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CMFDM: a methodology to guide the design of a conceptual model of farmers' decision-making processes

Abstract : The agricultural research community offers languages and approaches to model farmers' decision-making processes but does not often clearly detail the steps necessary to build an agent model underlying farmers' decision-making processes. We propose an original and readily applicable methodology for modelers to guide data acquisition and analysis, incorporate expert knowledge, and conceptualize decision-making processes in farming systems using a software engineering language to support the development of the model. We propose a step-by-step approach that combines decision-making analysis with a modeling approach inspired by cognitive sciences and software-development methods. The methodology starts with case-based analysis to study and determine the complexity of decision-making processes and provide tools to obtain a generic and conceptual model of the decisional agent in the studied farming system. A generic farm representation and decision diagrams are obtained from cross-case analysis and are modeled with Unified Modeling Language. We applied the methodology to a research question on water management in an emerging country (India). Our methodology bridges the gap between field observations and the design of the decision model. It is a useful tool to guide modelers in building decision model in farming system.
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Submitted on : Wednesday, May 27, 2020 - 11:51:37 PM
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Marion Robert, Jérôme Dury, Alban Thomas, Olivier Therond, Muddu Sekhar, et al.. CMFDM: a methodology to guide the design of a conceptual model of farmers' decision-making processes. Agricultural Systems, Elsevier Masson, 2016, 148, pp.86-94. ⟨10.1016/j.agsy.2016.07.010⟩. ⟨hal-02637697⟩



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