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Numerical and Graphical Measures to Facilitate the Interpretation of GGE Biplots
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Abstract
The genotype + genotype‐by‐environment (GGE) biplot technique has been widely used in the recent years for the analysis of multienvironment trials, as is evident by the large number of articles published where there is a reference to this technique. One question often raised by the users of this technique is how much of genotype and/or genotype‐by‐environment variability is captured by the GGE biplot axes. This article provides an answer to this question by establishing a link between the partitioning of the total sum of squares (TSS) of the genotype‐by‐environment‐centered matrix provided by singular value decomposition and the partitioning of this TSS provided by the analysis of variance technique. An artificial dataset is used to illustrate this link, which is visualized through a mosaic plot. This new GGE biplot interpretation tool is found to be useful and is discussed in contrast with other interpretation tools.
Citing Literature
Number of times cited according to CrossRef: 4
- Nicholas George, Mark Lundy, Quantifying Genotype × Environment Effects in Long‐Term Common Wheat Yield Trials from an Agroecologically Diverse Production Region, Crop Science, 10.2135/cropsci2019.01.0010, 59, 5, (1960-1972), (2019).
- Jean‐Louis Laffont, Kevin Wright, Mohamed Hanafi, Genotype Plus Genotype × Block of Environments Biplots, Crop Science, 10.2135/cropsci2013.03.0178, 53, 6, (2332-2341), (2013).
- Kraig L. Roozeboom, William T. Schapaugh, Mitchell R. Tuinstra, Richard L. Vanderlip, George A. Milliken, Testing Wheat in Variable Environments: Genotype, Environment, Interaction Effects, and Grouping Test Locations , Crop Science, 10.2135/cropsci2007.04.0209, 48, 1, (317-330), (2008).
- Hugh G. Gauch, Hans‐Peter Piepho, Paolo Annicchiarico, Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations, Crop Science, 10.2135/cropsci2007.09.0513, 48, 3, (866-889), (2008).
