A Mixed-Integer Framework for Operational Decision-Making in Sustainable Nutrient Management
43 Pages Posted: 23 Feb 2017 Last revised: 29 Jun 2017
Date Written: June 28, 2017
Global population and income trends continue to increase world food demand and an “upscaling” of diets to include more animal proteins. In response, cost and efficiency driven intensification of cropping and livestock operations has created substantive environmental concerns including deforestation, mono-culture versus diversified production systems, increased use of carbon intensive chemicals, increased greenhouse gas emissions, pathogen and antibiotic resistance health concerns, and nutrient runoff leading to large scale eutrophication and algal blooms. This paper shows that management of nutrients within commodity crop and livestock production can provide improved agricultural sustainability. Specifically, optimization and data driven models are used to improve economic and environmental performance using a combination of nutrient cycling, reduced chemical fertilizer application, and logistical enhancements due to manure separation and precision nutrient blending/application technology. Farm field level data from regulatory instruments can be incorporated into a process model foundation using a sophisticated, large scale, mixed integer programming approach to generate a rich, linked decision space for evaluating economic and environmental performance tradeoffs. The paper also details how the operational model can be enhanced to include new environmental constraints that are more in line with the long term health of the land, air and water supply, and furthermore shows how the model can be used to quantify the costs of implementing new policies within an optimized system. In particular, the model can elucidate key strategic tradeoffs that can be used to understand the costs and effects of separation, and can demonstrate the utility of these approaches in dealing with increased regulation of organic nitrogen and dry matter. It also provides policy makers with science and data-based mechanisms to value the impact of specific regulations on both typical and specific farm setups, in a way that can be used directly in a regulatory setting.
Keywords: mixed-integer programming, sustainable environmental management, farm operations
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