The effects of inbreeding on the lifetime performance of dairy cattle were examined using data for production, somatic cell score, and linear type for all Holstein cows that were scored between 1983 and 1993. The results of fixed and mixed animal models differed. Relative net income adjusted for opportunity cost for the 2,610,123 cows with an 84-mo opportunity for herdlife was depressed by $14.79 for fluid market pricing and by $12.40 for manufacturing pricing per 1% increase in inbreeding. Mixed model estimates of depression per 1% of increase in inbreeding were +0.55 d for age at first calving, -6 d for days of productive life, and -4.8 for days in milk. Inbreeding decreased the mature equivalent production of milk, fat, and protein during first lactation by 27, 0.9, and 0.8 kg and the lifetime production of milk, fat, and protein by 177, 6.0, and 5.5 kg, respectively, per 1% increase in inbreeding. Inbreeding had little effect on conformation traits. The effects of inbreeding were cumulative, and effects on lifetime profit functions were relatively larger than the effects on lactation traits. Registered cows had higher levels of inbreeding and larger standard deviations than did grade cows. Inbreeding in registered cows depressed relative net income adjusted for opportunity cost for fluid and manufacturing prices by $24.43 and $21.78, respectively; income was depressed $9.43 and $9.02, respectively, for grade cows. The difference between registered and grade cattle is likely due to the incomplete pedigree information in grade animals. Inbreeding among cows in this study was not high on average, but economic losses represented a significant cost to the producer.
Animals most related or least related to current members of their breed were revealed by calculating the expected inbreeding of their future progeny. A sample of potential mates was chosen by randomly selecting 600 females from a recent birth year (1995). Relationships among the sample were computed by the tabular method. Relationships of other animals to the sample population were computed quickly from the relationships of their parents or ancestors. To-Mar Blackstar-ET and Round Oak Rag Apple Elevation were most related to the Holstein breed with expected inbreeding of 7.9 and 7.7%, respectively. Corresponding Jersey bulls were Highland Magic Duncan and Soldierboy Boomer Sooner of CJF with expected inbreeding of 10.9 and 9.5%, respectively. The highest expected inbreeding was 11.1% for Selwood Bettys Commander, 8.6% for Forest Lawn Simon Jetway, 10.1% for Dutch Mill Telestars Fayette, and 7.4% for Korncrest Pacesetter for Ayrshire, Brown Swiss, Guernsey, and Milking Shorthorn breeds, respectively. Regression on inbreeding in the genetic evaluation model removed effects of past inbreeding. Future inbreeding effects could be included for each potential mating or by adjusting breeding values for average inbreeding expected with random mating. The correlation between Holstein breeding values unadjusted and adjusted for inbreeding was 0.9976. The estimated genetic trend was 6% lower with future inbreeding included.
Prediction of lactation yields and accuracies of yields for use in genetic evaluation can be improved by including information from test day correlations, especially for milk recording plans that vary in the numbers of milk weights recorded and component samples taken. Daily milk weights for 658 lactations of Canadian cows and monthly test records of milk, fat, and protein yields and somatic cell scores for 500,000 lactations of US cows were used to estimate phenotypic correlations between test days within herd-year. Correlations between daily yields for a designated interval between test days generally were highest for midlactation and were lowest for early and late lactation. Regression (two linear, two quadratic, and interaction effects) on mean DIM and interval between test days predicted correlations with a squared correlation of 0.94 for daily milk yields. Similar relationships were found for US monthly data. Variation in sampling was reduced, computer memory was minimized, and positive definiteness was guaranteed by fitting regressions on simply defined sources of correlation. An autoregressive matrix represented the within-trait correlations very well. The equations developed could be used to derive covariances and, subsequently, to estimate lactation yields and accuracies from combinations of individual daily milk, fat, and protein yields and somatic cell score.
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