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Optimizing Best Management Practice Selection to Increase Cost-effectiveness

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Paper number  032110,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.14949) @2003
Authors:   Margaret W. Gitau, Tamie L. Veith, William J. Gburek
Keywords:   genetic algorithm, BMP, SWAT, agricultural nonpoint source pollution

With Best Management Practices (BMPs) being used increasingly to control losses of major agricultural pollutants to surface waters, establishing the effectiveness of these practices has become important. A methodology was developed for determining cost-effective watershed scenarios. This technique combines three existing tools: a genetic algorithm (GA), a watershedlevel nonpoint source model (Soil and Water Assessment Tool, SWAT), and a BMP assessment tool. The GA combines initial pollutant loadings from SWAT with literature-based pollution reduction efficiencies provided by the assessment tool and BMP costs appropriate to the study area to determine cost-effective watershed scenarios. The methodology was successfully applied to a 300-ha farm within the Cannonsville Reservoir watershed in New York. The Cannonsville Reservoir is phosphorous (P) restricted, and planners are implementing BMPs to reduce P loading to the reservoir. The optimal scenario for the farm, under the presented methodology, achieved a cost-effectiveness of 0.6 kg dissolved P reduction per dollar spent. Additionally, the methodology determined alternative scenarios which met the pollution reduction criteria cost-effectively.

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