Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-02T22:58:36.925Z Has data issue: false hasContentIssue false

A comparison of alternative index procedures for multiple generation selection on non-linear profit

Published online by Cambridge University Press:  02 September 2010

A. F. Groen
Affiliation:
Department of Animal Breeding, Wageningen Agricultural University, PO Box 338, 6700 AH Wageningen, The Netherlands
T. H. E. Meuwissen
Affiliation:
Institute for Animal Science and Health (ID-DLO), Research Branch Zeist, PO Box 501, 3700 AM Zeist, The Netherlands
A. R. Vollema
Affiliation:
Department of Animal Breeding, Wageningen Agricultural University, PO Box 338, 6700 AH Wageningen, The Netherlands
E. W. Brascamp
Affiliation:
Department of Animal Breeding, Wageningen Agricultural University, PO Box 338, 6700 AH Wageningen, The Netherlands
Get access

Abstract

Alternative index procedures for selection on non-linear profit are quadratic indices, desired gains indices, group or mate selection indices, or direct optimization of responses over multiple generations. In this study a multiple generation time horizon was considered and several linear, quadratic and desired gains indices were compared. Genetic and economic responses over multiple generations were calculated considering a quadratic profit function combining protein yield and days open.

Directly optimizing reponse over multiple generations was found to yield slightly higher economic responses (+ < l.5%) than stepwise (each generation) adjustment of a linear index. A constant linear index using base population averages and a quadratic index were found to be less efficient. The quadratic index was less efficient than the linear index when considering multiple generations. Desired gains indices allowed stabilization of base population average for days open, however, forcing considerable economic losses. Relative efficiencies of methods depended on the degree of non-linearity of the profit function.

Type
Research Article
Copyright
Copyright © British Society of Animal Science 1994

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

Allaire, F. R. 1977. Corrective mating methods in context of breeding theory. journal of Dairy Science 60: 17991806.CrossRefGoogle Scholar
Allaire, F. R. 1980. Mate selection by selection index theory. Theoretical and Applied Genetics 57: 267272.CrossRefGoogle ScholarPubMed
Brascamp, E. W. 1984. Selection indices with constraints. Animal Breeding Abstracts 52: 645654.Google Scholar
Bulmer, M. G. 1980. The mathematical theory of quantitative genetics.Oxford University Press, Oxford.Google Scholar
Cunningham, E. P. 1969. Animal breeding theory. Internordic licenciat course in quantitative genetics. Institute for Animal Genetics and Breeding, Agricultural College of Norway, As.Google Scholar
Dijkhuizen, A. A., Renkema, J. A. and Stelwagen, J. 1985. Economic aspects of reproductive failure in dairy cattle. I Financial loss at farm level. Preventive Veterinary Medicine 3: 251263.CrossRefGoogle Scholar
Gibson, J. P. and Kennedy, B. W. 1990. The use of constrained selection indexes in breeding for economic merit. Theoretical and Applied Genetics 80: 801805.CrossRefGoogle ScholarPubMed
Goddard, M. E. 1983. Selection indices for non-linear profit functions. Theoretical and Applied Genetics 64: 339344.CrossRefGoogle ScholarPubMed
Groen, A. F. 1989. Economic values in cattle breeding. I Influences of production circumstances in situations without output limitations. Livestock Production Science 22: 116.CrossRefGoogle Scholar
Groen, A. F. and Korver, S. 1989. The economic value of feed intake capacity of dairy cows. Livestock Production Science 22: 269281.CrossRefGoogle Scholar
Hazel, L. N. 1943. The genetic basis for constructing selection indexes. Genetics, USA 28: 476490.CrossRefGoogle ScholarPubMed
Hovenier, R., Brascamp, E. W., Kanis, E., Werf, J. H. J.der, van and Wassenberg, A. P. A. M. 1993. Economic values of optimum traits; the example of meat quality in pigs. Journal of Animal Science 71: 14291433.CrossRefGoogle ScholarPubMed
Hovenier, R., Arendonk, J. A. M. van and Boer, W. de. 1988. Phenotypic and genetic association between fertility and production in dairy cows. Publication no. 9, Department of Animal Breeding, Wageningen Agricultural University, Wageningen.Google Scholar
Jansen, G. B. 1985. Selection and mating strategies to improve quadratic merit. Ph.D. thesis, Department of animal and Poultry Science, University of Guelph, Guelph.Google Scholar
Kempthorne, O. and Nordskog, A. W. 1959. Restricted selection indices. Biometrics 15: 1019.CrossRefGoogle Scholar
NRS. 1992. Royal Dutch Cattle Syndicate, Jaarstatistieken 1991, Arnhem.Google Scholar
Pasternak, H. and Weller, J. I. 1993. Optimum linear indices for non-linear profit functions. Animal Production 56: 4350.Google Scholar
Press, W. H., Flannery, B. P., Teukolsky, S. A. and Vetterling, W. T. 1989. Numerical recipes. Cambridge University Press, Cambridge.Google Scholar
Smith, S. P. and Maki-Tanila, A. 1990. Genotypic covariance matrices and their inverses for models allowing dominance and inbreeding. Genetics Selection Evolution 22: 6591.CrossRefGoogle Scholar
Vollema, A. R. 1993. De mogelijkheid en het nut van nietlineaire economische waarden in een selektie-index, uitgewerktloor “aantal open dagen” Scriptie Vakgroep Veefokkerij, Landbouwuniversiteit Wageningen, Wageningen.Google Scholar
Vries, A. G. de and Kanis, E. 1992. A growth model to estimate economic values for food intake capacity in pigs. Animal Production 55: 241246.Google Scholar
Wilmink, J. B. M. and de Graaf, F. M. 1986. Genetische parameters voor 305 dagen producties in de eerste lactatie en de bijstelling van de netto melkgeld index. Koninklijk Nederlands Rundvee Syndicaat, Arnhem.Google Scholar
Wilton, J. W., Evans, D. A. and Van Vleck, L. D. 1968. Selection indices for quadratic models of total merit. Biometrics 24: 937949.CrossRefGoogle Scholar