Comparing nature conservation policies using a Bayesian belief network: Modeling sequential effects on adoption, farming practices, and biodiversity

Abstract Identifying and implementing policy measures to reduce on‐farm biodiversity loss requires a better understanding of complex system interactions. Studies on policy challenges to enhance on‐farm biodiversity can be divided into those focusing on biodiversity‐enhancing practices on farms versus those focusing on the success of policies in terms of farmers' adoption rates. In this study, we combine both parts within an ad‐hoc decision analytic method to predict which policy scenario is most likely to improve biodiversity on smallholder farms across the European Union. We apply a Bayesian belief network to assess on‐farm biodiversity outcomes under five policy scenarios including (a) regulatory, (b) market‐based, (c) incentive‐based, (d) voluntary information‐driven, and (e) policy mix. We collect and combine information from existing literature along with expert elicitation from a workshop held with decision makers and other stakeholders. We found the highest probability of positive biodiversity outcomes (48%) under a regulatory policy, but the result was strongly dependent on the expected rate of adoption. We employ a novel approach combining knowledge from different knowledge strains and sources that accounts for layers of uncertainty while informing biodiversity policy decisions.