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Agronomy Journal Abstract - STATISTICS

Mixed Model Formulations for Multi-Environment Trials


This article in AJ

  1. Vol. 96 No. 1, p. 143-147
    Received: Oct 2, 2002

    * Corresponding author(s): k.e.basford@uq.edu.au
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  1. K. E. Basford *a,
  2. W. T. Federerb and
  3. I. H. DeLacya
  1. a The Univ. of Queensland, Brisbane, QLD 4072, Australia
    b Cornell Univ., Ithaca, NY 14850, USA


When studying genotype × environment interaction in multi-environment trials, plant breeders and geneticists often consider one of the effects, environments or genotypes, to be fixed and the other to be random. However, there are two main formulations for variance component estimation for the mixed model situation, referred to as the unconstrained-parameters (UP) and constrained-parameters (CP) formulations. These formulations give different estimates of genetic correlation and heritability as well as different tests of significance for the random effects factor. The definition of main effects and interactions and the consequences of such definitions should be clearly understood, and the selected formulation should be consistent for both fixed and random effects. A discussion of the practical outcomes of using the two formulations in the analysis of balanced data from multienvironment trials is presented. It is recommended that the CP formulation be used because of the meaning of its parameters and the corresponding variance components. When managed (fixed) environments are considered, users will have more confidence in prediction for them but will not be overconfident in prediction in the target (random) environments. Genetic gain (predicted response to selection in the target environments from the managed environments) is independent of formulation.

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