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In JoVE (1)
- Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Other Publications (1)
Articles by Sergey Rabotyagov in JoVE
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Sergey Rabotyagov1, Todd Campbell2, Adriana Valcu2, Philip Gassman2, Manoj Jha3, Keith Schilling4, Calvin Wolter4, Catherine Kling2
1School of Environmental and Forest Sciences, University of Washington, 2Center for Agricultural and Rural Development, Department of Economics, Iowa State University, 3Department of Civil, Architectural, and Environmental Engineering, North Carolina A&T University, 4Iowa Geological and Water Survey
This work demonstrates an integration of a water quality model with an optimization component utilizing evolutionary algorithms to solve for optimal (lowest-cost) placement of agricultural conservation practices for a specified set of water quality improvement objectives. The solutions are generated using a multi-objective approach, allowing for explicit quantification of tradeoffs.
Published December 9, 2012. Keywords: Environmental Sciences, Plant Biology, Civil Engineering, Forest Sciences, Water quality, multiobjective optimization, evolutionary algorithms, cost efficiency, agriculture, development
Other articles by Sergey Rabotyagov on PubMed
Ecological Applications : a Publication of the Ecological Society of America. Sep, 2010 | Pubmed ID: 20945758
In 2008, the hypoxic zone in the Gulf of Mexico, measuring 20 720 km2, was one of the two largest reported since measurement of the zone began in 1985. The extent of the hypoxic zone is related to nitrogen and phosphorous loadings originating on agricultural fields in the upper Midwest. This study combines the tools of evolutionary computation with a water quality model and cost data to develop a trade-off frontier for the Upper Mississippi River Basin specifying the least cost of achieving nutrient reductions and the location of the agricultural conservation practices needed. The frontier allows policymakers and stakeholders to explicitly see the trade-offs between cost and nutrient reductions. For example, the cost of reducing annual nitrate-N loadings by 30% is estimated to be US$1.4 billion/year, with a concomitant 36% reduction in P and the cost of reducing annual P loadings by 30% is estimated to be US$370 million/year, with a concomitant 9% reduction in nitrate-N.