Effects of close-up body condition score and selenium-vitamin E injection on lactation performance, blood metabolites, and oxidative status in high-producing dairy cows.

High-producing dairy cows with high pre-calving body condition score (BCS) are more susceptible to metabolic disorders and oxidative stress. The aim of present study was to evaluate the effects of close-up BCS and 3 times Se-vitamin E (SeE) injection on BCS change, blood metabolites, oxidative status, and milk yield in high-producing Holstein cows. A total of 136 multiparous cows were divided into 2 groups based on their BCS including high (HB = 4.00 ± 0.20) and moderate (MB = 3.25 ± 0.25) at 3 wk before expected calving time. Then, each group was divided into 2 subgroups: 3 rounds of SeE injection at 21 d before, and 0 and 21 d after calving (+SeE), and no SeE injection (-SeE). Four final experimental groups were HB+SeE, MB+SeE, HB-SeE, and MB-SeE (34 cows each). Results indicated that interaction effect of BCS and SeE affected serum glucose, and the MB+SeE group had the highest level. The HB cows lost more BCS compared with MB cows during the postcalving period. Moreover, serum insulin concentration increased after SeE injection. The HB cows had higher serum nonesterified fatty acids at 14 d after calving. The MB cows tended to have higher activity of blood glutathione peroxidase over the study period. Furthermore, the SeE-injected cows tended to have higher activity of blood glutathione peroxidase at 28 d after calving. Serum albumin level was increased by SeE injection. The HB cows had greater milk production than MB cows, and SeE-injected cows tended to have higher milk fat percentage and higher fat:protein ratio compared with nonsupplemented cows. It was concluded that SeE injection had beneficial effects on some blood metabolites, albumin as a blood antioxidative parameter, and lactation performance in high-producing dairy cows, especially cows with moderate close-up BCS.

The effect of using cow genomic information on accuracy and bias of genomic breeding values in a simulated Holstein dairy cattle population.

Using cow data in the training population is attractive as a way to mitigate bias due to highly selected training bulls and to implement genomic selection for countries with no or limited proven bull data. However, one potential issue with cow data is a bias due to the preferential treatment. The objectives of this study were to (1) investigate the effect of including cow genotype and phenotype data into the training population on accuracy and bias of genomic predictions and (2) assess the effect of preferential treatment for different proportions of elite cows. First, a 4-pathway Holstein dairy cattle population was simulated for 2 traits with low (0.05) and moderate (0.3) heritability. Then different numbers of cows (0, 2,500, 5,000, 10,000, 15,000, or 20,000) were randomly selected and added to the training group composed of different numbers of top bulls (0, 2,500, 5,000, 10,000, or 15,000). Reliability levels of de-regressed estimated breeding values for training cows and bulls were 30 and 75% for traits with low heritability and were 60 and 90% for traits with moderate heritability, respectively. Preferential treatment was simulated by introducing upward bias equal to 35% of phenotypic variance to 5, 10, and 20% of elite bull dams in each scenario. Two different validation data sets were considered: (1) all animals in the last generation of both elite and commercial tiers (n = 42,000) and (2) only animals in the last generation of the elite tier (n = 12,000). Adding cow data into the training population led to an increase in accuracy (r) and decrease in bias of genomic predictions in all considered scenarios without preferential treatment. The gain in r was higher for the low heritable trait (from 0.004 to 0.166 r points) compared with the moderate heritable trait (from 0.004 to 0.116 r points). The gain in accuracy in scenarios with a lower number of training bulls was relatively higher (from 0.093 to 0.166 r points) than with a higher number of training bulls (from 0.004 to 0.09 r points). In this study, as expected, the bull-only reference population resulted in higher accuracy compared with the cow-only reference population of the same size. However, the cow reference population might be an option for countries with a small-scale progeny testing scheme or for minor breeds in large counties, and for traits measured only on a small fraction of the population. The inclusion of preferential treatment to 5 to 20% of the elite cows led to an adverse effect on both accuracy and bias of predictions. When preferential treatment was present, random selection of cows did not reduce the effect of preferential treatment.