**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** GROWING ENOUGH FOOD IS A CHALLENGE, ESPECIALLY WITH CHANGING CLIMATE CONDITIONS. IMPROVED CROP CULTIVARS ARE REQUIRED TO MEET THIS CHALLENGE AS ARE IMPROVED METHODS FOR DEVELOPING CULTIVARS AND OPTIMIZING MANAGEMENT SCHEMES. IDEALLY, THESE METHODS WOULD CONSIDER GENETICS, ENVIRONMENT, AND MANAGEMENT STRATEGY ALL AT ONCE SO THAT CULTIVARS THAT WILL THRIVE IN SPECIFIC ENVIRONMENTAL CONDITIONS OR WITH CERTAIN MANAGEMENT PRACTICES CAN BE IDENTIFIED. THESE METHODS WILL SUPPORT THE DEVELOPMENT OF IMPROVED CULTIVARS, HELPING FARMERS SATISFY OUR NEEDS FOR FOOD AND FIBER.TO IMPROVE CULTIVAR DEVELOPMENT, THIS PROJECT USES A LARGE DATASET OF CORN YIELDS PLANTED IN DIVERSE ENVIRONMENTS ACROSS THE UNITED STATES TO PRODUCE DEEP LEARNING MODELS THAT ACCOUNT FOR THE INTERACTIONS BETWEEN GENOMIC, ENVIRONMENTAL, AND MANAGEMENT FACTORS. THESE MODELS CAN BE USED TO PREDICT YIELD AND DETERMINE WHAT FACTORS ARE ASSOCIATED WITH HIGH YIELD. THE YIELD ASSOCIATED FACTORS WILL BE POSSIBLE TARGETS FOR IMPROVEMENT IN THE FUTURE. DEEP LEARNING MODELS OFTEN NEED A LOT OF DATA TO BE ACCURATE. THIS PROJECT WILL DETERMINE IF THE AMOUNT OF NEEDED DATA CAN BE REDUCED BY USING DATA FROM ANOTHER CROP IN THIS CASE SUPPLEMENTING DATA ON WHEAT WITH DATA ON CORN. THE ULTIMATE GOAL SUPPORTED BY THIS WORK IS TO IMPROVE THE ABILITY TO OF BREEDERS TO DEVELOP CORN CULTIVARS IN DIVERSE ENVIRONMENTS WHICH IS AIDED BY PROVIDING THESE BREEDERS WITH ACCURATE MODELS AND MAKING IT EASIER TO MAKE MODELS FOR OTHER CROPS.