Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: March 8, 2007
Publication Date: July 8, 2007
Citation: Wiggans, G.R., Cole, J.B., Thornton, L.L. 2007. Multitrait evaluation for calving ease and stillbirth with separate genetic effects by parity. Journal of Dairy Science. 90(Suppl. 1):19(abstr. M49).
Genetic evaluations for calving ease and stillbirth were calculated with Holstein and Brown Swiss data from 14,164,522 calving reports in the USDA national dairy database. Calving ease was measured on a scale of 1 (no difficulty) to 5 (difficult birth); stillbirth status was designated as live or dead within 48 hr. Calving-ease scores were transformed separately for first and later parities and calf gender. The score used in the analysis was on a unit standard deviation scale in the middle of the range for each score. Stillbirth status was present for 53% of calving-ease observations. Variance components were estimated from a 103,909-record Holstein sample with no missing observations, which represented the 2,999 bulls with the most data. A multitrait sire-maternal grandsire (MGS) linear model included fixed effects for year-season, gender-parity, sire birth year, and MGS birth year and random effects for herd-year interaction, sire, and MGS. The correlation between first and later parities was 0.69 for sire and 0.19 for MGS solutions for calving ease and 0.84 for sire and 0.78 for MGS solutions for stillbirth. For first-parity, the correlation between calving ease and stillbirth was 0.86 for sire and 0.34 for MGS solutions. To calculate national evaluations for Holstein and Brown Swiss, a fixed effect for breed was added to the model. Correlations between solutions on the underlying scale from the current evaluation with those from this analysis averaged 0.85 for sire and 0.80 for MGS for calving ease and 0.67 for sire and 0.70 for MGS for stillbirth. The multitrait analysis provided stillbirth evaluations for bulls with missing observations based on correlated calving-ease data and accounted for genetic differences in calving performance between first and later parities. Evaluation stability should be improved as the portion of observations from different parities changes. Accuracy of the net merit index can be improved by adjusting weights to use evaluations for separate parities optimally.