**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THE SUSTAINABILITY OF IRRIGATED AGRICULTURE IN THE ARID WESTERN U.S. IS BEING THREATENED BY INCREASING WATER DEMANDS FROM A GROWING POPULATION, DIMINISHING AQUIFERS, RECURRING DROUGHTS, AND THE EFFECTS OF A CHANGING CLIMATE. THESE CONDITIONS ARE FORCING ALFALFA HAY PRODUCERS IN THE REGION TO IRRIGATE ALFALFA WITHOUT MEETING ITS FULL WATER DEMANDS. THIS PRACTICE, KNOWN AS DEFICIT IRRIGATION, INHERENTLY LEADS TO A REDUCTION IN YIELD, WHICH IN TURN ENDANGERS THE LIVELIHOOD OF RURAL COMMUNITIES IN THE WESTERN U.S. AND THREATENS THE ACCESS TO AFFORDABLE FOOD FOR URBAN COMMUNITIES IN THE SAME REGION.THE LONG-TERM GOAL OF THIS RESEARCH PROJECT IS TO HELP ALFALFA FARMERS IN THE WESTERN U.S. TO AMELIORATE THE ECONOMIC IMPACT OF PRODUCING ALFALFA WITH A LIMITED WATER SUPPLY BY DEVELOPING AN ALFALFA HAY YIELD FORECASTING TOOL (YFT) THAT CAN BE USED TO ANALYZE MANY IRRIGATION MANAGEMENT SCENARIOS AND IDENTIFY A DECISION FOR AN UPCOMING IRRIGATION EVENT (I.E., DON'T IRRIGATE OR HOW MUCH TO IRRIGATE) THAT MAXIMIZES YIELDS WITHOUT EXCEEDING A WATER QUOTA. TO ACHIEVE THIS GOAL, THE OVERALL OBJECTIVE OF THIS PROJECT IS TO IDENTIFY A COMPUTATIONAL TOOL CAPABLE OF ESTIMATING THE EFFECTS THAT AN IRRIGATION MANAGEMENT DECISION WILL HAVE IN THE SEASONAL ALFALFA HAY YIELD WITH A SATISFACTORY LEVEL OF ACCURACY AND LIMITED DATA INPUTS.WE'LL IDENTIFY SUCH A TOOL BY COMPARING FOUR COMPUTATIONAL TOOLS THAT CAN BE USED TO ESTIMATE ALFALFA HAY YIELD: TWO ARTIFICIAL INTELLIGENCE (AI) METHODS AND TWO COMPUTER PROGRAMS, KNOWN AS CROP GROWTH MODELS. WE'LL TRAIN THE AI METHODS TO ESTIMATE ALFALFA HAY YIELD AND CALIBRATE THE CROP GROWTH MODELS FOR THE SAME PURPOSE USING DATA COLLECTED IN NORTHERN NEVADA AND THE TEXAS HIGH PLAINS FROM TWO HISTORICAL AND TWO ONGOING ALFALFA EXPERIMENTS. WE'LL IDENTIFY THE AI METHOD OR CROP GROWTH MODEL THAT MORE ACCURATELY ESTIMATES ALFALFA HAY YIELD, AND WE'LL INCORPORATE THE BEST PERFORMING AI METHOD OR MODEL INTO THE YFT. THE RESULTING YFT WILL BE A COMPUTER PROGRAM THAT AGRICULTURAL RESEARCHERS OR CONSULTANTS IN THE WESTERN U.S. CAN USE TO ESTIMATE THE EFFECTS THAT DIFFERENT IRRIGATION MANAGEMENT SCENARIOS WILL HAVE IN THE ALFALFA HAY YIELD OBTAINED AT THE END OF A GROWING SEASON. WE'LL RELEASE TO THE PUBLIC THE COMPUTER CODE OF THE YFT SO THAT OTHER AGRICULTURAL RESEARCHERS OR CONSULTANTS CAN IMPROVE IT AND/OR MODIFY IT TO FIT THE NEEDS OF OTHER CROPS OR REGIONS.