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

^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

^* Corresponding author (hgg1{at}cornell.edu).

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 x 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 x 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.

Abbreviations: AEC, average environment coordinate • AMMI, additive main effects and multiplicative interaction • ANOVA, analysis of variance • E, environment • EGE, environment main effects and genotype x environment interaction • G, genotype • GE, genotype x environment • GGE, genotype main effects and genotype x environment interaction • GL, genotype x location • PC, principal component • PCA, principal components analysis • QTL, quantitative trait locus • RHS, right-hand side • SS, sum of squares • SSG, sum of squares for genotype • SSGE, sum of squares for genotype x environment • SV, singular value • SVD, singular value decomposition • SVP, singular value partitioning

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Received for publication September 18, 2007.