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Selection of farm animals for non-linear traits and profit

  • T. H. E. Meuwissen (a1) and M. E. Goddard (a1)

Abstract

According to animal breeding theory, profit after, say, 10 generations of selection is maximized when the usually non-linear profit function is approximated by a linear breeding goal where the linearization is at the population mean in generation 10 and the linear breeding goal is subsequently predicted by a linear index for which the animals are selected. The prediction of the population mean at generation 10 requires linear relationships among the traits that constitute the non-linear profit, because otherwise this prediction becomes very complicated.

A non-linear index is proposed that simply estimates the non-linear goal H =f(u) by Ĥ =f(û), where u = vector of genetic values for the traits and u is its (BLUP) estimate. This non-linear index does not require predictions of (future) population means and does not require linearly related traits.

To test these indices a simple meat production example was constructed where the non-linearity between the traits was due to the competition between weight and probability of survival for the same resources from food intake. In the model selection for weight and, in particular, for weight over costs (mainly food) led to reduced profits due to large reductions of survival rates. Although, the example was oversimplified, this should provide a warning for the use of oversimplified breeding goals, e.g. fitness traits may reduce by more than expected from base population genetic parameters.

When probability of survival and weight were measured, a non-linear index of these non-linear traits gave the greatest genetic gains. Failure to update genetic parameters each generation severely reduced genetic gain and, if linear indices were used, it was also important to update the economic weights. When probability of survival was measured, profit could be calculated on each animal and included as a trait in the calculation of estimated breeding value. This yielded high genetic gain and did not require updating of genetic parameters or economic weights.

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References

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Beilharz, R. G., Luxford, B. G. and Wilkinson, J. L. 1993. Quantitative genetics and evolution: is our understanding of genetics sufficient to explain evolution. Journal of Animal Breeding and Genetics 110:161170.
Bulmer, M. G. 1971. The effect of selection on genetic variability. The American Naturalist 105: 201211.
Dekkers, J. C. M., Birke, P. V. and Gibson, J. P. 1994. Multiple generation selection for nonlinear profit functions. Proceedings of the fifth world congress on genetics applied to livestock production, Guelph, vol. 18, pp. 209212.
Falconer, D. S. 1989. Introduction to quantitative genetics. Oliver and Boyd, London.
Goddard, M. E. 1983. Selection indices for non-linear profit functions. Theoreotical and Applied Genetics 64:339344.
Groen, A. F., Meuwissen, T. H. E., Vollema, A. R. and Brascamp, E. W. 1994. A comparison of alternative index procedures for multiple generation selection on non-linear profit. Animal Production 59:19.
Henderson, C. R. 1984. Applications of linear models in animal breeding. University of Guelph, Guelph, Canada.
Meuwissen, T. H. E. 1989. A deterministic model for the optimization of dairy cattle breeding based on BLUP breeding value estimates. Animal Production 49:193202.
Moav, R. and Hill, W. G. 1966. Specialised sire and dam lines. 4. Selection within lines. Animal Production 8: 375390.
Pasternak, H. and Weller, J. I. 1993. Optimum linear indices for non-linear profit functions. Animal Production 56: 4350.
Weller, J. I. 1994. Economic aspects of animal breeding. Chapman and Hall, London.
Wilton, J. W., Evans, D. A. and van Vleck, L. D. 1968. Selection indices for quadratic models of total merit. Biometrics 24: 937949.

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Selection of farm animals for non-linear traits and profit

  • T. H. E. Meuwissen (a1) and M. E. Goddard (a1)

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