Abstract
Physiological maturity, measured as carcass ossification [10 unit increments (100, 110, 120, …)], is used by the United States Department of Agriculture and the Meat Standards Australia carcass grading systems to reflect age-associated differences in beef tenderness and determine producer payments. In most commercial cattle herds, the exact age of animals is unknown; thus, prediction of ossification in association with phenotypic prediction systems has the capacity to assist producer decision making to improve carcass and eating quality. This study developed and evaluated prediction equations that use either live animal or carcass traits to predict ossification for use in phenotypic prediction systems to predict meat quality. The average ossification in the model development dataset was 138 with a SD of 21 and a range between 100 and 200. Model development involved regressing various combinations of live animal traits: age at recording, sex, live weight (BW), average daily gain, ultrasound scanned eye muscle area, 12/13th rib and subcutaneous P8 rump fat thickness; or carcass traits: age at slaughter, sex, hot standard carcass weight (HSCW), carcass eye muscle area, marble score, rib, and P8 rump fat (CP8) thickness, against ossification. The models were challenged with data from 3 independent datasets: 1) Angus steers produced by divergent selection for visual muscle score; 2) temperate (Angus, Hereford, Shorthorn and Murray Grey) steers and heifers; and 3) tropically adapted (Brahman and Santa Gertrudis) steers and heifers. Five models with adjusted R2adj above 0.55 were evaluated. When challenged with dataset 1, the absolute mean bias (MB) and root mean square error of prediction (RMSEP) ranged from 0.1 to 4.2, and 9.8 to 10.7, which are within the bounds of the 10 point increment on the ossification scale. When subsequently challenged with dataset 2, MB and RMSEP ranged from 2.8 to 13.4, and 19.6 to 23.7, respectively; and with dataset 3, MB and RMSEP ranged from 14.4 to 17.5, and 23.3 to 31.9, respectively. Generally, when compared in relation to the ossification scale, all evaluated models had similar accuracy. For predicting meat quality, the model containing live animal traits considered most useful was [85.35 + 0.16 × BW + 10.94 × sex - 0.09 × sex × BW (adjusted R2 = 0.59; SE = 13.51)] and the most useful model containing carcass traits was [107.15 + 11.53 × sex + 1.10 × CP8 + 0.16 × HSCW - 0.15 × sex × HSCW (adjusted R2 = 0.60; SE = 13.39)].