Influence of age at first calving in a continuous calving season on productive, functional, and economic performance in a Blonde d'Aquitaine beef population.

The lifetime production of 7,655 cows with known age at first calving and a total of 27,118 parity records from 301 purebred Blonde d'Aquitaine herds were used to demonstrate the economic benefits of 2 yr of age at first calving. Ages at first calving ranged from 20 to 48 mo, and cows were divided into 5 calving groups, starting with early calving from age 20 to 27 mo up to late calving from age 40 to 48 mo. The information was gathered into 2 data sets, one for only primiparous cows and the second for all cows. The traits analyzed in this study were grouped as functional, linear type, and production traits. Functional traits were calving interval, calving ease, and number of calvings. Skeletal, muscle, and functional appraisal were included as linear type traits. The production traits studied were BW and weaning weight, carcass growth, and conformation of the offspring. The only significant traits found in primiparous cows were late age at first calving, which resulted in heavier BW calves, and early age at first calving, which resulted in calves with greater carcass conformation scores. Age at first calving was found to be significant only in its effect on BW and the number of calvings over a cow's lifetime, with lighter calves for early age at first calving. Heritability for age at first calving was 0.17. Genetic correlation of age at first calving with direct calving ease was positive (0.27) and that with maternal calving ease was negative (-0.39). Age at first calving showed a negative genetic correlation with lifetime number of calvings (-0.29) and a positive correlation with calving interval (0.14). Correlations with production and type traits were low, except for skeletal development (-0.29). Based on phenotypic and genetic analysis, there is a tendency for early-calving cows to produce a greater lifetime number of calves with less muscle but good carcass growth. Age at first calving affected the number of heifers in the herd, replacement rate, and number of animals slaughtered each year. Shortening the age at first calving from 3 to 2 yr led to a reduction of heifer feeding cost of US$21.24 (17.7€), a reduction of production cost of $26.52 (22.1€), and a profit increase of $25.80 (21.50€) per slaughtered animal per year over lifetime cow production.

Accuracy of Genome-Enabled Prediction in a Dairy Cattle Population using Different Cross-Validation Layouts.

The impact of extent of genetic relatedness on accuracy of genome-enabled predictions was assessed using a dairy cattle population and alternative cross-validation (CV) strategies were compared. The CV layouts consisted of training and testing sets obtained from either random allocation of individuals (RAN) or from a kernel-based clustering of individuals using the additive relationship matrix, to obtain two subsets that were as unrelated as possible (UNREL), as well as a layout based on stratification by generation (GEN). The UNREL layout decreased the average genetic relationships between training and testing animals but produced similar accuracies to the RAN design, which were about 15% higher than in the GEN setting. Results indicate that the CV structure can have an important effect on the accuracy of whole-genome predictions. However, the connection between average genetic relationships across training and testing sets and the estimated predictive ability is not straightforward, and may depend also on the kind of relatedness that exists between the two subsets and on the heritability of the trait. For high heritability traits, close relatives such as parents and full-sibs make the greatest contributions to accuracy, which can be compensated by half-sibs or grandsires in the case of lack of close relatives. However, for the low heritability traits the inclusion of close relatives is crucial and including more relatives of various types in the training set tends to lead to greater accuracy. In practice, CV designs should resemble the intended use of the predictive models, e.g., within or between family predictions, or within or across generation predictions, such that estimation of predictive ability is consistent with the actual application to be considered.