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Crop Science Abstract - CROP BREEDING & GENETICS

GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data


This article in CS

  1. Vol. 47 No. 2, p. 643-653
    Received: June 9, 2006

    * Corresponding author(s): yanw@agr.gc.ca
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  1. Weikai Yan *a,
  2. Manjit S. Kangb,
  3. Baoluo Maa,
  4. Sheila Woodsc and
  5. Paul L. Corneliusd
  1. a Eastern Cereal and Oilseed Research Centre (ECORC), Agric. and Agri-Food Canada (AAFC), 960 Carling Ave., Ottawa, ON, Canada, K1A 0C6
    b Dep. of Agronomy & Environ. Mgmt., Louisiana State Univ. Agric. Center, Baton Rouge, LA 70803-2110
    c Cereal Research Center (CRC), AAFC, 195 Dafoe Road, Winnipeg, MB, Canada, R3T 2M9
    d Dep. of Plant and Soil Sciences and Dep. of Statistics, Univ. of Kentucky, Lexington, KY 40506. ECORC contribution number: 06-688


The use of genotype main effect (G) plus genotype-by-environment (GE) interaction (G+GE) biplot analysis by plant breeders and other agricultural researchers has increased dramatically during the past 5 yr for analyzing multi-environment trial (MET) data. Recently, however, its legitimacy was questioned by a proponent of Additive Main Effect and Multiplicative Interaction (AMMI) analysis. The objectives of this review are: (i) to compare GGE biplot analysis and AMMI analysis on three aspects of genotype-by-environment data (GED) analysis, namely mega-environment analysis, genotype evaluation, and test-environment evaluation; (ii) to discuss whether G and GE should be combined or separated in these three aspects of GED analysis; and (iii) to discuss the role and importance of model diagnosis in biplot analysis of GED. Our main conclusions are: (i) both GGE biplot analysis and AMMI analysis combine rather than separate G and GE in mega-environment analysis and genotype evaluation, (ii) the GGE biplot is superior to the AMMI1 graph in mega-environment analysis and genotype evaluation because it explains more G+GE and has the inner-product property of the biplot, (iii) the discriminating power vs. representativeness view of the GGE biplot is effective in evaluating test environments, which is not possible in AMMI analysis, and (iv) model diagnosis for each dataset is useful, but accuracy gain from model diagnosis should not be overstated.

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