Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Bio-based and Applied Economics BAE © 2024 Author(s). Open access article published, except where otherwise noted, by Firenze University Press under CC-BY-4.0 License for content and CC0 1.0 Universal for metadata. Firenze University Press | www.fupress.com/bae Citation: Pagliacci, F., & Salpina, D. (2024). Adapting to climate change: what really drives the choices of the produc- ers of Geographical Indications?. Bio- based and Applied Economics 13(3): 265-283. doi: 10.36253/bae-15221 Received: October 2, 2023 Accepted: April 9, 2024 Published: October 16, 2024 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: Valentina Raimondi, Luca Sal- vatici ORCID FP: 0000-0002-3667-7115 DS: 0000-0001-9663-2456 Adapting to climate change: what really drives the choices of the producers of Geographical Indications? Francesco Pagliacci1, Dana Salpina1,2,* 1 Department of Land, Environment, Agriculture and Forestry, University of Padua, Via dell’Università 16, 35020 Legnaro (PD) Italy 2 Euro-Mediterranean Center on Climate Change, Porta dell’Innovazione Building - 2nd Floor Via della Libertà 12 , 30175 Venice (VE), Italy *Corresponding author. E-mail: dana.salpina@cmcc.it Abstract. In an era of rapid climate change, there is an increasing call for the efforts directed at detecting best practices of climate change adaptation in agriculture and understanding the factors behind producers’ willingness to implement adaptation strat- egies. Many studies consider solely traditional agriculture and specific sectors (e.g., wine), while little attention has been paid to certified and high-quality products, as a whole. To fill this knowledge gap, in 2022 a questionnaire-based online survey was administered to 137 producers of agri-food Geographical Indications in the Veneto Region (north-eastern Italy). Using a multinomial logit model, this study highlights the factors explaining adaptation strategies distinguishing three cases: (i) farmers who have implemented adaptation strategies; (ii) farmers intending to implement them in the future; (iii) farmers neither having implemented nor willing to do so. Results suggest that socio-demographic characteristics, particularly education, matter, with produc- ers holding a high school degree in agriculture showing a greater willingness to adapt. Also, being full-time farmer couples with higher probability of having already imple- mented adaptation strategies. Lastly, also a direct observation of climate change in the production area affects farmers’ adaptation decisions. Keywords: climate change, adaptation, PDOs, PGIs, producers’ survey. JEL Codes: Q1, Q15, Q54. 1. INTRODUCTION One of the major recommendations of the United Nations Climate Change Conference COP27 (in 2022) is the recognition of the importance of sharing best adaptation practices among public and private key stakeholders, while adjusting them to country-specific context (UNFCCC, 2022). In such a setting, national governments have the direct responsibility of detecting best practices, highlighting the main factors behind climate change adaptation. For the agri-food sector, both incremental and transformational climate change adaptation strategies have a paramount importance (Howden et al., https://doi.org/10.36253/bae-15221 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-15221 https://orcid.org/0000-0002-3667-7115 https://orcid.org/0000-0001-9663-2456 mailto:dana.salpina@cmcc.it 266 Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Francesco Pagliacci, Dana Salpina 2010; Ingram, 2012; Fedele et al., 2019). Although incre- mental adaptation strategies alone are commonly con- sidered insufficient to achieve the zero-hunger target of Sustainable Development Goal 2 (SDG2) and address the impacts of climate change (FAO, 2018), they can indeed contribute to national and regional transformative adap- tation processes, especially in the case of local level strategies (Rahman et al., 2021). However, adaptation to climate change for agri- food geographical indication (GI) systems is even more complex process, due to the legislative and institutional framework characterising them. Indeed, for each GI, a Product Specification (hereinafter, PS) defines the delimited area of production as well as production rules (e.g., plant varieties, harvest dates, size and colour). According to the World Trade Organisation (WTO, 1994), GIs are indications aimed at identifying goods as produced in a given geographical area, whose qual- ity and reputation are attributable to the geographical origin itself. In practice, GIs are considered as a sort of social constructions (Belletti et al., 2017), which play a crucial role in fostering endogenous rural development, hence contributing to the preservation of the tradition- al agri-food systems and related social networks (Van- decandelaere et al., 2010), and thus, to socio-economic and environmental sustainability of the concerned rural areas (Owen et al., 2020). However, to contribute to this goal, GI management must be implemented effectively (Giacomini and Mancini, 2015) and GI regulations put producers under obligation to comply with the respec- tive PS. The complex policy and socio-economic pro- cesses on which GIs rely on (Thompson and Scoones, 2009) often makes the modification of their PS complex, even if for the urgent purpose of climate change adap- tation. In particular, the introduction of such changes requires an agreement among the involved producers, which can be concerned with long and costly authori- sation processes (Belletti et al., 2015; Quiñones-Ruiz et al., 2018) on the one hand, and with the product’s qual- ity and reputation at stake, on the other hand. All these reasons explain why agri-food GIs are quite vulnerable to climate change. In this setting, GIs adaptation to climate change depends on the capacity of agents and institutions to innovate, hence finding new solutions. Information on already-existing adaptation practices and a better understanding of the drivers behind the willingness of GI agents to adapt is crucial in informing public poli- cies. Indeed, policies can foster anticipatory adaptation strategies within the agri-food sector. This is particularly important when self-investment for adaptation is insuf- ficient, also due to the existence of major financial con- straints, both in high-income and low-income countries (Ignaciuk, 2015; Deressa et al., 2009). For the last decade, the studies addressing climate change adaptation of farmers have increased, especially in low-income countries. They have focused either on specific territories, e.g., the “char” islands in Bangladesh (Ahmed et al., 2021), the Amazon basin (Bauer et al., 2022), Laikipia District in Kenya (Ogalleh et al., 2012); or on specific productions, such as tea (Muench et al., 2021), coffee (Bro, 2020), honey (Vercelli, et al. 2021). In fact, only a few studies focused on the nexus of cli- mate change adaptation and GIs in high-income coun- tries. According to Marescotti et al. (2020), safeguarding Protected Designations of Origins (PDOs) and Protected Geographical Indications (PGIs) from the effects of cli- mate change is a rather new topic mostly disregarded by international literature. Some studies addressed climate change perception of wine producers (e.g., Lereboul- let, 2013; Lamonaca et al., 2021), while there is paucity of studies focusing on agri-food GIs, specifically. To this regard, a recent study by Henry (2023) suggested the chance for agricultural supply relocation as an option to adapt to climate change, even in the case of GI labels. Although this chance is currently excluded outside of the boundaries of the designed geographical area of pro- duction, it is still true that – at least in principle - chang- es in the geographical area of production are admit- ted as non-minor amendments by Regulation (EU) No 1151/2012 (Article 53). However, according to an analy- sis of the amendments in the fruit and vegetable sector in the EU by Marescotti et al. (2020), only one out of 81 non-minor amendments until 2018 affected the area of production, justifying the need to enlarge the produc- tion area with climate change. Actually, Henry (2023) also stressed the existence of expected negative impact on quality of products, limiting similar relocations. This research aims to shed new light on local adap- tation strategies in the case of agri-food GIs in the Vene- to Region (north-eastern Italy), i.e., one of the regions with the highest climate change risk in Italy (ARPAV, 2017). In particular, its objective is to highlight the main factors influencing the decision of producers to counter climate change impact. The main research questions of this study are: What are the main adaptation practices used by producers of agri-food GIs? And what are the main factors influencing the willingness of agri-food GI producers to adapt? In order to answer these questions, the study is based on a structured online survey, targeted to agri- food GI producers in the case-study area. With the help of primary data, it highlights the main adaptation prac- tices in place as well as the factors influencing the will- 267Adapting to climate change: what really drives the choices of the producers of Geographical Indications? Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 ingness of farmers to implement them (either currently or in the future). Adaptation revolves around the com- plex interplay of socio-demographic characteristics (e.g., age, education), management characteristics and net- working activity, production type, altitude as well as cli- mate change perception itself. To estimate these factors, a multinomial logit model is used. Findings suggest that despite a generalised awareness of climate change, this has not yet turned into widespread decision to imple- ment adaptation measures. Rather, developing peer-to- peer learning practices among farmers and fostering collaborations among those GI systems that face similar risks is of utmost importance. The paper is structured as follows. Section 2 pro- vides the theoretical background on adaptation to cli- mate change and on the factors affecting adaptation. Section 3 discusses the materials and methods used, by briefly describing the case study area, the sample, data collection and analysis. Section 4 presents the results of the study, while Section 5 discusses them. Section 6 explores the main policy implications of this study and Section 7 concludes. 2. THEORETICAL BACKGROUND: FACTORS OF CLIMATE CHANGE ADAPTATION Within climate change literature, adaptation is defined as a process of adjustment to current or future climate and its effects, so as to reduce harm or take advantage of some positive opportunities (IPCC, 2014). In agriculture, there are many climate change adapta- tion measures (e.g., technological and behavioural, reac- tive and anticipatory; tactical and strategical) (Ingram, 2012). Adaptation options can be grouped into the fol- lowing categories: cultivars and breed improvements; changing management practices; switching crops, breeds and farming systems; managing water; diversifying agri- cultural systems; managing fisheries and supply chain options (FAO, 2018). However, in the case of GIs, adap- tation is somehow hindered by the PSs, given that they define bounded production areas and well-codified pro- duction rules (Thompson and Scoones, 2009). For exam- ple, many GIs include the specifications on crop varie- ties and breeds (Salpina and Pagliacci, 2022a), hence adaptation requires a modification of the code of prac- tices, turning into long and costly authorisation pro- cesses (Belletti et al., 2015; Quiñones-Ruiz et al., 2018). However, despite the extensive literature on GI products, just a few studies provide insights into PSs amendments justified by climate change (e.g., Marescotti et al., 2020; Belletti et al., 2015). Thus, according to Marescotti et al. (2020), compliance with the PSs might be more difficult to attain due to climate change, which would limit adap- tation options. Overall, scholars distinguish between two types of adaptation processes (i.e., incremental and transforma- tional), based on the expected complexity of their imple- mentation, their costs, expected risks, and the number and heterogeneity of the different stakeholders who are engaged (Howden et al., 2010). Incremental adaptation refers to short-term measures implemented at local level based on farmers’ knowledge and experience (e.g., intro- duction of rain covers). Transformational adaptation refers to long-term measures implemented at a larger spa- tial scale (region, state), suitable when impact intensity is high (e.g., changes in the boundaries of the production area). FAO (2007) also distinguishes between 1) autono- mous or on-farm adaptation, i.e., the reaction of a single farmer to climate change; and 2) planned adaptation, as policy options or response strategies, which modify adap- tive capacity or ease the introduction of given adapta- tion strategies. Being GIs “social constructions” (Belletti et al., 2017), both types of adaptation matter, involving both single producers and broader managing authori- ties. In particular, the understanding of both incre- mental (autonomous) and transformational (planned) methods of adaptation is crucial for the agri-food sector (Ingram, 2012; Fedele et al., 2019). The transformational or planned adaptation is usually influenced by the socio- economic and political structure of a given country or region. However, at farm level, the factors behind climate change adaptation can vary considerably. In the case of GIs, these factors can be grouped into four areas: socio-demographic characteristics of produc- ers, farm management and networks; product character- istics; climate change magnitude and its perception. Socio-demographic characteristics. Studies on climate change adaptation usually claim that a number of socio- demographic variables influence the development and the transmission of innovations at the farm level, includ- ing age (Morel and Cartau, 2023), sex (Zamasiya et al., 2017) and education level (Guo et al. 2021). These factors affect the absorptive capacity of farmers towards innova- tions and the introduction of new agricultural practices, including adaptation to climate change, making easier the acquisition, assimilation, use, and transformation of external knowledge in the decision making process (Asrat and Simane, 2018; Abdala et al., 2022). Farm management and networks. Besides socio- demographic characteristics of producers, other char- acteristics of the farm management matter, as well as the networks in which a farm is involved (Below et al. 2012; Khan et al., 2020; Gao et al., 2022). With regard to 268 Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Francesco Pagliacci, Dana Salpina management practices, the difference between part-time and full-time farmers is important. Although declin- ing in Europe (Shahzad and Fischer, 2022), part-time farming is still present, hence affecting the decisions about climate change adaptation. Full-time farmers are more likely to have more information and knowledge on changes in climatic conditions than part-time farm- ers, hence the former being more prone to adaptation (Maponya and Mpandeli, 2012). This can also be associ- ated with less time dedicated to farm-related activities by a part-time farmer. Also, the presence of formal and informal networks as a part of social capital (Akahoshi & Binotto, 2016), within and outside each single GI sys- tem, can explain diffusion of innovation, hence encour- aging agri-food GI producers to innovate (Wang et al., 2021). According to Ingram & Kirwan (2011), informal relationships can facilitate the formation of joint ven- tures for information exchange and business partner- ships, and thus accelerate the adaptation in agriculture. Product characteristics. Adaptation to climate change can be also affected by some characteristics relat- ed to the type of production as well as regulatory issues. Firstly, the effects of climate change – hence, the adap- tation practices – differ considerably among, e.g., crop- based or animal-based productions (FAO, 2018). Second- ly, and in the case of GIs, type of the certification (i.e., either a PDO or a PGI) may influence adaptation to cli- mate change. Actually, the restrictions imposed by each denomination, particularly in terms of size of produc- tion area, provenance of raw materials and production processes, are different, with the former being tighter than the latter. Magnitude and perception of climate change. When addressing climate change-related risks, several stud- ies have claimed that adaptation decisions can depend both on the (measurable) magnitude of climate change and on individual perceptions. Therefore, in the con- text of adapting to climate change at the farm level, the effectiveness of adaptation measures can depend both on the overall increase of temperature and on farm- ers’ ability to perceive climate-related hazards, evalu- ating their impact on production. According to many theories aimed at explaining the risk-reducing behav- iour of economic agents against natural hazards, dif- ferent perception of climate change can emphasise subjective aspect in assessing the risks associated with it. For instance, according to the Protection Motiva- tion Theory (PMT), rooted in the theory of planned behaviour (Fishbein and Ajzen, 1975; Grothmann and Reusswig, 2006), individuals’ decisions to engage in a protective response against natural hazards are driven, among others, by threat appraisal (also known as ‘risk perception’), which encompasses perceived probability and perceived consequences that an individual associ- ates with a certain hazard (Fishbein and Ajzen, 2010; Fahad et al., 2020; Ahmed et al. 2021; Talanow et al., 2021). Similarly, in the case of climate change adapta- tion strategies, Guo et al. (2021) claimed that perceived temperature change can have a significant impact on farmer’s adaptive behaviour. However, it should also be noticed that farmers’ perception of climate change is usually aligned with observed real climatic trends in specific regions (Ogalleh et al. 2012; Alam et al., 2017; Bauer et al., 2022). 3. METHODS AND DATA 3.1. Study area The Veneto Region is located in the North-East of Italy. It is characterised both by several PDOs and PGIs produced in the area, and by large climate change haz- ard, making this area perfectly suitable for studying adaptation to climate change and for obtaining insights which might be expanded to agricultural areas in other temperate regions of the EU. Veneto is among the first Italian regions in terms of the economic impact of food GI, which amounted to € 433m in 2021, including about 800 economic agents. In the region, GIs represent 48% of the total agri-food sec- tor (well above the national average, which is equal to 21%) (ISMEA-Qualivita, 2022). Moreover, in the Vene- to Region, 36 different agri-food GIs (18 PDOs and 18 PGIs, respectively) can be produced, according to the PSs that set the boundaries for the production of each GI. Among them, there are some of the GIs with the highest production value in Italy, e.g., Grana Padano cheese, Asiago DOP cheese. Moreover, both crop-based GIs and animal-based GIs are produced. Among crop GIs, there are fruits and berries (e.g., cherries, chest- nuts), vegetables (e.g. radicchio chicory, asparagus), and olive oil. Animal-based GIs include processed meats (e.g., ham), cheeses, and honey. At sub-regional level, the production areas mainly concentrate in the NUTS3 regions (province, in Italian) of Treviso, Verona, and Vicenza, where some municipal- ities are eligible for the production of more than 9 differ- ent GIs each (Fig. 1). In addition to the widespread diffusion of GIs, the Veneto Region is also highly prone to climate change (Pagliacci and Salpina, 2022), having experienced a rapid increase in average temperatures (Regione Vene- to, 2021), since the 1990s. In particular, when compar- ing the decades 1961–1970 and 2009–2018, temperature 269Adapting to climate change: what really drives the choices of the producers of Geographical Indications? Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 increase has been large across the entire Veneto region, ranging from a minimum increase of less than +1°C in the plains near the Adriatic Sea to an increase of more than +3°C in some areas of the Alpine region (Pagliacci and Salpina, 2022). In terms of rainfall, there has been a significant change in precipitation patterns. Although the region’s annual total precipitation has not changed widely, there has been an increase in both the maxi- mum annual values of short-term rainfall, on the one hand (Sofia et al., 2017), and in the frequency, length, and severity of droughts and related heat spells, on the other (Bonzanigo et al., 2016). In general terms, it can be observed that climate variability has increased, with a large number of extreme events (e.g., heavy rain- fall, strong winds, hailstorms…) observed in almost every municipality of the region, in the decade 2010– 2020. Given these characteristics, climate change haz- ard can be considered high across the whole region, with almost all agri-food sectors being largely affected (Pagliacci and Salpina, 2022). 3.2. Data collection To answer the research questions, primary data was collected using a questionnaire-based online survey administered to agri-food GI producers in the study area. Consortia or Producer Organisation (POs) helped in identifying respondents. As not all of them agreed upon providing a full list of producers, due to privacy rea- sons, they were asked to send the questionnaire directly to their members/producers, or alternatively informa- tion available online were considered. After a pilot phase (December 2021), the entire survey was administered between January and August 2022. Comprehensively, 183 responses were collected, with 46 of them being dis- carded after the first data cleaning. Thus, the final data- base includes data from 137 producers that answered all the questions necessary for the analysis. Among the respondents, 29 producers of animal- based GIs1 and 108 producers of crop-based GIs partici- pated in the survey. It is approximately 18% of the over- all population of GI producers located in the region2, excluding the producers of the raw materials. The sam- ple size – ranging from 5 to 15% – can be considered as adequate for a household survey (Bartlett et al., 2001; Alam et al., 2017). All agri-food GIs factually produced in the region were considered in this study. To further classify them, the current analysis refers to the clusters of agri-food GIs of the Veneto Region identified by Salpina and Pagli- acci (2022a) on a broad set of variables (i.e., type of GI, category, total revenue, decade of registration, share of production occurring in the region). Their classification returned six clusters of GIs. Three of them include PDOs only, distinguished according to revenue, territorial con- centration at the local level and decade of registration (“Little revenue PDOs”; “Large-scale PDO cheeses”; “Sec- ond-generation PDOs”). The remaining clusters include PGIs (the “Unexploited opportunities”, namely GIs for which the production in Veneto is actually nil; “First- generation crop PGIs”, i.e., early PGIs, with higher rev- enue; “Second-generation crop PGIs with little revenue”, i.e., PGIs with little turnover, more territorially concen- trated, and registered more recently) (Salpina and Pagli- acci, 2022a). In the current analysis, all the clusters were considered, with the only exception of the ‘unexploited opportunities’, given that the four meat-based PGIs included are not produced in Veneto. Actually, the focus on clusters, rather than on single products, enables us to provide information that can be useful for GI products 1 In the case of animal-based GIs, producers of the final products (e.g., cheesemakers) were surveyed, asking them to report also details about their suppliers. Except for a few large dairy companies, often cheese- makers were also milk-producers, hence able to provide first-hand information. Moreover, the answers of a few cheesemakers producing 2 GIs were duplicated. 2 The total number of producers for all GIs is not available. According to the authors’ estimations based on data of Qualivita and numbers pro- vided by Consortia, there are approximately 800 producers of agri-food GIs in the region (around 700 in the case of crop-based, and around 100 for animal-based GIs), excluding the producers of raw materials, i.e., only milk or meat producers. Figure 1. Distribution of the GI production areas across the region. 270 Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Francesco Pagliacci, Dana Salpina with similar characteristics (e.g., size of production area, total turnover). With regard to the contents of the survey, the ques- tionnaire addressed a broad series of topics: farm man- agement and networks, GI production, perception about climate change, implementation of adaptation measures, barriers to adaptation, additional socio-demographic information of the respondents. Some of those ques- tions were used to retrieve some core variables for the econometric model (see the following section 3.3). This includes the questions on socio-demographic charac- teristics, farm management and network, product char- acteristics, and climate change perception and adapta- tion decisions (i.e., those mentioned in the theoretical background section). Additional questions included in the survey were instead used as ancillary variables to enhance the primary findings of the study, through some additional descriptive statistics. Among others, they include the questions on the impact of extreme weather events on agri-food GIs, on adaptation measures implemented or planned to be implemented by produc- ers and on cost-effectiveness evaluation of these meas- ures, as well as on barriers to adaptation. In particular, the adaptation practices proposed in the questionnaire are based on the results of Salpina and Pagliacci (2022b), who had used semi-structured inter- views and focus-group discussions involving managers of Consortia and POs in the same study area to under- stand how agents involved in agri‐food GIs production are adapting to climate change. 3.3. The econometric model Firstly, preliminary descriptive statistics are ana- lysed, considering: observations on climate change, extreme events and adaptation practices, distinguishing between crop and animal-based GIs. Secondly, the main drivers of adaptation of GIs pro- ducers to climate change are analysed through econo- metric models. As a dependent variable, the analysis considers the implementation of adaptation strategies by GI producers. In particular, three alternative situa- tions are distinguished: (i) the one in which producers have already implemented adaptation strategies at the farm level; (ii) the one in which producers are willing to implement them in the next future; (iii) the one in which producers neither have implemented them in the past nor are willing to do so in the future. A set of covariates is considered to analyse the occurrence of the different situations (Table 1). The table bases on the theoretical background presented in Sec- tion 2, thus distinguishing the core variables in terms of socio-demographic characteristics of the producers, farm management and networks, production charac- teristics, magnitude and perception of climate change. Lastly, a control variable (altitude) is considered as well, by distinguishing farms in the lowlands, hills, and mountains. All these factors affect farmers’ decision to imple- ment adaptation strategies. To test this hypothesis, a comprehensive multinomial model is used. In particular, we estimate five different models: Y = βpP + βaA + ε (1) Y = βfF + βaA + ε (2) Y = βdD + βaA + ε (3) Y = βcC + βaA + ε (4) Y = βpP + βfF + βdDe + βcC + βaA + ε (5) Where: – Y is the (n x 3) matrix, where n = 137 respondents, indicating the alternatives decisions about adapta- tion (No, Yes, Yes in the future), and assuming the unwillingness to implement any adaptation strate- gies (both in the past and in the future) as the refer- ence baseline. – P is the (n x 3) matrix of the proxies of the socio- demographic characteristics of producers (including age range, sex and education level) and is the (3 x 1) vector of respective unknown parameters. – F is the (n x 2) matrix of the proxies for farm man- agement (i.e., full-time/part-time activity) and the number of farm adhesions, or networks the farm is involved in, such as POs and associations (e.g., CIA – Confagri) and is the (2 x 1) vector of respective unknown parameters. – D is the (n x 3) matrix of the proxies for the product characteristics (type of certifications, clusters, and type of product) and is the (3 x 1) vector of respec- tive unknown parameters. – C is the (n x 3) matrix of the proxies for climate change variables, encompassing both variations in mean temperature between 2009-2018 and 1961- 1970, and producer perception of climate change and extreme events in the production area. Addi- tionally, is the (3 x 1) vector of respective unknown parameters. – A is the (n x 1) vector of control variables about alti- tude and is the (1 x 1) unknown parameter. – ε is the (n x 1) vector of error terms. 271Adapting to climate change: what really drives the choices of the producers of Geographical Indications? Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 The implementation of the models was performed by using the software R (R Core Team, 2021). 4. RESULTS 4.1. Characteristics of respondents All the statistics about the socio-demographic char- acteristics of the producers, farm management, and product type characteristics under consideration in this study are shown in Table 2 and commented in this sub- section. The following subsection 4.2 will focus on mag- nitude and perception of climate change. With regard to the geographical distribution of the respondents, most of them are from the NUTS-3 regions of Treviso (30%), Verona (23%), and Vicenza (19%) which also host the largest number of agri-food GIs in the region (Fig. 2). In terms of socio-demographic characteristics of the producers, most of the respondents are young producers. The rate of female respondents (23%) is low, but some- how similar to the share of women who are agricultural holders in the Veneto Region (26% of the total, accord- ing to Istat, 2022). The largest share of respondents has a diploma of non-agrarian high school, followed by those with a university degree in non-agricultural field. With regard to farm management and networks, respondents are mostly full-time farmers, with only 38 out of 137 being part-time farmers. They are small-size farms, 50% of the total cases being family-run. Moreo- Table 1. Classification of the core variables considered for the analysis of the factors of adaptation. Factor Label Levels (when categorical) Socio-demographic characteristics Age 0 = Less than 35 1 = 35-44 2 = 45-54 3 = 55-64 4 = more than 64 Sexa 1 = Male Education level 0 = Elementary school 1 = Middle school 2 = High school (agrarian) 3 = High school (non-agrarian) 4 = University degree (agrarian) 5 = University degree (non-agrarian) Farm management and networks Farm management 1 = Part-time Nr. of adhesions (memberships to different networks) Continuous Product characteristics Cluster, according to Salpina and Pagliacci (2022a) 0 = Custer “Little revenue PDOs” 1 = Custer “Large-scale PDO cheeses” 2 = Custer “Second-generation PDOs” 3 = Custer “First-generation crop PGIs” 4 = Custer “Second-generation crop PGIs with little revenue” Certification type (PDO vs. PGI) 1 = PGI Type of the product 1= Crop-based Climate change Climate change observation in the production area 1 = Yes Observation of extreme events in the production area 1 = Yes Long-term temperature change (Difference in °C of the mean temperature of the period 2009-2018 and the mean temperature of the period 1961-1970)b Continuous Control factor Altitude 0 = Mountains 1 = Hills 2 = Lowlands a The research uses a binary sex categorisation (male/female), as a set of biological attributes associated with physical and physiological fea- tures. b Data refers to the municipality where the producer is located. Data retrieved and adapted by https://climatechange.europeandatajournal- ism.eu/en/about (see Ferrari and Gjergji, 2020, for further methodological details). https://climatechange.europeandatajournalism.eu/en/about https://climatechange.europeandatajournalism.eu/en/about 272 Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Francesco Pagliacci, Dana Salpina ver, they are also members of only 1.66 networks on average. When considering the characteristics of the pro- duced GIs, most respondents are crop-based GI produc- ers (108 out of 137), and PGI producers (63 out of 137). With regard to the 6 GI cluster classification by Salpina and Pagliacci (2022a), most of the respondents belong to the cluster of “Second-generation crop PGIs”, “Little revenue PDOs”, and “Second-generation PDOs”, which include the largest number of agri-food GIs. 4.2. Climate change magnitude and perception and adapta- tion practices With regard to climate change, data on long-term temperature change provided by Ferrari and Gjergji (2020) and some of the ancillary variables collected in the survey help to better characterise the sample. On average, the set of the municipality in which the respondents are located have experienced an increase of +2.7 °C, when comparing the period 1961-1970 and the period 2009-2018. Thus, 95% of the respondents produc- ing crop-based GIs and 86% of the respondents produc- ing animal-based GIs had direct experience of climate change in the production area. The main concern for both groups is an increased irregularity of precipita- tion (80%, on average), followed by temperature increase (72%, on average) (Fig 3). In terms of extreme weather events, 73% of the respondents have directly observed them in their production areas, over the last decade. On average, the impact of the extreme events under consid- eration in this research is evaluated as medium, except for frost, which seem to have the lowest effect among the respondents. Producers of animal-based GIs reported also a high impact of drought (Table 3). Figure 2. Geographical distribution of the sample. 273Adapting to climate change: what really drives the choices of the producers of Geographical Indications? Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 More specifically, among the effects of climate change on crop-based GIs, respondents pointed out the effect on the volume of production and water availability. Conversely, the perceived effect on soil quality is relative- ly lower. Among animal-based GI producers, the major concern is heat stress, affecting cattle and milk produc- tion during summer, with negative consequences in terms of both product quality and quantities produced. Despite the large and direct experience of climate change, only 24% of the respondents have already adopt- ed some types of adaptation measures to cope with cli- mate change. Moreover, 33% of them are planning to adopt them in the next future. Among the managerial measures, which are imple- mented by both crop-based GI producers and animal- based GI producers, insurances (45%) are the most pop- ular anticipatory measures of adaptation, followed by the use of advisory services and training. Among more tech- nical measures (namely, those specific to either crop- based GIs or animal-based GIs), introduction of new crop varieties (49%), followed by increased efficiency of pests (46%), irrigation (45%) and crop rotation (45%) are the ones most mentioned by crop-based GI respondents. As for the producers of animal-based GIs, they mostly opt for barn cooling systems to deal with heat stress of animals (34%), followed by importing forage from out- side the production area (21%) (Fig. 4). In terms of costs and effectiveness of adaptation measures, the ranking for managerial and technical adaptation measures of crop-based GIs is quite hetero- geneous. For crop-based GIs, introduction of new irriga- tion systems and increased efficiency of pesticides were attributed the highest scores in terms of cost/effective- ness ratio (3.6/5.0), whereas pest increase received the lowest score (2.1/5.0). For animal-based GIs, the lowest Table 2. Producer, farm, and production characteristics of the respondents (137 total respondents). Factor Label Levels (when categorical) Value Missing values Socio-demographic characteristics Age 0 = Less than 35 17 24 1 = 35-44 25 2 = 45-54 30 3 = 55-64 19 4 = more than 64 22 Sex 1 = Male 81 25 Education level 0 = Elementary school 1 24 1 = Middle school 20 2 = High school (agrarian) 11 3 = High school (non-agrarian) 43 4 = University degree (agrarian) 10 5 = University degree (non-agrarian) 29 Farm management and networks Farm management 1 = Part-time 38 38 Nr. of adhesions (memberships to different networks) Average number (std. Dev.) 1.66 (1.15) 0 Product characteristics Cluster, according to Salpina and Pagliacci (2022a) 0 = Custer “Little revenue PDOs” 29 0 1 = Custer “Large-scale PDO cheeses” 18 2 = Custer “Second-generation PDOs” 27 3 = Custer “First-generation crop PGIs” 16 4 = Custer “Second-generation crop PGIs with little revenue” 48 Certification type (PDO vs. PGI) 1 = PGI 63 0 Type of the product 1= Crop-based 108 0 Figure 3. Observations on climate change. 274 Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Francesco Pagliacci, Dana Salpina score is for pasture management plans (1.0/5.0), while the highest one is for barn cooling systems (3.8/5.0) (Table 4). 4.3. Drivers affecting climate change adaptation The factors influencing farmer willingness to imple- ment adaptation measures are analysed under models (1)-(5) admitted in this study. Table 5 returns the results of these models. In (1), which includes sociodemographic variables, education plays an important role. As expected, the respondents with a high school degree in agriculture show a greater willingness to adapt (either in terms of already-implemented adaptation strategies or in terms of future adaptation). Conversely, age is never signifi- cant. In (2), part-time management negatively affects adaptation decisions, while larger number of adhesions to associations and other sectoral networks couples with a higher probability of having already introduced some forms of adaptation practices. When considering pro- duction features, in (3), no covariates are significant. In (4), direct perception of the effects of climate change plays a major role in driving adaptation decisions, while, as an unexpected result, an increase in aver- age temperature in the production areas shows a nega- tive coefficient. Lastly, when considering all covariates jointly, in (5), education level remains the main factor influencing on-farm adaptation to climate change. In particular, education in agrarian field is positively asso- ciated with adaptation strategies. It is also confirmed that part-time farmers are less willing to undertake adaptation measures. As for GI products, “large-scale PDO cheeses” (Cluster 2) show negative coefficients, in Table 3. Average impact of extreme weather events on agri-food GIs (as evaluated by producers, scale from 1 to 5) Impact (Yes) Drought Frost Hailstorm Heavy rainfall/ Flood Insects/ diseases outbreaks Crop-based GIs 84/108 3.1 2.6 3.3 3.0 3.4 Animal-based GIs 16/29 3.7 2.3 3.5 3.1 3.3 Average 100/137 3.2 2.5 3.3 3.0 3.4 Figure 4. Adaptation methods implemented or planned to be implemented by producers of agri-food GIs. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Sh or t- te rm fo re ca st Se as on al fo re ca st W ar ni ng sy st em s In su ra nc e Co op er at io n Tr ai ni ng s Ad vi so ry se rv ic es N ew cr op v ar ie tie s Ch an ge d da te s o f p la nt in g Di ve rs ifi ed cr op v ar ie tie s Cr op ro ta tio n So il co ns er va tio n Sh ad in g Su pp le m en ta ry ir ri ga tio n N ew ir ri ga tio n sy st em In cr ea se d ef fic ie nc y of p es ts Pe st s i nc re as e Im po rt ed fo ra ge St or ag e ca pa ci ty (f or ag e) Pa st ur e m an ag em en t p la ns Co ol in g sy st em s f or b ar ns N ew a ni m al b re ed s Le ss a ni m al s p er b ar n Managerial (average for crop- and animal-based GIs) Technical (crop-based GIs) Technical (animal-based GIs) 275Adapting to climate change: what really drives the choices of the producers of Geographical Indications? Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 terms of both current and future adaptation to climate change. Similarly, when considering the type of GIs, producers of crop-based GIs are less willing to adapt than those of animal-based GIs, both when considering already existing adaptation strategies and future ones. Nevertheless, adaptation to climate change remains significant among producers that do observe climate change in their production areas. In addition, altitude of the production areas only shows a small effect, sug- gesting a negative relation between flatland locations and adaptation strategies. Table 5 also shows the results of the McFadden test (Hausman and McFadden, 1984), the Akaike Informa- tion Criterion (Sakamoto et al., 1986), and the Bayes- ian information criterion (Schwarz, 1978), computed for each model. Although the computed tests do not point to the full model (5), however it is the one with the larg- est accuracy ratio. 5. DISCUSSION This study offered important insights into the extent of adaptation to climate change in the case of the high quality agri-food GIs of the Veneto Region (Italy). The results show that agri-food GI producers are highly aware of climate change, having experienced both its direct and indirect impacts. In the case of animal-based GI productions, mainly indirect impacts of climate change are observed (e.g., alteration in fodder quality and quantity). In the case of crop-based products, the spectrum of direct impacts seems to be larger. How- ever, although producers are perfectly aware of climate change and of its effects on GI production, adaptation has not reached its full potential among them. Only 50% of the respondents have already adapted to climate change or are expressing their willingness to do so in the next future. In particular, their decisions seem to be driven by a large number of factors. All the different types of admitted drivers (i.e., socio-demographic characteristics of producers, farm management, type of product, climate change obser- vation) matter in predicting adaptation measures at the farm level. Producers with an educational degree related to agriculture, who adhere to sectoral networks, and who perceive more directly climate change in their production area tend to be more willing to adapt to cli- mate change. These findings are consistent with previ- ous studies that claim the critical role played by risk perceptions (Fishbein and Ajzen, 2010; Grothmann and Reusswig, 2006; Menapace et al., 2015; Hasan and Kumar, 2019; Zagaria et al., 2021), involvement in social networks (Bairagi et al., 2021; Bazzana et al., 2022) and education (Muench et al., 2021; Guo et al. 2021), when explaining adaptation attitudes. The counterin- tuitive negative relationship between magnitude of cli- mate change and willingness to implement adaptation strategies (as observed in just one of the selected mod- els) might be explained with the intuition that further decreases in economic profitability, due to global warm- ing, could make any adaptation investments too costly compared to any potential future benefits. However, among the most interesting findings, rigidity of the PS deserves specific attention. Indeed, it can be observed that some of the adaptation practices implemented by conventional farmers, either in Italy (Bonzanigo et al., 2016) or elsewhere (Song et al., 2019; Antwi-Agyei et al., 2021; Nor Diana et al., 2022), are also adopted by some of the producers of agri-food GIs in the Veneto Region. This is the case, for example, of some varietal improvements as well as by the introduction of barn cooling systems. The main difference in adap- Table 4. Average score of adaptation measures (as evaluated by pro- ducers, scale from 1 to 5). Managerial methods of adaptation (average) Short-term forecast 3.0 Seasonal forecast 2.7 Warning systems 2.9 Insurance 3.2 Cooperation 2.7 Trainings 3.4 Advisory services 3.0 Involvement of external actors 2.7 Adaptation measures for crop-based GIs New crop varieties 2.8 Changed dates of planting 2.6 Diversified crop varieties 3.5 Crop rotation 3.5 Soil conservation 2.8 Shading 2.5 Supplementary irrigation 3.6 New irrigation system 3.6 Increased efficiency of pests 3.6 Pests increase 2.1 Adaptation measures for animal-based GIs Imported forage 2.3 Storage capacity (forage) 2.2 Pasture management plans 1.0 Cooling systems for barns 3.8 New animal breeds 2.8 Less animals per barn 2.0 276 Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Francesco Pagliacci, Dana Salpina Ta bl e 5. R es ul ts o f t he m od el s. (1 ) (2 ) (3 ) (4 ) (5 ) Ye s Fu tu re Ye s Fu tu re Ye s Fu tu re Ye s Fu tu re Ye s Fu tu re Se x (M al e) 0. 89 0 0. 24 6 1. 73 1 0. 64 9 (0 .6 33 ) (0 .5 75 ) (1 .0 97 ) (0 .8 61 ) A ge (3 5- 44 ) -0 .1 55 0. 87 2 1. 46 8 1. 49 1 (0 .9 38 ) (0 .9 69 ) (1 .3 75 ) (1 .2 30 ) A ge (4 5- 54 ) 0. 24 5 1. 59 2 0. 80 3 1. 90 0 (0 .9 56 ) (0 .9 83 ) (1 .3 93 ) (1 .2 62 ) A ge (5 5- 64 ) -0 .1 18 0. 76 8 1. 30 2 0. 78 2 (0 .9 83 ) (1 .0 23 ) (1 .6 08 ) (1 .4 43 ) A ge (o ve r6 4) -0 .4 93 -0 .1 56 0. 03 4 0. 05 5 (0 .8 93 ) (1 .0 23 ) (1 .4 94 ) (1 .2 32 ) Ed uc at io n (M id dl e) 15 .0 73 ** * 12 .5 02 ** * - - (0 .6 78 ) (0 .6 53 ) Ed uc at io n (H ig h no n- ag ra ria n) 16 .0 46 ** * 13 .4 03 ** * 2. 40 3* 1. 79 2 (0 .4 56 ) (0 .4 58 ) (1 .4 17 ) (1 .1 72 ) Ed uc at io n (H ig h ag ra ria n) 50 .9 49 ** * 49 .2 97 ** * 41 .6 79 ** * 42 .2 09 ** * (0 .3 61 ) (0 .3 61 ) (0 .6 52 ) (0 .6 52 ) Ed uc at io n (U ni ve rs ity n on -a gr ar ia n) 14 .8 30 ** * 12 .6 54 ** * 1. 02 1 2. 38 0* (0 .5 43 ) (0 .5 03 ) (1 .6 01 ) (1 .3 28 ) Ed uc at io n6 (U ni ve rs ity a gr ar ia n) 17 .0 94 ** * 14 .8 83 ** * 1. 84 0 2. 80 4 (0 .9 76 ) (0 .9 66 ) (1 .8 41 ) (1 .7 26 ) Fa rm m an ag em en t ( Pa rt -t im e) -2 .5 98 ** * -1 .6 77 ** * -3 .7 24 ** * -2 .4 00 ** (0 .7 55 ) (0 .6 43 ) (1 .1 73 ) (1 .0 33 ) N r. of a dh es io ns 0. 56 4* 0. 24 4 0. 51 4 -0 .0 30 (0 .3 01 ) (0 .2 83 ) (0 .3 84 ) (0 .3 75 ) C lu st er s C L2 0. 99 4 1. 31 6 -1 3. 67 1* ** -1 2. 40 5* ** (1 .1 64 ) (1 .2 30 ) (1 .5 88 ) (1 .5 43 ) C lu st er s C L3 0. 22 0 0. 19 1 -0 .6 11 1. 23 2 (0 .7 50 ) (0 .7 48 ) (1 .5 80 ) (1 .4 91 ) C lu st er s C L5 0. 19 0 0. 36 2 -1 .0 94 -0 .4 52 (0 .6 09 ) (0 .5 61 ) (0 .8 70 ) (0 .8 55 ) C lu st er s C L6 -0 .5 97 -0 .4 44 0. 21 1 0. 41 9 (0 .4 58 ) (0 .4 12 ) (0 .7 39 ) (0 .6 73 ) C er tifi ca tio n ty pe (P G I) -0 .4 08 -0 .0 82 -0 .8 83 -0 .0 33 (0 .4 68 ) (0 .4 37 ) (0 .7 60 ) (0 .6 60 ) (C on tin ue d) 277Adapting to climate change: what really drives the choices of the producers of Geographical Indications? Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 (1 ) (2 ) (3 ) (4 ) (5 ) Ye s Fu tu re Ye s Fu tu re Ye s Fu tu re Ye s Fu tu re Ye s Fu tu re Ty pe (c ro p) 0. 95 0 1. 43 1 -1 0. 46 7* ** -1 0. 93 9* ** (0 .8 68 ) (0 .9 50 ) (1 .4 19 ) (1 .4 16 ) C lim at e ch an ge o bs er va tio n (Y es ) 1. 88 9* 0. 73 1 7. 08 8* ** 4. 50 6* ** (1 .0 47 ) (1 .0 02 ) (1 .6 75 ) (1 .5 19 ) O bs er va tio n of e xt re m e ev en ts (Y es ) -0 .0 07 0. 76 7 -0 .0 62 1. 33 4 (0 .7 09 ) -0 .7 83 (1 .3 55 ) (1 .3 26 ) Lo ng -t er m te m pe ra tu re c ha ng e -1 .4 71 ** -0 .6 24 -1 .4 94 0. 14 6 (0 .7 35 ) (0 .6 95 ) (1 .2 38 ) (1 .1 06 ) A lti tu de (h ill s) -0 .3 12 -0 .1 21 -1 .1 36 -1 .0 06 -0 .4 63 -0 .3 99 0. 27 8 -0 .2 66 -1 .2 77 -1 .5 45 (0 .8 56 ) (0 .8 01 ) (0 .9 37 ) (0 .8 20 ) (0 .8 60 ) (0 .8 20 ) (0 .8 70 ) (0 .7 71 ) (1 .4 62 ) (1 .3 42 ) A lti tu de (l ow la nd s) -0 .0 94 -0 .0 93 -1 .7 46 * -1 .4 45 0. 27 6 0. 01 5 0. 06 1 -0 .3 51 -2 .5 72 * -1 .9 20 (0 .8 80 ) (0 .8 29 ) (1 .0 20 ) (0 .9 17 ) (0 .8 40 ) (0 .8 04 ) (0 .8 40 ) (0 .7 58 ) (1 .3 88 ) (1 .2 80 ) C on st an t -1 6. 06 3* ** -1 3. 91 0* ** 1. 38 2 1. 81 4* -0 .6 06 -0 .8 72 1. 88 9* 0. 73 1 7. 08 8* ** 4. 50 6* ** (1 .0 33 ) (1 .0 12 ) (1 .1 09 ) (1 .0 21 ) (1 .0 85 ) (1 .1 37 ) (1 .0 47 ) (1 .0 02 ) (1 .6 75 ) (1 .5 19 ) O bs . 10 6 93 11 5 99 86 A IC 25 3. 56 19 6. 86 27 3. 61 22 9. 16 21 8. 17 BI C 32 2. 81 22 2. 18 31 7. 53 25 5. 11 32 1. 25 M cF ad de n 0. 13 0. 13 0. 04 0. 04 0. 29 A cc ur ac y 53 .7 7 49 .4 6 49 .5 7 42 .4 2 63 .9 5 N ot es : s ta tis tic al ly si gn ifi ca nt * p< 0. 1; * *p <0 .0 5; * ** p< 0. 01 . Ta bl e 5. (C on tin ue d) . 278 Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Francesco Pagliacci, Dana Salpina tation strategies between agri-food GI producers and conventional ones is the existence of regulative barriers imposed by PSs. However, the fact that the certification type (i.e., producing either a PDO or a PGI) is never sig- nificant might suggest that rigidity of code of practices (i.e., more stringent PSs in the case of PDOs than in the case of PGIs) is not a big issue in climate change adapta- tion for GI producers. This finding seems to be support- ed also by the analysis of the main barriers, according to the respondents’ perspectives (Table 6). Indeed, the restriction imposed by PSs is one of the least perceived concerns by producers, who are worried much more by the lack of financial resources or by difficulties in having access to public funds (e.g., those of the Rural Develop- ment Policy). Moreover, information issues seem to play a key role in the adaptation process. Similarly, to what is observed across Europe (Simon- et and Leseur, 2019) or elsewhere (Alam et al., 2017; Belay et al., 2022), the economic aspect of adaptation is proved to matter, as on-farm adaptation mostly relies on producers’ own resources. On top of that, there is an issue of uncertainty, associated with the high cost of investments, and with the uncertain long-term benefits. In other words, uncertain future costs of climate risks compared to the certain and immediate costs of adapta- tion measures together with uncertain expected returns on investment represent one of the major barriers to cli- mate change adaptation (Lefebvre et al., 2014), also in the case of agri-food GI producers. The barriers discussed above couple with external factors, mainly involving policy and governance issues: observed complexity in having access to public funds, a lack of technical assistance in obtaining such help, mar- ket dynamics and the current geo-political conditions. In this context, climate change adaptation, which is of utmost importance given the impacts already affecting GI farmers and producers, seems to require specific pol- icy interventions. 6. POLICY IMPLICATIONS The results of this study represent an important con- tribution, not only to inform policymakers at regional level (i.e., in the Veneto Region), but also for national and EU policymakers and stakeholders. Indeed, the results of this study are highly generalizable in terms of suggested approach and adopted empirical strategy. In particular, the suggested strategy, distinguishing three alternative situations (farmers who have implemented adaptation strategies; farmers intending to implement them in the future; and farmers neither having imple- mented nor willing to do so in the future) holds prom- ise for delivering a relatively elevated degree of accuracy and interpretability, also when implemented in other case studies. Moreover, the results suggest that the main policy instruments for high-quality agri-food products might be largely improved across the EU. Firstly, a more tar- geted support within the new Common Agricultural Policy (2023–2027) will largely help. This is true also in a region such as Veneto, where in the 2014-2020 program- ming period just 1.5% of the total funds of the Rural Development Programme was earmarked to the measure aimed at supporting quality schemes (i.e., measure 03). Besides a larger public fund allocation, in this con- text, reliability of new technologies and clear informa- tion regarding their effectiveness might help. This will provide new incentives to the producers of agri-food GI, when considering their options of investing in new adaptation measures to climate change. Moreover, it could also be helpful developing peer-to-peer learning practices among producers together with fostering fur- ther collaborations among GI systems that face similar risks. Indeed, the role of public policies is not limited to allocation of financial resources to prevent the finan- cial barriers of adaptation, but it can also ease knowl- edge transfer (Ignaciuk, 2015), fostering collaborations between farms and Consortia, and across sectors (e.g., Table 6. Barriers to climate change adaptation, as perceived by pro- ducers of agri-food GIs (as evaluated by producers, scale from 1 to 5). Barriers (Number of respondents) Adaptation No (40) Yes (33) Yes_ future (45) Total (118) Lack of financial resources 3.6 3.1 3.9 3.6 High cost of investments and long-term benefits 3.9 3.2 3.5 3.5 Accession to RDP funds 3.6 3.9 3.8 3.7 Long waiting time for the accession RDP funds 3.8 3.5 3.9 3.7 Lack of technical assistance 3.4 3.0 3.4 3.3 Lack of information on effectiveness of certain adaptation measures 3.8 3.5 3.8 3.7 Restriction imposed by PSs 3.5 3.1 3.2 3.3 Land property 2.8 2.4 2.8 2.6 Lack of local and production networks 2.1 2.8 2.5 2.5 Lack of producers’ representation in the decision-making process 3.3 3.3 3.3 3.3 279Adapting to climate change: what really drives the choices of the producers of Geographical Indications? Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 public and private). To this regard, the framework of Agricultural Knowledge and Innovation Systems (AKIS) could be strengthened, in terms of a multi-stakeholder process (Cruz Maceín et al., 2023). Analogously, also the functions of Consortia and POs could be strengthened to better facilitate the coordination among the stake- holders for the implementation of adaptation strategies at GI level. These entities, which frequently offer advi- sory support, can serve as innovation intermediaries, cooperating with research organisations (Salpina and Pagliacci, 2022b) and facilitating horizontal and verti- cal diffusion of information. Thus, Consortia and POs can play a pivotal role in sustaining adaptation efforts, and guide farmers in investing in new adaptation meas- ures. Lastly, the findings of this empirical study hold the potential to contribute significantly to international discourse surrounding food policy, by providing an in- depth examination of climate change adaptation prac- tices within the GI agri-food sector, the policy area that has thus far received limited attention within academic circles. 7. CONCLUSION This study aimed at analysing climate change adap- tation strategies in the case of high-quality agri-food sector, shedding light on the main factors inf luenc- ing the decision of producers to adapt. In the past, this topic was largely neglected in the literature. Actually, to the authors’ best knowledge, only a few other stud- ies have already focused on the topic of climate change adaptation, taking agri-food GIs into consideration. The key findings of the research suggest that despite a gen- eralised (and high) awareness of climate change among GI farmers and producers, this has not yet turned into widespread adoption of adaptation measures. The main factors influencing the willingness of producers are con- firmed to revolve around the complex interplay of socio- demographic characteristics (e.g., age, education), farm management and networks, and production characteris- tics, in addition to the perception of climate change. Despite the potential limitations of any online sur- veys (e.g., some bias in respondents’ characteristics, in favour of younger and more educated ones), further studies could eventually replicate the questionnaire- based survey in other countries and regions, making use of the same methods proposed here. Moreover, it should be noticed that this study encompassed the cer- tified agri-food sector in general. Thus, future works, focusing on a specific sector (e.g., only cheese products), would allow for a more targeted examination of key variables affecting climate change adaptation. One addi- tional limitation of this study is the absence of a com- parison between farmers operating within GI schemes and the ones operating outside such schemes. However, such a limitation was due to the complexity of such a comparison and mostly to the data collection process, which was primarily done through Consortia and POs. Future research will eventually address this gap, provid- ing valuable insights into this phenomenon. Moreover, future lines of research will also involve the analysis of the drivers contributing to the adoption of specific adap- tation measures and will consider additional and more sophisticated proxies for climate change perception. ACKNOWLEDGEMENT The authors gratefully acknowledge the financial support from the project “What if the terroir moves under our feet? Addressing the effects of climate change on the use of geographical indications for agri-food products in Veneto, Italy” (PAGL_BIRD20_05 - BIRD 3 2020/2022 research grant). Project financed with BIRD 2020 funds, Department TESAF, University of Padova - Italy. The authors thank the editor and the anonymous referees for their precious suggestions that have led to a great improvement in the overall quality of the paper. REFERENCES Abdala, R.G., Binotto, E., Borges, J.A.R. (2022). Family farm succession: evidence from absorptive capacity, social capital and socioeconomic aspects. Revista de Economia e Sociologia Rural, 60(4), E235777. https:// doi.org/10.1590/1806-9479.2021.235777 Ahmed, Z., Guha, G.S., Shew, A.M., Alam, G.M. (2021). Climate change risk perceptions and agricultural adaptation strategies in vulnerable riverine char islands of Bangladesh. Land Use Policy 103, 105295. https://doi.org/10.1016/j.landusepol.2021.105295. Akahoshi, W.B., Binotto, E. (2016). Cooperativas e capi- tal social: caso da Copasul, Mato Grosso do Sul. Gestão & Produção 23(1), 104-117. https://doi. org/10.1590/0104-530X532-13. Alam, G.M.M., Alam, K., Mushtaq, S. (2017). Climate change perceptions and local adaptation strategies of hazard-prone rural households in Bangladesh. Climate Risk Management 17, 52-63. https://doi. org/10.1016/j.crm.2017.06.006. Antwi-Agyei, P., Nyantakyi-Frimpong, H. (2021). Evidence of Climate Change Coping and Adaptation Practices https://doi.org/10.1590/1806-9479.2021.235777 https://doi.org/10.1590/1806-9479.2021.235777 https://doi.org/10.1016/j.landusepol.2021.105295 https://doi.org/10.1590/0104-530X532-13 https://doi.org/10.1590/0104-530X532-13 https://doi.org/10.1016/j.crm.2017.06.006 https://doi.org/10.1016/j.crm.2017.06.006 280 Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Francesco Pagliacci, Dana Salpina by Smallholder Farmers in Northern Ghana. Sustain- ability 13, 1308. https://doi.org/10.3390/su13031308. ARPAV (2017). A proposito di cambiamenti climatici (seconda edizione). Available at: https://www.arpa. veneto.it/arpavinforma/pubblicazioni/a-proposito- di-.-cambiamenti-climatici-seconda-edizione (last accessed on 18th December 2022). Asrat, P., Simane, B. (2018). Farmers’ Perception of Cli- mate Change and Adaptation Strategies in the Dabus Watershed, North-West Ethiopia. Ecological Processes 7, 7. https://doi.org/10.1186/s13717-018-0118-8. Bairagi, S., Bhandari, H., Das, S.K., Mohanty, S. (2021). Flood-tolerant rice improves climate resilience, prof- itability, and household consumption in Bangladesh. Food Policy, 105, 102183. https://doi.org/10.1016/j. foodpol.2021.102183 Bartlett, J.E., Kotrlik, J.W., Higgins, C.C. (2001). Organi- zational research: determining appropriate sam- ple size in survey research. Information Technology, Learning, and Performance Journal 19(1), 43–50. Bauer, T.N., De Jong, W., Ingram. V. (2022). Percep- tion matters: An Indigenous perspective on climate change and its effects on forest-based livelihoods in the Amazon. Ecology and Society 27(1), 17. https:// doi.org/10.5751/ES-12837-270117. Bazzana, D., Foltz, J.D., Zhang, Y. (2022). Impact of cli- mate smart agriculture on food security: An agent- based analysis. Food Policy, 111, 102304. https://doi. org/10.1016/j.foodpol.2022.102304 Belay, A., Oludhe, C., Mirzabaev, A., Recha, J.W., Ber- hane, Z., Osano, P.M., Demissie, T., Olaka, L.A., Sol- omon, D. (2022). Knowledge of climate change and adaptation by smallholder farmers: evidence from southern Ethiopia, Heliyon, 8(12), e12089. https://doi. org/10.1016/j.heliyon.2022.e12089. Belletti, G., Marescotti, A., Brazzini, A. (2017). Old World Case Study: The Role of Protected Geographical Indi- cations to Foster Rural Development Dynamics: The Case of Sorana Bean PGI. In: van Caenegem, W., Cleary, J. (Eds.) “The Importance of Place: Geograph- ical Indications as a Tool for Local and Regional Development”, 253–276. Springer International Pub- lishing AG Belletti, G., Marescotti, A., Sanz-Cañada, J., Vakoufaris, H. (2015). Linking protection of geographical indica- tions to the environment: Evidence from the Europe- an Union olive-oil sector. Land Use Policy 48, 94–106. https://doi.org/10.1016/j.landusepol.2015.05.003. Below, T.B., Mutabazi, K.D., Kirschke, D., Franke, C., Sieber, S., Siebert, R., Tscherning, K. (2012). Can farmers’ adaptation to climate change be explained by socio-economic household-level variables? Glob- al Environmental Change 22, 223–235. https://doi. org/10.1016/j.gloenvcha.2011.11.012. Bonzanigo, L., Bojovic, D., Maziotis, A., Giupponi, C. (2016). Agricultural policy informed by farmers’ adaptation experience to climate change in Veneto, Italy. Regional Environmental Change 16, 245–258. https://doi.org/10.1007/s10113-014-0750-5. Bro, A.S. (2020). Climate Change Adaptation, Food Secu- rity, and Attitudes toward Risk among Smallholder Coffee Farmers in Nicaragua. Sustainability 12, 6946. https://doi.org/10.3390/su12176946. Cruz Maceín, J.L., Gonzalez-Fernandez, I., Barrutieta, A. et al. (2023) Adaptation strategies for dealing with global atmospheric change in Mediterranean agri- culture: a triple helix approach to the Spanish case study. Regional Environmental Change 23, 142. htt- ps://doi.org/10.1007/s10113-023-02131-1 Deressa, T.T., Hassan, R.M., Ringler, C., Alemu, T., Yesuf, M. (2009). Determinants of farmers’ choice of adap- tation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change, 19(2), 248- 255. https://doi.org/10.1016/j.gloenvcha.2009.01.002. Fahad, S., Inayat, T., Wang, J., Dong, L., Hu, G., Khan, S., Khan, A. (2020). Farmers’ awareness level and their perceptions of climate change: A case of Khyber Pakh- tunkhwa province, Pakistan. Land Use Policy 96, 104669. https://doi.org/10.1016/j.landusepol.2020.104669. FAO (2007). Adaptation to climate change in agricul- ture, forestry and fisheries: Perspective, framework and priorities, Rome. https://www.fao.org/3/au030e/ au030e.pdf (last accessed on 18th December 2022). FAO (2018). Climate Smart Agriculture Sourcebook, CSA E-Learning modules. FAO. https://www.fao.org/ climate-smart-agriculture-sourcebook/production- resources/en/ (last accessed on 18th December 2022). Fedele, G., Donatti, C.I., Harvey, C.A., Hannah, L., Hole, D.G. (2019). Transformative adaptation to climate change for sustainable social-ecological systems. Environmental Science & Policy 101, 116–125. https:// doi.org/10.1016/j.envsci.2019.07.001. Ferrari, L., Gjergji, O. (2020). Il riscaldamento clima- tico in Europa, comune per comune. In: European Data Journalism Network. Available at: https://www. europeandatajournalism.eu/ita/Notizie/Data-news/ Il-riscaldamento-climatico-in-Europa-comune-per- comune (last accessed on 19th December 2022). Fishbein, M., Ajzen I. (1975). Beliefs, attitude, intention and behaviour. Addison-Wesley: Reading, MA. Fishbein, M., Ajzen, I. (2010). Predicting and Chang- ing Behavior: The Reasoned Action Approach. Psy- chology Press: New York, NY, USA. https://doi. org/0.4324/9780203838020. https://doi.org/10.3390/su13031308 https://www.arpa.veneto.it/arpavinforma/pubblicazioni/a-proposito-di-.-cambiamenti-climatici-seconda-edizione https://www.arpa.veneto.it/arpavinforma/pubblicazioni/a-proposito-di-.-cambiamenti-climatici-seconda-edizione https://www.arpa.veneto.it/arpavinforma/pubblicazioni/a-proposito-di-.-cambiamenti-climatici-seconda-edizione https://doi.org/10.1186/s13717-018-0118-8 https://doi.org/10.1016/j.foodpol.2021.102183 https://doi.org/10.1016/j.foodpol.2021.102183 https://doi.org/10.5751/ES-12837-270117 https://doi.org/10.5751/ES-12837-270117 https://doi.org/10.1016/j.foodpol.2022.102304 https://doi.org/10.1016/j.foodpol.2022.102304 https://doi.org/10.1016/j.heliyon.2022.e12089 https://doi.org/10.1016/j.heliyon.2022.e12089 https://doi.org/10.1016/j.landusepol.2015.05.003 https://doi.org/10.1016/j.gloenvcha.2011.11.012 https://doi.org/10.1016/j.gloenvcha.2011.11.012 https://doi.org/10.1007/s10113-014-0750-5 https://doi.org/10.3390/su12176946 https://doi.org/10.1007/s10113-023-02131-1 https://doi.org/10.1007/s10113-023-02131-1 https://doi.org/10.1016/j.gloenvcha.2009.01.002 https://doi.org/10.1016/j.landusepol.2020.104669 https://www.fao.org/3/au030e/au030e.pdf https://www.fao.org/3/au030e/au030e.pdf https://www.fao.org/climate-smart-agriculture-sourcebook/production-resources/en/ https://www.fao.org/climate-smart-agriculture-sourcebook/production-resources/en/ https://www.fao.org/climate-smart-agriculture-sourcebook/production-resources/en/ https://doi.org/10.1016/j.envsci.2019.07.001 https://doi.org/10.1016/j.envsci.2019.07.001 https://www.europeandatajournalism.eu/ita/Notizie/Data-news/Il-riscaldamento-climatico-in-Europa-comune-per-comune https://www.europeandatajournalism.eu/ita/Notizie/Data-news/Il-riscaldamento-climatico-in-Europa-comune-per-comune https://www.europeandatajournalism.eu/ita/Notizie/Data-news/Il-riscaldamento-climatico-in-Europa-comune-per-comune https://www.europeandatajournalism.eu/ita/Notizie/Data-news/Il-riscaldamento-climatico-in-Europa-comune-per-comune https://doi.org/0.4324/9780203838020 https://doi.org/0.4324/9780203838020 281Adapting to climate change: what really drives the choices of the producers of Geographical Indications? Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Gao, J., Shahid, R., Ji, X., Li, S. (2022). Climate change resilience and sustainable tropical agriculture: Farm- ers’ perceptions, reactive adaptations and deter- minants of reactive adaptations in Hainan, China. Atmosphere 2022 13, 955. https://doi.org/10.3390/ atmos13060955. Giacomini, C., Mancini, M.C. (2015). Organisation as a key factor in Localised Agri-Food Systems (LAFS). Bio-Based and Applied Economics 4(1), 17-32. https:// doi.org/10.13128/BAE-15088. Grothmann, T. and Reusswig, F. (2006). People at Risk of Flooding: Why Some Residents Take Precautionary Action While Others do not. Natural Hazards, 38: 101–120. DOI 10.1007/s11069-005-8604-6 Guo, R., Li, Y., Shang, L., Feng, C., Wang, X. (2021). Local farmer’s perception and adaptive behavior toward climate change. Journal of Cleaner Production 287, 125332. https://doi.org/10.1016/j.jclepro.2020.125332. Hausman, J.A., McFadden, D. (1984). A Specification Test for the Multinomial Logit Model, Econometrica 52, 1219–1240. Hasan, M. K., Kumar, L. (2019). Comparison between meteorological data and farmer perceptions of cli- mate change and vulnerability in relation to adap- tation, Journal of Environmental Management, 237, 54-62. https://doi.org/10.1016/j.jenvman.2019.02.028. Henry, L. (2023). Adapting the designated area of geo- graphical indications to climate change. American Journal of Agricultural Economics 105, 1088-1115. https://doi.org/10.1111/ajae.12358. Howden, S., Crimp, S., Nelson, R. (2010). Australian agriculture in a climate of change. In Proceedings of Greenhouse 2009 Conference, Perth, Australia, 23–26 March 2009, CSIRO Publishing, Collingwood, Aus- tralia, 101–111. Ignaciuk, A. (2015). Adapting Agriculture to Cli- mate Change: A Role for Public Policies, OECD Food, Agriculture and Fisheries Papers, No. 85, OECD Publishing, Paris. https://doi. org/10.1787/5js08hwvfnr4-en. Ingram, J. (2012). Agricultural adaptation to climate change: New approaches to knowledge and learning. In Climate change Impact and Adaptation in Agri- cultural Systems. Wallingford (UK): CAB Interna- tional, 253–270. Ingram, J., Kirwan, J. (2011). Matching new entrants and retiring farmers through farm joint ventures: Insights from the Fresh Start Initiative in Cornwall, UK. Land Use Policy 28(4), 917-927. https://doi.org/10.1016/j. landusepol.2011.04.001. IPCC (2014). Annex II: Glossary. IPCC: Geneva (CH). https://www.ipcc.ch/site/assets/uploads/2018/02/ WGIIAR5-AnnexII_FINAL.pdf (last accessed on 20th December 2022). Ismea-Qualivita (2022). Rapporto sulle produzioni agroal- imentari e vitivinicole italiane DOP IGP STG. Fon- dazione Qualivita, Siena, Italia. https://www.qualivita. it/osservatorio/rapporto-ismea-qualivita/#toggle-id-1 (last accessed on 18th December 2022). Istat (2022). 7° Censimento generale dell’agricoltura: Inte- grazione dei primi risultati: https://www.istat.it/it/ archivio/273753. Khan, I., Lei, H., Shah, I.A., Ali, I., Khan, I., Muham- mad, I., Huo, X., Javed, T. (2020). Farm households’ risk perception, attitude and adaptation strategies in dealing with climate change: Promise and perils from rural Pakistan. Land Use Policy 91, 104395. https:// doi.org/10.1016/j.landusepol.2019.104395. Lamonaca, E., Santeramo, F. G., & Seccia, A. (2021). Cli- mate changes and new productive dynamics in the global wine sector. Bio-Based and Applied Econom- ics, 10(2), 123–135. https://doi.org/10.36253/bae-9676 Lefebvre, M., De Cuyper, K., Loix, E., Viaggi, D., Gomez, Y., Paloma, S. (2014). European farmers` intentions to invest in 2014-2020: survey results. EUR 26672. Luxembourg: Publications Office of the European Union, JRC90441. https://doi.org/10.2791/82963. Lereboullet, A-L. (2013). How do Geographical Indica- tions interact with the adaptive capacity and resil- ience of viticultural systems facing global change? [Conference Short Paper] 25th ESRS Congress. 29 July – 1 August 2013, Florence, Italy, 331-332 Maponya, P., Mpandeli, S. (2012). Climate Change and Agricultural Production in South Africa: Impacts and Adaptation options. Journal of Agricultural Science 4(10), 48-60. https://doi.org/10.5539/jas.v4n10p48. Marescotti, A., Quiñones-Ruiz, X.F., Edelmann, H., Bel- letti, G., Broscha, K., Altenbuchner, C., Penker, M., Scaramuzzi, S. (2020). Are Protected Geographical Indications Evolving Due to Environmentally Related Justifications? An Analysis of Amendments in the Fruit and Vegetable Sector in the European Union. Sustainability 12(9), 3571. https://doi.org/10.3390/ su12093571. Menapace, L., Colson, G., Raffaelli, R. (2015). Climate change beliefs and perceptions of agricultural risks: An application of the exchangeability method. Global Environmental Change 35, 70-81. https://doi. org/10.1016/j.gloenvcha.2015.07.005. Mitter, H., Larcher, M., Schönhart, M., Schmid, E. (2019). Exploring Farmers’ Climate Change Perceptions and Adaptation Intentions: Empirical Evidence from Aus- tria. Environmental Management 63, 804–821. https:// doi.org/10.1007/s00267-019-01158-7. https://doi.org/10.3390/ https://doi.org/10.13128/BAE-15088 https://doi.org/10.13128/BAE-15088 https://doi.org/10.1016/j.jclepro.2020.125332 https://doi.org/10.1016/j.jenvman.2019.02.028 https://doi.org/10.1111/ajae.12358 https://doi.org/10.1787/5js08hwvfnr4-en https://doi.org/10.1787/5js08hwvfnr4-en https://doi.org/10.1016/j.landusepol.2011.04.001 https://doi.org/10.1016/j.landusepol.2011.04.001 https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-AnnexII_FINAL.pdf https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-AnnexII_FINAL.pdf https://www.qualivita.it/osservatorio/rapporto-ismea-qualivita/#toggle-id-1 https://www.qualivita.it/osservatorio/rapporto-ismea-qualivita/#toggle-id-1 https://www.istat.it/it/archivio/273753 https://www.istat.it/it/archivio/273753 https://doi.org/10.1016/j.landusepol.2019.104395 https://doi.org/10.1016/j.landusepol.2019.104395 https://doi.org/10.36253/bae-9676 https://doi.org/10.2791/82963 https://doi.org/10.5539/jas.v4n10p48 https://doi.org/10.3390/su12093571 https://doi.org/10.3390/su12093571 https://doi.org/10.1016/j.gloenvcha.2015.07.005 https://doi.org/10.1016/j.gloenvcha.2015.07.005 https://doi.org/10.1007/s00267-019-01158-7 https://doi.org/10.1007/s00267-019-01158-7 282 Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Francesco Pagliacci, Dana Salpina Morel, K., Cartau, K. (2023). Adaptation of organic vege- table farmers to climate change: An exploratory study in the Paris region. Agricultural Systems, 210. 103703. https://doi.org/10.1016/j.agsy.2023.103703 Muench, S., Bavorova, M., Pradhan, P. (2021). Climate Change Adaptation by Smallholder Tea Farmers: A Case Study of Nepal. Environmental Science & Policy 116, 136- 146. https://doi.org/10.1016/j.envsci.2020.10.012. Nor Diana, M.I., Zulkepli, N.A., Siwar, C., Zainol, M.R. (2022). Farmers’ Adaptation Strategies to Cli- mate Change in Southeast Asia: A Systematic Lit- erature Review. Sustainability 14, 3639. https://doi. org/10.3390/su14063639. Ogalleh, S.A., Vogl, C.R., Eitzinger, J., Hauser, M. (2012). Local Perceptions and Responses to Climate Change and Variability: The Case of Laikipia District, Ken- ya. Sustainability 4(12), 3302-3325. https://doi. org/10.3390/su4123302. Owen, L., Udall, D., Franklin, A., Kneafsey, M. (2020). Place-Based Pathways to Sustainability: Exploring Alignment between Geographical Indications and the Concept of Agroecology Territories in Wales. Sustainability 12, 4890. https://doi.org/10.3390/ su12124890 Pagliacci, F., Salpina D. (2022). Territorial hotspots of exposure to climate disaster risk. The case of agri- food geographical indications in the Veneto Region. Land Use Policy 123, 106404. https://doi. org/10.1016/j.landusepol.2022.106404. Quiñones‐Ruiz, X., Edelmann, H., Penker, M., Bel- letti, G., Marescotti, A., Scaramuzzi, S., Broscha, K., Braito, M., Altenbuchner, C. (2018). How are food Geographical Indications evolving? – An analysis of EU GI amendments. British Food Journal 120, 1876– 1887. https://doi.org/10.1108/BFJ‐02‐2018‐0087. Rahman, T.H.M., Albizua, A., Soubry, B., Tourangeau, W. (2021). A framework for using autonomous adapta- tion as a leverage point in sustainable climate adap- tation. Climate Risk Management 34, 100376. https:// doi.org/10.1016/j.crm.2021.100376. Regione Veneto (SISTAR) (2021). I cambiamenti climat- ici ormai tangibili. Statistiche Flash. https://statistica. regione.veneto.it/Pubblicazioni/StatisticheFlash/statis- tiche_flash_ottobre_2021.pdf (last accessed on 20 June 2022). R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project. org/. Sakamoto, Y., Ishiguro, M., and Kitagawa, G. (1986). Akaike Information Criterion Statistics. D. Reidel Publishing Company. Salpina, D., Pagliacci, F. (2022a). Contextual vulner- ability to climate change of heterogeneous agri-food geographical indications: A case study of the Veneto region (Italy). Environmental Science & Policy, 136: 103–113. https://doi.org/10.1016/j.envsci.2022.06.005. Salpina, D., Pagliacci, F. (2022b). Are We Adapting to Climate Change? Evidence from the High-Quality Agri-Food Sector in the Veneto Region. Sustainability 14(18), 11482. https://doi.org/10.3390/su141811482. Schwarz, G. (1978). Estimating the Dimension of a Mod- el. The Annals of Statistics 6(2), 461-464. Shahzad, M. A., Fischer, C. (2022). The decline of part- time farming in Europe: an empirical analysis of trends and determinants based on Eurostat panel data. Applied Economics 54(42), 4812-4824. https:// doi.org/10.1080/00036846.2022.2036687. Simonet, G., Leseur, A. (2019). Barriers and drivers to adaptation to climate change – a field study of ten French local authorities. Climatic Change 155, 621– 637. https://doi.org/10.1007/s10584-019-02484-9. Sofia, G., Roder, G., Dalla Fontana, G., Tarolli, P. (2017). Flood dynamics in urbanised landscapes: 100 years of climate and humans’ interaction. Scientific Reports 7, 40527. https://doi.org/10.1038/srep40527. Song, C.‐X., Liu, R.‐F., Les Oxley, Ma, H.‐Y. (2019). Do farmers care about climate change? Evidence from five major grain producing areas of China. Journal of Integrative Agriculture 18, 1402–1414. https://doi. org/10.1016/S2095-3119(19)62687-0. Talanow, K., Topp, E.N., Loos, J. Martín-López, B. (2021). Farmers’ perceptions of climate change and adap- tation strategies in South Africa’s Western Cape. Journal of Rural Studies 81, 203-219. https://doi. org/10.1016/j.jrurstud.2020.10.026. Thompson, J., Scoones, I. (2009). Addressing the dynam- ics of agri-food systems: an emerging agenda for social science research. Environmental Science & Policy 12(4), 386–397. https://doi.org/10.1016/j.envs- ci.2009.03.001. UNFCCC (2022). Joint work on implementation of cli- mate action on agriculture and food security. Deci- sion -/CP.27, the United Nations Climate Change Conference COP27, Sharm el-Sheikh, 18 Nov 2022 https://unfccc.int/documents/624317 (last accessed on 16th December 2022). Vandecandelaere, E., Arfini, F., Belletti, G., Marescotti, A. (2010). Linking people, places and products. A guide for promoting quality linked to geographical origin and sustainable Geographical Indications. 2nd edi- tion. FAO, Rome. https://www.fao.org/documents/ card/en/c/debded43-9d99-5c74-a440-e8db347941ac (last accessed on 20th December 2022). https://doi.org/10.1016/j.agsy.2023.103703 https://doi.org/10.1016/j.envsci.2020.10.012 https://doi.org/10.3390/su14063639 https://doi.org/10.3390/su14063639 https://doi.org/10.3390/su4123302 https://doi.org/10.3390/su4123302 https://doi.org/10.3390/su12124890 https://doi.org/10.3390/su12124890 https://doi.org/10.1016/j.landusepol.2022.106404 https://doi.org/10.1016/j.landusepol.2022.106404 https://doi.org/10.1108/BFJ https://doi.org/10.1016/j.crm.2021.100376 https://doi.org/10.1016/j.crm.2021.100376 https://statistica.regione.veneto.it/Pubblicazioni/StatisticheFlash/statistiche_flash_ottobre_2021.pdf https://statistica.regione.veneto.it/Pubblicazioni/StatisticheFlash/statistiche_flash_ottobre_2021.pdf https://statistica.regione.veneto.it/Pubblicazioni/StatisticheFlash/statistiche_flash_ottobre_2021.pdf https://www.R-project.org/ https://www.R-project.org/ https://doi.org/10.1016/j.envsci.2022.06.005 https://doi.org/10.3390/su141811482 https://doi.org/10.1080/00036846.2022.2036687 https://doi.org/10.1080/00036846.2022.2036687 https://doi.org/10.1007/s10584-019-02484-9 https://doi.org/10.1038/srep40527 https://doi.org/10.1016/S2095-3119(19)62687-0 https://doi.org/10.1016/S2095-3119(19)62687-0 https://doi.org/10.1016/j.jrurstud.2020.10.026 https://doi.org/10.1016/j.jrurstud.2020.10.026 https://doi.org/10.1016/j.envsci.2009.03.001 https://doi.org/10.1016/j.envsci.2009.03.001 https://unfccc.int/documents/624317 https://www.fao.org/documents/card/en/c/debded43-9d99-5c74-a440-e8db347941ac https://www.fao.org/documents/card/en/c/debded43-9d99-5c74-a440-e8db347941ac 283Adapting to climate change: what really drives the choices of the producers of Geographical Indications? Bio-based and Applied Economics 13(3): 265-283, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15221 Vercelli, M., Novelli, S., Ferrazzi, P., Lentini, G., Ferracini, C. A. (2021). Qualitative Analysis of Beekeepers’ Per- ceptions and Farm Management Adaptations to the Impact of Climate Change on Honey Bees. Insects 12, 228. https://doi.org/10.3390/insects12030228. Wang, W., Zhao, X., Li, H., Zhang, Q. (2021). Will social capital affect farmers’ choices of climate change adap- tation strategies? Evidences from rural households in the Qinghai-Tibetan Plateau, China. Journal of Rural Studies 83, 127-137. https://doi.org/10.1016/j. jrurstud.2021.02.006. WTO (1994). Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS Agreement). https://www.wto.org/english/docs_e/legal_e/27- trips_01_e.htm (last accessed on 20th November 2022). Zagaria, C., Schulp, C.J.E., Zavalloni, M., Viaggi, D., Ver- burg, P.H. (2021). Modelling transformational adap- tation to climate change among crop farming systems in Romagna, Italy. Agricultural Systems, 188. 103024. https://doi.org/10.1016/j.agsy.2020.103024. Zamasiya, B., Nyikahadzoi, K., Mukamuri, B.B. (2017). Factors influencing smallholder farmers’ behavioural intention towards adaptation to climate change in transitional climatic zones: A case study of Hwedza District in Zimbabwe. Journal of Environmental Man- agement, 198 (1), 233-239, https://doi.org/10.1016/j. jenvman.2017.04.073. https://doi.org/10.3390/insects12030228 https://doi.org/10.1016/j.jrurstud.2021.02.006 https://doi.org/10.1016/j.jrurstud.2021.02.006 https://www.wto.org/english/docs_e/legal_e/27-trips_01_e.htm https://www.wto.org/english/docs_e/legal_e/27-trips_01_e.htm https://doi.org/10.1016/j.agsy.2020.103024 https://doi.org/10.1016/j.jenvman.2017.04.073 https://doi.org/10.1016/j.jenvman.2017.04.073 Guns, Germs and Climate: Food Security and Food Systems in a Risky World Francesco Pagliacci1,*, Valentina Raimondi2, Luca Salvatici3,* The implications of the Russian invasion of Ukraine for African economies: A CGE analysis for Ethiopia Amsalu Woldie Yalew1,2,*, Victor Nechifor3, Emanuele Ferrari3 Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator Tulia Gattone Adapting to climate change: what really drives the choices of the producers of Geographical Indications? Francesco Pagliacci1, Dana Salpina1,2,* Exploring preferences for contractual terms in a scenario of ecological transition for the agri-food sector: a latent class approach Stefano Ciliberti, Angelo Frascarelli*, Gaetano Martino, Andrea Marchini Intention and behavior toward eating whole grain pasta on a college dining campus: Theory of Planned Behavior and message framing Giovanni Sogari1, Rungsaran Wongprawmas1, Giulia Andreani1, Michele Lefebvre2, Nicoletta Pellegrini3, Miguel I. Gómez4, Cristina Mora1, Davide Menozzi1,*