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Genetic evaluation of Holstein Friesian sires for daughter condition-score changes using a random regression model

Published online by Cambridge University Press:  18 August 2016

H. E. Jonest ,
I. M. S. White  and
S. Brotherstone
H. E. Jonest*
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT
I. M. S. White
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT
S. Brotherstone
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT
*
†Present address; Animal Biology Division, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG.
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Abstract

In dairy cattle type classification schemes, heifers are condition scored (CS) only once during their first lactation. Although genetic analysis of condition-score changes is not possible using an animal model, the data can be analysed as repeated observations on the sire.

CS records for 100 078 Holstein Friesian heifers, the progeny of 797 sires, were available. Sires differed in the shape of the regression of mean daughter CS on stage of lactation at both the phenotypic and genetic level. Genetic analysis was carried out using a random regression model (RRM) which can account for differences between sires in the shape of the CS curves. CS curves for individual sires were modelled using a cubic polynomial.

Heritability estimates for CS at each stage of lactation generally increased through the lactation from 0·20 in stage 2 (days in milk 31 to 60) to 0·28 in later lactation stages. Genetic correlations between CS at different stages were generally high (0·80), with the exception of correlations with stage 1 (days in milk 1 to 30) which decreased to 0·63 with stages 6 and 7, suggesting that CS at stage 1 is under different biological control from CS at other stages of the lactation. Using RRM, sire estimated breeding values (EBVs) for CS at each stage of the lactation were estimated. Sire rankings on EBV at each stage were seen to change through early, mid and later lactation stages.

Keywords

dairy cattlecondition scoregenetic parametersrandom regression
Type
Research Article
Information
Animal Science , Volume 68 , Issue 3 , April 1999 , pp. 467 - 475
Copyright
Copyright © British Society of Animal Science 1999

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References

Arendonk, J. A. M.van, Nieuwhof, G. J., Vos, H. and Korver, S. 1991. Genetic aspects of feed intake and efficiency in lactating dairy heifers. Livestock Production Science 29: 263–275.Google Scholar
Blake, R. W. and Custodio, A. A. 1984. Feed efficiency: a composite trait of dairy cattle. Journal of Dairy Science 67: 2075–2083.Google Scholar
Brotherstone, S. 1994. Genetic and phenotypic correlations between linear type traits and production traits in Holstein-Friesian dairy cattle. Animal Production 59: 183–187.Google Scholar
Brotherstone, S., McManus, C. M. and Hill, W. G. 1990. Estimation of genetic parameters for linear type traits in Holstein-Friesian dairy cattle. Livestock Production Science 26: 177–192.Google Scholar
Butler, W. R. and Smith, R. D. 1989. Interrelationships between energy balance and post partům reproductive function in dairy cattle. Journal of Dairy Science 72: 767–783.CrossRefGoogle ScholarPubMed
Domecq, J. J., Skidmore, A. L., Lloyd, J. W. and Kaneene, J. B. 1997. Relationship between body condition scores and conception at first artificial insemination in a large dairy herd of high yielding Holstein cows. Journal of Dairy Science 80: 113–120.Google Scholar
Gilmour, A. R., Thompson, R., Cullis, B. R. and Welham, S. J. 1998. ASREML manual. NSW Dept of Agriculture, occasional publication.Google Scholar
Jamrozik, J. and Schaeffer, L. R. 1997. Estimates of genetic parameters for a test day model with random regressions for yield traits of first lactation Holsteins. Journal of Dairy Science 80: 762–770.Google Scholar
Jamrozik, J., Schaeffer, L. R. and Dekkers, J.C.M. 1996. Random regression models for production traits in Canadian Holsteins. Proceedings o f an Interbull open meeting, Veldhoven, 23–24 June 1996.Google Scholar
Jamrozik, J., Schaeffer, L. R. and Dekkers, J. C. M. 1997. Genetic evaluation of dairy cattle using test day yields and random regression model. Journal of Dairy Science 80: 1217–1226.Google Scholar
Kirkpatrick, M., Lofsvold, D. and Bulmer, M. 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124: 979–993.Google Scholar
Koenen, E. P.C. and Veerkamp, R. F. 1997. Genotype by diet interactions for live weight and condition score during lactation in heifers. Proceedings of the British Society of Animal Science, 1997, p. 34.Google Scholar
Koenen, E. P.C. and Veerkamp, R. F. 1999. Genetic covariance functions for live-weight, condition score, and dry matter intake measured at different lactation stages of lactating Holstein Friesian heifers. Livestock Production Science In press.Google Scholar
Lowman, B. G., Scott, N. and Somerville, S. 1976. Condition scoring of cattle, revised edition. Bulletin of the East of Scotland College of Agriculture, no. 6.Google Scholar
Minitab, . 1992. Minitab reference manual, release 9. Minitab Inc., PA, USA.Google Scholar
Numerical Algorithms Group. 1986. The NAG Pascal Library handbook, vol. 2, p. 6. Numerical Algorithms Group, Oxford.Google Scholar
Ptak, E. and Schaeffer, L. R. 1993. Use of test day yields for genetic evaluation of dairy sires and cows. Livestock Production Science 34: 23–34.Google Scholar
Schaeffer, L. R. and Dekkers, J. C. M. 1994. Random regressions in animal models for test-day production in dairy cattle. Proceedings of the fifth world congress on genetics applied to livestock production, Guelph, vol. 18, pp. 443–446.Google Scholar
Stanton, T. L., Jones, L. R., Everett, R. W. and Kachman, S. D. 1992. Estimating milk, fat and protein lactation curves with a test day model. Journal of Dairy Science 75: 1691–1700.Google Scholar
Veerkamp, R. F. 1994. Genetic improvement of economic performance in dairy cattle. Ph.D. thesis, University of Edinburgh. Google Scholar
Veerkamp, R. F. and Brotherstone, S. 1997. Genetic correlations between linear type traits, food intake, live weight and condition score in Holstein Friesian dairy cattle. Animal Science 64: 385–392.Google Scholar
Veerkamp, R. F. and Emmans, G. C. 1995. Sources of genetic variation in energetic efficiency of dairy cows: a review. Livestock Production Science 44: 87–97.Google Scholar
Waltner, S. S., McNamara, J. P. and Hillers, J. K. 1993. Relationships of body condition score to production variables in high producing Holstein dairy cattle. Journal of Dairy Science 76: 3410–3419.Google Scholar
Wildman, E. E., Jones, G. M., Wagner, P. E., Bowman, R. L., Troutt, H. F. Jr and Lesch, T. N. 1982. A dairy cow body condition scoring system and its relationship to selected production characteristics. Journal of Dairy Science 65: 495–501.Google Scholar