Multi-Environment Trial Analysis of Food Barley in Ethiopia Using AMMI and GGE Biplot Methods

Girma Fana, Diriba Tadese, Hiwot Sebsibe, Ramesh P.S. Verma


Food barley released varieties were tested in 2012 for performance across major environments in Ethiopia consisting of 12 varieties Diribe, Tilla, Abbay, Biftu, Defo, Dinsho, Mulu, Setegn, Misiratch, Basso, Mezezo and local checks over six locations Gergera, Estayish, Shambu, Arjo, Robe and Sinana. The objective was to determine genotype by environment interaction using AMMI and GGE biplot, compare the two models for identifying the adaptable and stable genotypes. Sinana was identified as the high yielding environment and MULU the high yielding variety with mean yields of 3466.31 and 3137.67 kg/ha, respectively. The mean yield at Estayish was lower (1535 kg/ha) than other environments whereas lower yield (2212.16 kg/ha) was also obtained from the variety DINSHO. The AMMI analysis of Variance indicated that 47% of the total sum of squares is attributed to the Environmental effect, 8% to the genotypic effect and 25% to the interaction. The first three principal components of the GEI explained 81% of the variation. Genotypes Basso, Biftu and Setegn were the most stable whereas Diribe was unstable. Variety Mulu was identified as the winner genotype by AMMI model whereas Diribe was identified as the winner by the GGE model. GGE model better explains the which-won-where scenario and hence preferred to AMMI model. The discriminating and representative view of the GGE biplot depicted that Sinana and Shambu are discriminating environments whereas Sinana, Estayish and Gergera are representative environments. Therefore, Sinana is the ideal environment for discriminating genotypes and representing other environments for selecting ideal genotypes.


Multi-environment; GGE biplot; stability; AMMI; interaction; adaptability

Full Text:



Annicchiarico, P., Perenzin, M., 1994. Adaptation Patterns and Definition of Macro-environments for Selection and Recommendation of Common-wheat Genotypes in Italy. Plant Breeding 113, 197-205.

Bekele, B., Alemayehu, F., Lakew, B., 2005. Food barley in Ethiopia, in: Grando, S., Macpherson, H.G. (Eds.), Food Barley: Importance, Uses, and Local Knowledge. International Center for Agricultural Research in the Dry Areas (ICARD), Aleppo, Syria, pp. 53-82.

Bradu, D., Gabriel, K.R., 1978. The Biplot as a Diagnostic Tool for Models of Two-Way Tables. Technometrics 20, 47.

Crossa, J., 1990. Statistical Analyses of Multilocation Trials, Advances in Agronomy. Elsevier, Amsterdam, Netherlands, pp. 55-85.

Ding, M., Tier, B., Yan, W., Wu, H.X., Powell, M.B., McRae, T.A., 2007. Application of GGE biplot analysis to evaluate Genotype (G), Environment (E) and GxE interaction on P. radiata: a case study. 15p, Hobart, Australia.

Dixon, A.G.O., Nukenine, E.N., 1997. Statistical analysis of cassava yield trials with the additive main effects and multiplicative interaction (AMMI) model. African Journal of Root and Tuber Crops 3, 46-50.

Fan, X., Hagos, E.G., Xu, B., Sias, C., Kawakami, K., Burdine, R.D., Dougan, S.T., 2007. Nodal signals mediate interactions between the extra-embryonic and embryonic tissues in zebrafish. Developmental Biology 310, 363-378.

FAO, 2014. Food and Agriculture Organization. United Nations, New York, United States.

Gabriel, K.R., 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika 58, 453-467.

Gauch, H.G., 1988. Model Selection and Validation for Yield Trials with Interaction. Biometrics 44, 705.

Gauch, H.G., 2006. Statistical Analysis of Yield Trials by AMMI and GGE. Crop Science 46, 1488.

Gauch, H.G., Zobel, R.W., 1988. Predictive and postdictive success of statistical analyses of yield trials. Theoretical and Applied Genetics 76, 1-10.

Gauch Jr, H.G., 1992. Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Elsevier Science Publishers, Amsterdam, Netherlands.

Gollob, H.F., 1968. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33, 73-115.

Grüneberg, W.J., Manrique, K., Zhang, D., Hermann, M., 2005. Genotype × Environment Interactions for a Diverse Set of Sweetpotato Clones Evaluated across Varying Ecogeographic Conditions in Peru. Crop Science 45, 2160.

Kandus, M., Almorza, D., Boggio Ronceros, R., Salerno, J.C., 2010. Statistical models for evaluating the genotype-environment interaction in maize (Zea mays L.). Phyton-Revista Internacional de Botanica Experimental 79, 39.

Kroonenberg, P.M., 1995. Introduction to biplots for G× E tables. Department of Mathematics, University of Queensland, Brisbane, Australia, p. 51.

Ma, B.L., Yan, W., Dwyer, L.M., Fregeau-Reid, J., Voldeng, H.D., Dion, Y., Nass, H., 2004. Graphic analysis of genotype, environment, nitrogen fertilizer, and their interactions on spring wheat yield. Agronomy Journal 96, 169-180.

Rad, M.R.N., Kadir, M.A., Rafii, M.Y., Jaafar, H.Z.E., Naghavi, M.R., Ahmadi, F., 2013. Genotype environment interaction by AMMI and GGE biplot analysis in three consecutive generations of wheat (Triticum aestivum) under normal and drought stress conditions. Australian Journal of Crop Science 7, 956.

Samonte, S.O.P.B., Wilson, L.T., McClung, A.M., Medley, J.C., 2005. Targeting Cultivars onto Rice Growing Environments Using AMMI and SREG GGE Biplot Analyses. Crop Science 45, 2414.

Stojaković, M., Ivanović, M., Jocković, Đ., Bekavac, G., Purar, B., Nastasić, A., Stanisavljević, D., Mitrović, B., Treskić, S., Laišić, R., 2010. NS maize hybrids in production regions of Serbia. Ratarstvo i povrtarstvo 47, 93-102.

Vargas, M., Crossa, J., 2000. The AMMI analysis and graphing the biplot. Biometrics & Statistics Unit, Genetic Resources Program, International Maize and Wheat Improvement Center, Edo Mex, Mexico.

Yan, W., 2001. GGEbiplot—A Windows Application for Graphical Analysis of Multienvironment Trial Data and Other Types of Two-Way Data. Agronomy Journal 93, 1111.

Yan, W., 2002. Singular-Value Partitioning in Biplot Analysis of Multienvironment Trial Data. Agronomy Journal 94, 990.

Yan, W., Hunt, L.A., Sheng, Q., Szlavnics, Z., 2000. Cultivar Evaluation and Mega-Environment Investigation Based on the GGE Biplot. Crop Science 40, 597.

Yan, W., Kang, M.S., 2002. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC press, Boca, Raton, FL, United States.

Yan, W., Kang, M.S., Ma, B., Woods, S., Cornelius, P.L., 2007. GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data. Crop Science 47, 643.

Yan, W., Tinker, N.A., 2006. Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86, 623-645.

Zerihun, J., 2011. GGE Biplot Analysis of Multi-environment Yield trials of Barley (Hordeium vulgare L.) Genotypes in South-eastern Ethiopia Highlands. International journal of plant breeding and genetics 5, 59-75.

Zobel, R.W., Wright, M.J., Gauch, H.G., 1988. Statistical Analysis of a Yield Trial. Agronomy Journal 80, 388.



  • There are currently no refbacks.

Copyright (c) 2018 Girma Fana, Diriba Tadese, Hiwot Sebsibe, Ramesh P.S. Verma

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

Journal of Plant Breeding and Genetics
ISSN: 2305-297X (Online), 2308-121X (Print)
© EScience Press. All Rights Reserved.