**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** WIND DAMAGE COSTED THE U.S. GOVERNMENT APPROXIMATELY 1.2 BILLION IN CROP INSURANCE PAYMENTS BETWEEN 2015 TO 2020 OF WHICH AROUND HALF WERE PAID FOR THE DAMAGE CLAIMS IN THE MIDWEST REGION. AS ONE OF THE AGROFORESTRY PRACTICES, WINDBREAKS REDUCE WIND SPEED AND OFFER OTHER ENVIRONMENTAL BENEFITS. HOWEVER, MOST OF THE STUDIES ARE CONDUCTED AT A FARM LEVEL, AND LIMITED EVIDENCE EXISTS ON THEIR ROLE IN REDUCING CROP LOSS AT A REGIONAL LEVEL.THE PRIMARY GOAL OF THIS PROJECT IS TO EVALUATE THE EFFECTIVENESS OF WINDBREAKS IN REDUCING WIND-RELATED CROP LOSS AT A REGIONAL LEVEL. USING DATA FROM REMOTE SENSING, CROP INSURANCE, AND WEATHER, THE PROJECT AIMS TO DEVELOP COUNTY-LEVEL MODEL TO ASSESS WINDBREAK EFFECTIVENESS IN REDUCING CROP LOSS USING ECONOMETRIC AND MACHINE LEARNING TECHNIQUES. NEBRASKA, KANSAS, SOUTH DAKOTA, AND NORTH DAKOTA AS OUR STUDY AREA AS THEY HAVE THE HIGHEST CONCENTRATION OF WINDBREAKS IN THE COUNTRY. WE SELECTED CORN, WHEAT, SOYBEAN, AND SUNFLOWER AS THEY REPORTED MAJOR WIND-RELATED LOSS.THE PROJECT FOCUSES ON THREE OBJECTIVES: (1) QUANTIFY WINDBREAKS AND TREE COVER ON AND AROUND AGRICULTURAL LAND USING HIGH-RESOLUTION LANDCOVER MAPS; (2) DEVELOP PREDICTIVE MODELS TO ASSESS EFFECTIVENESS OF WINDBREAKS USING ECONOMETRIC METHODS AND MACHINE LEARNING ALGORITHMS; AND (3) CONDUCT SPATIOTEMPORAL ANALYSES OF OTHER CLIMATE RISKS AND CROP LOSS USING SPATIAL AND MACHINE LEARNING TECHNIQUES. THIS PROJECT WILL GENERATE GREATER UNDERSTANDING OF WINDBREAK PERFORMANCE IN REDUCING CROP LOSS AT A REGIONAL SCALE. THE STUDY ALIGNS WITH THE PRIORITY AREA OF DEVELOPING DECISION-SUPPORT TOOLS THAT USE BIG DATA ANALYTICS.