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Comparison of models using different censoring scenarios for days open in Spanish Holstein cows

Published online by Cambridge University Press:  09 March 2007

O. González-Recio*
Affiliation:
Departamento de Producción Animal, ETSI Agrónomos – Universidad Politécnica de Madrid, Ciudad Universitaria s/n 28040, Madrid, Spain
Y.M. Chang
Affiliation:
Department of Dairy Science, University of Wisconsin, Madison, 53706, USA
D. Gianola
Affiliation:
Department of Dairy Science, University of Wisconsin, Madison, 53706, USA
K. A. Weigel
Affiliation:
Department of Dairy Science, University of Wisconsin, Madison, 53706, USA
*
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Abstract

Days open data from 113 569 lactation records in 774 Spanish Holstein herds were analysed using standard linear models under two different editing procedures, and with two alternative methodologies that account for censoring: a censored linear model (CLM) and a Weibull survival analysis (SA) model. The first editing procedure excluded from the linear model all censored records for days open (LMnc), and the second defined days open as days from calving to the last known insemination or culling date, treating censored records as complete (LM). Sire variance estimates for days open were 61, 70 and 139 for LMnc, LM and CLM, respectively, and 0·026 for SA on a logarithmic scale. Heritability estimates were 0·05, 0·06 and 0·08 with LMnc, LM and CLM, respectively. Rankings of sires varied between methodologies: sire evaluations from LMnc and LM had rank correlations with evaluations from SA equal to −0·65 and −0·82, respectively, and of 0·71 and 0·87 with evaluations from CLM. The rank correlation between evaluations from SA and CLM was −0·98, suggesting stronger agreement of sire rankings between models that take censoring into account.The SA model had a better predictive ability of daughter fertility at early stages of lactation than the other methods, as measured by chi-squared statistics for predicted pregnancy status at 75, 103, 140, or 200 days post partum in a split data set. The CLM also predicted daughter fertility more accurately than any of the two standard linear models.

Type
Research Article
Copyright
Copyright © British Society of Animal Science 2006

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References

Abdallah, J. M. and McDaniel, B. T. 2000. Genetic parameters and trends of milk, fat, days open, and body weight after calving in North Carolina experimental herds. Journal of Dairy Science 83: 13641370.CrossRefGoogle ScholarPubMed
Caraviello, D. Z., Weigel, K. A. and Gianola, D. 2004. Prediction of longevity breeding values for US Holstein sires using survival analysis methodology. Journal of Dairy Science 87: 35183525.CrossRefGoogle ScholarPubMed
Carriquiry, A. L., Gianola, D. and Fernando, R. L. 1987. Mixed model analysis of a censored normal distribution with reference to animal breeding. Biometrics 43: 929939.CrossRefGoogle ScholarPubMed
Cox, D. R. 1972. Regression models and life–tables (with discussion). Journal of the Royal Statistical Society, B 34: 187220.Google Scholar
Dekkers, J. C. M., Ten Hag, J. H. and Weersink, A. 1998. Economic aspects of persistency of lactation in dairy cattle. Livestock Production Science 53: 237252.CrossRefGoogle Scholar
Dematawewa, C. M. B. and Berger, P. J. 1998. Genetic and phenotypic parameters for 305-day yield, fertility, and survival in Holstein. Journal of Dairy Science 81: 27002709.CrossRefGoogle Scholar
Donoghue, K. A., Rekaya, R., Bertrand, J. K. and Misztal, I. 2004. Genetic evaluation of calving to first insemination using natural and artificial insemination mating data. Journal of Animal Science 82: 362367.CrossRefGoogle ScholarPubMed
Ducrocq, V. and Casella, G. 1996. A Bayesian analysis of mixed survival models. Genetics, Selection, Evolution 28: 505529.CrossRefGoogle Scholar
Ducrocq, V. and Solkner, J. 1998. The Survival Kit–V3.0: a package for large analyses of survival data. Vil. Proceedings of the sixth world congress on genetics applied to livestock production,Armidale, Australia, vol 27, pp. 447–448.Google Scholar
Gelfand, A. and Smith, A. F. M. 1990. Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association 85: 398409.CrossRefGoogle Scholar
Guo, S. F., Gianola, D., Rekaya, R. and Short, T. 2001. Bayesian analysis of lifetime performance and prolificacy in Landrace sows using a linear mixed model with censoring. Livestock Production Science 72: 243252.CrossRefGoogle Scholar
INTERBULL. 2004. National GES information. http://www–interbull.slu.se/national_ges_info2/framesida–ges.htm. Accessed 31 March 2005.Google Scholar
Klein, J. P. and Moeschberger, M. L. 2003. Survival analysis: techniques for censored and truncated data, second edition. Springer, New York.Google Scholar
Korsgaard, I. R., Andersen, A. H. and Jensen, J. 2002. Prediction error variance and expected response to selection, when selection is based on the best predictor–for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits. Genetics, Selection, Evolution 34: 307333.Google Scholar
Kuhn, M. T., Van Raden, P. M. and Hutchison, J. L. 2004. Use of early lactation days open records for genetic evaluation of cow fertility. Journal of Dairy Science 87: 22772284.CrossRefGoogle ScholarPubMed
Moreno, C., Sorensen, D., García–Cortes, L. A., Varona, L. and Altarriba, J. 1997. On biased inferences about variances components in the binary threshold model. Genetics, Selection, Evolution 29: 145160.Google Scholar
Oseni, S., Tsuruta, S., Misztal, I. and Rekaya, R. 2004. Genetic parameters for days open and pregnancy rates in US Holstein using different editing criteria. Journal of Dairy Science 87: 43274333.CrossRefGoogle ScholarPubMed
Pösö, J., Mäntysaari, E. A. 1996. Genetic relationship between reproductive disorders, operational days open and milk yield. Livestock Production Science 46: 4148.CrossRefGoogle Scholar
Schneider, M. P., Strandberg, E., Ducrocq, V. and Roth, A. 2005. Survival analysis applied to genetic evaluation for female fertility in dairy cattle. Journal of Dairy Science 88: 22532259.CrossRefGoogle ScholarPubMed
Sorensen, D., Gianola, D. and Korsgaad, I. 1998. Bayesian mixed–effects model analysis of a censored normal distribution with animal breeding applications. Acta Agriculturæ Scandinavica Section A, Animal Science 48: 222229.Google Scholar
Tanner, M. A. and Wong, W. H. 1987. The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association 81: 8286.Google Scholar
Van Arendonk, J. A. M., Hovenier, R. and Deboer, W. 1989. Phenotypic and genetic association between fertility and production in dairy cows. Livestock Production Science 21: 112.CrossRefGoogle Scholar
Van Raden, P. M., Sanders, A. H., Tooker, M. E., Miller, R. H., Norman, H. D., Kuhn, M. T. and Wiggans, G. R. 2004. Development of a national genetic evaluation for cow fertility. Journal of Dairy Science 87: 22852292.CrossRefGoogle ScholarPubMed
Vargas, B., Van der Lende, T., Viajen, M. and Van Arendonk, J. A. M. 1998. Event–time analysis of reproductive traits of dairy heifers. Journal of Dairy Science 81: 28812889.CrossRefGoogle ScholarPubMed
Wiggans, G. R. and Goodling, R. C. 2005. Accounting for pregnancy diagnosis in predicting days open. Journal of Dairy Science 88: 18731877.Google Scholar