** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** IN COMING DECADES, CLIMATE CHANGE WILL JEOPARDIZE PRODUCTION OF STAPLE CROPS IN THE U.S., HIGHLIGHTING THE NEED FOR SUSTAINABLE INTENSIFICATION AND IMPROVED CLIMATE-CROP RESPONSE PREDICTION. THIS PROJECT SEEKS TO ADDRESS THIS CHALLENGE THROUGH THE APPLICATION OF MACHINE LEARNING TO DEVELOP MULTI-SCALE (WITHIN-FIELD TO NATIONAL SCALES), PIXEL-BASED BIOPHYSICAL SUITABILITY MAPS FOR CORN (ZEA MAYS) AND SOYBEAN (GLYCINE MAX). USING AN EXTENSIVE YIELD MONITOR DATASET (OVER 60 FARMS THROUGHOUT THE U.S. SPANNING 3-10 YEARS PER FARM) A MODEL WILL BE TRAINED TO PREDICT RELATIVE YIELD BASED ON CLIMATE DATA FROM THE PARAMETER-ELEVATION REGRESSIONS ON INDEPENDENT SLOPES MODEL, SOIL DATA FROM THE SOIL SURVEY GEOGRAPHIC DATABASE, AND DIGITAL ELEVATION MODELS. ADDITIONALLY, THE MODEL WILL BE USED TO PREDICT SUITABILITY UNDER CLIMATE CHANGE SCENARIOS IN THE NEXT THREE DECADES USING DATA FROM THE CLIMATE MODEL INTERCOMPARISON PROJECT. THIS PROJECT WILL RESULT IN THE CREATION OF NOVEL CROP SUITABILITY MODELS FOR CORN AND SOYBEAN WHICH, COUPLED WITH PREDICTIVE WEATHER TREND TOOLS WILL ALLOW FOR IMPROVED RESPONSE TO STOCHASTIC WEATHER EVENTS. DYNAMIC SUITABILITY MAPS FROM THIS PROJECT WILL BE MADE AVAILABLE ON CLOUD-BASED APPLICATIONS FOR RESEARCH AND AGRICULTURAL PRODUCER PLANNING. THIS FELLOWSHIP WILL SUPPORT THE PROFESSIONAL DEVELOPMENT OF THE APPLICANT AND ENABLE HIM TO CONDUCT RESEARCH THAT WILL ADDRESS THE CHALLENGE OF SUSTAINABLE INTENSIFICATION AND ADVANCE THE STATE OF AGRICULTURAL CLIMATE CHANGE ADAPTATION.