Quantifying the importance of farmers' behavioral factors in ex-ante assessments of policies supporting sustainable farming practices.

Behavioral factors have been identified to determine farmers' uptake of the adoption of sustainable farming practices. However, the coherent consideration of empirically identified behavioral factors in ex-ante model-based policy assessments is still rare. This study presents an agent-based modelling framework that integrates empirical data on farmers' cognitive, social, and dispositional characteristics. Using this framework, we test and quantify the impact of including behavioral factors in ex-ante assessments of agricultural policies aimed at promoting sustainable farming practices. Thereby, we apply the same modelling framework to quantify and compare the effectiveness of results-based payments for climate change mitigation measures and precision agricultural technologies in two Swiss case studies. Our results indicate that farmers' cognitive and dispositional factors (e.g., reluctance to change) reduce the uptake of sustainable farming practices by 20–70% compared to simulations using income maximization as the underlying decision-making concept. In contrast, social factors can increase adoption by up to 40%. We conclude that including behavioral factors allows to improve ex-ante policy assessments in the context of sustainable farming practices. In addition, these approaches can highlight the importance of policy instruments that complement traditional economic measures, such as public support for the creation of networks. • We integrate behavioral economics in ex-ante assessments of agricultural policies. • The study combines agent-based modelling and empirical data on farmer behavior. • Results allow for a comparison of different decision-making models. • Behavioral factors reduce the uptake of sustainable farming practices. • Social factors can increase the adoption and the efficiency of agricultural policies. [ABSTRACT FROM AUTHOR]