** 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 AIMS TO REDUCE THE POTENTIAL INFORMATION GAP BETWEEN LARGE TRADERS (E.G., INSTITUTIONAL TRADERS; GRAIN MERCHANDISERS; LARGE AGRIBUSINESSES) AND SMALL / MEDIUM SIZED FARMS THROUGH THE DEPLOYMENT OF NEAR REAL-TIME FORECASTS OF CROP YIELD AND PRODUCTION FOR A SET OF GRAIN COMMODITIES. THUS, THE PRIMARY ACTIVITIES OF THIS PROJECT ARE TWOFOLD. FIRST, ASSESS THE ACCURACY OF CROP YIELD AND PRODUCTION FORECASTS MADE USING A MACHINE LEARNING ALGORITHM, XGBOOST, WITH SATELLITE AND WEATHER DATA, AND COMPARE THEIR ACCURACY TO THE WASDE YIELD AND PRODUCTION FORECASTS. SECOND, CREATE AN AUTOMATED FORECASTING SYSTEM THAT GENERATES DAILY YIELD AND PRODUCTION FORECASTS BASED ON THIS MACHINE LEARNING ALGORITHM, WITH THE ESTIMATES RELEASED TO THE PUBLIC THROUGH AN AUTOMATED REPORT DISTRIBUTED THROUGH A WEB PORTAL.INDEED, MANY INSTITUTIONAL TRADERS, GRAIN MERCHANDISERS, AND OTHER LARGE AGRIBUSINESSES PAY LARGE SUMS FOR PRIVATE FORECASTS OF KEY PRODUCTION VARIABLES. THESE PRIVATE FORECASTS CAN BE PROHIBITIVELY EXPENSIVE FOR SMALLER AND MEDIUM-SIZED FARMS AND AGRIBUSINESSES, RESULTING IN AN INFORMATION GAP OR INFORMATION DISADVANTAGE TO THESE SMALLER BUSINESSES. A LOW COST, WIDELY DISTRIBUTED, INFORMATIONALLY EFFICIENT FORECAST UTILIZING MACHINE LEARNING ALGORITHMS MAY BENEFIT PRODUCERS AND MARKET PARTICIPANTS ALONG THE GRAIN SUPPLY CHAIN.THEREFORE, THE PROPOSED ACTIVITIES OF BOTH TESTING AND DEPLOYING NEAR REAL-TIME MACHINE-LEARNING BASED FORECASTS OF YIELD AND PRODUCTION, AT MULTIPLE LEVELS OF AGGREGATION, WILL HELP PRODUCERS BETTER ANTICIPATE CROP MARKET PRICES AND MARKET SUPPLY.FURTHERMORE, THIS RESEARCH WILL HELP BROADEN OUR UNDERSTANDING OF THE EFFICACY OF MACHINE LEARNING AND BIG DATA APPROACHES TO FORECASTING KEY PRODUCTION VARIABLES IMPORTANT FOR AGRICULTURE.