COST-EFFECTIVE BMP PLACEMENT: OPTIMIZATION VERSUS TARGETING

Abstract

Cost-effectiveness of nonpoint-source pollution reduction programs in an agricultural watershed depends on theselection and placement of control measures within the watershed. Locations for best management practices (BMPs) are commonlyidentified through targeting strategies that define locations for BMP implementation based on specific criteria uniformlyapplied across the watershed. The goal of this research was to determine if cost-effectiveness of BMP scenarios couldbe improved through optimization rather than targeting. The optimization procedure uses a genetic algorithm (GA) to searchfor the combination of site-specific practices that meets pollution reduction requirements, and then continues searching forthe BMP combination that minimizes cost. Population size, replacement level, crossover, and mutation parameters for theGA were varied to determine the most efficient combination of values. A baseline scenario, a targeting strategy, and threeoptimization plans were applied to a 1014 ha agricultural watershed in Virginia. All three optimization plans identified BMPplacement scenarios having lower cost than the targeting strategy solution for equivalent sediment reduction. The targetingstrategy reduced average annual sediment loss compared to the baseline at a cost of $42 per kg sediment reduction/ha. Theoptimization plan with the same BMP choices achieved the same sediment reduction at a cost of $36 per kg/ha. Allocationof BMPs varied among optimization solutions, a possibility not available to the targeting strategy. In particular, the optimizationsolutions placed BMPs on several stream-edge fields that did not receive BMPs in the targeting strategy.