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AMMI and GGE biplot analyses of genotype-environment interaction in spring barley lines

https://doi.org/10.18699/VJ17.283

Abstract

Yield stability depends on the resistance of varieties and hybrids to stressful environmental factors. Assessment of genotype-environment interaction helps breeders select the best genotypes for submission to the state variety trials. The article presents results of AMMI (Additive Main effects and Multiplicative Interaction) and GGE (Genotype plus Genotype-Environment interaction) biplot analyses of the grain yield data in eight promising spring barley lines bred at the Plant Production Institute nd. a V.Ya. Yuryev of NAAS and two standard varieties in 2012–2015. The objective of this study was to determine the effect of genotype, environment and their interaction for grain yield and identify stable and performance genotypes. The experimental layout was randomized complete block design with four replications. The analysis of variance on grain yield data showed that the mean squares of environments, genotypes and genotype-environment interaction (GEI) accounted for 85.8, 8.1 and 6.1 % of treatment combination sum of squares, respectively. To find out the effects of GEI on grain yield, the data were subjected to AMMI and GGE biplot analysis. The AMMI model presented greater efficiency by retaining most of the variation in the first two main components, 95.7 %, followed by the GGE biplot model, 82.9 %. Lines 09-837 (G8) and 08-1385 (G9) presented an elevated grain yield and stability as determined by the AMMI and GGE biplot methodologies. These lines named as “Avgur” and “Veles” were submitted to the state variety trial. The results finally indicated that AMMI and GGE biplot are informative methods to explore stability and adaptation pattern of genotypes in practical plant breeding and in subsequent variety recommendations.

 

About the Author

P. N. Solonechnyi
Plant Production Institute nd. a V.Ya. Yuryev, NAAS of Ukraine, Kharkov
Ukraine


References

1. Annicchiarico P. Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy. Euphytica. 1997;94:53-62. DOI 10.1023/A:1002954824178.

2. Bajpai P.K., Prabhakaran V.T. A new procedure of simultaneous selection for high yielding and stable crop genotypes. Indian J. Genet. Pl. Br. 2000;60:141-146.

3. Balestre M., Von Pinho R.G., Souza J.C., Oliveira R.L. Genotypic stability and adaptability in tropical maize based on AMMI and GGE biplot analysis. Genet. Mol. Res. 2009;8(4):1311-1322. DOI 10.4238/vol8-4gmr658.

4. Cornelius P.L., Crossa J., Seyedsadr M.S. Statistical tests and estimators of multiplicative models for genotype-by-environment interaction. In: M.S. Kang, H.G. Gauch (Eds.). Genotype-by-Environment Interaction. FL, USA: CRC Press, Boca Raton, 1996;199-234.

5. Crossa J., Gauch H.G., Zobel R.W. Additive main effects and multiplicative analysis of two international maize cultivar trials. Crop Sci. 1990;30:493-500.

6. Dashiell K.E., Ariyo O.J., Bello L. Genotype × environment interaction and simultaneous selection for high yield and stability in soybeans (Glycine max (L.) Merr.). Ann. Appl. Biol. 1994;124:133-139.

7. Eskridge K.M. Selection of stable cultivars using a safety-first rule. Crop Sci. 1990;30:369-374.

8. Farshadfar E. Incorporation of AMMI stability value and grain yield in a single non-parametric index (GSI) in bread wheat. Pak. J. Biol. Sci. 2008;11(14):1791-1796.

9. Gauch H.G., Zobel R.W. AMMI analysis of yield trials. Ch. 4. In: M.S. Kang, H.G. Gauch (Eds.). Genotype-by-Environment Interaction. FL, USA: CRC Press, Boca Raton, 1996:85-122.

10. Kang M.S. Simultaneous selection for yield and stability in crop performance trials: Consequences for growers. Agron. J. 1993;85:754-757. DOI 10.2134/agronj1993.00021962008500030042x.

11. Kiliç H. Additive main effects and multiplicative interactions (AMMI) analysis of grain yield in barley genotypes across environments. Tarım Bilimleri Dergisi = J. Agric. Sci. 2014;20:337-344.

12. Mirosavljević M., Pržulj N., Čanak P. Analysis of new experimental barley genotype performance for grain yield using AMMI biplots. Selekcija i Semenarstvo. 2014;XX(1):27-36.

13. Mohammadi R., Abdulahi A., Haghparast R., Armion M. Interpreting genotype-environment interactions for durum wheat grain yields using non-parametric methods. Euphytica. 2007;57:239-251. DOI 10.1007/s10681-007-9417-3.

14. Mohammadi R., Amri A. Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica. 2008;159:419-432. DOI 10.1007/s10681-007-9600-6.

15. Moreno-González J., Crossa J., Cornelius P.L. Genotype × environment interaction in multi-environment trials using shrinkage factors for AMMI models. Euphytica. 2004;137:119-127.

16. Mortazavian S.M.M., Nikkhah H.R., Hassani F.A., Sharif-al-Hosseini M., Taheri M., Mahlooji M. GGE biplot and AMMI analysis of yield performance of barley genotypes across different environments in Iran. J. Agr. Sci. Tech. 2014;16:609-622.

17. Oliveira E.J., Freitas J.P.X., Jesus O.N. AMMI analysis of the adaptability and yield stability of yellow passion fruit varieties. Sci. Agric. 2014;71(2):139-145.

18. Purchase J.L., Hatting H., van Deventer C.S. Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: Stability analysis of yield performance. South Afric. J. Plant Soil. 2000;17:101-107. DOI 10.1080/02571862.2000.10634878.

19. Rao A.R., Prabhakaran V.T. Use of AMMI in simultaneous selection of genotypes for yield and stability. Ind. Soc. Agric. Statist. 2005; 59(1):76-82.

20. Solonechnyi P., Vasko N., Naumov O., Solonechnaya O., Vazhenina O., Bondareva O., Logvinenko Yu. GGE biplot analysis of genotype by environment interaction of spring barley varieties. Zemdirbyste-Agriculture. 2015;102(4):431-436. DOI 10.13080/z-a.2015.102.055.

21. Yan W. GGEbiplot – a Windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agron. J. 2001;93:1111-1118. DOI 10.2134/agronj2001.9351111x.

22. Yan W. Singular-value partitioning in biplot analysis of multi-environment trial data. Agron. J. 2002;94:990-996. DOI 10.2134/agronj 2002.9900.

23. Yan W. Crop Variety Trials: Data Management and Analysis. John Wiley and Sons, 2014. DOI 10.1002/9781118688571.

24. Yan W., Hunt L.A., Sheny Q., Szlavnics Z. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 2000;40:597-605. DOI: 10.2135/cropsci2000.403597x.

25. Yan W., Kang M.S. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists and Agronomists. Boca Raton, USA: CRC Press, 2003.

26. Yan W., Kang M.S., Ma B., Woods S., Cornelius P.L. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 2007; 47:643-655. DOI 10.2135/cropsci2006.06.0374.

27. Yan W., Tinker N.A. Biplot analysis of multi-environment trial data: principles and applications. Can. J. Plant Sci. 2006;86:623-645. DOI 10.4141/P05-169.

28. Zobel R.W., Wright A.J., Gauch H.G. Statistical analysis of a yield trial. Agron. J. 1988;80:388-393.


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