Improving Agri Environmental Incentive Programs with Biophysical, Economic, and Spatial Targeting

Public programs to incentivize sustainable agricultural management face competing challenges of budget constraints and ambitious demands to improve water quality across scales. However, incentive mechanisms often overlook spatial heterogeneity in potential benefits, significantly reducing cost-effectiveness and limiting success in meeting targets. In this work, we demonstrate a novel approach to incentive policy design based on biophysical modeling, economic analysis, and classification regression to allow spatial targeting of incentive payments. We use regression tree analysis to classify land units based on cost-effectiveness of different management practices followed by an agent-based bi-level optimization to set incentive offers separately for each land unit class. Policies based on these biophysically-derived decision trees lead to large savings over policies set for average watershed conditions and …