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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Reference

Marion Robert, Jérôme Dury, Alban Thomas, Olivier Therond, Muddu Sekhar, Shrinivas Badiger, Laurent Ruiz, and Jacques Eric Bergez, “CMFDM: A methodology to guide the design of a conceptual model of farmers' decision-making processes”, Agricultural Systems, vol. 148, October 2016, pp. 86–94.