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ABSTRACTThe objective of this study was to investigate the effects of rumen-protected methionine (RPM) supplementation during early lactation on performance and metabolic parameters of dairy cows. Forty-two Holstein cows were blocked by parity (22 primiparous and 20 multiparous) and calving date, then randomly assigned to two groups, Control and RPM. From calving through 29 ± 8 d in milk, cows received an early lactation diet [1.59 Mcal/kg of DM, 10.7% rumen-degradable protein and 6.5% rumen-undegradable protein] with no added methionine (Control, n = 21) or with supplementation of Smartamine M (RPM, Adisseo Inc, n = 21). RPM cows were supplemented with 12 g/day Smartamine M (7.2 g of metabolizable methionine), individually top-dressed over the total mixed ration. Blood and milk samples were collected during the first two weeks of lactation and milk yield recorded until 30 days in milk. No differences in milk yield or blood metabolites were observed. Cows supplemented with RPM increased milk fat and total solids contents and fat-to-protein ratio by 0.48% units, 0.66% units, and 0.09 units, while tended to increase milk total protein content by 0.13% units, respectively. This study showed beneficial effects of early postpartum RPM supplementation on milk composition of dairy cows.

Bull fertility has been recognized as an important factor affecting dairy herd fertility. The objective of this study was to assess the feasibility of predicting male fertility in Brown Swiss cattle using genomic data. The data set consisted of 1,102 Italian Brown Swiss bulls with sire conception rate (SCR) records and genotype data for roughly 480k SNP. The analyses included the use of linear kernel-based regression models fitting all SNPs or incorporating markers with large effect. Predictive performance was evaluated in 5-fold cross-validation using the correlation between observed and predicted SCR values and mean squared error of prediction. The entire SNP set exhibited predictive correlations around 0.19. Interestingly, the inclusion of 2 markers with large effect yielded predictive correlations around 0.32. Overall, using linear kernel-based models fitting markers with large effect is a promising approach. Our findings could help Brown Swiss breeders make enhanced genome-guided management and selection decisions on male fertility.