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

Abstract

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Ambition Meets Reality: Achieving GHG Emission Reduction Targets in the Livestock Sector of Latin America

“…For both the dissemination of information and policy formulation, it is likewise important to understand how livestock producers make decisions, i.e., regarding the adoption of technologies and mitigation strategies, and how their decision-making process is influenced by e.g., trust (in the information provided or in its sources), risks, social networks and socio-cultural contexts. Although this is a growing field of research with interesting approaches (e.g., Robert et al, 2016;Singh et al, 2016), evidence has so far mainly been provided for agricultural (e.g., Stuart et al, 2014;de Sousa et al, 2018;Azadi et al, 2019;Gatto et al, 2019) and non-bovine livestock production (e.g., Jones et al, 2013;Ambrosius et al, 2019;Hidano et al, 2019), and only to a limited extent for (bovine) livestock production in Latin American countries (e.g., Martínez-García et al, 2013;Rossi Borges and Oude Lansink, 2016). This indicates a knowledge gap which needs to be addressed in order to assure a more widespread adoption of mitigation strategies.…”

A call to focus on farmer intuition for improved management decision-making

“…Several authors agree that formal tools are rarely designed with a detailed understanding of the relationship between farmers’ specific knowledge, the decisions they make and the actions they take, and farmers are often not consulted in the design process until release of the final product (Lynch et al, 2000; Öhlmér, 2007; Robert et al, 2016). As a result, early use of new information management systems is often stressful for farmers accustomed to using an intuitive, experience-based management style, and these systems are subsequently not prioritized (Eastwood et al, 2006).…”

An adaptive, probabilistic, cognitive agent architecture for modelling sugarcane growers’ operational decision-making

“…A number of computational models that aim to capture the complexity of farmers' decisionmaking have been created. For example, Dury et al (2010) and Robert, Dury et al (2016) modelled the tactical-level crop selection decision of farmers amidst the uncertainty of expected rainfall, underground water availability and changing market prices. In these papers, the authors used the Belief-Desire-Intention (BDI) architecture (M. E. Bratman et al, 1988) to represent their farmer agents.…”

“…The BDI architecture is suitable for representing sugarcane growers, since it mimics human decision processes and has been used to model other types of farmers (e.g Dury et al, 2010;Liang et al, 2016;Robert, Dury et al, 2016;Truong et al, 2015). However, typical implementations of the BDI architecture have represented beliefs symbolically e.g.…”