Using genetic evaluations for growth and maternal gain from birth to weaning to predict energy requirements of Line 1 Hereford beef cows.

The maintenance energy required to sustain the cow herd is a major cost of beef production. This work proposes modifying parameter estimates for a population-specific lactation curve with genetic evaluations for the maternal genetic effect on calf gain from birth to weaning to provide inputs for a commonly used prediction of energy requirement. Daily milk production (y) was modeled as a function of stage of lactation (T, d) using the function y = AT(B)exp(-CT) modified to incorporate effects of genetic evaluation for the maternal effect on calf gain from birth to weaning and age of dam. A 1-kg increase in predicted maternal breeding value for calf gain from birth to weaning from within-herd genetic evaluation increased the lactation curve parameter A by 10.3+/-4.6% and reduced the B parameter by 1.0+/-0.6%. Similarly, a 1-kg increase in maternal breeding value for gain from birth to weaning from national cattle evaluation increased the A parameter by 1.7+/-0.2%. Corresponding estimates of peak milk yield and time of peak lactation were derived for individual animals from their genetic evaluation. Additional inputs for predicting maintenance energy requirements were derived from genetic evaluations for birth weight and mature size. The methodology is demonstrated using genetic evaluations of sires from the Miles City Line 1 Hereford population. Further refinement and application of this methodology may facilitate characterization of beef cattle seedstock for their potential genetic contributions to profitability.

Genetic analysis of gain from birth to weaning, milk production, and udder conformation in Line 1 Hereford cattle.

The objective of this research was to partition phenotypic variation in calf gain from birth to weaning, and milk production measured, by the weigh-suckle-weigh method, and udder score of cows into genetic and nongenetic components. Data were from the Line 1 Hereford population maintained by USDA-ARS at Miles City, MT, and included observations of pre-weaning gain (n = 6,835) from 2,172 dams, milk production (n = 692) from 403 cows, and udder score (n = 1,686) from 622 cows. Data were analyzed using a Gibbs sampler for multiple-trait animal models. Results are reported as means +/- SD derived from the posterior distributions of parameter estimates. Mean estimates of the phenotypic variance of preweaning gain, milk production, and udder score were 476.3 kg2, 8.88 kg2, and 1.89 (1 to 9 scale), respectively. Estimates of phenotypic correlations between preweaning gain and milk production, preweaning gain and udder score, and milk production and udder score were 0.37 +/- 0.04, - 0.07 +/- 0.04, and - 0.09 +/- 0.05, respectively. Estimates of heritability for direct and maternal preweaning gain, milk production, and udder score were 0.13 +/- 0.03, 0.25 +/- 0.04, 0.25 +/- 0.06, and 0.23 +/- 0.05, respectively. Genetic correlations of milk production with maternal preweaning gain and udder score were estimated as 0.80 +/- 0.08 and - 0.36 +/- 0.16, respectively. Posterior distributions of the other genetic correlations all contained 0.00 within the respective 90% probability density posterior intervals. Estimates of repeatability of maternal preweaning gain, milk production, and udder score were 0.43 +/- 0.03, 0.39 +/- 0.05, and 0.34 +/- 0.03, respectively. Breeding value for maternal gain from birth to weaning was highly predictive of breeding value for milk production. Direct measurement of milk production to use in genetic improvement may not be justified because it is difficult to measure, and selection based on the breeding value for maternal preweaning gain may be nearly as effective in changing milk production as direct selection. A potentially undesirable consequence of selection to increase milk production is the degradation of udder quality. However, this correlation is not so strong as to preclude simultaneous improvement of milk production and udder quality using appropriate predicted breeding values for each trait.