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Within-Herd Effects of Age at Test Day and Lactation Stage on Test-Day Yields

J. Bormann^*,, G. R. Wiggans, T. Druet^*, and N. Gengler^*,

^* Animal Science Unit, Gembloux Agricultural University, B-5030 Gembloux, Belgium
Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
National Fund for Scientific Research, B-1000 Brussels, Belgium

Corresponding author: G. R. Wiggans; email: wiggans{at}aipl.arsusda.gov.

Variance ratios were estimated for random within-herd effects of age at test day and lactation stage, on test-day yield and somatic cell score to determine whether including these effects would improve the accuracy of estimation. Test-day data starting with 1990 calvings for the entire US Jersey population and Holsteins from California, Pennsylvania, Wisconsin, and Texas were analyzed. Test-day yields were adjusted for across-herd effects using solutions from a regional analysis. Estimates of the relative variance (fraction of total variance) due to within-herd age effects were small, indicating that regional adjustments for age were adequate. The relative variances for within-herd lactation stage were large enough to indicate that accuracy of genetic evaluations could be improved by including herd stage effects in the model for milk, fat, and protein, but not for somatic cell score. Because the within-herd lactation stage effect is assumed to be random, the effect is regressed toward the regional effects for small herds, but in large herds, lactation curves become herd specific. Model comparisons demonstrated the greater explanatory power of the model with a within-herd-stage effect as prediction error standard deviations were greater for the model without this effect. The benefit of the within-herd-stage effects was confirmed in a random regression model by comparing variance components from models with and without random within-herd regressions and through log-likelihood ratio tests.

Key Words: test-day model • genetic evaluation • yield traits • lactation curves

Abbreviation key: RRM = random regression model

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