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Understanding the participation in agri-environmental schemes: evidence from Tuscany Region


  • Fabio Bartolini


  • Gianluca Brunori
  • Laura Fastelli
  • Massimo Rovai


Since introduction with accompanying measures of MacSharry Reform, Agri-Environmental Schemes (AESs) represents the main agricultural policy instrument which address environmental objectives in Common Agricultural Policy. In spite of twenty years application and its high share of RDP budget, several evaluation reports and scientific literature have measured a low environmental impacts compared with the expectations. Economic literature has identified in low target level of schemes, low participation rates, spatial distribution heterogeneity and asymmetric information between farmers and public administration the main reasons for such AESs lower impact. AESs are compensation payments, aimed to encourage farmers to adopt agricultural practices/prescriptions which affect positively environment. The agri-environmental payments are designed in order to cover (on the average) income foregone, and operational and transaction cost increase due to AESs farm implementation. Agricultural economic literature has highlighted positive effects of incentive mechanisms in increasing farmers' participation. Such literature has identified farm, farmers and household characteristics which determine an expected compliance cost lower than compensation payments, and then are determinant of participation. The agricultural economics literature has also highlighted how uncertainty in market and climatic condition is one of the main driver of adoption of AESs. Due to farmers' risk aversion, farmers prefer to receive lower but with certain payments (i.e. payments by landscape measure or twenty years set-aside) instead of uncertain income (even if is higher). A growing literature has investigated the role of transaction costs and social capital as determinants of AESs participation. Such literature branch has identified elements of trust and networking, as main motivational factors which affect AESs participation. The objective of the paper is to provide a comprehensive analysis of determinants of agri-environmental adoption. The objective is pursued combining results of farm level adoption analysis with spatial analysis of participation rate. The first analysis is undertaken applying farmers' behavior choice model, in order to identified the main farm, farmer and household (micro) characteristics which positively or negatively affect the probability to participate in Tuscany AESs. Data are obtained by Tuscany 2010 Census. The second model is undertaken applying spatial analysis (ESDA and spatial econometrics) in order to identifies alternative spatial patterns and to investigate meso determinants of participation to AESs. Data are obtained by ARTEA (regional RDP payments agency) and are referred to the entire RDP 2007-2013. Preliminary results shown that both micro and meso characteristics strongly affect participation to AESs. In fact, farm and household structure, quality of advice services and territory endogeneity, significantly affect AESs adoption. Altogether results allow to better understand the participation mechanism. Results will support an improvement of selection mechanism which is aimed to better target AESs measures.

Suggested Citation

  • Fabio Bartolini & Gianluca Brunori & Laura Fastelli & Massimo Rovai, 2013. "Understanding the participation in agri-environmental schemes: evidence from Tuscany Region," ERSA conference papers ersa13p1084, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa13p1084

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    References listed on IDEAS

    1. Maria Espinosa-Goded & Jesús Barreiro-Hurlé & Eric Ruto, 2010. "What Do Farmers Want From Agri-Environmental Scheme Design? A Choice Experiment Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 61(2), pages 259-273.
    2. Evy Mettepenningen & Ann Verspecht & Guido Van Huylenbroeck, 2009. "Measuring private transaction costs of European agri-environmental schemes," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 52(5), pages 649-667.
    3. Eva Schmidtner & Christian Lippert & Barbara Engler & Anna Maria Häring & Jaochim Aurbacher & Stephan Dabbert, 2012. "Spatial distribution of organic farming in Germany: does neighbourhood matter?," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 39(4), pages 661-683, September.
    4. Bartolini, Fabio & Raggi, Meri & Viaggi, Davide, 2012. "A spatial analysis of participation in RDP measures: a case study in Emilia Romagna Region," Congress Papers 124103, Italian Association of Agricultural and Applied Economics (AIEAA).
    5. Bartolini, Fabio & Andreoli, Maria & Brunori, Gianluca, 2013. "Explaining the determinants of on-farm diversification: The case Study of Tuscany Region," 2013 Second Congress, June 6-7, 2013, Parma, Italy 149903, Italian Association of Agricultural and Applied Economics (AIEAA).
    6. Aude Ridier, 2012. "Farm Level Supply of Short Rotation Woody Crops: Economic Assessment in the Long-Term for Household Farming Systems," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 60(3), pages 357-375, September.
    7. Giannoccaro, Giacomo & Bartolini, Fabio & Raggi, Meri & Viaggi, Davide, 2013. "Assessing the CAP influence on European farmers’ preferences towards the adoption of renewable energy production," 2013 Second Congress, June 6-7, 2013, Parma, Italy 149914, Italian Association of Agricultural and Applied Economics (AIEAA).
    8. Giovanopoulou, Eirini & Nastis, Stefanos A. & Papanagiotou, Evagelos, 2011. "Modeling farmer participation in agri-environmental nitrate pollution reducing schemes," Ecological Economics, Elsevier, vol. 70(11), pages 2175-2180, September.
    9. J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
    10. Pierre Dupraz & Karine Latouche & Nadine Turpin, 2009. "Threshold effect and co-ordination of agri-environmental efforts," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 52(5), pages 613-630.
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    Cited by:

    1. Justin Dijk & Erik Ansink & Daan van Soest, 2017. "Buyouts and Agglomeration Bonuses in Wildlife Corridor Auctions," Tinbergen Institute Discussion Papers 17-036/VIII, Tinbergen Institute.

    More about this item


    Agri-environmental schemes; Determinants; Econometrics model; Spatial econometrics;

    JEL classification:

    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets


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