From primary data to formalized decision-making: open challenges and ways forward to inform representations of farmers’ behavior in agent-based models

Model-based analyses can effectively contribute to investigating leverage points for sustainability transformations in agriculture. They allow for a systematic evaluation of policies under changing environmental, economic, or institutional conditions, and can be used to assess the effectiveness and efficiency of different policy designs. For analyzing agricultural systems, agent-based modeling is particularly useful because it can represent individual farmers—the crucial actors in land use systems—their interactions and emerging patterns at the landscape level. In order to provide policy-makers with relevant and accurate information, an adequate representation of farmers’ decision-making is essential. However, formalizing empirically observed farmers’ behavior into model rules is challenging, in particular when the observations are qualitative. With this article, we aim to guide modelers through the process of formalizing farmers’ decision-making based on empirical findings. First, we discuss which primary data collection designs are appropriate for inferring particular aspects of farmers’ behavior, focusing in particular on when a theory-driven design is helpful and when inductive approaches are needed. Second, we compile aspects that need to be covered in empirical data to best inform agent-based models. Finally, we present approaches for translating empirical findings into formalized decision rules. We underpin our discussion with model examples from the literature and our own model developed to represent farmers’ decision-making on the adoption of agri-environmental schemes in Europe. With this methodological contribution, we aim to help make agent-based models less stylized, thereby providing greater potential to support policy-makers in identifying leverage points for a sustainable transformation of agriculture.