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Do Farm Characteristics or Social Dynamics Explain the Conversion to Organic Farming by Dairy Farmers? An Agent-Based Model of Dairy Farming in 27 French Cantons

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The drivers of conversion to organic farming, which is still a residual choice in agriculture, are poorly understood. Many scholars argue that farm characteristics can determine this choice but do not exclude the role of social dynamics. To study this issue, we developed an agent-based model in which agents' decisions to shift to organic farming are based on a comparison between satisfaction with the current situation and potential satisfaction with an alternative farming strategy. A farmer agent’s satisfaction is modelled using the Theory of Reasoned Action. This makes it necessary to compare an agent's productions over time with those of other agents to whom the former attributes considerable credibility (“important others†). Moreover, farmers make technical changes that affect their productions by imitating other credible farmers. While we first used this model to examine simple and abstract farm populations, here we also adapted it for use with data from an Agricultural Census concerning the farm characteristics of dairy farming in 27 French “cantons†. Based on domain expertise, data and previous research, we propose certain laws for modelling the impact of conversion on the farm production of milk and the environment. The simulations with “real†populations of farms confirm the important impact of farm characteristics. However, our results also suggest a complex impact of social dynamics that can favour or impede the diffusion of organic farming through dynamic implicit networks of similarity and credibility. We confirm the great importance of demographic changes in farm characteristics.

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  • Qing Xu & Sylvie Huet & Eric Perret & Guillaume Deffuant, 2020. "Do Farm Characteristics or Social Dynamics Explain the Conversion to Organic Farming by Dairy Farmers? An Agent-Based Model of Dairy Farming in 27 French Cantons," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(2), pages 1-4.
    • Handle: RePEc:jas:jasssj:2018-53-3
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    Cited by:

    1. Aubron, Claire & Vigne, Mathieu & Philippon, Olivier & Lucas, Corentin & Lesens, Pierre & Upton, Spencer & Salgado, Paulo & Ruiz, Laurent, 2021. "Nitrogen metabolism of an Indian village based on the comparative agriculture approach: How characterizing social diversity was essential for understanding crop-livestock integration," Agricultural Systems, Elsevier, vol. 193(C).
    2. Shang, Linmei & Heckelei, Thomas & Gerullis, Maria K. & Börner, Jan & Rasch, Sebastian, 2021. "Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction," Agricultural Systems, Elsevier, vol. 190(C).
    3. Marilena Gemtou & Konstantina Kakkavou & Evangelos Anastasiou & Spyros Fountas & Soren Marcus Pedersen & Gohar Isakhanyan & Kassa Tarekegn Erekalo & Serafin Pazos-Vidal, 2024. "Farmers’ Transition to Climate-Smart Agriculture: A Systematic Review of the Decision-Making Factors Affecting Adoption," Sustainability, MDPI, vol. 16(7), pages 1-34, March.
    4. Beatrice Nöldeke & Etti Winter & Ulrike Grote, 2020. "Seed Selection Strategies for Information Diffusion in Social Networks: An Agent-Based Model Applied to Rural Zambia," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(4), pages 1-9.
    5. Fritz Wittmann & Michael Eder, 2023. "Farmers facing changed urban dietary patterns: whether and what to adapt?," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 28(7), pages 1-26, October.
    6. Shang, Linmei & Heckelei, Thomas & Börner, Jan & Rasch, Sebastian, 2020. "Adoption and Diffusion of Digital Farming Technologies – Integrating Farm-Level Evidence and System-Level Interaction," 60th Annual Conference, Halle/ Saale, Germany, September 23-25, 2020 305586, German Association of Agricultural Economists (GEWISOLA).

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