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Current IssueEditorial BoardOnline First ArticlesArchive

Research Article

volume 53 issue 5 (october 2019) : 529-535,   Doi: 10.18805/IJARe.A-5244

A new index for evaluation of G×E interaction in pearl millet using AMMI and GGE biplot analyses

M
Mamata
B
B.K. Hooda
E
Ekta Hooda
1<div>Department of Mathematics and Statistics,&nbsp;<br />Chaudhary Charan Singh Haryana Agricultural University, Hisar-125 004, Haryana, India.</div>
Cite article:- Mamata, Hooda B.K., Hooda Ekta (2019). A new index for evaluation of G&times;E interaction in pearl millet using AMMI and GGE biplot analyses. Indian Journal of Agricultural Research. 53(5): 529-535. doi: 10.18805/IJARe.A-5244.

ABSTRACT

Performance of a genotype is the result of its genetic constitution and the environment in which it has been grown. In practice, a particular variety may not exhibit the same phenotypic performance under different environments.  Also, different varieties may respond differently to a specific environment. Additive Main effects and Multiplicative Interaction (AMMI) and GGE biplot analyses are the most frequently used models to explain G×E interaction of multi-environment cultivar trials. Based on climatic conditions, the pearl millet cultivation in India is divided in 3 major zones A1, A and B for effective evaluation of the pearl millet breeding material. In the present study, the G×E interaction in pearl millet genotypes from zone-A of India has been evaluated using the techniques of AMMI and GGE biplot analyses. A new Weighted Stability Index (WSI) has been proposed for determining the high yielding and stable genotypes based on the normalized indices for grain yield and ASV indices. The three interaction principal component axes (IPCA1, IPCA2 and IPCA3) have been found to be significant for this zone. AMMI Stability Value (ASV) and Stability Index have been used to find the most stable genotypes while indices YSI and WSI have been used to find both the most stable and high yielding genotypes. On the basis of ASV, genotypes MH 2120, MH 2109 and MH 2116 have been found to be the most stable for this Zone. The Spearman’s rank correlation coefficient between YSI and WSI was found to be significant at 1% level of significance indicating that the two indices have almost similar performance in determining high yielding stable genotypes.   

REFERENCES

  1. Birla, D. and Ramgiry, S.R. (2015). AMMI analysis to comprehend genotype-by-environment (G × E) interactions in rainfed grown soybean [Glycine max. (L) Merrill]. Indian Journal of Agricultural Research. 49:39-45.
  2. Crossa, J. (1990). Statistical analyses of multilocation trials. Advances in Agronomy, 44: 55–85.
  3. Gauch, H.G. Jr. (1992). Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Elsevier, Amsterdam.
  4. 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.
  5. Gauch, H.G.Jr. (1988). Model selection and validation of yield trials with interaction. Biometrics, 44: 705-715.
  6. Giridhar, K., Kumari, S. Surya, Sarada C., Naidu L. Naram (2016). Stability for seed yield in ajwain based on Gentoype Selection Index. Indian Journal of Agricultural Research. 50: 244-248.
  7. Gollob, H.F. (1968). A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika, 33: 73-115.
  8. Hooda, E., Hooda, B.K. and Manocha, V. (2017). Dynamics of inter-district developmental disparities in Haryana. Journal of Applied and Natural Science, 9(2): 983-991.
  9. Kempton, R.A. (1984). The use of biplots in interpreting variety by environment interactions. Journal of Agricultural Science, 103: 123-135.
  10. Khanzadeh, H., Vaezi, B., Mohammadi, R., Mehraban, A., Hosseinpor, T. and Shahbazi, K. (2018). Grain yield stability of barley genotypes in uniform regional yield trails in warm and semi warm dry land area. Indian Journal of Agricultural Research. 52: 16-21.
  11. Kumar, B., Hooda, E. and Hooda, B. K. (2018). GGE Biplot technique for analysis of data from Multi-environment Yield Trials for Wheat in Northern India. Advances in Research. 16(2): 1-9. 
  12. Purchase, J.L., Hatting, H. and Van, D.C.S. (2000). Genotype x Environment interaction of winter wheat (T. aestivum) in South Africa: stability analysis of yield performance. South African Journal of Plant and Soil, 17: 101-107.
  13. Rao, A.R. and Prabhakaran, V.T. (2005). Use of AMMI in Simultaneous Selection of Genotypes for Yield and Stability. Journal of Indian Society of Agricultural Statistics, 59(1): 76-82.
  14. Yan, W. and Kang, M.S. (2003). GGE biplot Analysis: a Graphical Tool for Breeders, Geneticists and Agronomists. CRC Press, Boca Raton.
  15. Yan, W., Hunt, L.A., Sheng, Q. and Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science, 40: 597–605.
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    Research Article

    volume 53 issue 5 (october 2019) : 529-535,   Doi: 10.18805/IJARe.A-5244

    A new index for evaluation of G×E interaction in pearl millet using AMMI and GGE biplot analyses

    M
    Mamata
    B
    B.K. Hooda
    E
    Ekta Hooda
    1<div>Department of Mathematics and Statistics,&nbsp;<br />Chaudhary Charan Singh Haryana Agricultural University, Hisar-125 004, Haryana, India.</div>
    Cite article:- Mamata, Hooda B.K., Hooda Ekta (2019). A new index for evaluation of G&times;E interaction in pearl millet using AMMI and GGE biplot analyses. Indian Journal of Agricultural Research. 53(5): 529-535. doi: 10.18805/IJARe.A-5244.

    ABSTRACT

    Performance of a genotype is the result of its genetic constitution and the environment in which it has been grown. In practice, a particular variety may not exhibit the same phenotypic performance under different environments.  Also, different varieties may respond differently to a specific environment. Additive Main effects and Multiplicative Interaction (AMMI) and GGE biplot analyses are the most frequently used models to explain G×E interaction of multi-environment cultivar trials. Based on climatic conditions, the pearl millet cultivation in India is divided in 3 major zones A1, A and B for effective evaluation of the pearl millet breeding material. In the present study, the G×E interaction in pearl millet genotypes from zone-A of India has been evaluated using the techniques of AMMI and GGE biplot analyses. A new Weighted Stability Index (WSI) has been proposed for determining the high yielding and stable genotypes based on the normalized indices for grain yield and ASV indices. The three interaction principal component axes (IPCA1, IPCA2 and IPCA3) have been found to be significant for this zone. AMMI Stability Value (ASV) and Stability Index have been used to find the most stable genotypes while indices YSI and WSI have been used to find both the most stable and high yielding genotypes. On the basis of ASV, genotypes MH 2120, MH 2109 and MH 2116 have been found to be the most stable for this Zone. The Spearman’s rank correlation coefficient between YSI and WSI was found to be significant at 1% level of significance indicating that the two indices have almost similar performance in determining high yielding stable genotypes.   

    REFERENCES

    1. Birla, D. and Ramgiry, S.R. (2015). AMMI analysis to comprehend genotype-by-environment (G × E) interactions in rainfed grown soybean [Glycine max. (L) Merrill]. Indian Journal of Agricultural Research. 49:39-45.
    2. Crossa, J. (1990). Statistical analyses of multilocation trials. Advances in Agronomy, 44: 55–85.
    3. Gauch, H.G. Jr. (1992). Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Elsevier, Amsterdam.
    4. 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.
    5. Gauch, H.G.Jr. (1988). Model selection and validation of yield trials with interaction. Biometrics, 44: 705-715.
    6. Giridhar, K., Kumari, S. Surya, Sarada C., Naidu L. Naram (2016). Stability for seed yield in ajwain based on Gentoype Selection Index. Indian Journal of Agricultural Research. 50: 244-248.
    7. Gollob, H.F. (1968). A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika, 33: 73-115.
    8. Hooda, E., Hooda, B.K. and Manocha, V. (2017). Dynamics of inter-district developmental disparities in Haryana. Journal of Applied and Natural Science, 9(2): 983-991.
    9. Kempton, R.A. (1984). The use of biplots in interpreting variety by environment interactions. Journal of Agricultural Science, 103: 123-135.
    10. Khanzadeh, H., Vaezi, B., Mohammadi, R., Mehraban, A., Hosseinpor, T. and Shahbazi, K. (2018). Grain yield stability of barley genotypes in uniform regional yield trails in warm and semi warm dry land area. Indian Journal of Agricultural Research. 52: 16-21.
    11. Kumar, B., Hooda, E. and Hooda, B. K. (2018). GGE Biplot technique for analysis of data from Multi-environment Yield Trials for Wheat in Northern India. Advances in Research. 16(2): 1-9. 
    12. Purchase, J.L., Hatting, H. and Van, D.C.S. (2000). Genotype x Environment interaction of winter wheat (T. aestivum) in South Africa: stability analysis of yield performance. South African Journal of Plant and Soil, 17: 101-107.
    13. Rao, A.R. and Prabhakaran, V.T. (2005). Use of AMMI in Simultaneous Selection of Genotypes for Yield and Stability. Journal of Indian Society of Agricultural Statistics, 59(1): 76-82.
    14. Yan, W. and Kang, M.S. (2003). GGE biplot Analysis: a Graphical Tool for Breeders, Geneticists and Agronomists. CRC Press, Boca Raton.
    15. Yan, W., Hunt, L.A., Sheng, Q. and Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science, 40: 597–605.
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