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Modélisation déterministe et optimisation d'un schéma de sélection séquentiel : Exemple d'un schéma "volaille de chair"

Abstract : A sequential selection scheme, where candidates are ranked using a multiple trait BLUP selection index, was modelled deterministically. This model accounts for overlapping generations and for the reduction of genetic variances under selection, in order to predict the asymptotic genetic gain. Sires and dams are selected among the pairs already created whose progeny have maximum expected average genetic merit. This procedure allows for an optimal use of the available information when the pairs are selected. Effects of selection on the mean and variance of the traits measured on selected animals are accounted for using the Tallis formulae, while a matrix formula is used in order to simultaneously derive genetic lags and gains. The evolution of inbreeding rate was not modelled. Numerical applications were related to a turkey breeding plan. The impact of the relative weight given to growth (male and female body weight, measured at 12 and 16 weeks) and reproduction traits (three partial egg number records) on the expected genetic gains was investigated. Influence of demographic parameters was also studied. Different selection strategies were compared. When the selection objective is mainly to improve laying ability, it is more relevant to increase the amount of information on laying performance, and to apply selection of best mated pairs, rather than to reduce generation intervals by only using the youngest sires. This modelling can be viewed as a useful tool, in order to foresee the consequences of any change in the breeding plan for the long-term genetic gain.
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  • HAL Id : hal-02686755, version 1
  • PRODINRA : 132382


Herve Chapuis, Vincent Ducrocq, Florence Phocas, Y. Delabrosse. Modélisation déterministe et optimisation d'un schéma de sélection séquentiel : Exemple d'un schéma "volaille de chair". Genetics Selection Evolution, BioMed Central, 1997, 29 (4), pp.327-356. ⟨hal-02686755⟩



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