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Multi-trait covariance functions to estimate genetic correlations between milk yield, dry-matter intake and live weight during lactation

Published online by Cambridge University Press:  27 February 2018

R. F. Veerkamp  and
R. Thompson
R. F. Veerkamp
Affiliation:
DLO-Institute for Animal Science and Health, P.O. Box 65, 8200 AB Lelystad, The Netherlands
R. Thompson
Affiliation:
IACR Statistics Department, IACR-Rothamsted, Harpenden, Hertfordshire AL5 2JQ
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Abstract

Energy balance is a function of dry-matter intake (DMI), live weight and milk yield over a certain time period. To investigate potential strategies to use genetic selection for the improvement of the negative energy balance, genetic co-variances were estimated among DMI, live weight and milk yield during the first 15 weeks of lactation (no.=628). Rather than estimating the full 45 by 45 matrix a random regression model was used to estimate a second order covariance functions for the additive genetic and permanent environmental effects. Fixed effects were test-day, a group effect and week in lactation. Estimates for the genetic covariance function demonstrated that a high level of milk yield is only moderately correlated with a high level of DMI (0.21) but very strongly correlated to an increase of intake (0.97) and a loss of live weight (-0.46) during the first 15 weeks of lactation. Levels of weight and intake were correlated strongly (0.81). Estimates for the genetic correlations between weeks 1 and 15 were 0.79, 0.34 and 0.83 for milk yield, DMI and live weight respectively. DMI during early lactation was negatively correlated with milk yield but DMI during the later weeks was positively correlated with milk yield. The implication is that when selection is for a linear combination of milk yield, DMI and live weight (i.e. energy balance or efficiency) the moment in lactation of measuring each trait on the cow is of importance

Type
Research Article
Information
BSAP Occasional Publication , Volume 24: Metabolic stress in dairy cows , 1999 , pp. 147 - 151
Copyright
Copyright © British Society of Animal Science 1999

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References

Berglund, B. and Darteli, B. 1987. Live-weight changes, feed consumption, milk yield and energy balance in dairy cattle during the first period of lactation. Acta Agricultura; Scandinavica 37:495–509.Google Scholar
Gilmour, A. R., Thompson, R., Cullis, B. R. and Welham, S. J. 1998. ASREML. Biometric bulletin no. 3, NSW Agriculture.Google Scholar
Goddard, M. E. and Thompson, R. 1998. Use of prior information in forming selection indices. Journal of Animal Breeding and Genetics 115:327–340.Google Scholar
Harrison, R. O., Ford, S. P., Young, J. W., Conley, A. J. and Freeman, A. E. 1990. Increased milk production versus reproductive and energy status of high producing dairy cows. Journal of Dairy Science 73:2749–2758.Google Scholar
Hill, W. G. and Thompson, R. 1978. Probabilities of non-positive definite between-group or genetic covariance matrices. Biometrics 34:429–439.Google Scholar
Kirkpatrick, M. and Heekman, N. 1989. A quantitative genetic model for growth, shape, reaction norms and other infinite-dimensional characters. Journal of Mathematical Biology 27:429–450.CrossRefGoogle ScholarPubMed
Kirkpatrick, M., Hill, W. G. and Thompson, R. 1994. Estimating the covariance structure of traits during growth and ageing, illustrated with lactation in dairy cattle. Genetical Research Cambridge 64:57–69.CrossRefGoogle ScholarPubMed
Koenen, E. P.C. and Veerkamp, R. F. 1998. Genetic covariance functions for live-weight, condition score and dry-matter intake of lactating Holstein Friesian heifers. Livestock Production Science 57:67–77.Google Scholar
Robinson, J. J. 1996. Nutrition and reproduction. Animal Reproduction Science 42:25–34.CrossRefGoogle Scholar
Sales, J. and Hill, W. G. 1976. Effect of sampling errors on efficiency of selection indices. 2. Use of information on associated traits for improvement of a single important trait. Animal Production 23:1–14.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 18:443–446.Google Scholar
Veerkamp, R. F. 1998. Selection for economic efficiency of dairy cattle using information on live weight and feed intake: a review. Journal of Dairy Science 81:1109–1119.Google Scholar
Veerkamp, R. F., Emmans, G.C., Cromie, A. R. and Simm, G. 1995. Variance components for residual feed intake in dairy cows. Livestock Production Science 41:111–120.Google Scholar
Veerkamp, R. F. and Goddard, M. 1998. Covariance functions across herd production levels for test day records on milk, fat and protein yields. Journal of Dairy Science 81: 1690–1701.Google Scholar
Visscher, P. M. 1995. Bias in multiple genetic correlation from half sib designs. Genetics, Selection, Evolution 27: 335–345.Google Scholar
Werf, J. H. J. van der, Goddard, M. E. and Meyer, K. 1999. The use of covariance functions and random regression for genetic evaluation of milk production based on test day records. Journal of Dairy Science In press.Google Scholar