Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-18T08:21:17.006Z Has data issue: false hasContentIssue false
  • English
  • Français

An improved model for the French genetic evaluation of dairy bulls on length of productive life of their daughters

Published online by Cambridge University Press:  09 March 2007

V. Ducrocq
V. Ducrocq*
Affiliation:
Station de Génétique Quantitative et Appliquée, Département de Génétique Animale, Institut National de la Recherche Agronomique, 78352 Jouy en Josas, France
*
†E-mail: [email protected]
Get access

Abstract

Functional longevity of dairy cows has been routinely evaluated in France since 1997 using a survival analysis model. Recently, we proposed a genetic trend validation test that could be used before including national data in an international evaluation of bulls on longevity of their daughters. Its application to the French Holstein data revealed a large overestimation of the genetic trend. It was found that the bias is the result of a change in the baseline hazard rate over time. A new proportional hazards model is proposed which accounts for this change. In the new model, the baseline is described as a stratified, piecewise Weibull hazard function within lactation, i.e. a function of the number of days since the most recent calving. Stratification is within year and parity. Different Weibull hazard functions are used over four periods: 0 to 270 days, 271 to 380 days, 381 days to day when dried, dry period until the next calving. The non-genetic effects included in the model were slightly different from the previous one. In particular the interaction effects between the within herd-year class of production and lactation number × stage of lactation on the one hand and year-season were accounted for. The estimated genetic variance was smaller than with the old model. The new genetic trend is almost flat. An illustration of the efficiency of selection on the estimated breeding values for longevity is presented.

Keywords

dairy cattlegenetic analysislongevitysire evaluationsurvival
Type
Research Article
Information
Animal Science , Volume 80 , Issue 3 , June 2005 , pp. 249 - 256
Copyright
Copyright © British Society of Animal Science 2005

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Cox, D. R. 1972. Regression models and life tables (with discussion). Journal of the Royal Statistical Society B 34: 187–220.Google Scholar
Damgaard, L. H., Korsgaard, I. R., Simonsen, J., Dalsgaard, O. and Andersen, A. H. 2003. Weibull log-normal frailty models. Proceedings of the 54th annual meeting of the European Association for Animal Production,Rome,August 31-Sept 3, book of abstracts, p. 73 (abstr.).Google Scholar
Dennis, J. E. and Schnabel, R. B. 1983. Numerical methods for unconstrained optimization and nonlinear equations. Prentice-Hall Inc., Englewood Cliffs, NJ.Google Scholar
Ducrocq, V. 1999. Two years of experience with the French genetic evaluation of dairy bulls on production-adjusted longevity of their daughters. Interbull Bulletin 20: 60–67.Google Scholar
Ducrocq, V. 2002. A piecewise Weibull mixed model for the analysis of length of productive life of dairy cows. Proceedings of the seventh world congress on genetics applied to livestock production,Montpellier,19–23 August, 2002, communication no. 20–04.Google Scholar
Ducrocq, V. 2004. Illustration of a trend validation test for longevity evaluations. Proceedings of the Interbull technical workshop, Sousse, Tunisia, 05 29–31, 2004. Interbull Bulletin 32: 151–156.Google Scholar
Ducrocq, V., Boichard, D., Barbat, A. and Larroque, H. 2001. Implementation of an approximate multitrait BLUP evaluation to combine production traits and functional traits into a total merit index. Proceedings of the 52nd annual meeting of the European Association for Animal Production,Budapest,26–29 August, 2001, book of abstracts, p. 2 (abstr.).Google Scholar
Ducrocq, V. and Casella, G. 1996. A Bayesian analysis of mixed survival models. Genetics, Selection, Evolution 28: 505–529.CrossRefGoogle Scholar
Ducrocq, V., Delaunay, I., Boichard, D. and Mattalia, S. 2003. A general approach for international genetic evaluations robust to inconsistencies of genetic trends in national evaluations. Interbull Bulletin 30: 101–111.Google Scholar
Ducrocq, V. and Sölkner, J. 1998a. Implementation of a routine breeding value evaluation for longevity of dairy cows using survival analysis techniques. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, 12–16 01, vol. 26, pp. 359–363.Google Scholar
Ducrocq, V. and Sölkner, J. 1998b. The survival kit-V3·0: a package for large analyses of survival data. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, 12–16 01, vol. 27, pp. 447–448.Google Scholar
Interbull. 2004. Interbull trend validation procedure. http://www.interbull.slu.se/service_documentation/General/framesida-general.htmGoogle Scholar
Kalbfleisch, J. D. and Prentice, R. L. 1980. The statistical analysis of failure time data. Wiley, New York.Google Scholar
Kaplan, E. L. and Meier, P. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53: 457–481.CrossRefGoogle Scholar
Klein, J. P. and Moeschberger, M. 1997. Survival analysis. John Wiley and Sons, New York.CrossRefGoogle Scholar
Lindé van de, C. and Jong, G. de. 2003. MACE for longevity traits. Interbull Bulletin 30: 1–9.Google Scholar
Liu, D. C. and Nocedal, J. 1989. On the limited memory {BFGS} method for large scale optimization. Mathematical Programming 45: 503–528.CrossRefGoogle Scholar
Meuwissen, T. H. E., Veerkamp, R. F., Engel, B. and Brotherstone, S. 2002. Single and multitrait estimates of breeding values for survival using sire and animal models. Animal Science 75: 15–24.CrossRefGoogle Scholar
Röxström, A., Ducrocq, V. and Strandberg, E. 2003. Survival analysis of longevity in dairy cattle on a selection basis. Genetics, Selection, Evolution 35: 305–318.CrossRefGoogle Scholar
Veerkamp, R. F., Brotherstone, S., Engel, B. and Meuwissen, T. H. E. 2001. Analysis of censored survival data using random regression models. Animal Science 72: 1–10.CrossRefGoogle Scholar
Yazdi, M. H., Visscher, P. M., Ducrocq, V. and Thompson, R. 2002. Heritability, reliability of genetic evaluations and response to selection in proportional hazard models. Journal of Dairy Science 85: 1563–1577.CrossRefGoogle ScholarPubMed