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This article in CS

  1. Vol. 48 No. 3, p. 866-889
     
    Received: Sept 18, 2007
    Published: May, 2008


    * Corresponding author(s): hgg1@cornell.edu
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doi:10.2135/cropsci2007.09.0513

Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations

  1. Hugh G. Gauch *a,
  2. Hans-Peter Piephob and
  3. Paolo Annicchiaricoc
  1. a Crop and Soil Sciences, Cornell Univ., Ithaca, NY 14853
    b Bioinformatics Unit, Institut für Pflanzenbau und Grünland, Universität Hohenheim, 70599 Stuttgart, Germany
    c CRA-Istituto Sperimentale per le Colture Foraggere, Viale Piacenza 29, 26900 Lodi, Italy

Abstract

Recent review articles in this journal have compared the relative merits of two prominent statistical models for analyzing yield-trial data: Additive main effects and multiplicative interaction (AMMI) and genotype main effects and genotype × environment interaction (GGE). This review addresses more than 20 issues that require clarification after controversial statements and contrasting conclusions have appeared in those recent reviews. The AMMI2 mega-environment display incorporates more of the genotype main effect and captures more of the genotype × environment (GE) interaction than does GGE2, thereby displaying the which-won-where pattern more accurately for complex datasets. When the GE interaction is captured well by one principal component, the AMMI1 display of genotype nominal yields describes winning genotypes and adaptive responses more simply and clearly than the GGE2 biplot. For genotype evaluation within a single mega-environment, a simple scatterplot of mean and stability is more straightforward than the mean vs. stability view of a GGE2 biplot. Diagnosing the most predictively accurate member of a model family is vital for either AMMI or GGE, both for gaining accuracy and delineating mega-environments.

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Copyright © 2008. Crop Science Society of AmericaCrop Science Society of America

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