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ARS Home » Northeast Area » University Park, Pennsylvania » Pasture Systems & Watershed Management Research » Research » Publications at this Location » Publication #160000
Title: COST-EFFECTIVE BMPPLACEMENT: OPTIMIZATION VERSUS TARGETING

Author
item Veith, Tameria - Tamie
item WOLFE, M - VA TECH
item HEATWOLE, C - VA TECH

Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/30/2004
Publication Date: 9/20/2004
Citation: Veith, T.L., Wolfe, M.L., Heatwole, C.D. 2004. Cost-effective BMP placement: optimization versus targeting. Transactions of the ASAE. 47(5):1585-1594.

Interpretive Summary: Reducing agricultural nonpoint source pollution through implementation of best management practices (BMPs) has received growing emphasis due to government regulations such as the Clean Water Act. However, selecting and placing best management practices in a way that cost-effectively reduces nonpoint source pollution is complex. Two methods were compared: targeting and optimization. Targeting uses a universal set of rules to apply BMPs uniformly across the watershed. Optimization determines the optimal combination of BMPs for a watershed based on specific characteristics of the watershed. Optimization requires more data and knowledge about the watershed than does targeting. The optimization strategy was found to be more selective in placing BMPs. As a result, fewer BMPs were needed to control the same level of pollution which caused a reduction in pollution control costs. The optimization strategy provides a tool for improving water quality through more cost-effective watershed management.

Technical Abstract: Cost-effectiveness of nonpoint source pollution reduction measures from an agricultural watershed depends on selection and placement of these control measures. The goal of this research was to increase, relative to critical area targeting recommendations, cost-effectiveness of pollution reduction measures within a watershed. A procedure developed to optimize best management practice (BMP) placement at the watershed level via a genetic algorithm (GA) was evaluated to determine if it achieved this goal. The procedure searches for the combination of site-specific practices that meet pollution reduction requirements, then continues searching for the BMP combination that minimizes cost. Population size, replacement level, crossover, and mutation parameters for the GA were varied to determine the most efficient combination of values. A baseline scenario, a targeting strategy, and three optimization plans were applied to a 1014-ha agricultural watershed in Virginia. All three optimization plans successfully optimized BMP placement, identifying lower cost solutions than the targeting strategy solution for equivalent pollution reduction. The targeting strategy reduced costs from the baseline by $42 per kg/ha while the optimization plan with the same BMP choices used different placement strategies to reduce costs by an additional $6 per kg/ha. Land allocation varied among solutions. In particular, the optimization solutions placed BMPs on several non-targeted fields along streams.