A multiple criteria decision analysis model to improve performance of Federal conservation programs: A case study of Indiana Environmental Quality Incentives Program (EQIP)
Abstract
For conservation programs, such as Conservation Reserve Program and Environmental Quality Incentives Program (EQIP), United States Department of Agriculture program administrators identify eligibility criteria, suitable conservation practices, and a process to score, rank, and select applications for funding. The existing selection processes of these conservation programs feature weaknesses documented by previous decision theory research including mismatched criteria, spatial homogeneity, and unintentional conversion of qualitative criteria to quantitative criteria. This dissertation conducted an in-depth research to assess the underlying multiple criteria decision analysis model of conservation programs, which ties the individual program components together in a formal, consistent model, and then apply the decision model to Indiana's EQIP program using application data from 2005. The elements of Federal conservation programs are organized in a hierarchical structure. This analysis focuses on identification and measurement of attributes to assess the water quality objectives that are also outputs of Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) hydrologic simulation model. Then, I formulated the mathematical specification, selected the adequate multiple criteria method, and ranked the applications based on the environmental value they provided for each dollar spent. As expected, the total environmental value of selected applications with new ranking exceeded the total value of the 2005 Indiana EQIP selection process. Assessment and design of Federal conservation programs following formal procedures of multiple criteria decision analysis can improve program performance by enrolling more cost efficient applications and help avoiding common mistakes.
Degree
Ph.D.
Advisors
Farnsworth, Purdue University.
Subject Area
Agriculture|Agricultural economics|Environmental science
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