**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 EFFICIENCY AND REDUCING METHANE EMISSIONS OF DAIRY HERDS BY BRIDGING THE GAP BETWEEN ANIMAL AND DATA SCIENCEINVESTIGATORS: DRS. DIPTI PITTA, KEVIN HARVATINE, STEPHANIE WARD, DARKO STEFANOVSKI, RYAN URBANOWICZ, LINDA BAKER, JOSEPH BENDER, AND ROBERT GOODLING, JRRATIONALE:THE USE OF PRECISION TECHNOLOGIES ON DAIRY FARMS PROVIDES OPPORTUNITIES TO MONITOR HERD HEALTH AND ESTRUS IN DAIRY COWS. THE DATA DERIVED FROM SENSORS ON ANIMAL BEHAVIOR SUCH AS EATING TIME (ET), RUMINATION TIME (RT), STANDING, LYING, AND MOVEMENT WITHIN THE BARN HAVE HUGE POTENTIAL TO LEAD TO A BETTER UNDERSTANDING OF RUMEN FUNCTION AND PHENOTYPIC RESPONSES IN DAIRY COWS. HOWEVER, THE LINKS BETWEEN THIS PRECISION DATA, RUMEN MICROBIAL COMPOSITION, METHANE (CH4) PRODUCTION, AND MILK YIELD AND COMPONENTS IS NOT WELL UNDERSTOOD. THEREFORE, THE LONG-TERM GOAL OF THIS PROJECT IS TO INVESTIGATE THE POTENTIAL OF PRECISION TECHNOLOGIES TO IMPROVE PRODUCTIVITY AND REDUCE CH4 EMISSIONS IN DAIRY COWS USING GROUND-BREAKING RESEARCH AS WELL AS TO DIVERSIFY THE SKILLSET OF NEXT-GENERATION ANIMAL SCIENTISTS AND TO INCREASE AWARENESS OF APPLICATION OF THESE TECHNOLOGIES AMONG STAKEHOLDERS.HYPOTHESIS: WE HYPOTHESIZE THAT RUMINATION TIME (RT) AND RUMINAL MICROBIOTA ARE LINKED AND THAT BY ALTERING RT-MICROBIOTA ASSOCIATIONS VIA DIETARY CHANGES, WE WILL DETERMINE THEIR EFFECT ON MILK YIELD, MILK COMPONENTS, AND CH4 EMISSIONS.SPECIFIC OBJECTIVES:OBJECTIVE 1: CHARACTERIZE THE COMPLEX ASSOCIATIONS AMONG DIFFERENT TYPES OF BEHAVIORAL ACTIVITY DATA (RT, EATING TIME [ET], CHEWING) AND THEIR LINK TO MILK YIELD AND COMPONENTS IN DAIRY COWS USING MACHINE LEARNING (ML) APPROACHES.OBJECTIVE 2: DETERMINE WHETHER BEHAVIORAL ACTIVITY DATA, PARTICULARLY RT, MAY SERVE AS A PREDICTOR FOR CH4-YIELD PHENOTYPE BY CHARACTERIZING THE CONNECTIONS BETWEEN BEHAVIORAL ACTIVITY DATA AND RUMINAL MICROBIOTA USING BOTH AN IN VIVO EXPERIMENT AND ML APPROACHES.OBJECTIVE 3: DETERMINE TO WHAT EXTENT DIETARY FACTORS ALTER THE RUMINATION ACTIVITY-MICROBIOME CONNECTION IN COWS AND INFLUENCE HEALTH AND PRODUCTION PERFORMANCE.OBJECTIVE 4: CREATE TRAINING PROGRAMS FOR THE CURRENT AND FUTURE WORKFORCE ON THE APPLICATION OF SMART TECHNOLOGIES TO IMPROVE SUSTAINABILITY OF DAIRY HERDS.IV. EXPECTED OUTCOMES(I) UNDERSTANDING OF HOW MICROBIOTA AND ACTIVITY ARE CONNECTED; (II) GROUPING OF COWS BASED ON BEHAVIORAL ACTIVITY METRICS; (III) TESTING WHETHER BEHAVIORAL ACTIVITY METRICS CAN PREDICT PRODUCTION RESPONSES AND CH4 EMISSIONS; (IV) DEVELOPING SUPERVISED AND UNSUPERVISED ML ALGORITHMS TO IDENTIFY PRECISION INDICATORS FOR PHENOTYPIC RESPONSES; (V) DEVELOPING NEW STRATEGIES TAILORED TO MANAGE SPECIFIC GROUPS BASED ON BEHAVIORAL METRICS (EXAMPLE: HIGH AND LOW RT); (VI) DEVELOPING AN INNOVATIVE EDUCATION CURRICULUM FOR STUDENTS ON APPLICATION OF PRECISION TECHNOLOGIES (PT) AND DATA SCIENCE CONCEPTS TO IMPROVE SUSTAINABILITY ON FARMS; AND (VII) DEVELOPING INNOVATIVE EXTENSION PROGRAMS TO TRAIN EXTENSION AGENTS TO WORK WITH PRODUCERS ON THE USE OF PT ON DAIRY HERDS.V. ANTICIPATED IMPACTTHIS PROJECT WILL ENHANCE OUR KNOWLEDGE OF RUMEN FUNCTION AND WILL DEFINE A NOVEL DIRECTION IN IMPROVING EFFICIENCY AND PRODUCTIVITY OF NOT ONLY DAIRY COWS, BUT ALSO OTHER RUMINANT SYSTEMS. WE ANTICIPATE THIS PROJECT WILL REVEAL DIFFERENCES IN HIGH AND LOW CH4-YIELD PHENOTYPES AND IDENTIFY LINKAGES BETWEEN DIET COMPOSITION, BEHAVIORAL ACTIVITY, MICROBIAL INFORMATION, FERMENTATION, AND MILK PRODUCTION, LEADING TO OPPORTUNITIES TO IMPROVE PRODUCTION EFFICIENCY AND REDUCE THE CARBON FOOTPRINT ON DAIRY FARMS TO PROMOTE SUSTAINABILITY OF DAIRY PRODUCTS.