** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THIS PROJECT LEVERAGES UNIVERSITY-BASED RESEARCH AT THE INTERSECTION BETWEEN DATA SCIENCE/ARTIFICIAL INTELLIGENCE AND AGRICULTURAL AREAS TO IMPROVE RESOURCE MANAGEMENT AND ADVANCE US FOOD AND AGRICULTURE ENTERPRISES. SPECIFICALLY, THIS PROJECT: (1) TRACKS AND MODELS CHANGES IN IRRIGATION LAND COVER PATTERNS USING A FULLY AI-BASED SUITE OF BIG DATA APPLICATIONS, (2) BETTER INFORMS THE FUTURE OF IRRIGATED ROW CROPS THROUGH MACHINE LEARNING, AND (3) PROVIDES NEW RURAL REVENUE PATHWAYS BY PROACTIVELY IDENTIFYING REGIONS WHERE FUTURE IRRIGATION IS ECONOMICALLY AND ENVIRONMENTALLY FAVORABLE IN UNDER-UTILIZED REGIONS OF THE COUNTRY. OUR PROJECT SPANS 1980-2075, GOING BOTH FORWARD AND BACKWARD IN TIME, WHERE OUR AI APPLICATIONS ARE TRAINED IN OVER 20 YEARS OF OBSERVED DATA RECORDS. OUR PROJECT ADDRESSES FOUR OF THE SIX AFRI PRIORITIES (PLANT HEALTH AND PRODUCTION AND PLANT PRODUCTS; BIOENERGY, NATURAL RESOURCES AND ENVIRONMENT; AGRICULTURE SYSTEMS AND TECHNOLOGY; AGRICULTURE ECONOMICS AND RURAL COMMUNITIES) AND THREE OF THE SIX AI PILLARS (ADVANCING TRUSTWORTHY AI; EDUCATION AND TRAINING; APPLICATIONS). WE MEET ALL OF THE CENTERS OF EXCELLENCE ELIGIBILITY CRITERIA, AND WE FURTHER INTRODUCE A NOVEL BROADER IMPACTS INITIATIVE CREATING CUSTOM LEARNING MATERIALS FOR K-12 ENVIRONMENTS, AMONG OTHER NOTABLE BORDER IMPACT EFFORTS. COLLECTIVELY, OUR PROJECT ALIGNS DIRECTLY WITH THE USDA RURAL DEVELOPMENT KEY PRIORITIES AND CAN FOUNDATIONALLY ADVANCE BOTH UNDERSTANDING AND AGRICULTURAL APPLICATIONS WITHIN IRRIGATED PRODUCTION ZONES.