Reproductive performance as affected by lactation status and body fat in beef cows.

Seventy-one mature Brangus cows, 38 nonlactating (NL) and 33 in late stage of lactation (L) were fed for 192 days (Phase I) a low energy diet (L). During Phase II (65 days) 19 NL and 17 L cows were fed a high energy diet (H). The other nonlactating (19) and lactating (16) cows remained on the low energy diet. Energy restriction during Phase I did not affect (P> 0.05) cyclic ovarian activity although losses in body weight and condition were substantial. Rapid changes in body weight, condition, and percent empty body lipe (EBLP) during Phase II did not substantially influencefertility, although a five-fold difference in EBLP was observed (NL0H vs. L-L). Treatment groups did not differ (P> 0.05) in conception rate, days from the beginning of the breeding season to breeding and to conception, conception at first service, and number of services per conception. Values observed for these parameters for NL-H, L-H, NL-L, and L-L groups were respectively: 68,4%, `3,.2, 23.3, 36.8% and 1.68; 82,4% 12.7, 19.5, 58.8% and 1.29; 68.4%, 10.2, 17.4, 47.4%, and 1.41; 68.8%, 12.4, 19.5, 43.7%, and 1.50.

Genetic associations between scrotal circumference and body weight measured at different ages in Canchim cattle.

2015

Genetic parameters for carcass traits and body weight using a Bayesian approach in the Canchim cattle.

Correlation between genetic parameters and factors such as backfat thickness (BFT), rib eye area (REA), and body weight (BW) were estimated for Canchim beef cattle raised in natural pastures of Brazil. Data from 1648 animals were analyzed using multi-trait (BFT, REA, and BW) animal models by the Bayesian approach. This model included the effects of contemporary group, age, and individual heterozygosity as covariates. In addition, direct additive genetic and random residual effects were also analyzed. Heritability estimated for BFT (0.16), REA (0.50), and BW (0.44) indicated their potential for genetic improvements and response to selection processes. Furthermore, genetic correlations between BW and the remaining traits were high (P > 0.50), suggesting that selection for BW could improve REA and BFT. On the other hand, genetic correlation between BFT and REA was low (P = 0.39 ± 0.17), and included considerable variations, suggesting that these traits can be jointly included as selection criteria without influencing each other. We found that REA and BFT responded to the selection processes, as measured by ultrasound. Therefore, selection for yearling weight results in changes in REA and BFT.

Relationships between residual intake and gain, performance, feeding behavior and body measures in Brahman young bulls

2016