FARM-LEVEL OPTIMIZATION OF BMP PLACEMENT FOR COST-EFFECTIVE POLLUTION REDUCTION

Authors: GITAU, MARGARET - PENN STATE UNIV.; Veith, Tameria - Tamie; Gburek, William

Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/20/2004
Publication Date: 11/1/2004
Citation: Gitau, M., Veith, T.L., Gburek, W.J. 2004. Farm-level optimization of BMP placement for cost-effective pollution reduction. Transactions of the ASAE. 47(6):1923-1931.

Interpretive Summary: It is possible through field studies or simple modeling to estimate the extent to which a specific group of best management practices can improve water quality on a specific farm or watershed. It is more complex to select and place a combination of best management practices on the farm or watershed in a manner that is both inexpensive and minimizes pollution. However, solving such a selection and placement problem is important in keeping farming practices profitable while minimally impacting the environment. This study used an optimization technique to select combinations of best management practices based on phosphorous reduction data reported in the literature and on results from a watershed-level water quality model. The tool was demonstrated on a New York farm within the Cannonsville Reservoir. The Cannonsville Reservoir watershed supplies drinking water to New York City. The reservoir currently contains high levels of phosphorous, creating a need for reducing phosphorous runoff from farms through implementation of best management practices. The tool determined a combination of best management practices which reduced phosphorus by 0.6 kg for each dollar spent in implementation and maintenance. This tool is important in helping farmers identify low cost ways of improving water quality.

Technical Abstract: With Best Management Practices (BMPs) being used increasingly to control losses of major agricultural pollutants to surface waters, establishing the environmental effectiveness of these practices has become important. Additionally, cost implications of establishing and maintaining environmentally effective BMPs are often a crucial factor in selecting and adopting BMPs. This paper considers both water quality and economic concerns by presenting a methodology developed for determining cost-effective farm- or watershed-level scenarios through optimization. This optimization technique uniquely incorporates three existing tools: a genetic algorithm (GA), a watershed-level 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 BMP assessment tool and with 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 New York Cannonsville Reservoir watershed, which supplies potable water to New York City. The Cannonsville Reservoir is phosphorous (P) restricted, creating a need for increased control of nonpoint source pollution through BMPs. The presented methodology provides a more cost-effective means of selecting and placing BMPs than has previously been available to watershed planners. For evaluation, a 60% reduction in dissolved P was set as the pollutant target, a baseline scenario was established assuming no BMPs were in place, and a maximum allowable annual cost of $28,551 was determined by assuming all considered BMPs were implemented across the farm. 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 criterion cost-effectively.