COMPUTER VISION AND DATA-DRIVEN APPROACHES ARE USED TO ENHANCE EXISTING DNA AND GROWTH BEEF INDUSTRY DATASETS (&GT;150,592 CATTLE WITH 50 MILLION DNA VARIANTS) WITH DENSE DATA FROM THREE-DIMENSIONAL (3D) CAMERAS (~5,000 COWS, ~7 MILLION POINTS PER COW) TO ANSWER 170-YEAR-OLD QUESTIONS IN ECOLOGY AND EVOLUTION (BERGMANN'S RULE). USING YIELD, ENVIRONMENTAL STRESS, AND PATHOGEN RESISTANCE, THE PROJECT CREATES DNA PREDICTIONS TO HELP FARMERS SELECT CATTLE THAT THRIVE IN THE ENVIRONMENT AT THEIR RANCH, ULTIMATELY IMPROVING THE ENVIRONMENTAL EFFICIENCY OF BEEF PRODUCTION. IDENTIFYING ADAPTED, ENERGY EFFICIENT CATTLE ALLOWS FAMILY FARMS AND RANCHES ACROSS THE USA TO BE MORE PROFITABLE AND IMPROVES ANIMAL WELFARE, AN IMPORTANT SOCIETAL ISSUE. IN OBJECTIVE 1, 3D COMPUTER VISION IS USED TO MEASURE THE SURFACE AREA AND VOLUME OF CATTLE TO TEST IF SURFACE-AREA-TO-VOLUME RATIO (SA:V) INFLUENCES GENETICS FOR GROWTH UNDER COLD STRESS OR GROWTH UNDER HIGH FEED RESOURCES. LARGER ANIMALS HAVE A LOWER SA:V, ALLOWING THEM TO RETAIN HEAT MORE EFFICIENTLY IN WINTER. ONE HYPOTHESIS SUGGESTS LARGER ANIMALS LIVE AT HIGHER LATITUDES (FURTHER NORTH IN THIS STUDY) BECAUSE THEY USE THE LOWER SA:V TO BETTER DEAL WITH COLD STRESS. AN ALTERNATIVE HYPOTHESIS SUGGESTS THAT ANIMALS ARE LARGER AT HIGHER LATITUDES BECAUSE MORE FOOD IS AVAILABLE WHEN THE ANIMALS ARE GROWING. THIS RESEARCH CREATES TWO DNA PREDICTIONS, ONE TAILORED TO COLD STRESS AND THE SECOND TAILORED TO HIGH FEED RESOURCES. GENETIC MERITS OF ANIMALS WITHIN EACH OF THESE PRODUCTION CONTEXTS WILL BE DIRECTLY COMPARED TO THEIR SA:V RATIOS. IN OBJECTIVE 2, THIS PROJECT TESTS THE HYPOTHESIS THAT RANDOM MODELS VERSUS MACHINE LEARNING METHODS BETTER PREDICT VARIOUS CATTLE TRAITS (FEED INTAKE, FAT RESERVES, BODY WEIGHT, ETC.) USING PROCESSED 3D POINT CLOUDS AND ENVIRONMENTAL DATA. THE PROJECT USES RANDOM MODELS TYPICALLY USED IN DNA PREDICTIONS, BUT RATHER THAN DNA VARIANTS AS THE PREDICTORS, THE ANALYSIS USES THE LARGE 3D CAMERA DATA AS PREDICTORS.BESIDES ADVANCES IN COMPUTER VISION ALGORITHMS (E.G. STRUCTURE FROM MOTION) AND NEW APPROACHES TO APPLY DATA-DRIVEN MACHINE LEARNING TO AGRICULTURE PROBLEMS, THIS PROJECT PROVIDES A DEFINITIVE TEST OF JAMES'S RULE (INTRASPECIFIC BERGMANN'S RULE) THAT ANIMAL SIZE INCREASES WITH LATITUDE DUE TO AN INCREASED ABILITY TO MAINTAIN BODY TEMPERATURE WITH A SMALLER SA:V RATIO. THIS IS CONTRASTED WITH GEIST'S RULE THAT BODY SIZE INCREASES DUE TO ADVANTAGES OF LARGER ANIMALS TO UTILIZE SEASONAL BURST OF FOOD AT HIGHER LATITUDES. EXPLICITLY COMPARING DIRECT SA:V MEASUREMENTS WITH PRECISE CLIMATE-BY-GROWTH AND RESOURCE-BY-GROWTH PREDICTIONS, THIS PROJECT WILL ANSWER WHICH FORCE DRIVES EVOLUTION IN BODY SIZE ACROSS LATITUDES. THIS WILL BE THE FIRST DEFINITIVE TEST OF BERGMANN'S RULE. BY COMPARING RANDOM MODELS VERSUS DEEP LEARNING TO PREDICT A VARIETY OF BEEF COW PRODUCTION TRAITS USING 3D CAMERA DATA, THIS PROJECT INDICATES WHETHER RANDOM MODELS CAN REPLACE OR CLARIFY ARTIFICIAL INTELLIGENCE IN MANY CONTEXTS. THIS PROJECT CREATES NEW DATA RECORDING AND SELECTION TOOLS BASED ON INTERNET OF THINGS AND 3D COMPUTER VISION ALGORITHMS TO ADDRESS THE PRESSING NEED TO MEASURE AND IMPROVE EFFICIENCY IN PASTURE, COW-CALF PRODUCTION (A $64 BILLION INDUSTRY). THIS ENABLES CONVENIENT AND AFFORDABLE MEASUREMENT OF BEEF COW EFFICIENCY (A VITAL ASPECT OF BEEF PRODUCTION THAT IS NOT CURRENTLY PREDICTED).PARTNERING WITH THE UNIVERSITY OF MISSOURI SCHOOL OF JOURNALISM STRATEGIC COMMUNICATION CAPSTONE COURSE PROVIDES STRATEGIC PLANS TO EDUCATE A VARIETY OF AUDIENCES ON THE TRUE ENVIRONMENTAL IMPACT OF CATTLE AND HOW TECHNOLOGY ADOPTION DECREASES THIS IMPACT. THE THREE-PRONGED APPROACH 1) EDUCATES LAY AUDIENCES 2) DELIVERS ONLINE EDUCATIONAL OUTREACH TO FARMERS/RANCHERS AND 3) PROVIDES TRADITIONAL EXTENSION PROGRAMMING TO FARMERS/RANCHERS THROUGH REGIONAL AND NATIONAL IN-PERSON PRESENTATIONS. THE FIRST PRONG WILL REACH A NATIONAL AUDIENCE THROUGH CABLE TELEVISION. THE SECOND AND THIRD PRONG WILL REACH APPROXIMATELY 7,000 PEOPLE PER YEAR. WITH THE NATIONAL CENTER FOR APPLIED REPRODUCTION AND GENOMICS, A DATA SCIENCE BEST PRACTICES MODULE IS CREATED TO GIVE VETERINARY AND GRADUATE STUDENTS A PRIMER ON DATA-DRIVEN AGRICULTURE. PARTNERSHIPS WITH INDUSTRY CREATE APPLIED GENETIC EVALUATIONS TO PREDICT GENETIC, ENVIRONMENT, AND MANAGEMENT INTERACTIONS, WHICH ALLOW PRECISION SELECTION NOT CURRENTLY POSSIBLE WITH ONE-SIZE-FITS-ALL NATIONAL EVALUATIONS. THE COW SCANNING SYSTEM WILL BE COMMERCIALIZED AND ADDED ONTO EXISTING COMMERCIAL PLATFORMS THAT COLLECT FEED AND WATER INTAKE. THE 3D DATA IS USED TO PREDICT COW EFFICIENCY. COLLECTIVELY, THESE IMPACTS IMPROVE THE FINANCIAL SECURITY OF RURAL FARMERS.