** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** ONE OF THE MAIN PRIORITIES OF ALFALFA GROWERS IN THE UNITED STATES IS TO IMPROVE ALFALFA YIELD AND QUALITY WHILE MINIMIZING ENVIRONMENTAL IMPACTS. MAKING OPTIMUM PRODUCTION DECISIONS UNDER LIMITED RESOURCES E.G., WATER REQUIRES INFORMATION AND DECISION SUPPORT TOOLS THAT CAN HELP FARMERS PREDICT ALFALFA YIELD AND QUALITY DURING THE SEASON AS AFFECTED BY MANAGEMENT DECISIONS AND ENVIRONMENTAL CONDITIONS. IN A PRIOR PROJECT, WE DEVELOPED AN ALFALFA MODEL CAPABLE OF PREDICTING YIELD. GROWER'S FEEDBACK SHOWED AN URGENT NEED TO ENHANCE THE MODEL'S ABILITY TO PREDICT FORAGE QUALITY E.G., DIGESTIBILITY, NEUTRAL DETERGENT FIBER, CRUDE PROTEIN, AND ENERGY CONTENT, FOR ANY GIVEN DAY AFTER THE PREVIOUS HARVEST. THE SPECIFIC OBJECTIVES ARE TO 1) AGGREGATE DATA FOR COMMON FORAGE QUALITY TRAITS FROM HISTORIC AND NEW EXPERIMENTAL TRIALS, 2) USE NEW AND EXISTING DATA TO IMPROVE THE CROPGRO-ALFALFA MODEL FOR PREDICTION OF ALFALFA QUALITY AS A FUNCTION OF CROP AND WATER MANAGEMENT, GENETICS, AND ENVIRONMENTAL CONDITIONS, 3) EVALUATE THE PERFORMANCE OF THE CROPGRO-ALFALFA MODEL AGAINST DATA ON ALFALFA YIELD AND QUALITY, 4) CONDUCT SCENARIO ANALYSIS FOR FORAGE YIELD AND QUALITY PREDICTIONS, USING EXTENSIVE WEATHER, SOIL, AND MANAGEMENT DATA FROM MAJOR ALFALFA-GROWING REGIONS ACROSS THE US, AND 5) DEVELOP A MOBILE-FRIENDLY FARMS WEB APP THAT PREDICTS FORAGE QUALITY AND YIELD AS A FUNCTION OF IRRIGATION AND HARVESTING SCHEDULE. THE OUTCOMES OF OUR STUDY WILL BE AN ENHANCED ABILITY FOR IN-SEASON PREDICTION OF ALFALFA YIELD AND QUALITY, AND AN IMPROVED FARMS WEB APP THAT ENABLES FARMERS TO MAKE OPTIMAL HARVESTING AND IRRIGATION SCHEDULING DECISIONS THAT ENHANCE PROFITABILITY.