**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** INCREASING THE PREDICTABILITY OF CROP VARIETY PERFORMANCE IN RESEARCH AND PRODUCTION ENVIRONMENTS IS CRITICAL FOR NATIONAL FOOD AND NUTRITION SECURITY. TO ADDRESS THESE CHALLENGES, PLANT SCIENTISTS NEED TO HAVE A BETTER UNDERSTANDING OF WHICH GENES CONTROL TRAIT VARIATION AND HOW THESE GENES INTERACT WITH THE ENVIRONMENT ACROSS PLANT GROWTH AND DEVELOPMENT. WITH THE EASE AT WHICH LOW-COST GENOMICS DATA CAN BE GENERATED, EFFORTS OF THE SCIENTIFIC COMMUNITY HAVE NOW TURNED TO DEVELOPING LOW-COST, HIGH-THROUGHPUT PHENOMICS METHODS FOR COLLECTING AND ANALYZING MULTIPLE TYPES OF SENSOR AND IMAGE DATA ACROSS DIFFERENT TEMPORAL AND SPATIAL SCALES, PROVIDING MORE DETAILED INFORMATION ON DYNAMIC PROCESSES DRIVING PLANT GROWTH AND DEVELOPMENT. THROUGH THE JOINT MODELING OF GENOMICS AND PHENOMICS DATA, IT SHOULD BE POSSIBLE TO GAIN A DEEPER UNDERSTANDING OF THE UNDERLYING GENETIC AND ENVIRONMENTAL FACTORS RESPONSIBLE FOR VARIATION IN TRAITS OVER THE LIFE HISTORY OF CROP PLANTS, ALLOWING FOR ENHANCED PREDICTION AND FORECASTING OF VARIETY PERFORMANCE. OUR PROJECT WILL DEVELOP THE OPEN-SOURCE DIGITAL TOOLS NEEDED TO MODEL AND PREDICT AGRONOMICALLY AND ECONOMICALLY IMPORTANT PHENOTYPES BY EXTRACTING AND UNIFYING FEATURES OF HIGH-DIMENSIONAL DATASETS TO BETTER DISSECT AND MODEL GENOTYPE-BY-ENVIRONMENT INTERACTIONS IN FIELD ENVIRONMENTS. THE DEVELOPED INTEROPERABLE DIGITAL ECOSYSTEM, CONSISTING OF INTEGRATED DATA, SOFTWARE TOOLS, AND MODELING APPROACHES, WILL EXIST IN THE PUBLIC DOMAIN SO THAT IT CAN BE AVAILABLE AND CO-EVOLVE WITH THE GREATER SCIENTIFIC COMMUNITY.