INFEWS/T3: Innovations for Sustainable Food, Energy, And Water Supplies In Intensively Cultivated Regions: Integrating Technologies, Data, And Human Behavior

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To keep pace with the demands of a growing global population, innovations are needed to meet the unprecedented challenge of producing more food in intensively cultivated regions with less energy and lower environmental impacts. In this project, researchers from the biophysical, socioeconomic, and computational sciences investigate two types of innovations using data from the northern U.S. Corn Belt. First, a novel oilseed crop, winter camelina, is being studied for its potential incorporation into existing corn-soybean rotations to produce a new supply of biodiesel energy while lowering water resource impacts and creating positive ecological benefits. Second, emerging systems of sustainability certification are being studied for their potential to lead to broad-scale adoption of this new cropping system. Detailed computational models are being evaluated and applied for systems-level assessments of two innovations: developing novel approaches to influence beneficial land use, and accounting for energy and environmental impacts within food supply chains. Because of the importance of the project results on the local economy, outreach activities are targeted towards the rural community, policy makers, the general public, and local watershed planners. Although this project focuses on the Northern Corn Belt, the approaches used in the research could be adopted to achieve beneficial outcomes for food, energy, and water systems elsewhere. This research project I scomprised of four overlapping and interdependent research teams. The biophysical research team conducts cropping systems studies of corn-soybean in rotation with winter camelina at two Minnesota research stations. Experimental treatments vary by winter timing of planting and harvest and fertilization rates. Meteorological data along with soil, water, and crops data is being collected to develop management strategies for producers and to calibrate and evaluate the crop models that will be used. The socioeconomic research team collects data from surveys and randomized control trials to study the forces determining whether, and by whom, new cropping systems are likely to be adopted under different policy and market conditions. Of particular interest is the role of incentives from certification programs, including the feedback effects of using producer data for peer benchmarking. The data science team applies novel deep learning computational approaches to identify crops, including winter cover crops, from satellite imagery. Study plots for the biophysical plots provides training data for crop identification and the final statewide datasets are being incorporated in the socioeconomic analysis. Finally, the integrated modeling team develops a suite of connected modeling tools to quantify systems-level outcomes. These simulations shed light on the feasibility and impacts of innovations in the food-energy-water system under different scenarios, including spatial patterns and the role of socio-economic drivers.