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CMFDM: A methodology to guide the design of conceptual model of farmers’ decision-making processes

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  • Marion Robert

    (AGIR - AGroécologie, Innovations, teRritoires - INRA - Institut National de la Recherche Agronomique - Toulouse INP - Institut National Polytechnique (Toulouse) - UT - Université de Toulouse)

  • Alban Thomas

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique)

  • Jacques-Eric Bergez

    (AGIR - AGroécologie, Innovations, teRritoires - INRA - Institut National de la Recherche Agronomique - Toulouse INP - Institut National Polytechnique (Toulouse) - UT - Université de Toulouse)

Abstract

The agricultural research community uses languages and approaches to model farmers' decision-making processes but does not clearly detail the steps necessary to build a decision model. We propose an original and easily applicable methodology for modelers to guide data acquisition and analysis, incorporate expert knowledge, and conceptualize decision-making processes in farming systems. It combines decision-making analysis with a modeling approach inspired by cognitive sciences and software-development methods. It is organized into four steps:1)Problem Definition Specify the context and the initial research question. Chose the type of data to collect. 2)Case Study Selection Select case studies based on theoretical sampling approach to search for diversity instead of representativeness. 3)Data Collection and Analysis of Individual Case Studies Collect preliminary knowledge on the context domain. Select appropriate knowledge representations for the task. Use critical decision method to collect farmer's knowledge and identify critical incidents disrupting farming management. Present an initial transcription of the knowledge collected to the farmer for verification, refinement, revision. Represent farmers' knowledge with UML diagrams.4)The Generic Conceptual Model List similarities and differences between individual analysis and UML graphs. Use literature, experts and modelers to strengthen development of the generic model by formulating and adding complex concepts. Format the generic conceptual model with UML graphs.We applied the methodology to two research questions on water management, one in adeveloped country (France) and one in an emerging country (India). This methodology can be used in different contexts and will be a useful tool to guide modelers in building decision model in farming system

Suggested Citation

  • Marion Robert & Alban Thomas & Jacques-Eric Bergez, 2016. "CMFDM: A methodology to guide the design of conceptual model of farmers’ decision-making processes," Post-Print hal-02739506, HAL.
    • Handle: RePEc:hal:journl:hal-02739506
    Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-02739506
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    1. James Risbey & Milind Kandlikar & Hadi Dowlatabadi & Dean Graetz, 1999. "Scale, context, and decision making in agricultural adaptation to climate variability and change," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 4(2), pages 137-165, June.
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    1. Baccar, Mariem & Raynal, Hélène & Sekhar, Muddu & Bergez, Jacques-Eric & Willaume, Magali & Casel, Pierre & Giriraj, P. & Murthy, Sanjeeva & Ruiz, Laurent, 2023. "Dynamics of crop category choices reveal strategies and tactics used by smallholder farmers in India to cope with unreliable water availability," Agricultural Systems, Elsevier, vol. 211(C).
    2. Novotny, Ivan P. & Boul Lefeuvre, Nastasia & Attiogbé, Koffi S. & Wouyo, Atakpama & Fousseni, Folega & Dray, Anne & Waeber, Patrick O., 2024. "Exploring farmer choices in Southern Togo: Utilizing a strategy game to understand decision-making in agricultural practices," Agricultural Systems, Elsevier, vol. 218(C).
    3. Jahel, C. & Augusseau, X. & Lo Seen, D., 2018. "Modelling cropping plan strategies: What decision margin for farmers in Burkina Faso?," Agricultural Systems, Elsevier, vol. 167(C), pages 17-33.
    4. Bakker, Craig & Zaitchik, Benjamin F. & Siddiqui, Sauleh & Hobbs, Benjamin F. & Broaddus, Elena & Neff, Roni A. & Haskett, Jonathan & Parker, Cindy L., 2018. "Shocks, seasonality, and disaggregation: Modelling food security through the integration of agricultural, transportation, and economic systems," Agricultural Systems, Elsevier, vol. 164(C), pages 165-184.
    5. Robert, Marion & Thomas, Alban & Sekhar, Muddu & Badiger, Shrinivas & Ruiz, Laurent & Raynal, Hélène & Bergez, Jacques-Eric, 2017. "Adaptive and dynamic decision-making processes: A conceptual model of production systems on Indian farms," Agricultural Systems, Elsevier, vol. 157(C), pages 279-291.

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