Prediction of slaughter cow composition using live animal and carcass traits

Slaughter cows (n = 120), represent- ing four genotypes (British, continental, Bos indicus, and dairy) and three body condition classes (thin, moderate, and fat), n = 10 per subclass, were used to identify practical and accurate prediction equations for the yield of boneless manufacturing beef of specific fat percentages. Cows and their carcasses were weighed and evaluated for USDA yield and quality grade factors and for physical muscle and fat indica- tors. Carcass sides were fabricated; total fat percen- tage (TFP) was calculated as total fat (trimmed and chemical) divided by side weight, and tissue lean percentage (TLP) was calculated as boneless fat-free lean divided by soft tissue weight. Data were analyzed using maximum R 2 multiple regression. The best live trait prediction model for TFP included live prelimi- nary yield grade (LPYG), body condition score (LCOND), visual live muscle score (LMUSC), and live weight (LWT), R 2 = .83. The best carcass trait TFP prediction model included adjusted preliminary yield grade (CPYGA); kidney, pelvic, and heart fat adjustment (CKPHADJ); marbling score (CMARB); and hot carcass weight (HCW), R 2 = .92. The best live trait TLP prediction model included LPYG, LCOND, LMUSC, and LWT, R 2 = .82. The best carcass trait TLP prediction model included CPYGA, CKPHADJ, CMARB, and lean maturity, R 2 = .91. These data indicate that TFP and TLP of slaughter cows can be accurately and practically predicted using live animal and carcass traits.