GGE biplot analysis of genotype × environment interaction in spring barley varieties

Authors

  • П. М. Солонечний Plant Production Institute nd. a V.Ya. Yuryev of NAAS, Ukraine
  • М. Р. Козаченко Plant Production Institute nd. a V. Ya. Yuryev NAAS, Ukraine
  • Н. І. Васько Plant Production Institute nd. a V. Ya. Yuryev NAAS, Ukraine
  • О. Г. Наумов Plant Production Institute nd. a V. Ya. Yuryev NAAS, Ukraine
  • О. Є. Важеніна Plant Production Institute nd. a V. Ya. Yuryev NAAS, Ukraine
  • О. В. Солонечна Plant Production Institute nd. a V. Ya. Yuryev NAAS, Ukraine
  • П. П. Дмитренко Donetsk Experiment Station NAAS, Ukraine
  • О. Л. Коваленко Research Station of Bast Crops of the Institute of Agriculture of North-East of NAAS, Ukraine

DOI:

https://doi.org/10.30835/2413-7510.2014.42134

Keywords:

GGE biplot, spring barley, genotype  environment interaction, environmental variety trial, performance, stability

Abstract

Purpose. To show advantages of  GGE biplot analysis for visualization and interpretation of results of environmental variety trials as exemplified by testing seven varieties spring barley in terms of performance in 2013. To identify genotypes with high performance and stability.

Materials and Methods. The article describes GGE biplot analysis of the results of environmental variety trials of seven spring barley varieties bred at the Plant Production Institute nd. a V.Ya. Yuryev NAAS in three locations.

Results. The results of GGE biplot analysis showed that the first two basic components (PC1 and PC2) determined 88.2% of the total variability caused by genotype  environment interaction. The polygon view of GGE biplot showed that genotype G1 was the most productive among genotypes in locations E3 and E2, and genotype G6 - in location E2. Genotypes G1, G3 and G6 were found to be the closest to the "ideal" genotype in terms of performance and stability. According to the average productivity varieties can be ranked in the following order: G6> G1> G3> G5> G7> G2> G4. The productivity of genotypes G6, G1 and G5 was the most variable, and genotype G3 was noticeable for its high performance and stability. Discriminatory and representative capacities of the environmental variety trial locations were estimated as a tester for assessing genotypes. Locations E1 and E3 had long vectors and a large inclination angle with the AEC abscissa, indicating their unsuitability as testers for selecting the best genotypes, but at the same time the applicability of their using as testers for selection for stability. Location E2 had a long vector and a small inclination angle with the abscissa AEC, which makes it optimal for estimating genotypes.

Conclusions. Benefits of using GGE biplot analysis for visualization and interpretation of results of environmental variety trials were proven.

References

Pourdad SS, Moghaddam MJ. Study on seed yield stability of sunflower inbred lines through GGE biplot. Helia. 2013; 36(58):19-28.

Hagos HG, Abay F. AMMI and GGE biplot analysis of bread wheat genotypes in the northen part of Ethopia. J. Plant Breed. and Genet. 2013; 01:12-18.

Flores FA, Moreno M.T., Cubero JI. comparison of univariate and multivariate methods to analyze environments. Field Crop Res. 1998; 56:271-286.

Farshadfar E, Rashidi M, Jowkar MM, Zali H. GGE Biplot analysis of genotype × environment interaction in chickpea genotypes. European Journal of Experimental biology. 2013; 3(1):417-423.

Gabriel KR. The biplot graphic display of matrices with application to principal component analysis. Biometrika. 1971; 58(3):453-467.

Yan W, Tinker NA. Biplot analysis of multi-environment trial data: Principles and applications. Can. J. Plant Sci. 2006; 86:623–645.

Gauch HG. Statistical analysis of yield trials by AMMI and GGE. Crop Science. 2006; 46:1488-1500.

Yan W, Kang MS, Ma B, Woods S, Cornelius PL. GGE biplot vs. AMMI analysis of genotype – by- environment data. Crop Science. 2007; 47:643-655.

Yan W. Singular-value partitioning in biplot analysis of multi-environment trial data. Agronomy Journal. 2002; 94:990-996.Yan W, Hunt L, A., Sheny Q, Szlavnics Z. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science. 2000; 40:597-605.

Yan W, Kang MS. GGE biplot analysis: A graphical tool for breeders, geneticists and Agronomists. CRC press : Boca Raton (FL). 2003.

Solonechnii PM. Homeostaticity and breeding value of current spring barley varieties. Selektsia I nasinnitstvo. 2013; 103:36–41.

Solonechnii PM. Stability of productivity elements in spring barley varieties under ecological testing conditions. Selektsia I nasinnitstvo. 2014; 105:194–203.

GGEbiplot [Internet]. 2011. Available from: http://www.ggebiplot.com.

Fan XM, Kang MS, Chen H, Zhang Y, Tan J, Xu C. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agronomy Journal. 2007; 99:220-228.

Downloads

Published

2014-12-28

Issue

No. 106 (2014)

Section

METHODS AND RESULTS SELECTION

License

Copyright (c) 2015 П. М. Солонечний, М. Р. Козаченко, Н. І. Васько, О. Г. Наумов, О. Є. Важеніна, О. В. Солонечна, П. П. Дмитренко, О. Л. Коваленко

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

When placing the text in electronic resources, the copyright is reserved by the author of a publication.

Authors may not agree with referee’s(s’) comments and remarks of the Editorial Board, rationalizing their point of view.

Authors may require clarification from or the Editorial Board or changes in the event of significant errors in their article.

Authors can use materials published in the journal Selection and Seed Industry in their work, mandatorily referring to our journal.