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Title: GENETIC EVALUATION AND BEST PREDICTION OF LACTATION PERSISTENCY

Author
item Cole, John
item Vanraden, Paul

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/1/2006
Publication Date: 7/1/2006
Citation: Cole, J.B., Van Raden, P.M. 2006. Genetic evaluation and best prediction of lactation persistency. Journal of Dairy Science. 89(7):2722-2728.

Interpretive Summary: Persistency of lactation refers to the rate of decline in production after peak yield is reached. Routine genetic evaluation of persistency in the United States is feasible, and increased persistency may have economic benefits resulting from decreased feed costs or improved health. Further research is needed to determine if increased persistency is associated with lower incidences of periparturient metabolic disease. Many lactations are now longer than 305 d, and cows with high persistency may not need yearly calving intervals to be profitable. Selection goals that account for persistency could become important as new technologies such as bST and sexed semen are introduced.

Technical Abstract: Cows with high persistency tend to milk less than expected at the beginning of lactation and more than expected at the end. Best prediction of persistency was calculated as a function of a trait-specific standard lactation curve and the linear regression of a cow’s test day deviations on days in milk. The objectives of this study were to calculate (co)variance components and breeding values for best predictions of persistency of milk (PM), fat (PF), protein (PP), and SCS (PSCS) in Holstein cows. Data included 8,682,138 lactations from 4,375,938 cows calving since 1997, and 39,354 sires were evaluated. Sire EBV for PM, PF, and PP were similar and ranged from -0.70 to 0.75 for M; EBV for PSCS ranged from -0.37 to 0.28. Regressions of sire EBV on birth year were near zero (< 0.003) but positive for PM, PF, and PP, and negative for PSCS. Genetic correlations of PM, PF, and PP with PSCS were moderate and favorable, indicating that increasing SCS decreases yield traits, as expected. Genetic correlations among yield and persistency were low to moderate and ranged from -0.09 (PSCS) to 0.18 (PF). This definition of persistency may be more useful than those used in test-day models, which are often correlated with yield. A measure not confounded with yield may provide for simpler understanding of persistency. Routine genetic evaluations for persistency are feasible and may allow for improved predictions of yield traits but better understanding of its implications are needed before selection is implemented.