THE LONG-TERM PROJECT OUTCOME IS TO IMPROVE THE ENVIRONMENTAL AND ECONOMIC PERFORMANCE OF U.S. CROP PRODUCTION BY INCREASING INPUT USE EFFICIENCY BY INTEGRATING MACHINE LEARNING METHODS INTO EXISTING PRECISION AG AND VARIABLE RATE TECHNOLOGIES. THIS INCREASED INPUT EFFICIENCY WILL ENHANCE THE PROFITABILITY AND GLOBAL COMPETITIVENESS OF U.S. FARMERS AND IMPROVE AGRICULTURAL SUSTAINABILITY WITH LESS INPUTS SUCH AS FERTILIZERS AND PESTICIDES LOST TO THE ENVIRONMENT. THE PROJECT WILL USE REINFORCEMENT LEARNING AND BAYESIAN OPTIMIZATION TO OPTIMALLY INTEGRATE ON-FARM EXPERIMENTATION INTO STANDARD CROP PRODUCTION PRACTICES (SOIL SAMPLING, VARIABLE RATE APPLICATION, YIELD MONITORING). SURVEYS SHOW THAT >50% OF FARMERS USE FIELD SAMPLING AND HAVE VARIABLE RATE TECHNOLOGIES AND YIELD MONITOR CAPABILITIES, BUT FEEL THAT THEY ARE UNDERUTILIZED. AS FARM CONSOLIDATION CONTINUES AND THE NUMBER OF PUBLICLY-FUNDED APPLIED RESEARCHERS DWINDLES, IT BECOMES CRITICAL THAT FARMERS BECOME MORE ACTIVE CITIZEN SCIENTISTS ON THE LANDS THEY MANAGE. BASED ON THESE ON-FARM EXPERIMENTS, FARM-, FIELD- AND SUB-FIELD-SPECIFIC INPUT USE RECOMMENDATIONS WILL BE DEVELOPED AND ANNUALLY UPDATED TO IMPROVE INPUT USE EFFICIENCY OF CROP FARMERS. THE PROJECT TEAM WILL DEVELOP BASIC SOFTWARE AND POSSIBLY APPS AS DECISION AIDS, AS WELL A VARIETY OF OUTREACH MATERIALS TO EDUCATE FAMERS AND CROP CONSULTANTS IN THE UNDERLYING LOGIC AND THE PRACTICAL USE OF THESE DECISION AIDS. AN ADDITIONAL GOAL OF THE PROJECT IS TO DEVELOP CURRICULUM TO INCORPORATE INTO NEW MACHINE LEARNING COURSES FOR AGRICULTURAL ECONOMICS GRADUATE STUDENTS AND ADVANCED UNDERGRADUATES TO MEET THE GROWING DEMAND FOR THESE SKILLS AMONG STUDENTS AND EMPLOYERS.

University Of Wisconsin System, Madison WI

Investigators