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* University of Wisconsin–Madison, Department of Dairy Science, Madison 53706
Alta Genetics, Inc., Watertown, WI 53094
Dairy Records Management Systems, Raleigh, NC 27695
2 Corresponding author: kweigel{at}wisc.edu
Producer-recorded clinical mastitis data from 77,791 cows in 418 herds were used to determine the potential for genetic improvement of mastitis resistance using data from on-farm management software programs. The following threshold sire models were applied: 1) a single-trait lactation model, where mastitis was recorded as 0 or 1 in first lactation only; 2) a 3-trait lactation model, where mastitis was recorded as 0 or 1 in each of the first 3 lactations, and 3) a 12-trait, lactation-segment model, where mastitis was recorded as 0 or 1 in each of 4 segments (0 to 50, 51 to 155, 156 to 260, and 261 to 365 d postpartum) in each of the first 3 lactations. Lactation incidence rates were 0.16, 0.20, and 0.24 in first, second, and third lactation, respectively, and incidence rates within various segments of these lactations ranged from 0.036 in late first lactation to 0.093 in early third lactation. Estimated heritability of liability to clinical mastitis ranged from 0.07 to 0.15, depending on the model and stage of lactation. Heritability estimates were higher in first lactation than in subsequent lactations, but estimates were generally similar for different segments of the same lactation. Genetic correlations between lactations from the 3-trait model ranged from 0.42 to 0.49, while correlations between segments within lactation from the 12-trait model ranged from 0.26 to 0.64. Based on the results presented herein, it appears that at least 2 segments are needed per lactation, because mastitis in early lactation is lowly correlated with mastitis in mid or late lactation. Predicted transmitting abilities of sires ranged from 0.77 to 0.89 for probability of no mastitis during the first lactation and from 0.36 to 0.59 for probability of no mastitis during the first 3 lactations. Overall, this study shows that farmer-recorded clinical mastitis data can make a valuable contribution to genetic selection programs, but additional systems for gathering and storing this information must be developed, and more extensive data recording in progeny test herds should be encouraged.
Key Words: clinical mastitis • genetic selection • threshold model • breeding value prediction
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