurizio Prosperi the author’s prior knowledge and experience (Munn et al., 2018). Th e scoping method was thus suitable for this study in exploring how risk has been incorporated into SFA agricultural productivity analysis and how the endogeneity issues have been handled in literature. Aft er stating the research question, the subsequent steps of this approach are the identifi cation of relevant studies, study selection, data extraction and charting, and reporting of the results. In order to get a representa- tive sample of the literature, an initial set of articles was identifi ed. Th e Scopus bibliographic database was used to research the relevant studies, including articles writ- ten in English and published in peer-reviewed journals earlier than 30 June 2021. We opted to focus on articles indexed in Scopus since it is one of the two most used bibliographic databases, and it includes most (about 99%) of the journals indexed in Web of Science (Singh et al., 2021), particularly in the social sciences topics (Mon- geon and Paul-Hus, 2016). Th e search was characterized by a combination of three keyword groups included in the paper abstract, title, or keywords. Th e following structured query devel- oped using Boolean operators and wildcards was used for the research: [“stochastic frontier” OR “stochastic production” OR “technical efficiency”] AND [“risk” OR “uncertain*”] AND [“ farm*” OR “agricultur*” OR “ food” OR “crop” OR “livestock”]. While the fi rst set of keywords included the terms related to the SFA, the second related to the risk, and the third to the agricultural context. Th e fi nal set of articles was exported to the Mende- ley referencing tool for assessment. For consistency pur- poses, all the authors screened the initial set of articles. We screened the same publications and discussed our chosen studies for review. To be included in the sam- ple, the eligibility criteria used the following: (i) research topic on agricultural production (ii) inclusion of risk and risk management in farm productivity and effi ciency analysis; (iii) the adoption of SFA to model technical effi - ciency and agricultural productivity. Th e selection process followed several steps which gradually reduced the number of studies accord- ing to the eligibility criteria, as shown in Figure 1. Th e search output initially included 162 peer-reviewed arti- cles. In the first screening step, titles and abstracts were examined, where papers focusing on issues relat- ed to risk analysis in the agricultural sector using the SFA approach were retained. Th en, the full text of the remaining 94 studies were analysed, excluding 35 arti- cles according to the rejection criteria. Finally, in the last screening step, 15 papers were excluded because they utilized a stochastic production function instead of the frontier. However, these papers were examined to consider their insights as regarding endogeneity issues. At the end of the screening process, 44 articles were retained. Of the 162 articles, 11 were disqualifi ed because they were not focused on agricultural econom- ics, and 40 for the lack of risk considerations. Finally, 67 papers were excluded for their use of methods other than SFA, for instance, stochastic production function (e.g., Griffi ths, 1986; Eggert and Tveteras, 2004; Di Falco et al., 2007), or non-parametric approaches such as DEA (e.g., Serra and Oude Lansink, 2014; Chambers et al., 2015; Oude Lansink et al., 2015), or fuzzy mathematical models (Guo et al., 2019; Wang et al., 2020). 3. RESULTS Th e results of the analysis showed that there are sev- eral approaches adopted in estimating stochastic produc- tion frontiers with risk considerations. Figure 2 below presents a histogram of the distribution of the common approaches employed in the retained articles. Th e most commonly used methods were those of Just and Pope (1978), Battese and Coelli (1995), Battese et al. (1997), and Kumbhakar (2002). In addition, 15 articles adopted other methods that studied risk in their analysis1. 1 Among them, there are the approaches proposed by Aigner et al. (1977), Antle (1983), Blarel et al. (1992), Caudill et al. (1995), Koop Figure 1. PRISMA-ScR Flow diagram. Source: Own elaboration based on Tricco et al. (2018). 343Dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: A scoping review Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13516 However, not all approaches allow the inclusion of risk within the stochastic production framework, such as Battese and Coelli (1995). Among the techniques that include risk within the production frontier, the most com- mon methods used were the ones proposed by Just and Pope (1978), Battese et al. (1997), and Kumbhakar (2002)2. Six diff erent thematic groups were identifi ed with- in the literature analysed, as shown in Figure 3. In this analysis, it was found that two articles incorporated risk in the SFA approach by focussing on the relation- ship between effi ciency, risk aspects, and investment, such as the timing of investment decisions (Lambarraa et al., 2016) or the adoption of new technology (Ghosh et al., 1994). In addition, nineteen articles investigated the eff ect of farmer risk attitudes, risk mitigation prac- tices, and risk management tools on farm performance. Furthermore, six papers examined the impact of agricul- tural policies on production risk and technical effi ciency. Additionally, two studies investigated the diff erences in production risk and technical effi ciency among distinct production technologies, such as intensive or exten- sive (Nguyen et al., 2020) and organic or conventional production (Tiedemann and Latacz-Lohmann, 2013). In addition, four papers investigated the climate eff ect or market volatility on farm performance and/or risk. Finally, eleven articles focused on the assessment of the impact of input on production risk and technical effi - ciency. In Figure 3, the articles that dealt with endogene- et al. (1997), Greene (2003, 2005), Tsionas (2006), Yesuf et al. (2008), O’Donnell et al. (2010), Power et al. (2011), Bravo-Ureta et al. (2012), Karagiannis and Tzouvelekas (2012), Kumbhakar et al. (2014), and O’Donnell (2016). 2 While all the studies consider risk, not all explicitly include it within the estimated production frontier. Some articles assessed it outside the model as a prerequisite or a follow-up step aft er the estimations. ity and those that did not are diff erentiated with colour schemes. Th e colour red represents the articles that dealt with endogeneity. As a result, only nine studies out of 44 (about 20%) considered the issue of endogeneity. Among them, fi ve articles focused on the risk-management the- matic area, two on agricultural policy, one on produc- tion technology, and one on input eff ects. Th e diff erent methods implemented to account for endogeneity are presented in Table 1. Among the articles in the risk management thematic area, Chang and Wen (2011) investigated the off -farm work eff ect on techni- cal effi ciency and production risk in Taiwan rice farm- ing, Mishra et al. (2019, 2020) examined the impact of contract farming on production risk, technical effi- ciency, and risk attitudes for diff erent crops in Nepal, and Rizwan et al. (2020) studied the eff ect of off -farm employment on production risk and technical effi ciency. All these articles developed a stochastic frontier follow- ing the model proposed by Kumbhakar (2002), account- ing for self-selection by separating adopters and non- adopters. Khanal et al. (2021) investigated the infl uence of farmers’ climate change adaptations on smallholder farm effi ciency and productivity in Nepal rice produc- tion. Th e authors treated the self-selection endogeneity bias among adopters and non-adopters for observed and unobserved characteristics. In particular, they utilized the Propensity Score Matching (PSM) technique to cor- rect for observed heterogeneity, obtaining samples of farmers homogenous in terms of socioeconomic charac- teristics. Th en, they estimated a stochastic frontier using the model proposed by Bravo-Ureta et al. (2012) to cor- rect for unobserved heterogeneity. In the agricultural policy thematic area, Key and Mcbride (2014) estimated the effects on production mean and variance caused by the ban of antibiotics on the US hog industry. Th ey developed a stochastic fron- Figure 2. Th eoretical and methodological framework to estimate the production frontier. Source: Own elaboration. Note: Th e sum is 45 because one article compared the Just and Pope and Kumbhakar models. Figure 3. Literature thematic areas accounting for the articles that dealt with endogeneity issues. Source: Own elaboration. 344 Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13516 Simone Russo, Lerato Phali, Maurizio Prosperi tier following the approach proposed by Karagian- nis and Tzouvelekas (2012). The authors addressed the potential selection bias as the application of antibiotics treatment may be related to other unobserved aspects influencing the production process. In particular, they matched the different treatment effects (antibiotics) to create similar groups based on the observable charac- teristics. Singbo et al. (2020) analysed the impact of the revenue insurance program and environmental regula- tions on Canadian hog farmers’ behaviour and farm per- formance indicators. The authors addressed the poten- tial endogeneity of input changes related to production shocks by estimating the meta-technology production frontier model developed by O’Donnell (2016). Within the production technology thematic area, Tiedemann and Latacz-Lohmann (2013) evaluated pro- duction risk and technical efficiency in organic and con- ventional arable crop farms in Germany. The authors developed a stochastic frontier approach stemming from the model developed by Just and Pope (1978). They used the propensity score matching to compare groups, accounting for the self-selection problem due to farm size and soil quality. Finally, among the input effects thematic area, the only study that dealt with endogeneity is Nauges et al. (2011), who analysed Finnish grain production under both inefficiency and risk conditions. They developed a state-contingent production frontier following the mod- el proposed by O’Donnell and Griffiths (2006). They accounted for the endogeneity of inputs considering the different states of nature. In particular, they considered that farmers allocate inputs differently to manage risk in relation to the meteorological conditions in the relative states of nature. To summarise, seven articles considered endogeneity bias resulting from self-selection, while two considered endogeneity stemming from input use alterations after adverse shocks. In addition to results related to SFA, some other articles which emerged from the search string accounted for endogeneity in the production function. These papers are reported in Table 2. All these articles were classified into the risk-management thematic area. Among these articles, Di Falco and Chavas (2009) analysed the crop genetic diversity effects on productiv- ity and production risk of Ethiopian farmers engaged with barley production, following the Antle (1983) approach. The authors estimated the mean function, the variance, and the skewness equations using a three- stage least squares (3SLS) estimator to correct the self- selection bias, treating biodiversity as endogenous in all equations. Following the approach proposed by Antle (1983), Di Falco and Veronesi (2014) investigated the influence of climate change adaptations on farm expo- sure to downside risk for several crops in Ethiopia. The decision on whether to adapt or not to climate change is voluntary and may result in self-selection bias. The authors accounted for the endogeneity of the adaptation decision by estimating a switching regression model. By using the same approach, Kassie et al. (2015) analysed the effect of sustainable intensification practices on pro- ductivity and production risk in maize-legume inter- cropping production in Malawi, while Amondo et al. (2019) investigated the impact of using drought-tolerant Table 1. Articles dealing with endogeneity in the production frontier estimates. Category/Study Frontier Theoretical Framework Endogeneity Source Methodology Risk Management Chang and Wen (2011) Kumbhakar (2002) Self-Selection Separating Groups Mishra et al. (2019) Kumbhakar (2002) Self-Selection Separating Groups Mishra et al. (2020) Kumbhakar (2002) Self-Selection Separating Groups Rizwan et al. (2020) Kumbhakar (2002) Self-Selection Separating Groups Khanal et al. (2021) Bravo-Ureta et al. (2012) Self-Selection PSM Agricultural Policy Key and Mcbride (2014) Karagiannis and Tzouvelekas (2012) Self-Selection PSM Singbo et al. (2020) O’Donnell (2016) Input Endogeneity Meta-Technology Production Technology Tiedemann and Latacz-Lohmann (2013) Just and Pope (1978) Self-Selection PSM Input Effect Nauges et al. (2011) O’Donnell and Griffiths (2006) Input Endogeneity State-Contingent Source: Own elaboration. 345Dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: A scoping review Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13516 maize varieties on farm productivity, yield variance, and downside risk exposure in Zambian maize-grow- ing farms. The research proposed by Wang et al. (2018) studied the importance of irrigation infrastructure in enhancing farmers’ ability to adapt to drought and its efficacy in managing drought risk in rice production in China. The authors estimated a production function fol- lowing the approach proposed by Antle (1983). In addi- tion, they implemented a two-stage instrumental vari- able method to control for the endogeneity of the adap- tation decision. Finally, following the state-contingent method proposed by Quiggin and Chambers (2006), Mallawaarachchi et al. (2017) estimated the production function of dairy farms in Australia to analyse the effect of water allocation on farm performance. They account- ed for endogeneity related to the change in the usage of productive inputs under different states of nature according to the productivity shocks. Moreover, they proposed a two-stage instrumental variables approach to correct the endogeneity bias due to self-selection. 4. DISCUSSION Consistent with Just (2003), the results of this research confirm the low prevalence of risk-related agri- cultural production studies, showing the failure of risk researchers in convincing the broader profession of the importance of risk effects on farmers’ decision-making. The vast majority of the articles using SFA in agricultur- al production did not consider risk despite its relevance in the field. For example, by omitting the keywords related to risk from the search query, the number of arti- cles increases from 162 to 2595. Given that risk effects on productivity and technical efficiency are unavoid- able, the stochastic production frontier must include risk sources to accurately account for and predict the techni- cal efficiency of producers (Battese et al., 1997). Howev- er, it was alarming to discover that relatively few articles account for risk by implementing a SFA approach. This may be attributed to the fact that this approach is still in development and the model is rather complex, regarding both the modelling and estimating procedure (Kumbha- kar et al., 2015). It is worth noting that when the effects of risk are included in the model, the endogeneity sources are often ignored, resulting in biased estimates of parameters. Therefore, studies considering risk in the SFA approach seem to fail to represent the complexities of agricultural production modelling, such as accounting for endogene- ity issues. Despite the methods of dealing with the endo- geneity issues in production frontiers being well docu- mented in the recent literature (Shee and Stefanou, 2014; Amsler et al., 2016, 2017; Karakaplan and Kutlu, 2017; Latruffe et al., 2017), most of the studies analysed in this review, do not generally account for endogeneity bias due to the input relationship with production shocks. In addi- tion, other endogeneity sources may arise with the tak- ing up of risk management tools or risk mitigation prac- tices. According to Vigani and Kathage (2019), there are four possible cases. First, it is necessary to account for the possibility of reverse causality between the choice of adopting risk management instruments and productivity (Nelson and Loehman, 1987; Ramaswami, 1993). More productive farms, for example, are more likely to have the financial and managerial resources for risk mitigation (Enjolras et al., 2012; Santeramo et al., 2016). In addition, the self-selection problem needs to be addressed to avoid inconsistent estimates of risk mitigation tools on farm results. It is because, generally, the adoption is voluntary, and a particular strategy may be adopted by farms that have more advantages in adopting, i.e., they have differ- ent unobservable characteristics that may have an impact on both the adoption decision and performance such as Table 2. Articles dealing with endogeneity in the function production instead of the frontier. Category/Study Frontier Theoretical Framework Endogeneity Source Methodology Risk Management Di Falco and Chavas (2009) Antle (1983) Self-Selection Three-Stage Least Squares (3SLS) approach Di Falco and Veronesi (2014) Antle (1983) Self-Selection Endogenous Switching Regressor Kassie et al. (2015) Antle (1983) Self-Selection Endogenous Switching Regressor Mallawaarachchi et al. (2017) Quiggin and Chambers (2006) Self-Selection Input Endogeneity Two-Stage IV approach State-Contingent Wang et al. (2018) Antle (1983) Self-Selection Two-Stage IV approach Amondo et al. (2019) Antle (1983) Self-Selection Endogenous Switching Regressor Source: Own elaboration. 346 Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13516 Simone Russo, Lerato Phali, Maurizio Prosperi risk aversion or perceived barriers to adopting risk man- agement tools (Coletta et al., 2018; Di Falco and Vero- nesi, 2013; Giampietri et al., 2020). In addition, another potential source of endogeneity may arise from the sub- stitution effect between risk management practices and input use since the adoption of risk-mitigating practices may change the level of input used (Ramaswami, 1992; Russo et al., 2022). Finally, researchers need to account for omitted variables endogeneity by including the most adopted risk management tools. In fact, the estimates of risk mitigation practice effects may be biased because the total impact of adopting several risk mitigation practices simultaneously might not be equivalent to the sum of the influences when considering each strategy separately (Wu and Babcock, 1998). However, among the articles within the risk management thematic area, the few that dealt with endogeneity mainly considered the self-selection bias. None of these treated the endogeneity due to the input correlation with production shocks. The lack of studies that deal with endogeneity by using the SFA approach in agricultural economics may be explained as follows. First, the stochastic frontier lit- erature has largely ignored the advances made in the production function framework to control for endoge- neity issues (Shee and Stefanou, 2014). Moreover, deal- ing with endogeneity is relatively more complex in the SFA approach than in the standard regression models. In fact, due to the nature of the error term in the sto- chastic frontier models, which include both the techni- cal efficiency and statistical error terms, this is a rela- tively more difficult task (Karakaplan and Kutlu, 2017), which drastically reduces the number of researchers that are able to deal with these problems. Agricultural econo- mists have to push for the advancement of more sophis- ticated methodologies to account for these issues since farming production is much more complex than other productive sectors. Indeed, agricultural production stud- ies have to take into account the biological production cycle and environmental conditions, factors that are less relevant in other sectors. Our findings show a gap in the literature in identi- fying a comprehensive approach capable of dealing with either risk and endogeneity concurrently when assess- ing farm productivity and technical efficiency in the SFA framework. This apparent deficiency in literature in the field may be related to the lack of consolidated knowledge in terms of standardized methodologies. As emerged in the current analysis, the authors applied different production frontier models by using several strategies to deal with both risk and endogeneity issues. The use of several statistical platforms leads to a situa- tion where the routines are available in a fragmented way. For example, only certain softwares may be more appropriate to treat a specific problem. There is not yet a software where all the estimators are available (Kumb- hakar et al., 2020). Furthermore, despite its widespread use, only the most basic implementations of the SFA are available across the broad array of statistical plat- forms. As such, the lack of existing routines requires researchers to be able to program or code (e.g., creating new command or algorithms) to develop a frontier that accounts for all these factors. 5. CONCLUSION With the increasing availability of data compared to the past and access to appropriate analytical meth- ods/routines and statistical softwares, SFA may repre- sent a useful approach to yield valuable results that can improve the effectiveness of policies in the agricultural sector. This is also imperative for the future development of well-suited policy instruments. To this end, a scoping literature review was conducted to overview the existing knowledge in farm risk analysis within the SFA frame- work. In particular, this article aimed to investigate the methods proposed in the literature to deal with endoge- neity in SFA risk analysis. The main limitation of this study is related to the inclusion of only peer-reviewed articles published in aca- demic journals. However, this was deemed to be enough to highlight the gap in the literature. Therefore, for future studies of this domain, we suggest the review of grey literature as the approaches proposed in the study are still under development. The findings of this research highlight the need for more studies that investigate the farm productivity and efficiency which also account for risk and endogene- ity issues. This result is quite critical since the research- ers’ goal is often related to providing policy indications to enhance farm performance without focusing on the accuracy of data analysis. Neglecting risk and endogene- ity in benchmarking studies may yield biased estimates and thus lead to incorrect policy recommendations. A comprehensive approach might help to achieve more accurate estimates that could yield recommendations that ensure improved productivity and technical efficien- cy of farmers. However, it is plausible to conclude that much still needs to be done in order to get a comprehen- sive approach to represent the complexity of agricultural production modelling. Despite the relevant implications of risk and risk management tools in agricultural decision-making and economic performances, the SFA literature which focus- 347Dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: A scoping review Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13516 es on these aspects is still underrepresented. Research should be focused on measuring the impact of the differ- ent sources of risk when assessing farm productivity and technical efficiency. This can ensure that policy recom- mendations are based on more representative results. As such policy formulation can integrate possible mitigation strategies needed to enhance performance. Researchers should develop more accurate and sophisticated methodologies to take into account the complexity of the agricultural production modelling. Therefore, expert researchers are strongly encouraged to provide more information to ensure the replicability of their findings, for example, providing their own pro- gramming codes and guidelines for practitioners and policy analysts. REFERENCES Ahsan, S. M., Ali, A. A. G., and Kurian, N. J. (1982). Toward a Theory of Agricultural Insurance. American Journal of Agricultural Economics 64: 510–529. Aigner, D., Lovell, C. A. K., and Schmidt, P. (1977). For- mulation and estimation of stochastic frontier pro- duction function models. Journal of Econometrics 6: 21–37. Amondo, E., Simtowe, F., Rahut, D. B., and Erenstein, O. (2019). Productivity and production risk effects of adopting drought-tolerant maize varieties in Zambia. International Journal of Climate Change Strategies and Management 11: 570–591. Amsler, C., O’Donnell, C. J., and Schmidt, P. (2017). Sto- chastic metafrontiers. Econometric Reviews 36: 1007– 1020. Amsler, C., Prokhorov, A., and Schmidt, P. (2016). Endo- geneity in stochastic frontier models. Journal of Econometrics 190: 280–288. Antle, J. M. (1983). Testing the stochastic structure of production: A flexible moment-based approach. Jour- nal of Business and Economic Statistics 1: 192–201. Arksey, H., and O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology: Theory and Practice 8: 19–32. Battese, G. E., and Coelli, T. J. (1995). A Model for Tech- nical Inefficiency Effects in a Stochastic Frontier Pro- duction Function for Panel Data. Empirical Econom- ics 20: 325–332. Battese, G. E., Rambaldi, A. N., and Wan, G. H. (1997). A Stochastic Frontier Production Function with Flex- ible Risk Properties. Journal of Productivity Analysis 8: 269–280. Blarel, B., Hazell, P., Place, F., and Quiggin, J. (1992). The eco- nomics of farm fragmentation: Evidence from Ghana and Rwanda. World Bank Economic Review 6: 233–254. Bogetoft, P., and Otto, L. (2010). Benchmarking with DEA, SFA, and R. Springer Science & Business Media. Bravo-Ureta, B. E., Greene, W., and Solís, D. (2012). Technical efficiency analysis correcting for biases from observed and unobserved variables: An appli- cation to a natural resource management project. Empirical Economics 43: 55–72. Caudill, S. B., Ford, J. M., and Grqpper, D. M. (1995). Frontier estimation and firm-specific inefficiency measures in the presence of heteroscedasticity. Jour- nal of Business and Economic Statistics 13: 105–111. Cerroni, S. (2020). Eliciting farmers’ subjective probabili- ties, risk, and uncertainty preferences using contex- tualized field experiments. Agricultural Economics (United Kingdom) 51: 707–724. Chambers, R. G., Serra, T., and Stefanou, S. E. (2015). Using ex ante output elicitation to model state-con- tingent technologies. Journal of Productivity Analysis 43: 75–83. Chang, H.-H., and Wen, F.-I. (2011). Off-farm work, technical efficiency, and rice production risk in Tai- wan. Agricultural Economics 42: 269–278. Chavas, J. P., Chambers, R. G., and Pope, R. D. (2010). Production economics and farm management: A century of contributions. American Journal of Agri- cultural Economics 92: 356–375. Coletta, A., Giampietri, E., Santeramo, F. G., Severini, S., and Trestini, S. (2018). A preliminary test on risk and ambiguity attitudes, and time preferences in decisions under uncertainty: Towards a better explanation of participation in crop insurance schemes. Bio-Based and Applied Economics 7: 265–277 Colquhoun, H. L., Levac, D., O ’brien, K. K., Straus, S., Tricco, A. C., Perrier, L., Kastner, M., and Moher, D. (2014). Scoping reviews: Time for clarity in defini- tion, methods and reporting Scoping reviews: Time for clarity in definition How to cite TSpace items. Journal of Clinical Epidemiology 67: 3–13. Di Falco, S., and Chavas, J. P. (2009). On crop biodiver- sity, risk exposure, and food security in the highlands of Ethiopia. American Journal of Agricultural Eco- nomics 91: 599–611. Di Falco, S., Chavas, J. P., and Smale, M. (2007). Farmer management of production risk on degraded lands: The role of wheat variety diversity in the Tigray region, Ethiopia. Agricultural Economics 36: 147–156. Di Falco, S., and Veronesi, M. (2013). How can African agriculture adapt to climate change? A counterfactual analysis from Ethiopia. Land Economics 89: 743–766. 348 Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13516 Simone Russo, Lerato Phali, Maurizio Prosperi Di Falco, S., and Veronesi, M. (2014). Managing Environ- mental Risk in Presence of Climate Change: The Role of Adaptation in the Nile Basin of Ethiopia. Environ- mental and Resource Economics 57: 553–577. Duong, T. T., Brewer, T., Luck, J., and Zander, K. (2019). A global review of farmers’ perceptions of agricultur- al risks and risk management strategies. Agriculture (Switzerland) 9. Eggert, H., and Tveteras, R. (2004). Stochastic produc- tion and heterogeneous risk preferences: commercial fishers’ gear choices. American Economic Review. 86: 199–212. Enjolras, G., Capitanio, F., and Adinolfi, F. (2012). The demand for crop insurance: Combined approaches for France and Italy. Agricultural Economics Review 13: 5–22. Farrell, M. J. (1957). The Measurement of Productive Effi- ciency. Journal of the Royal Statistical Society. Series A (General) 120: 253–290. Finger, R. (2013). Expanding risk consideration in inte- grated models - The role of downside risk aversion in irrigation decisions. Environmental Modelling and Software 43: 169–172. Ghosh, S., McGuckin, J. T., and Kumbhakar, S. C. (1994). Technical efficiency, risk attitude, and adoption of new technology: The case of the U.S. dairy industry. Tech- nological Forecasting and Social Change 46: 269–278. Giampietri, E., Yu, X., and Trestini, S. (2020). The role of trust and perceived barriers on farmer’s intention to adopt risk management tools. Bio-Based and Applied Economics 9: 1–24. Greene, W. (2003). Simulated Likelihood Estimation of the Normal-Gamma Stochastic Frontier Function. Journal of Productivity Analysis 19: 179–190. Greene, W. (2005). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Jour- nal of Econometrics 126: 269–303. Griffiths, W. E. (1986). a Bayesian Framework for Opti- mal Input Allocation With an Uncertain Stochastic Production Function. Australian Journal of Agricul- tural Economics 30: 128–152. Guo, S., Zhang, F., Zhang, C., Wang, Y., and Guo, P. (2019). An improved intuitionistic fuzzy interval two-stage stochastic programming for resources planning management integrating recourse penalty from resources scarcity and surplus. Journal of Clean- er Production 234: 185–199. Just, R. E. (2003). Risk research in agricultural econom- ics: Opportunities and challenges for the next twen- ty-five years. Agricultural Systems 75: 123–159. Just, R. E., and Pope, R. D. (1978). Stochastic specifica- tion of production functions and economic implica- tions. Journal of Econometrics 7: 67–86. Just, R. E., and Pope, R. D. (1979). Production Function Estimation and Related Risk Considerations. Ameri- can Journal of Agricultural Economics 61: 276–284. Karagiannis, G., and Tzouvelekas, V. (2012). The damage- control effect of pesticides on total factor productiv- ity growth. European Review of Agricultural Econom- ics 39: 417–437. Karakaplan, M. U., and Kutlu, L. (2017). Handling endo- geneity in stochastic frontier analysis. Economics Bul- letin 37: 889–901. Kassie, M., Teklewold, H., Marenya, P., Jaleta, M., and Erenstein, O. (2015). Production Risks and Food Security under Alternative Technology Choices in Malawi: Application of a Multinomial Endogenous Switching Regression. Journal of Agricultural Eco- nomics 66: 640–659. Key, N., and Mcbride, W. D. (2014). Sub-therapeutic anti- biotics and the efficiency of U.S. hog farms. American Journal of Agricultural Economics 96: 831–850. Khanal, U., Wilson, C., Rahman, S., Lee, B. L., and Hoang, V. N. (2021). Smallholder farmers’ adaptation to cli- mate change and its potential contribution to UN’s sustainable development goals of zero hunger and no poverty. Journal of Cleaner Production 281: 124999. Komarek, A. M., De Pinto, A., and Smith, V. H. (2020). A review of types of risks in agriculture: What we know and what we need to know. Agricultural Systems 178: 102738. Koop, G., Osiewalski, J., and Steel, M. F. J. (1997). Bayes- ian efficiency analysis through individual effects: Hospital cost frontiers. Journal of Econometrics 76: 77–105. Kumbhakar, S. C. (2002). Specification and estimation of production risk, risk preferences and technical effi- ciency. American Journal of Agricultural Economics 84: 8–22. Kumbhakar, S. C., Lien, G., and Hardaker, J. B. (2014). Technical efficiency in competing panel data models: A study of Norwegian grain farming. Journal of Pro- ductivity Analysis 41: 321–337. Kumbhakar, S. C., Parmeter, C. F., and Zelenyuk, V. (2020). Stochastic Frontier Analysis : Foundations and Advances I. Handbook of Production Economics : 1–40. Kumbhakar, S. C., Wang, H.-J., and Horncastle, A. P. (2015). A Practitioner’s Guide to Stochastic Frontier Analysis Using Stata. Cambridge University Press. Lambarraa, F., Stefanou, S., and Gil, J. M. (2016). The analysis of irreversibility, uncertainty and dynamic technical inefficiency on the investment decision in the Spanish olive sector. European Review of Agricul- tural Economics 43: 59–77. 349Dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: A scoping review Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13516 Latruffe, L., Bravo-Ureta, B. E., Carpentier, A., Desjeux, Y., and Moreira, V. H. (2017). Subsidies and techni- cal efficiency in agriculture: Evidence from European dairy farms. American Journal of Agricultural Eco- nomics 99: 783–799. MacMinn, R. D., and Holtmann, A. G. (1983). Tech- nological Uncertainty and the Theory of the Firm. Southern Economic Journal 50: 120–136. Mallawaarachchi, T., Nauges, C., Sanders, O., and Quig- gin, J. (2017). State-contingent analysis of farmers’ response to weather variability: irrigated dairy farm- ing in the Murray Valley, Australia. Australian Jour- nal of Agricultural and Resource Economics 61: 36–55. Meeusen, W., and van Den Broeck, J. (1977). Efficiency Estimation from Cobb-Douglas Production Func- tions with Composed Error. International Economic Review 18: 435–444. Mishra, A. K., Rezitis, A. N., and Tsionas, M. G. (2019). Estimating Technical Efficiency and Production Risk under Contract Farming: A Bayesian Estimation and Stochastic Dominance Methodology. Journal of Agri- cultural Economics 70: 353–371. Mishra, A. K., Rezitis, A. N., and Tsionas, M. G. (2020). Production under input endogeneity and farm-spe- cific risk aversion: evidence from contract farming and Bayesian method. European Review of Agricul- tural Economics 47: 591–618. Mongeon, P., and Paul-Hus, A. (2016). The journal cov- erage of Web of Science and Scopus: a comparative analysis. Scientometrics 106: 213–228. Moschini, G., and Hennessy, D. A. (2001). Uncertainty, risk aversion, and risk management for agriculture producers. Volume 1, Part A. Handbook of Agricul- tural Economics 1: 88–153. Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McAr- thur, A., and Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choos- ing between a systematic or scoping review approach. BMC Medical Research Methodology 18: 1–7. Nauges, C., O’Donnell, C. J., and Quiggin, J. (2011). Uncertainty and technical efficiency in Finnish agri- culture: A state-contingent approach. European Review of Agricultural Economics 38: 449–467. Nelson, C. H., and Loehman, E. T. (1987). Further Toward a Theory of Agricultural Insurance. American Journal of Agricultural Economics 69: 523–531. Nguyen, K. A. T., Nguyen, T. A. T., Jolly, C., and Ngueli- fack, B. M. (2020). Economic efficiency of extensive and intensive shrimp production under conditions of disease and natural disaster risks in khánh hòa and trà vinh provinces, Vietnam. Sustainability (Switzer- land) 12. O’Donnell, C. J. (2016). Using information about tech- nologies, markets and firm behaviour to decompose a proper productivity index. Journal of Econometrics 190: 328–340. O’Donnell, C J, and Griffiths, W. E. (2006). Estimating State-Continget Production Frontiers. American Jour- nal of Agricultural Economics 88: 249–266. O’Donnell, Christopher J., Chambers, R. G., and Quiggin, J. (2010). Efficiency analysis in the presence of uncer- tainty. Journal of Productivity Analysis 33: 1–17. Oude Lansink, A., Stefanou, S. E., and Kapelko, M. (2015). The impact of inefficiency on diversification. Journal of Productivity Analysis 44: 189–198. Piñeiro, V., Arias, J., Dürr, J., Elverdin, P., Ibáñez, A. M., Kinengyere, A., Opazo, C. M., Owoo, N., Page, J. R., Prager, S. D., and Torero, M. (2020). A scoping review on incentives for adoption of sustainable agri- cultural practices and their outcomes. Nature Sustain- ability 3: 809–820. Power, B., Rodriguez, D., deVoil, P., Harris, G., and Pay- ero, J. (2011). A multi-field bio-economic model of irrigated grain-cotton farming systems. Field Crops Research 124: 171–179. Quiggin, J., and Chambers, R. G. (2006). The state-con- tingent approach to production under uncertainty. Australian Journal of Agricultural and Resource Eco- nomics 50: 153–169. Ramaswami, B. (1992). Production Risk and Optimal Input Decisions. American Journal of Agricultural Economics 74: 860–869. Ramaswami, B. (1993). Supply Response to Agricultural Insurance: Risk Reduction and Moral Hazard Effects. American Journal of Agricultural Economics 75: 914– 925. Rizwan, M., Qing, P., Saboor, A., Iqbal, M. A., and Nazir, A. (2020). Production risk and competency among categorized rice peasants: Cross-sectional evidence from an emerging country. Sustainability (Switzer- land) 12. Roll, K. H. (2019). Moral hazard : the effect of insurance on risk and efficiency. Agricultural Economics 50: 367–375. Russo, S., Caracciolo, F., and Salvioni, C. (2022). Effects of Insurance Adoption and Risk Aversion on Agricul- tural Production and Technical Efficiency : A Panel Analysis for Italian Grape Growers. Economies 10. Santeramo, F. G., Goodwin, B. K., Adinolfi, F., and Capi- tanio, F. (2016). Farmer Participation, Entry and Exit Decisions in the Italian Crop Insurance Programme. Journal of Agricultural Economics 67: 639–657. Serra, T., and Oude Lansink, A. (2014). Measuring the impacts of production risk on technical efficiency: A 350 Bio-based and Applied Economics 11(4): 339-350, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13516 Simone Russo, Lerato Phali, Maurizio Prosperi state-contingent conditional order-m approach. Euro- pean Journal of Operational Research 239: 237–242. Shee, A., and Stefanou, S. E. (2014). Endogeneity cor- rected stochastic production frontier and technical efficiency. American Journal of Agricultural Economics 97: 939–952. Singbo, A., Larue, B., and Tamini, L. D. (2020). Total fac- tor productivity change in hog production and Que- bec’s revenue insurance program. Canadian Journal of Agricultural Economics 68: 21–46. Singh, V. K., Singh, P., Karmakar, M., Leta, J., and Mayr, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Sci- entometrics 126: 5113–5142. Tiedemann, T., and Latacz-Lohmann, U. (2013). Produc- tion Risk and Technical Efficiency in Organic and Con- ventional Agriculture - The Case of Arable Farms in Germany. Journal of Agricultural Economics 64: 73–96. Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine 169: 467–473. Tsionas, E. G. (2006). Inference in dynamic stochastic frontier models. Journal of Applied Econometrics 21: 669–676. Vigani, M., and Kathage, J. (2019). To Risk or Not to Risk? Risk Management and Farm Productiv- ity. American Journal of Agricultural Economics 101: 1432–1454. Wang, N., Sun, M., Yu, L., and Jiang, F. (2020). Fuzzy mathematical risk preferences based on stochastic production function among medium-scale hog pro- ducers. Journal of Intelligent and Fuzzy Systems 39: 4859–4868. Wang, Y., Huang, J., Wang, J., and Findlay, C. (2018). Mitigating rice production risks from drought through improving irrigation infrastructure and management in China. Australian Journal of Agricul- tural and Resource Economics 62: 161–176. Wu, J., and Babcock, B. A. (1998). The Choice of Till- age, Rotation, and Soil Testing Practices: Economic and Environmental Implications. American Journal of Agricultural Economics 80: 494–511. Yesuf, M., Di Falco, S., Deressa, T., Ringler, C., and Koh- lin, G. (2008). The impact of climate change and adaptation on food production in low-income coun- tries: Evidence from the Nile Basin, Ethiopia. IFPRI Discussion Paper. Volume 11, Issue 4 - 2022 Firenze University Press Systems Thinking, Mapping and Change in Food and Agriculture Domenico Dentoni1,*, Carlo Cucchi2, Marija Roglic1, Rob Lubberink3, Rahmin Bender-Salazar4, Timothy Manyise5 Vulnerability and resilience to food and nutrition insecurity: A review of the literature towards a unified framework Pierluigi Montalbano1, Donato Romano2,* The local economic impact of climate change mitigation in agriculture Cathal Geoghegan1,2,*, Cathal O’Donoghue1,3, Jason Loughrey2 Dealing with endogeneity in risk analysis within the stochastic frontier approach in agricultural economics: a scoping review Simone Russo1,2,*, Lerato Phali3, Maurizio Prosperi1 Provision of public goods and bads by agriculture and forestry. An analysis of stakeholders’ perception of factors, issues and mechanisms Stefano Targetti1,*, Valentina Marconi1, Meri Raggi2, Annette Piorr3, Anastasio José Villanueva4, Kati Häfner3, Mikko Kurttila5, Natalia Letki6, Mihai Costica7, Dimitre Nikolov8, Davide Viaggi1