Farm management optimization under uncertainty with impacts on water quality and economic risk

Abstract

Farm management decisions under uncertainty are important, not only for farmers trying to maximize their net income, but also for policy makers responsible for incentives and regulations to achieve environmental goals. We focus on corn production as a significant contributor to the economy of the US Midwest. Nitrogen is one of the key nutrients needed to increase production efficiency, but its leaching and loss as nitrate through subsurface flow and agricultural drainage systems poses a threat to water quality. We build a novel two-stage stochastic mixed-integer program to find the annual farm management decisions that maximize the expected farm profit. A decomposition-based solution strategy is suggested to reduce the computational complexity resulting from the predominance of binary variables and complicated constraints. Case study results indicate that farmers may compensate for the additional risks associated with nutrient reduction strategies by increasing the planned nitrogen application rate. Significant financial incentives would be required for farmers to achieve substantial reductions in nitrate loss by fertilizer management alone. The complicated interactions between fertilizer management and crop insurance decisions observed in the numerical study suggest that crop insurance programs can affect water quality by influencing the adoption of environmentally beneficial practices.

Keywords:

• Farm management optimization
• stochastic programming
• nitrate loss
• crop insurance
• fertilizer application

Notes on contributors

Görkem Emirhüseyinoğlu is a Ph.D. candidate in industrial engineering at Iowa State University. He received his B.S. (2014) and M.S (2017) from Ozyegin University, Turkey, both in industrial engineering. His primary research interests are in combinatorial optimation and decision-making under uncertainty. Since 2014, he has worked on various projects in both industry and academia, in areas including supply chain management, primarily on transportation and distribution, finance, and agriculture.

Sarah M. Ryan is the Joseph Walkup Professor of Industrial and Manufacturing Systems Engineering at Iowa State University. She teaches courses in stochastic modeling and optimization under uncertainty. Professor Ryan directs the DataFEWSion National Research Traineeship for Innovations at the Nexus of Food Production, Renewable Energy and Water Quality Systems. Her research has been supported by the National Science Foundation, including a CAREER Award, the US Department of Energy, the Iowa Energy Center, an AT&T Industrial Ecology Faculty Fellowship, and industry consortia. She is past editor-in-chief of The Engineering Economist and Fellow of the Institute of Industrial and Systems Engineers.

Data availability statement

Data can be made available on request.

Additional information

Funding

This material is based upon work supported by the National Science Foundation under Grant No. DGE-1828942.