IDEAS home Printed from https://ideas.repec.org/a/ags/aieabj/361312.html
   My bibliography  Save this article

Predicting the effect of the Common Agricultural Policy post-2020 using an agent-based model based on PMP methodology

Author

Listed:
  • Baldi, Lisa
  • Calzolai, Sara
  • Arfini, Filippo
  • Donati, Michele

Abstract

The objective of this study is to perform an ex-ante assessment of the poten-tial impacts of agro-environmental measures included in the post-2020 Common Agri-cultural Policy (CAP), by estimating farmers’ responsiveness in adopting organic agri-cultural practices and an eco-scheme that incentivises extensive forage systems. T his research is conducted by means of an Agent-Based Model (ABM), based on Positive Mathematical Programming (PMP), implemented in GAMS. T he ABM facilitates the simulation of interaction among farmers, allowing for an analysis of farm heterogene-ity. T he PMP methodology adds a non-rational dimention to the farmers’ economic drivers. T he model is calibrated using 2019 Farm Accountancy Data Network (FADN) data specific to the Emilia Romagna region in Italy. Our findings reveal significant impacts on land use, with a notable decrease in cereal cultivation in favour of protein and fodder crops. Moreover, structural shifts are observed, notably a decrease in the number of small-scale farms. We also assess environmental and economic implications, observing a modest reduction in CO2 equivalent emissions per hectare, an increase in water demand, and an overall economic stability among farms, as indicated by changes in gross margin per hectare.

Suggested Citation

  • Baldi, Lisa & Calzolai, Sara & Arfini, Filippo & Donati, Michele, 2024. "Predicting the effect of the Common Agricultural Policy post-2020 using an agent-based model based on PMP methodology," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 13(4).
    • Handle: RePEc:ags:aieabj:361312
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/361312/files/OP09086_333-351.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James Nolan & Dawn Parker & G. Cornelis Van Kooten & Thomas Berger, 2009. "An Overview of Computational Modeling in Agricultural and Resource Economics," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 417-429, December.
    2. Małgorzata Holka & Jolanta Kowalska & Magdalena Jakubowska, 2022. "Reducing Carbon Footprint of Agriculture—Can Organic Farming Help to Mitigate Climate Change?," Agriculture, MDPI, vol. 12(9), pages 1-21, September.
    3. Kremmydas, Dimitris & Athanasiadis, Ioannis N. & Rozakis, Stelios, 2018. "A review of Agent Based Modeling for agricultural policy evaluation," Agricultural Systems, Elsevier, vol. 164(C), pages 95-106.
    4. Richard E. Howitt, 1995. "Positive Mathematical Programming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(2), pages 329-342.
    5. Thomas Berger & Christian Troost, 2014. "Agent-based Modelling of Climate Adaptation and Mitigation Options in Agriculture," Journal of Agricultural Economics, Wiley Blackwell, vol. 65(2), pages 323-348, June.
    6. Britz, Wolfgang & van Ittersum, Martin K. & Oude Lansink, Alfons G.J.M. & Heckelei, Thomas, . "Tools for Integrated Assessment in Agriculture. State of the Art and Challenges," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(2), pages 1-26.
    7. Lobianco, Antonello & Esposti, Roberto, 2010. "The Regional Multi-Agent Simulator (RegMAS): an open-source spatially explicit model to assess the impact of agricultural policies," MPRA Paper 25817, University Library of Munich, Germany.
    8. Quirino Paris & Richard E. Howitt, 1998. "An Analysis of Ill-Posed Production Problems Using Maximum Entropy," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 124-138.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lisa Baldi & Arfini, Filippo & Calzolai, Sara & Donati, Michele, 2023. "CAP reform and GHG emissions: policy assessment using a PMP agent-based model," 97th Annual Conference, March 27-29, 2023, Warwick University, Coventry, UK 334520, Agricultural Economics Society - AES.
    2. Ran Sun & James Nolan & Suren Kulshreshtha, 2022. "Agent-based modeling of policy induced agri-environmental technology adoption," SN Business & Economics, Springer, vol. 2(8), pages 1-26, August.
    3. Huber, Robert & Bakker, Martha & Balmann, Alfons & Berger, Thomas & Bithell, Mike & Brown, Calum & Grêt-Regamey, Adrienne & Xiong, Hang & Le, Quang Bao & Mack, Gabriele & Meyfroidt, Patrick & Millingt, 2018. "Representation of decision-making in European agricultural agent-based models," Agricultural Systems, Elsevier, vol. 167(C), pages 143-160.
    4. Lisa Baldi & Filippo Arfini & Sara Calzolai & Michele Donati, 2023. "An Impact Assessment of GHG Taxation on Emilia-Romagna Dairy Farms through an Agent-Based Model Based on PMP," Land, MDPI, vol. 12(7), pages 1-24, July.
    5. Robert Huber & Hang Xiong & Kevin Keller & Robert Finger, 2022. "Bridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling framework," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 35-63, February.
    6. Kremmydas, Dimitris & Athanasiadis, Ioannis N. & Rozakis, Stelios, 2018. "A review of Agent Based Modeling for agricultural policy evaluation," Agricultural Systems, Elsevier, vol. 164(C), pages 95-106.
    7. Britz, Wolfgang & Arata, Linda, 2016. "How important are crop shares in managing risk for specialized arable farms? A panel estimation of a programming model for three European regions," 2016 Fifth AIEAA Congress, June 16-17, 2016, Bologna, Italy 242316, Italian Association of Agricultural and Applied Economics (AIEAA).
    8. Severini, Simone & Valle, Stefano, "undated". "The Abrogation Of Set Aside And The Increase Of Cereal Prices: Can They Revert The Decline Of Cereal Production Generated By Decoupling?," 109th Seminar, November 20-21, 2008, Viterbo, Italy 44782, European Association of Agricultural Economists.
    9. Kooten, G. Cornelis van, 2013. "Modeling Forest Trade in Logs and Lumber: Qualitative and Quantitative Analysis," Working Papers 149182, University of Victoria, Resource Economics and Policy.
    10. Petsakos, Athanasios & Rozakis, Stelios, 2022. "Models and muddles: comment on ‘Calibration of agricultural risk programming models using positive mathematical programming’," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(03), January.
    11. Alain Carpentier & Elodie Letort, 2014. "Multicrop Production Models with Multinomial Logit Acreage Shares," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 59(4), pages 537-559, December.
    12. Arfini, Filippo & Donati, Michele & Marongiu, Sonia & Cesaro, Luca, 2012. "Farm production costs estimation trough PMP Models: an application in three Italian Regions," 2012 First Congress, June 4-5, 2012, Trento, Italy 124117, Italian Association of Agricultural and Applied Economics (AIEAA).
    13. Alain Carpentier & Elodie Letort, 2009. "Modeling acreage decisions within the multinomial Logit framework," Working Papers SMART 09-17, INRAE UMR SMART.
    14. Mugurel Ionel JITEA & Diana Elena DUMITRAȘ & Vasile Alexandru SIMU, 2015. "An ex-ante impact assessment of the Common Agricultural Policy reform in the North-Western Romania," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 61(2), pages 88-103.
    15. Catherine L. Kling & Raymond W. Arritt & Gray Calhoun & David A. Keiser, 2016. "Research Needs and Challenges in the FEW System: Coupling Economic Models with Agronomic, Hydrologic, and Bioenergy Models for Sustainable Food, Energy, and Water Systems," Center for Agricultural and Rural Development (CARD) Publications 16-wp563, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    16. Msangi, Siwa & Howitt, Richard E., 2006. "Estimating Disaggregate Production Functions: An Application to Northern Mexico," 2006 Annual meeting, July 23-26, Long Beach, CA 21080, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    17. Jon Duan & G. Cornelis van Kooten & A. T. M. Hasibul Islam, 2023. "Calibration of Grid Models for Analyzing Energy Policies," Energies, MDPI, vol. 16(3), pages 1-21, January.
    18. Steren, Aviv & Slater, Yehuda & Rubin, Ofir D. & Fleischer, Aliza & Kan, Iddo, 2025. "Toward sustainable practices in photovoltaic and agricultural greenhouse systems: An ecosystem services framework," Energy Economics, Elsevier, vol. 144(C).
    19. Xuan Liu & Gerrit Cornelis van Kooten & Jun Duan, 2020. "Calibration of agricultural risk programming models using positive mathematical programming," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(3), pages 795-817, July.
    20. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).

    More about this item

    Keywords

    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aieabj:361312. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aieaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.