Developing a Geo-Spatial Optimization Approach for Evaluating Alternative Agricultural Management Practices.
Abstract
Agricultural production in the Midwestern United States is dominated by corn and soybeans grown in a two-year rotation. Traditional management practices for this rotation have led to multiple environmental concerns including loss of soil organic matter and export of sediment and excess nutrients, which lead to decreased water quality. Alternative management practices such as implementation of winter cover crops or establishing a perennial crop species have been proposed as options to mitigate these problems, but the potential impact of alternative management on farm profitability remains a barrier to widespread adoption. In order to analyze and evaluate potential policies that may affect alternative farm management practices, we applied a geo-spatial optimization programming approach. First, we developed a watershed scale model for an agricultural landscape located within the Minnesota River Basin. The model is capable of simulating crop productivity as well as environmental impacts that can result from a variety of agricultural management practices. We then combined watershed model outputs with additional agricultural and economic data in order to employ an optimization programming model to predict environmental impacts of different agro-environmental policies. Policies evaluated include hypothetical adjustments to federal agricultural policies as well as a program to pay farmers to grow alternative crops. These results will be able to inform policy-makers with an aim toward identifying options that increase environmental quality while maintaining or improving farm probability.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMGC53F1019L
- Keywords:
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- 1630 Impacts of global change;
- GLOBAL CHANGEDE: 1834 Human impacts;
- HYDROLOGYDE: 4805 Biogeochemical cycles;
- processes;
- and modeling;
- OCEANOGRAPHY: BIOLOGICAL AND CHEMICALDE: 6309 Decision making under uncertainty;
- POLICY SCIENCES