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Acta Agron Sin ›› 2010, Vol. 36 ›› Issue (11): 1805-1819.doi: 10.3724/SP.J.1006.2010.01805

• REVIEW •     Next Articles

Optimal Use of Biplots in Analysis of Multi-Location Variety Test Data

YAN Wei-Kai   

  1. Eastern Cereal and Oilseed Research Centre (ECORC), Agriculture and Agri-Food Canada (AAFC), Neatby Building, 960 Carling Ave., Ottawa, Ontario, Canada, K1A 0C6 
  • Received:2010-03-29 Revised:2010-08-09 Online:2010-11-12 Published:2010-08-30

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Abstract: Biplot analysis has been increasingly used in visual analysis of genotype-by-environment data and other types of two-way data. While many plant breeders and agricultural researchers are enthusiastic about the capacity of biplot analysis in helping them to understand their research data, some statisticians consider the use of biplots as a sidetrack to genotype-by-environment interaction analyses. Confusion also exists among statisticians on what is or is not a biplot. Admittedly, some users of biplot analysis are not always clear on how to select a proper type of biplot for a particular research objective and how to interpret a biplot correctly, accurately, and adequately. Some criticisms of biplot analysis may arise from incomplete understanding of the practitioners’ research problems as well as of the biplot methodology. In this review, I summarize the experiences and understanding in biplot analysis of genotype-by-environment data achieved during the last decade and discuss the following issues: (1) how to choose a proper biplot; (2) how to choose a proper GGE (genotype + genotype-by-environment interaction) biplot; (3) how to use the key functions of a GGE biplot for genotype evaluation, test-environment evaluation, and mega-environment delineation; (4) how to judge the adequacy of a 2-D biplot; and (5) how to test the statistical significance of a biplot pattern.

Key words: Biplot, Genotype-by-environment interaction, Genotype evaluation, Test-environment evaluation, Mega-environment delineation

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