Reducing Agricultural Water Pollution in Texas: An Application of Linear Optimization
Date
2013-11
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Department of Applied Economics and Statistics, University of Delaware, Newark, DE.
Abstract
Linear optimizations models have been used for many practical purposes throughout the years – maximization and minimization models have proved to be key tools when striving to reach a goal. This case study employs such a model with the goal of maximizing an approximate reduction of pollutant loads from individual parcels per year with the implementation of BMPs throughout Fort Bend, Texas. The motivations for this study are the ever growing levels of Nitrogen, Phosphorus and sediment pollutant levels throughout the San Bernard Watershed, in which Fort Bend belongs. To do this, several BMPs related to livestock pollutant loads are examined and selected to be included in the model. This model will choose BMP and parcel combinations in order to provide maximum potential pollutant reductions for each parcel. As a result we obtained 32 BMP implementation recommendations across 31 parcels for a maximum reduction in pollutant loads of roughly 4.3 million pounds per year. A parameter analysis on the maximum budget concluded that the budget could increase until approximately $6
million where it begins to level off at 65 million pounds of pollutant reduction per year. There are several opportunities to expand this research, including developing a watershed wide model.