Professor Bullock, in conjunction with members of the team behind the recent NSF INFEWS application, submitted the following abstract for consideration in the upcoming INLRS Annual Workshop’s Research Showcase:

We propose to create an integrated FEW model and with user-friendly software to 1) enable individual farmers to examine the predicted economic and environmental impacts of their fertilizer management strategies, and 2) provide policy makers with a user-friendly tool in order to design policies that will lead to efficient reduction of N-nitrate contamination in the Mississippi River Basin. The model will be based on the ideal CyberGIS computation platform, and expand to the scale of the Mississippi River Basin the DSSAT crop growth model and the SWAT water drainage model. The model will also integrate the BioScope model of biomass supply, and a partial equilibrium economic model of crop, energy, and biomass markets in the U.S. Midwest and beyond. We argue that the principal shortcoming of current agricultural “Big Data” is that there is little record of variance in managed input use. Of course, managing inputs efficiently is the whole point of farm management, whether the goal is to increase monetary returns or environmental sustainability. Therefore, we propose to parameterize our integrated model with data we generate from large-scale, on-farm agronomic field trials over an entire small watershed. Those trials will randomize N application rates and cover-crop management strategies to measure both yield and water quality results of varying these managed input variables. We will use the generated data with existing agricultural “Big Data” to create “decision tool” software to improve private and public crop fertilization strategies. The proposed research will rely heavily on the proven abilities and infrastructure of the CyberGIS Center for Advanced Digital and Spatial Studies, and on the software, administrative capacity, and scientist-farmer relationships developed in an on-going USDA-NIFA Food Security project on data-intensive fertilizer management. In addition, we will provide opportunities for underrepresented undergraduates to gain research experience through an extension of the WE CAN program.