Development and evaluation of a regression equation of prediction for fat-free soft tissue in heterogenous populations of cattle.

Abstract

Regression equations to predict kilograms of fat-free soft tissue (the sum of water and protein from chemical analyses) were developed from data collected on 526 steers and heifers. Straightbred animals representing Angus, Braunvieh, Charolais, Gelbvieh, Hereford, Limousin, Pinzgauer, Red Poll, and Simmental breeds of cattle contributed to the data set. Cattle ranged in slaughter weight and age from approximately 350 to 575 kg and from 13 to 23 mo, respectively. Diets (100% ground alfalfa, 67% ground alfalfa and 33% ground corn or 33% ground alfalfa and 67% ground corn) were cross-classified with breed and sex. Estimative traits included in the equation were warm carcass weight, fat depth at the 12th rib, and body impedance. Carcass soft-tissue samples were taken for determination of chemical constituents. The prediction equation accounted for 94% of the variation in fat-free soft tissue of the carcass. Adjusting for breed-sex-diet contemporary groups increased the R2 value by 2% units. The prediction model was evaluated using data collected on 65 steers sired by Charolais or by Hereford bulls at the Ft Keogh Livestock and Range Research Laboratory (Miles City, MT). Postweaning feeding strategies and slaughter ages varied among these animals. Carcass weight, back fat depth, and resistive impedance measures were recorded. Carcass soft-tissue samples were taken for determination of chemical constituents. Values of estimator variables recorded at Ft. Keogh were used in the regression equation to predict fat-free soft tissue for each animal. The values for kilogram of fat-free soft tissue determined from chemical analysis were regressed on predicted fat-free soft tissue. the results indicate that fat-free soft tissue of carcasses can be accurately predicted using estimative traits that do not diminish carcass value.