**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** RATIONALE AND SIGNIFICANCECOVID-19 UNDERSCORED THE NEED TO UNDERSTAND RELATIONSHIPS BETWEEN RESILIENCY AND SUSTAINABILITY. EXISTING MODELS ARE INSUFFICIENT, BUT WE CAN ENHANCE THE EXISTING FOOD SYSTEM SUPPLY-CHAIN SUSTAINABILITY (FOODS3) MODEL DEVELOPED BY CO-IS SCHMITT AND PELTON TO MAKE IT SUFFICIENT. SOME OF THESE UPDATES WILL COME FROM CUTTING EDGE DATA SCIENCE/ML. THEN WITH OUR NEW MODEL WE LOOK AT BOTH RESILIENCE AND SUSTAINABILITY. THE NEW MODELING FRAMEWORK WILL ULTIMATELY ACHIEVE MULTIPLE CRITICAL OBJECTIVES: DEEP INSIGHT ON APS STRUCTURAL ELEMENTS THAT LEAD TO MORE RESILIENCE AND IMPROVED SYSTEM PERFORMANCE IN RESPONSE TO FUTURE SHOCKS (COVID-VARIANTS, CYBER-ATTACKS, EXTREME WEATHER, ANIMAL DISEASE OUTBREAKS, ETC.); BETTER POLICY (NATIONAL, STATE, LOCAL); INVESTMENT STRATEGIES FOR MAJOR SUPPLY CHAIN COMPANIES; INNOVATION OPPORTUNITIES FOR START-UP'S; ENHANCED AND MORE EQUITABLE CONSUMER ACCESS TO HIGH QUALITY PROTEIN, WITH NUTRITION AND PUBLIC HEALTH BENEFITS; AND PRODUCER INCOME STABILITY.OUR PROJECT INTEGRATES DATA SCIENCE WITH ENVIRONMENTAL LCA THROUGH ENHANCED UNDERSTANDING (VIA FOODS3 MODELING) OF VULNERABILITIES AND RESPONSES OF THE ANIMAL PROTEIN SYSTEM TO EXOGENOUS SHOCKS.DESIGN, VALIDATE AND IMPLEMENT NEW ALGORITHMS AND METHODS FOR DEPICTING AND LEVERAGING MASSIVE DATA.DEVELOP DATA-INTEGRATION AND DATA-QUALITY ALGORITHMS TO IMPROVE ANALYTIC CAPABILITY.CREATE NEW METHODOLOGIES AND FRAMEWORKS FOR TRACKING AND PROCESSING DATA.DEVELOP DECISION-SUPPORT TOOLS THAT USE DIVERSE DATA SOURCES AND BIG DATA ANALYTICS MODELING OF SHORT-TERM IMPACTS OF VARIOUS FACTORS TO CREATE BEST VALUE TO THE U. S. AGRICULTURAL ENTERPRISE.OUR LONG-TERM GOAL IS TO DEVELOP AN INNOVATIVE DATA-BASED MODELING FRAMEWORK FOR EVALUATION OF INTERVENTIONS INTENDED TO MITIGATE TRADEOFFS BETWEEN RESILIENCE AND SUSTAINABILITY (DEFINED BROADLY TO INCLUDE SOCIOECONOMIC FACTORS). STAKEHOLDER-INPUT ON IMPORTANT PROBLEM DEFINITION ISSUES, INCLUDING THE IDENTIFICATION OF RELEVANT DATA SOURCES AT APPROPRIATE SPATIAL AND TEMPORAL SCALES, MUST BE GATHERED, IN ORDER TO ENSURE THAT THE RESULTANT MODELING FRAMEWORK IS MEASURING APS PERFORMANCE AND TRADEOFFS IN A MEANINGFUL WAY. AS DETAILED BELOW, THIS INPUT WILL BE GATHERED AS THE PRIMARY DELIVERABLE OF OBJECTIVE 1. UNDER OBJECTIVE 2, WE WILL DEVELOP NEW ADVANCES IN DATA SCIENCE THAT WILL MAKE IT POSSIBLE TO ENHANCE THE CAPABILITIES OF FOODS3 TO MODEL APS SUPPLY CHAINS AND TO SIMULATE SUPPLY AND DEMAND SHOCKS. OBJECTIVE 3 IS FOCUSED ON THE ADVANCEMENT OF DATA SCIENCE CAPABILITIES TO MEASURE AND MONITOR FARM MANAGEMENT PRACTICES DEPLOYED IN THE APS, INCLUDING FROM CROP AND LIVESTOCK PRODUCTION, AND AGAIN IMPLEMENT THESE WITHIN FOODS3 TO IMPROVE SPATIALLY EXPLICIT ENVIRONMENTAL IMPACT MODELS. OBJECTIVE 4 INVOLVES EXPANSION OF FOODS3 CAPABILITIES TO REPORT ON REGIONAL AND DEMOGRAPHIC PATTERNS IN SOCIOECONOMIC RESPONSE TO THE COVID SHOCK. LASTLY, UNDER OBJECTIVE 5, THE NEW MODELING FRAMEWORK WILL BE USED TO SIMULATE APS RESPONSE,TO INTERVENTIONS THAT ARE PROPOSED BY STAKEHOLDERS FOR MITIGATING RESILIENCE AND SUSTAINABILITY TRADEOFFS IN THE APS