Key points are not available for this paper at this time.
Climate-smart agriculture (CSA) aims to address climate change, climate variability, and food security while sustaining productivity. The literature on acceptance and adoption of CSA technologies recognizes the importance of the policy environment in shaping farmers' decisions, particularly the policy mix, including economic, regulatory, and information instruments to stimulate CSA acceptance. Behavioral models have been used to better explain CSA acceptance, however, only a few studies integrate farmers' behavioral drivers and their policy mix appraisal, which in combination, are important determinants for the successful acceptance of CSA. This paper proposes a model that integrates behavioral drivers and policy mix appraisal influencing the acceptance of CSA technologies. We aim to examine how farmers' behavioral drivers and their appraisal of the policy environment influence the acceptance of CSA technologies and practices. We studied the Costa Rican coffee sector and conducted 523 surveys with coffee farmers and two focus groups with experts, extension agents, and cooperatives. An ordered probit model was used to identify factors explaining CSA acceptance. The results indicate that besides the influence of behavioral drivers, policy consistency and comprehensiveness and the type of instrument targeting farmers' behaviors play an important role in explaining CSA acceptance. Our results suggest that a positive appraisal of policy consistency and comprehensiveness are important for increasing farmers' acceptance of CSA and sustainable practices. This deepens earlier thinking on “policy packages” effectiveness by showing that the farmers' appraisal of the overall policy mix influences their decisions to engage with CSA. Our study shows the importance of considering system context effects (policy environment) on farmers' decision-making. Since the integration of behavioral drivers and the appraisal of the policy mix characteristics is relatively underexplored for CSA, our empirical results may help to unravel farmers' decision-making processes. Thus, it can be used for rethinking and adjusting policy interventions toward more balanced and comprehensive policy mixes, as it enables feedback from policy implementation and can further induce policy learning.
Rodríguez-Barillas et al. (Thu,) studied this question.