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Economic weights and index selection of milk production traits when multiple production quotas apply

Published online by Cambridge University Press:  02 September 2010

J. P. Gibson
J. P. Gibson
Affiliation:
Centre for Genetic Improvement of Livestock, Department of Animal and Poultry Science, University of Guelph, Guelph, Ontario N1G 2W1, Canada
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Abstract

The generation of profit in dairy production can be approximated by a generalized profit equation, which is a function of the genotype of the animals used. In the absence of legislated quotas on production, the economic weights for traits contributing to profit, for use in a selection index, have been shown to be simple functions of the partial derivatives of profit with respect to output of the traits. These functions reflect the fact that output in most agricultural industries will already be maximized, either because of saturated markets or limitations on total inputs. When a single quota applies, different functions result, which reflect the downward rescaling of enterprise size as output per animal of a trait under quota is increased. Difficulties arise when multiple non-independent quotas apply, such as in the United Kingdom (UK) milk market where quotas are triggered by both milk volume and fat concentration. The functions describing the economic weights are then dependent on the form of the dependency between the quota criteria and on the genetic change resulting from the applied selection index. Economic weights for milk volume, fat, protein and lactose yield applicable to Holstein'Friesian cattle in the UK were found to be –1·6 p/1, 76·6 p/kg, 170·0 p/kg and 7·0 p/kg, scaled to 1986 prices. These weights would not change much if the quota were changed to fat yield only. Use of appropriate selection indexes should result in genetic increases of milk volume, fat, protein and lactose yield, with gradual increases in fat and protein concentrations and the fat to protein ratio. In most situations, selecting on the combined evaluation for fat plus protein yield would be a simple procedure with high efficiency (0·995 of maximum efficiency).

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Type
Papers
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Animal Production , Volume 49 , Issue 2 , October 1989 , pp. 171 - 181
Copyright
Copyright © British Society of Animal Science 1989

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References

REFERENCES

Agricultural Research Council. 1980. The Nutrient Requirements of Ruminant Livestock. Commonwealth Agricultural Bureaux, Slough.Google Scholar
Cunningham, E. P. and Mahon, G. A. T. 1977. Selind. A Fortran computer program for genetic selection indexes. University of Dublin, Ireland.Google Scholar
Dommerholt, J. and Wilmink, J. B. M. 1986. Optimal selection responses under varying milk prices and margins for milk production. Livestock Production Science 14: 109–121.CrossRefGoogle Scholar
Fisher, R. A. 1936. The use of multiple measurements in taxonomic problems. Annals of Eugenics 7: 179–189.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
Gibson, J. P. 1987. The options and prospects for genetically altering milk composition in dairy cattle. Animal Breeding Abstracts 55: 231–243.Google Scholar
Gibson, J. P. 1989a. Selection on the major components of milk. Alternative methods of deriving economic weights. Journal of Dairy Science. In press.Google Scholar
Gibson, J. P. 1989b. The effect of pricing systems, economic weights and population parameters on economic response to selection on milk components. Journal of Dairy Science. In press.Google Scholar
Gibson, J. P. 1989c. Altering milk composition through genetic selection. Journal of Dairy Science. In press.Google Scholar
Hazel, L. N. 1943. The genetic basis for constructing selection indexes. Genetics, USA 28: 476–490.CrossRefGoogle ScholarPubMed
Heady, E. O. and Chandler, W. 1958. Linear Programming Methods. Iowa State University Press, Ames.Google Scholar
Hill, W. G., Edwards, M. R., Ahmed, M. K. A. and Thompson, R. 1983. Heritability of milk yield and composition at different levels and variability of production. Animal Production 36: 59–68.Google Scholar
McArthur, A. T. G. 1987. Weighting breeding objectives, an economic approach. Proceedings of the 6th Annual Conference of Australian Association of Animal Breeding and Genetics, Perth, pp. 179–187.Google Scholar
Maijala, K. and Hanna, M. 1974. Reliable phenotypic and genetic parameters in dairy cattle. Proceedings of the 1st World Congress on Genetic Applied to Livestock Production, Madrid, Vol. 1, pp. 541–563.Google Scholar
Meyer, K. 1987. Estimates of variance due to sire × herd interactions and environmental covariances between paternal half-sibs for first lactation dairy production. Livestock Production Science 17: 95–115.Google Scholar
Milk Marketing Board. 1986. Milk Costs 1985-86. Booklet 1 — Economic and Physical Features of Dairy Herds. Milk Marketing Board, Thames Ditton.Google Scholar
Smith, C. 1984. Rates of genetic change in farm livestock. Research and Development in Agriculture 1: 79–85.Google Scholar
Smith, C., James, J. W. and Brascamp, E. W. 1986. On the derivation of economic weights in livestock improvement. Animal Production 43: 545–551.Google Scholar
Smith, H. F. 1936. A discriminant function for plant selection. Annals of Eugenics 7: 240–250.Google Scholar
Sutton, J. D. 1989. Altering milk composition by feeding. Journal of Dairy Science. In press.Google Scholar
Van Arendonk, J. A. M., Wilmink, J. B. M. and Dukhuizf, N. A. A. 1985. Consequences of a restriction of the herd milk production for the selection on milk, fat and protein. In Adapting EEC Cattle Breeding Programmes to Market Realities (ed. Krauβlich, H. and Lutterback, A.), pp. 211–220. Commission of the European Communities, Brussels.Google Scholar