** 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 2020, CHINA HAD ACCOUNTED FOR 20% OF THE UNITED STATES' TOTAL AGRICULTURAL EXPORTS. CHINA'SHUGE AND UNEXPECTED IMPORTS OF US CORN, SOYBEAN, AND PORK PRODUCTS MAY OR MAY NOT CONTINUEIN THE NEXT DECADE. THERE IS A LACK OF RELIABLE STATISTICS ON CHINA'S KEY AGRICULTURAL COMMODITIES.THIS CREATES PRICE VOLATILITY AND MIGHT REDUCE THE TRANSPARENCY OF US AND WORLD AGRICULTURALMARKETS.WE PROPOSE TO DEVELOP RELIABLE DATA/STATISTICS FOR CORN, SOYBEANS, AND PORK. FIRST, WE WILL USEPRICE, TRADE, AND SATELLITE DATA TO GET RELIABLE STATISTICS ON PRODUCTION, CONSUMPTION AND STOCKS.SECOND, WE WILL WORK WITH OUR PARTNERS IN THE HUAZHONG AGRICULTURAL UNIVERSITY TO CONDUCTPLANTING INTENTION SURVEYS IN KEY COMMODITY GROWING AREAS TO UNDERSTAND THE ECONOMIC ANDPOLICY DETERMINANTS OF CHINESE CROP PRODUCERS' DECISIONS.WE PROPOSE TO DEVELOP MACHINE LEARNING (ML) MODELS TO FORECAST CHINA'S AGRICULTURAL IMPORTS.WE WILL COMPARE THE PREDICTION ACCURACIES OF ML MODELS WITH TRADITIONAL GRAVITY MODELS, WHICHLEADS TO AN UNDERSTANDING OF THE APPLICABILITY OF ML TECHNIQUES IN FORECASTING AGRICULTURAL TRADEAND QUANTIFYING THE IMPACTS OF TRADE AND ECONOMIC POLICIES.WE HAVE SHOWN THE APPLICABILITY OF ML METHODS IN PREDICTING AGRICULTURAL TRADE FLOWS AND ALSOIN PREDICTING STOCK LEVELS WHEN SOME ECONOMIC DATA IS ACCURATE, AND SOME IS NOT. THE PROPOSEDDATA COLLECTION AND MODELING SYSTEM WILL PROVIDE INSIGHTS INTO WORLD AGRICULTURAL TRADE PATTERNSAND ENHANCE OUR ABILITY TO QUANTIFY FUTURE SHOCKS TO THE GLOBAL AGRICULTURAL MARKETS. THE ULTIMATEGOAL IS TO IMPROVE THE LONG-TERM SUSTAINABILITY AND RESILIENCY OF US AGRICULTURE AND FOOD SYSTEMS.