Using conservation auctions informed by environmental performance models to reduce agricultural nutrient flows into Lake Erie

Cost-effectively mitigating agricultural nutrient export requires an understanding of the biophysical characteristics of cropland as well as the behavioral and economic factors that drive land management decisions. Conservation auctions informed by models that simulate environmental outcomes have the potential to allocate conservation payments cost-effectively by funding practices that provide high predicted environmental benefits per dollar spent. This research tested two forms of conservation auctions. First, experimental auctions were used to analyze farmer preferences for different types of financial incentives for voluntary conservation, including direct payments, insurance, tax credits, and stewardship certification benefits. Second, conservation auctions were conducted in two Ohio counties to evaluate performance under real-world conditions. Supporting both types of auctions, the Soil and Water Assessment Tool (SWAT) predicted reductions in phosphorus exported as a function of the type of conservation practice and farm location. Results of the experimental auctions showed direct payments and tax credits to be the most cost-effective incentives to mitigate phosphorus export. The real auctions yielded two important lessons: 1) participation was very low, due to perceived transaction costs of participation especially on rented fields and for group bids, and 2) the cost-effectiveness ranking of bids was highly sensitive to the parameters for soluble reactive phosphorus concentrations in the SWAT model. Future socio-economic research into payment for environmental services programs should seek cost-effective mechanisms with lower transaction costs for participants. Future biophysical research should strengthen our understanding of the factors governing soluble reactive phosphorus movement, so that models like SWAT can be more reliably parameterized. (C) 2016 International Association for Great Lakes Research. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.