Legume Research

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Legume Research, volume 40 issue 2 (april 2017) : 306-312

AMMI & GGE biplot analysis of multi environment yield trial of soybean in North Western Himalayan state Uttarakhand of India

Anuradha Bhartiya*, J.P. Aditya, Kamendra Singh, Pushpendra, J.P. Purwar, Anjuli Agarwal
1<p>ICAR-Vivekananda Parvatiya Krishi Anusandhan Sansthan,&nbsp;Almora-263 601,Uttarakhand, India.</p>
Cite article:- Bhartiya* Anuradha, Aditya J.P., Singh Kamendra, Pushpendra, Purwar J.P., Agarwal Anjuli (2016). AMMI &amp; GGE biplot analysis of multi environment yield trial of soybeanin North Western Himalayan state Uttarakhand of India . Legume Research. 40(2): 306-312. doi: 10.18805/lr.v0iOF.3548.

The investigation was carried out to study Genotype × Environment (G×E) interaction for seed yield in 36 soybean genotypes including check PS1092 over 3 diverse environments represented by different altitudes in Uttarakhand. Grain yield performances of soybean genotypes were evaluated during Kharif 2013 season using a randomized complete block design. The AMMI analysis indicated that environment, genotypes and genotype by environment interactions had significantly affected seed yield and accounted for 9.76, 28.97 and 47.55% of the total variation, respectively. GGE biplot clearly displayed interrelationships between test locations as well as genotypes and facilitated visual comparisons based on Principal Component Analysis (PCA). The first two principal components PCI and PCII were used to create a two-dimensional GGE biplot that accounted for 45.68 and 38.88% variations respectively and based on discriminating and representative ability, E2 (Majhera) was most suitable location for selecting generally adapted genotypes. Soybean genotype C1 (PS1539) was identified as ideal genotype with high yield and low G×E interaction i.e. high stability. 


  1. Agarwal D.K., Billore S.D., Sharma A.N., Dupare B.U. and Srivastava S.K. (2013) Soybean: introduction, improvement and utilization in India-problems and prospects. Agricultural Research 2: 293–300.

  2. Anandan A., Eswaran R., Sabesan T. and Prakash M. (2009) Additive Main effects and Multiplicative Interactions analysis of yield performances in rice genotypes under coastal saline environments. Advances in Biological Research 3: 43-4.

  3. Anonymous (2014-15) Director’s Report and Summary Tables of Experiments, All India Coordinated Research Project on Soybean, Directorate of Soybean Research, Indore, Madhya Pradesh.

  4. Arnoldi A. (2013) Health benefits of soybean consumption. Legume Perspectives 1: 25-27.

  5. Asfaw A., Alemayehu F., Gurum F. and Atnaf M. (2009) AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia. Scientific Research and Essays 4: 1322-1330.

  6. Atnaf M., Kidane S., Abadi S. and Fisha Z. (2013) GGE biplots to analyze soybean multi-environment yield trial data in north Western Ethiopia. Journal of Plant Breeding Crop Science, 5: 245-254. 

  7. Choukan R. (2011) Genotype, environment and genotype × environment interaction effects on the performance of maize (Zea mays L.) inbred lines. Crop Breeding Journal,1: 97-103.

  8. Cucolotto M., Pipolo V.C., Garbuglio D.D., Junior N.S.F., Destroand D. and Kamikoga M.K. (2007) Genotype x environment interaction in soybean :evaluation through three methodologies. Crop Breeding and Applied Biotechnology,    7: 270-277. 

  9. Department of Agriculture & Cooperation (2013-14) http://eands.dacnet.nic.in/APY_96_To_06.htm

  10. Ebdon J.S. and Gauch H.G. (2002) Additive main effect and mul­tiplicative interaction analysis of national turf grass performance trials: I. Interpretation of genotype × environment interaction. Crop Science, 42: 489-496.

  11. Gabriel K.R. (1971) Thebiplot graphic display of matrices with application to principal component analysis. Biometrika, 58: 453-467.

  12. Gauch H.G. (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Science, 46:1488-1500.

  13. Gauch H.G., Piepho H.P. and Annicchiarico P. (2008) Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Science, 48: 866-889.

  14. Gurmu F., Mohammed H. and Alemaw G. (2009) Genotype x Environment interactions and stability of soybean for grain yield and nutrition quality. African Crop Science Journal 17:87-99.

  15. Hammer G.L. and Cooper M. (1996) Plant Adaptation and Crop Improvement. CABI, Wallingford.

  16. Jha S.K., Singh N.K., Kumar R.A., Agrawal P.K., Bhatt J.C., Guleria S.K., Lone A.A., Sudan R.S., Singh K.P. and Mahajan V. (2013) Additive main effects and multiplicative interaction analysis for grain yield of short duration maize hybrids in North-Western Himalayas. Indian Journal of Genetics & Plant Breeding, 73: 29-35

  17. Karimizadeh R., Mohammadi M., Sabaghni N., Mahmoodi A.A., Roustami B., Seyyedi, F. and Akbari, F. (2013) GGE biplot analysis of yield stability in multi-environment trials of lentil genotypes under rainfed condition. Not. Sci. Biol,. 5:256-262.

  18. Kaya Y., Akcura M. and Taner S. (2006) GGE biplot analysis of multi-environment yield trials in bread wheat. Turk. J. Agric. For., 30: 325-337.

  19. Mikic V., Dordevic and Peric V. (2013) Origin of the word soy. Legume Perspectives 1:5-6.

  20. Oliveira A.B., Duarte J.B. and Pinheiro J.B. (2003) Application of AMMI analysis in the assessment of yield stability in soybean. Pesq. Agropec. Bras., 38: 357-364.

  21. Samonte S.O.P.B., Wilson L.T., McClung A. and Mand Medley J.C. (2005) Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analysis. Crop Science 45: 2414-2424.

  22. Shah N.C. (2006) Black soybean: An ignored nutritious and medicinal food crop from the Kumaon region of India. Asian Agri-History 10: 33–42.

  23. Yan W. (2002) Singular value partitioning in biplot analysis of multi-environment trial data. Agronomy Journal 94: 990-    996.

  24. Yan W., Kang M.S., Wood S. and Cornelius P.L. (2007) GGE biplot vs. AMMI analysis of genotype-by environment data. Crop Science, 47: 643-655.

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

  26. Zobel R.W., Wright M.J. and Gauch H.G. (1988) Statistical analysis of yield trial. Agronomy Journal,80: 388-393.

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