Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-13T07:18:13.917Z Has data issue: false hasContentIssue false
  • English
  • Français

Genetic and phenotypic parameters for yield, food intake and efficiency of dairy cows fed ad libitum 1. Estimates for ‘total’ lactation measures and their relationship with live-weight traits

Published online by Cambridge University Press:  02 September 2010

P. Persaud ,
G. Simm  and
W. G. Hlll
P. Persaud
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT Scottish Agricultural College, Edinburgh School of Agriculture, West Mains Road, Edinburgh EH9 3JG
G. Simm
Affiliation:
Scottish Agricultural College, Edinburgh School of Agriculture, West Mains Road, Edinburgh EH9 3JG
W. G. Hlll
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT
Get access

Abstract

Records on milk yield, fat plus protein yield, food intake, food efficiency, calving live weight and mean live weight, up to 26 and 38 weeks of lactation, were obtained from dairy cows, fed ad libitum, in the Edinburgh School of Agriculture's Langhill herd. The data were divided into first and later lactations and restricted maximum likelihood analyses carried out on heifer, cow and pooled data, fitting an animal model, with repeat lactations as an additional random effect. Univariate analyses were done after canonical transformation of heifer data and approximate canonical transformation of cow and pooled data. Heritability estimates for food efficiency and food intake, from pooled data, were 0·13 (s.e. 0·09) and 0·37 (s.e. 0·11) for 26-week and 0·13 (s.e. 0·12) and 0·52 (s.e. 0·14) for 38-week lactation periods, respectively. Over the same periods, estimates for milk yield were 0·20 (s.e. 0·08) and 0·20 (s.e. 0·11), respectively. Estimates from the analyses of cow and heifer data separately were higher, as were their standard errors. Genetic correlations between milk production traits and efficiency, from the pooled data analysis, ranged from 0·44 to 0·61 and those between milk production traits and food intake from 0·32 to 0·74. Genetic correlations between live-weight traits and efficiency ranged from −0·81 to −;0·99, and those between food intake and live-weight traits from 0·28 to 0·46. The results indicate that when selection is on yield, the correlated responses in efficiency may be smaller under ad libitum feeding, compared with published values where cows were given food according to yield. Including live weight in the selection criterion may give higher responses in efficiency compared with selection on yield alone. In MOET nucleus schemes it may be worthwhile to include food intake or efficiency directly in the selection criteria.

Keywords

dairy cowsfood intakegenetic correlationheritabilitymilk yield
Type
Research Article
Information
Animal Production , Volume 52 , Issue 3 , June 1991 , pp. 435 - 444
Copyright
Copyright © British Society of Animal Science 1991

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

REFERENCES

Dempfle, L. 1989. Increasing the efficiency of the dairy cow with regard to body size. Research Bulletin No. 4. Livestock Improvement Corporation, New Zealand Dairy Board, Hamilton, New Zealand.Google Scholar
Freeman, A. E. 1967. Genetic aspects of the efficiency of nutrient utilization for milk production. Journal of Animal Science 26: 976–983.CrossRefGoogle ScholarPubMed
Freeman, A. E. 1975. Genetic variation in nutrition of dairy cattle. In The Effect of Genetic Variation on Nutrition of Animals, pp. 19–46. National Academy of Science, Washington, DC.Google Scholar
Gibson, J. P. 1986. Efficiency and performance of genetically high and low milk-producing British Friesian and Jersey cattle. Animal Production 42: 161–182.Google Scholar
Gravert, H. O. 1985. Genetic factors controlling feed efficiency in dairy cows. Livestock Production Science 3: 87–99.CrossRefGoogle Scholar
Hill, W. G. and Thompson, R. 1978. Probabilities of non-positive definite between-group or genetic covariance matrices. Biometrics 34: 429–439.CrossRefGoogle Scholar
Hooven, N. W., Miller, R. H. and Plowman, R. D. 1968. Genetic and environmental relationships among efficiency, yield, consumption and weight of Holstein cows. Journal of Dairy Science 51: 1409–1419.CrossRefGoogle Scholar
Jensen, J. and Mao, I. L. 1988. Transformation algorithms in analysis of single trait and of multitrait models with equal design matrices and one random factor per trait: a review. Journal of Animal Science 66: 2750–2761.CrossRefGoogle Scholar
Kennedy, B. W. 1984. Breeding for feed efficiency: swine and dairy cattle. Canadian Journal of Animal Science 64: 505–512.CrossRefGoogle Scholar
Korver, S. 1988. Genetic aspects of feed intake and feed efficiency in dairy cattle: a review. Livestock Production Science 20: 1–13.CrossRefGoogle Scholar
Lee, A. J., Sliger, L. A., Lin, C. Y., McAllister, A. J., Batra, T. R., Roy, G. L., Vesely, J. A., Wauthy, J. M. and Winter, K. A. 1989. Feed efficiency of dairy cows during first lactation. Canadian Journal of Animal Science 69: 877–889.CrossRefGoogle Scholar
McGuirk, B. J. 1990. Operational aspects of a MOET nucleus dairy breeding scheme. Proceedings of the 4th World Congress on Genetics Applied to Livestock Production, Edinburgh, Vol. 14, pp. 259–262.Google Scholar
Mason, I. L., Robertson, A. and Gejlstad, B. 1957. The genetic connexion between body size, milk production and efficiency in dairy cattle. Journal of Dairy Research 24: 135–143.CrossRefGoogle Scholar
Meyer, K. 1985. Maximum likelihood estimation of variance components for a multivariate mixed model with equal design matrices. Biometrics 41: 153–165.CrossRefGoogle ScholarPubMed
Meyer, K. 1988. DFREML- programs to estimate variance components for individual animal models by restricted maximum likelihood (REML). User Notes, University of Edinburgh.Google Scholar
Oldenbroek, J. K. 1988. Feed intake and energy utilization in dairy cows of different breeds. Ph.D. Thesis, Research Institute for Animal Production, “Schoonoord”, The Netherlands.Google Scholar
Patterson, H. D. and Thompson, R. 1971. Recovery of inter-block information when block sizes are unequal. Biometrika 58: 545–554.CrossRefGoogle Scholar
Persaud, P., Simm, G. and Hill, W. G. 1990a. Genetic parameters of feed efficiency and feed intake for dairy cattle fed ad libitum. Proceedings of the 4th World Congress on Genetics Applied to Livestock Production, Edinburgh, Vol. 14, pp. 237–240.Google Scholar
Persaud, P., Simm, G., Parkinson, H. and Hill, W. G. 1990b. Relationships between sires' transmitting ability for production and daughters' production, food intake and efficiency in a highyielding dairy herd. Animal Production 51: 245–253.Google Scholar
Simm, G., Persaud, P., Neilson, D. R., Parkinson, H. and McGuirk, B. J. 1991. Predicting food intake in dairy heifers from early lactation records. Animal Production 52: 421–434.Google Scholar
Thompson, R. and Hill, W. G. 1990. Univariate REML analyses for multivariate data with the animal model. Proceedings of the 4th World Congress on Genetics Applied to Livestock Production, Edinburgh, Vol. 13, pp. 484–487.Google Scholar