CMFDM: A methodology to guide the design of a conceptual model of farmers' decision-making processes. (October 2016) - Robert, Marion; Dury, Jérôme; Thomas, Alban; Therond, Olivier; Sekhar, Muddu; Badiger, Shrini; Ruiz, Laurent; Bergez, Jacques-Eric
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. Highlights: CMFDM is a useful guideline for modelers to design conceptual decision model. Cognitive task analysis helps structure the decision-making problem. The use of UML as a formal language ensures consistency and re-usability.