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Factors influencing length of productive life and replacement rates in the Bruna dels Pirineus beef breed

Published online by Cambridge University Press:  18 August 2016

J. Tarrés
Affiliation:
Departament de Ciència Animal i dels Aliments, Grup de Recerca en Remugants
P. Puig
Affiliation:
Departament de Matemàtiques, Servei d’Estadística, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
V. Ducrocq
Affiliation:
Station de Génétique Quantitative et Appliquée, Institut National de la Recherche Agronomique, 78352 Jouy-en-Josas, France
J. Piedrafita*
Affiliation:
Departament de Ciència Animal i dels Aliments, Grup de Recerca en Remugants
*
Corresponding author. E-mail:jesus.piedrafita@uab.es
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Abstract

An analysis of the length of productive life in the Bruna dels Pirineus beef breed was performed with a non-parametric approach giving an average value of 9 years of productive life, and a corresponding replacement rate of 11%. Using a proportional hazards model stratified by herd, the influence of calf birth weight and weight gain until weaning, calving difficulty, calving interval and age at first calving on length of productive life were studied. Two models were explored: the first one included time-dependent variables taking the current value of the covariate at each calving date, while the second one also comprised time-dependent interactions between the value of the covariate of the current calving and its mean value during the last (up to) three previous calvings. Results from the first model showed that the risk of culling increases with very high ages at first calving, increasing calving difficulties, very large calf birth weights, very small weight gains until weaning and very long calving intervals. Furthermore, results from the second model showed that these increases also depend upon a sequence of values for the same covariate in previous calvings. Finally, these higher risks of culling implied lower survival functions that increased replacement rates but only slightly decreased average performances.

Type
Breeding and genetics
Copyright
Copyright © British Society of Animal Science 2004

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References

Buxadé, C. 1997. Vacuno de carne: aspectos claves. Mundi-Prensa, Madrid.Google Scholar
Casellas, J. and Piedrafita, J. 2002. Correction factors for weight productive traits up to weaning in the Bruna dels Pirineus beef cattle breed. Animal Research 51: 4350.CrossRefGoogle Scholar
Cox, D. R. 1972. Regression models and life tables (with discussion). Journal of the Royal Statistical Society, Series B 34: 187220.Google Scholar
Díaz, C., Chirinos, Z., Moreno, A. and Carabaño, M. J. 2002. Preliminary analysis of functional longevity in the Avileña Negra Ibérica beef cattle breed. Proceedings of the seventh world congress on genetics applied to livestock production, Montpellier, vol. 29, pp. 697700.Google Scholar
Ducrocq, V. P. 1997. Survival analysis, a statistical tool for length of productive life data. Proceedings of the 48th annual meeting of the European Association for Animal Production, Vienna. Available at http: //dga. jouy. inra. fr/sgqa/ diffusions/survkit/e97surv. pdf Google Scholar
Ducrocq, V. P., Quaas, R. L., Pollak, E. J. and Casella, G. 1988. Length of productive life of dairy cows. 1. Justification of a Weibull model. Journal of Dairy Science 71: 30613070.CrossRefGoogle Scholar
Ducrocq, V. P. and Solkner, J. 1998. ‘The Survival Kit v3·0’, a FORTRAN package for large analysis of survival data. Proceedings of the sixth world congress on genetics applied to livestock production, Armidale, vol. 27, pp. 447450.Google Scholar
Essl, A. 1998. Length of productive life in dairy cattle breeding: a review. Livestock Production Science 57: 7989.Google 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: 457481.CrossRefGoogle Scholar
Luo, M. F., Boettcher, P. J., Schaeffer, L. R. and Dekkers, J. C. M. 2002. Estimation of genetic parameters of calving ease in first and second parities of Canadian Holsteins using Bayesian methods. Livestock Production Science 74: 175184.Google Scholar
Rajala-Schultz, P. J. and Gröhn, Y. T. 2001. Comparison of economically optimized culling recommendations and current culling decisions of Finnish Ayrshire cows. Preventive Veterinary Medicine 49: 2939.Google Scholar
Tarrés, J., Puig, P. and Piedrafita, J. 2002. Non-genetic factors influencing productive life length and replacement rates in the Bruna dels Pirineus beef cattle breed. Proceedings of the seventh world congress on genetics applied to livestock production, Montpellier, vol. 29, pp. 693696.Google Scholar
Torrent, M. 1991. La oveja y sus producciones. AEDOS, Madrid.Google Scholar
Veerkamp, R. F., Brotherstone, S. and Meuwissen, T. H. E. 1999. Survival analysis using random regression models. Interbull Bulletin 21: 3640.Google Scholar
Yazdi, M. H., Rydhmer, L., Ringmar-Cederberg, E., Lundeheim, N. and Johansson, K. 2000. Genetic study of length of productive life in Swedish Landrace sows. Livestock Production Science 63: 255264.CrossRefGoogle Scholar