Herd-specific random regression carcass profiles for beef cattle after adjustment for animal genetic merit

Contribution of herd characteristics to best linear unbiased estimates of slaughter traits in beef cattle

Genetic evaluations separate phenotypes into their contributing additive genetic effects and non-(additive) genetic effects, with the former termed best linear unbiased predictions, and the latter termed best linear unbiased estimates (BLUEs). For the purpose of the present study, genetic evaluations, along with phenotypic data from 4 137 376 animals, were used to generate herd, year of slaughter and sex contemporary group BLUEs for various slaughter-related traits. These slaughter traits included carcass weight (CW), carcass conformation (CC) and carcass fat (CF). For the 4 665 herds that were consistently slaughtering ≥10 animals/year between the years 2014 and 2018, inclusive, all relevant contemporary group BLUEs were collapsed into a single herd-year value; results herein relate to these herds. The within-year herd-year BLUE correlations between CW and CC, between CW and CF, and between CC and CF were 0.51, 0.10 and -0.04, respectively. The repeatability across years of the herd-year BLUEs for CW, CC and CF was 0.66, 0.59 and 0.50, respectively. Furthermore, when the herds were stratified, within year, on the percentile rank of their herd-year BLUEs, herds had the greatest probability of remaining in the same BLUE stratum from one year to the next. In addition, results from the present study determined that various herd characteristics are associated with differences in the herd BLUEs. Results from the present study could be used to advise beef producers on the most promising strategy to improve the carcass merit of their animals.

Potential exists to change, through breeding, the yield of individual primal carcass cuts in cattle without increasing overall carcass weight1

The ability to alter the morphology of cattle towards greater yields of higher value primal cuts has the potential to increase the value of animals at slaughter. Using weight records of 14 primal cuts from 31,827 cattle, the objective of the present study was to quantify the extent of genetic variability in these primal cuts; also of interest was the degree of genetic variability in the primal cuts adjusted to a common carcass weight. Variance components were estimated for each primal cut using animal linear mixed models. The coefficient of genetic variation in the different primal cuts ranged from 0.05 (bavette) to 0.10 (eye of round) with a mean coefficient of genetic variation of 0.07. When phenotypically adjusted to a common carcass weight, the coefficient of genetic variation of the primal cuts was lesser ranging from 0.02 to 0.07 with a mean of 0.04. The heritability of the 14 primal cuts ranged from 0.14 (bavette) to 0.75 (topside) with a mean heritability across all cuts of 0.48; the heritability estimates reduced, and ranged from 0.12 (bavette) to 0.56 (topside), when differences in carcass weight were accounted for in the statistical model. Genetic correlations between each primal cut and carcass weight were all ≥0.77; genetic correlations between each primal cut and carcass conformation score were, on average, 0.59 but when adjusted to a common carcass weight, the correlations weakened to, on average, 0.27. The genetic correlations among all 14 primal cut weights was, on average, strong (mean correlation of 0.72 with all correlations being ≥0.37); when adjusted to a common carcass weight, the mean of the genetic correlations among all primal cuts was 0.10. The ability of estimated breeding values for a selection of primal cuts to stratify animals phenotypically on the respective cut weight was demonstrated; the weight of the rump, striploin, and fillet of animals estimated to be in the top 25% genetically for the respective cut, were 10 to 24%, 12 to 24%, and 7 to 17% heavier than the weight of cuts from animals predicted to be in the worst 25% genetically for that cut. Significant exploitable genetic variability in primal carcass cuts was clearly evident even when adjusted to a common carcass weight. The high heritability of many of the primal cuts infers that large datasets are not actually required to achieve high accuracy of selection once the structure of the data and the number of progeny per sire is adequate.