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Comparison of piecewise Weibull baseline survival models for estimation of true and functional longevity in Brown cattle raised in small herds

Published online by Cambridge University Press:  18 June 2013

J. Jenko ,
V. Ducrocq  and
M. Kovač
J. Jenko*
Affiliation:
Agricultural Institute of Slovenia, Hacquetova ulica 17, 1000 Ljubljana, Slovenia
V. Ducrocq
Affiliation:
INRA, UMR 1313 Gènètique Animale et Biologie Intègrative, F-78350 Jouy-en-Josas, France
M. Kovač
Affiliation:
Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Groblje 3, 1230 Domžale, Slovenia
*
†E-mail: janez.jenko@kis.si
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Abstract

Piecewise Weibull proportional hazard models were used to investigate the effect of genetic and nongenetic factors on functional and true longevity traits of the Slovenian Brown cattle breed. Records of 37 908 Brown cows from 2401 Slovenian herds were used. As these herds were characterised by a relatively small average herd size starting from 6.7 in 1999 and increasing to 8.7 Brown cows per herd in 2008, milk yield classification was made within different herd size groups. The hazard rate was the lowest in the first part of each lactation and was increasing for later stages. Culling risk was lower for cows from herds increasing in size, for cows with higher milk production and for cows from a region with smaller herd sizes and tougher conditions for cattle breeding. The latter result is surprising and may be related to better attention to maintain the animals, despite their lower milk production. The introduction of the milk quota system and drought was found to have an important effect on culling policy between the last seasons of the years 2001 and 2003. Seasonal effects were not related to the milk quota year (from April to March), but to the effect of shortage in fodder during the winter time. The effect of age at first calving and the interaction between year and milk yield class were not found to be significant. Heritability for functional and for true longevity were similar at around 10% each. Inclusion of a correction for class of milk yield to approximate functional longevity increased the herd-year random effect variance by 53%, whereas the sire variance increased by only 14%. The correlation coefficient between ranks of breeding values for functional and true longevity was high (0.91), whereas genetic trends were not found to be significant. To assess their predictive ability, models were compared looking at the survival rate of 4212 second-crop daughters not included in the initial models. The average correlation between estimated breeding values and survival at different stages was 0.39 for true longevity and 0.43 for functional longevity. Results showed that ranking milk yield at population level is appropriate to correct for voluntary culling on low production in small herds.

Keywords

dairy cattlelongevitysurvivalgenetic analysissmall farms
Type
Breeding and genetics
Information
animal , Volume 7 , Issue 10 , October 2013 , pp. 1583 - 1591
Copyright
Copyright © The Animal Consortium 2013 

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References

Abramowitz, M, Stegun, IA 1964. Handbook of mathematical functions: with formulas, graphs, and mathematical tables. Currier Dover Publications, New York.Google Scholar
Beaudeau, F, Ducrocq, V, Fourichon, C, Seegers, H 1995. Effect of disease on length of productive life of French Holstein dairy cows assessed by survival analysis. Journal of Dairy Science 78, 103–117.Google Scholar
Bielfeldt, JC, Tölle, K-H, Badertscher, R, Krieter, J 2006. Longevity of Swiss Brown cattle in different housing systems in Switzerland. Livestock Science 101, 134–141.Google Scholar
Boichard, D, Brochard, M 2012. New phenotypes for new breeding goals in dairy cattle. Animal 6, 544–550.Google Scholar
Buenger, A, Ducrocq, V, Swalve, HH 2001. Analysis of survival in dairy cows with supplementary data on type scores and housing systems from a region of northwest Germany. Journal of Dairy Science 84, 1531–1541.Google Scholar
Ceglar, A, Kajfež-Bogataj, L 2008. Obravnava meteorološke suše z različnimi indikatorji. Acta Agriculturae Slovenica 91, 407–425.CrossRefGoogle Scholar
Chirinos, Z, Carabaño, MJ, Hernández, D 2007. Genetic evaluation of length of productive life in the Spanish Holstein-Friesian population. Model validation and genetic parameters estimation. Livestock Science 106, 120–131.Google Scholar
Ducrocq, V 1987. An analysis of length of productive life in dairy cattle. PhD, Cornell University, USA.Google Scholar
Ducrocq, V 1994. Statistical analysis of length of productive life for dairy cows of the Normande breed. Journal of Dairy Science 77, 855–866.Google Scholar
Ducrocq, V 2001. A two-step procedure to get animal model solutions in Weibull survival models used for genetic evaluations on length of productive life. Interbull Bulletin 27, 147–152.Google Scholar
Ducrocq, V 2005. An improved model for the French genetic evaluation of dairy bulls on length of productive life of their daughters. Animal Science 80, 249–256.Google Scholar
Ducrocq, V, Casella, G 1996. A Bayesian analysis of mixed survival models. Genetics Selection Evolution 28, 505–529.Google Scholar
Dürr, JW, Monardes, EG, Cue, RI 1999. Genetic analysis of herd life in Quebec Holsteins using Weibull models. Journal of Dairy Science 82, 2503–2513.Google Scholar
Forabosco, F, Jakobsen, JH, Fikse, WF 2009. International genetic evaluation for direct longevity in dairy bulls. Journal of Dairy Science 92, 2338–2347.Google Scholar
Holtsmark, M, Heringstad, B, Ødegård, J 2009. Predictive abilities of different statistical models for analysis of survival data in dairy cattle. Journal of Dairy Science 92, 5730–5738.Google Scholar
Interbull 2011. Evaluation summary for direct longevity traits. Retrieved October 1, 2011, from http://www-interbull.slu.se/longevity/framesida-long.htmGoogle Scholar
Jairath, L, Dekkers, JC, Schaeffer, LR, Liu, Z, Burnside, EB, Kolstad, B 1998. Genetic evaluation for herd life in Canada. Journal of Dairy Science 81, 550–562.Google Scholar
Kalbfleisch, JD, Prentice, RL 1980. The statistical analysis of failure time data. Wiley, New York.Google Scholar
Kaplan, EL, Meier, P 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53, 457–481.CrossRefGoogle Scholar
Mèszáros, G, Sölkner, J, Ducrocq, V 2013. The survival kit: software to analyze survival data including possibly correlated random effects. Computer Methods and Programs in Biomedicine 110, 503–510.CrossRefGoogle ScholarPubMed
Mèszáros, G, Pálos, J, Ducrocq, V, Sölkner, J 2010. Heritability of longevity in Large White and Landrace sows using continuous time and grouped data models. Genetics Selection Evolution 42, Article no. 13.Google Scholar
Mèszáros, G, Fuerst, C, Fuerst-Waltl, B, Kadlečík, O, Kasarda, R, Sölkner, J 2008. Genetic evaluation for length of productive life in Slovak Pinzgau cattle. Archives of Animal Breeding 51, 438–448.CrossRefGoogle Scholar
Potočnik, K, Gantner, V, Krsnik, J, Štepec, M, Logar, B, Gorjanc, G 2011. Analysis of longevity in Slovenian Holstein cattle. Acta Agriculturae Slovenica 98, 93–100.CrossRefGoogle Scholar
Roxström, A, Strandberg, E 2002. Genetic analysis of functional, fertility-, mastitis-, and production-determined length of productive life in Swedish dairy cattle. Livestock Production Science 74, 125–135.Google Scholar
Roxström, A, Ducrocq, V, Strandberg, E 2003. Survival analysis of longevity in dairy cattle on lactation basis. Genetics Selection Evolution 35, 305–318.CrossRefGoogle ScholarPubMed
Sadar, M, Opara, A, Perpar, T, Jeretina, J, Logar, B, Podgoršek, P, Žabjek, A, Glad, JIvanovič, B 2010. Rezultati kontrole prireje mleka in mesa, Slovenija 2009. Kmetijski inštitut Slovenije, Govedorejska služba Slovenije, Ljubljana, Slovenija.Google Scholar
Sewalem, A, Kistemaker, GJ, Ducrocq, V, Van Doormaal, BJ 2005. Genetic analysis of herd life in Canadian dairy cattle on a lactation basis using a Weibull proportional hazards model. Journal of Dairy Science 88, 368–375.CrossRefGoogle ScholarPubMed
Terawaki, Y, Ducrocq, V 2009. Nongenetic effects and genetic parameters for length of productive life of Holstein cows in Hokkaido, Japan. Journal of Dairy Science 92, 2144–2150.Google Scholar
Terawaki, Y, Katsumi, T, Ducrocq, V 2006. Development of a survival model with piecewise Weibull baselines for the analysis of length of productive life of Holstein cows in Japan. Journal of Dairy Science 89, 4058–4065.Google Scholar
Van Doormaal, BJ, Schaeffer, LR, Kennedy, BW 1985. Estimation of genetic parameters for stayability in Canadian Holsteins. Journal of Dairy Science 68, 1763–1769.Google Scholar
van der Linde, C, Harbers, A, de Jong, G 2007. From functional to productive longevity in the Netherlands. Interbull Bulletin 37, 203–207.Google Scholar
Vlada Republike Slovenije 2004. Uredba o uvedbi kvot za mleko in mlečne proizvode za kvotno leto 2004/2005. Uradni list RS, št. 22/2004. Retrieved March 7, 2012 from http://zakonodaja.gov.si/rpsi/r06/predpis_URED3196.htmlGoogle Scholar
Vollema, AR, Groen, AF 1996. Genetic parameters of longevity traits of an upgrading population of dairy cattle. Journal of Dairy Science 79, 2261–2267.CrossRefGoogle ScholarPubMed
Vukasinovic, N, Moll, J, Casanova, L 2001. Implementation of a routine genetic evaluation for longevity based on survival analysis techniques in dairy cattle populations in Switzerland. Journal of Dairy Science 84, 2073–2080.Google Scholar
Wathes, DC, Brickell, JS, Bourne, NE, Swali, A, Cheng, Z 2008. Factors influencing heifer survival and fertility on commercial dairy farms. Animal 2, 1135–1143.Google Scholar
Yazdi, MH, Thompson, R, Ducrocq, V, Visscher, PM 1999. A comparison of two survival analysis methods with the number of lactations as a discrete time variate. Interbull Bulletin 21, 48–51.Google Scholar
Yazdi, MH, Visscher, PM, Ducrocq, V, Thompson, R 2002. Heritability, reliability of genetic evaluations and response to selection in proportional hazard models. Journal of Dairy Science 85, 1563–1577.Google Scholar
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