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Research Article

volume 52 issue 1 (february 2018) : 16-21,   Doi: 10.18805/IJARe.A-290

Grain yield stability of barley genotypes in uniform regional yield trails in warm and semi warm dry land area

H
Hassan Khanzadeh
B
Behroz Vaezi
R
Rahmatolah Mohammadi
A
Asghar Mehraban
T
Tahmaseb Hosseinpor
K
Kamal Shahbazi.
1Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ardabil, Iran
Cite article:- Khanzadeh Hassan, Vaezi Behroz, Mohammadi Rahmatolah, Mehraban Asghar, Hosseinpor Tahmaseb, Shahbazi. Kamal (2017). 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(1): 16-21. doi: 10.18805/IJARe.A-290.

ABSTRACT

The aim of this study was to assess the effect of GEI on grain yield of barley advanced lines and exploit the positive GEI effect using AMMI and SREG GGE biplot analysis. Therefore, 18 lines were evaluated at five research stations (Ghachsaran, Mogan, Lorestan, Gonbad and Ilam) of Dryland Agricultural Research Institute (DARI), in the semi-warm regions in Iran, in 2012, 2013 and 2014 cropping seasons under rain-fed conditions. Analysis of variance showed that grain yield variation due to the environments, genotypes and GE interaction were highly significant (p<0.01), which accounted for 68.9%, 9.3% and 22.7% of the treatment combination sum of squares, respectively. To determine the effects of GEI on yields, the data were subjected to AMMI and GGE biplot analysis. The first five AMMI model terms were highly significant (p<0.01) and the first two terms explained 59.56% of the GEI. There were two mega-environments according to the SREG GGE model. The best genotype in one location was not always the best in other test locations. According to AMMI1 biplot, G2, G4, G5 and G6 were better than all other genotypes across environments. G2 was the ideal genotype to plant in Gachsaran. It seems that Ghachsaran is the stable environment between the environments studied and next in rank was Gonbad. In finally, the ATC method indicated that G1, G3, G4 and G6 were more stable as well as high yielding. 

REFERENCES

  1. Admassu, S., Nigussie, M. and Zelleke H., (2008). Genotype-environment interaction and stability analysis for grain yield of maize (Zea mays L.) in Ethiopia. Asian Journal of Plant Science. 7:163-169.
  2. Ahmadi, J., Vaezi, B. and Fotokian M.H. (2012). Graphical analysis of multi-environment trials for barley yield using AMMI and GGE-biplot under rain-fed conditions. Journal of Plant Physiology and Breeding. 2:43-54.
  3. Akcura, M., Kaya, Y. and Taner S., (2005). Genotype-environment interaction and phenotypic stability analysis for grain yield of durum wheat in the Central Anatolian Region. Turkish Journal of Agriculture and Forestry. 29: 369-375.
  4. Becker, H.C. and Leon J., (1988). Stability analysis in plant breeding. Plant Breeding. 101: 1-23.
  5. Baker, R.J., (1988). Tests for crossover genotype-environmental interactions. Canadian Journal of Plant Science. 68:405-410.
  6. 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 J. Agric. Res., 49(1):39-45.
  7. Ceccarelli, S. and Grando. S., (2000). Barley landraces from the fertile crescent: a lesson for plant breeders. In: Brush S.B., (eds). Genes in the field: On farm conservation of crop diversity. IPGRI Rome, IDRC Ottawa, Lewis Boca Raton.
  8. Cornelius, P.L., Seyed sadr M.S. and Crossa. J. (1992). Using the shift ed multiplicative model to search for “reparability” in crop cultivar trials. Theoretical Applied Generics. 84:161-172.
  9. Crossa J., Cornelius P. L. and Yan., W. (2002). Biplots of linear-bilinear models for studying crossover genotype×environment interaction. Crop Sciences. 42: 619-633.
  10. Dehghani, H., Ebadi, A. and Yousefi. A., (2008). Biplot analysis of genotype by environment interaction for barley yield in Iran. Agronomy Journal. 98:388-393.
  11. De Lacy, I.H., Basford, K.E., Cooper M., Bull, J.K. and Mclaren. C.G., (1996). Analysis of multi-environment trail an historical perspective. In: [Cooper, M. and G.L. Hammer, (eds)]. Plant Adaptation and Crop Improvement. CAB International, Wallingford, UK 39-124. 
  12. Gauch, H.G., (1988) Model selection and validation for yield trials with interaction. Biometrics. 44: 705-715.
  13. Gauch, H.G. and Zobel. R.W., (1997). Identifying mega-environments and targeting genotypes. Crop Science. 37:311-326.
  14. Gauch, H.G., (2006). Statistical analysis of yield trials by AMMI and GGE. Crop Science. 46:1488-1500. 
  15. 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.
  16. GenStat Release., (2010). VSN International, Hemel Hempstead
  17. Huehn, M., (1996). Nonparametric analysis of genotype × environment interactions by ranks. Genotype by environment interaction. CRC Press, Boca Raton. 213-228.
  18. Karimzadeh, R., Asghari A., Chinipardaz R., Sofalian O., and Ghaffari. A.A., (2016). Determining Yield Stability and Model Selection by AMMI Method in Rain-fed durum Wheat Genotypes. Turkish Journal of Field Crops. 21:174-183.
  19. Mladenov, V., Banjac, B. and Milosevic. M., (2012). Evaluation of Yield and Seed Requirements Stability of Bread Wheat (Triticum aestivum L.) Via AMMI Model. Turkish Journal of Field Crops. 7:203-207.
  20. Mohammadi, M., Karimizadeh R., Noorinia A.A., Ghojogh H., Hosseinpour T., Khalilzadeh G.R., Mehraban A., Roustaii M., and Hasanpor Hosni. M., (2013). Analysis of yield stability in multi-environment trials of barley (Hordeum vulgar L.) genotypes using AMMI model. Current Opinion in Agriculture. 2:20-24.
  21. Mohammadi, R. and Mahmodi. K.N., (2008). Stability analysis of grain yield in barley (Hordeum Vulgare L.). International Journal of Plant Breeding. 2:74-78.
  22. Mortazavian, S.M.M., Nikkhah H.R., Hassani F.A., Sharif-al-Hosseini M., Taheri M., and Mahlooji. M., (2014). GGE biplot and AMMI analysis of yield performance of barley genotypes across different environments in Iran. Journal of Agricultural Science and Technology. 16:609-622.
  23. Rodriguez, M., Rau D., Papa R., and Attene. G., (2008). Genotype by environment Interactions in barley (Hordeum vulgare L.): Different responses of landraces, recombinant inbred lines and varieties to Mediterranean environments. Euphytica. 163:231-247.
  24. Romagosa I and Fox PN (1993) Genotype×environment interaction and adaptation. In: [Hayward, M.D., Bosemark, N.O. and Romagosa I., (eds.)] Plant Breeding, Principles and Prospects. Chapman and Hall, London, UK. 374-390.
  25. Sahay G. and Sarma. B.K., (2005). Yield stability of some groundnut (Arachis Hypogea) genotypes in Meghalaya. Indian J. Agric. Res., 39(3):22-224.
  26. Sarkar, B., Sharma R.C., Verma R.P.S., Sarkar A., and Sharma. I., (2014). Identifying superior feed barley genotypes using GGE biplot for diverse environments in India. Indian Journal of Genetics. 74:26-33.
  27. SAS INSTITUTE .(1996). SAS/STAT user’s guide. v. 6, 4th ed. SAS Inst., Cary, NC.
  28. Vir, O. and Sultan. S.M., (2012). Identification of genotypes of wheat (Triticum aestivum L.) for specific adaptation using qualitative and quantitative genotype × environment interaction and regression analysis in the rainfed condition of Hymalyas . Indian J. Agric. Res., 46(2):178-83.
  29. Voltas, J., Van Eeuwijk F., Sombrero A., Lafarga A., Igartua E., and Romagosa. I., (1999). Integrating statistical and ecophysiological analyses of genotype by environment interaction for grain filling of barley.I. individual grain weight. Field Crops Research. 62:63-74.
  30. Yan, W. and Hunt. L.A., (1998). Genotype by environment interaction and crop yield. Plant Breeding Review. 16:135-178.
  31. Yan, W., (2001). GGE biplot–A windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal, 93:1111-1118. 
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Indian Journal of Agricultural Research
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    Research Article

    volume 52 issue 1 (february 2018) : 16-21,   Doi: 10.18805/IJARe.A-290

    Grain yield stability of barley genotypes in uniform regional yield trails in warm and semi warm dry land area

    H
    Hassan Khanzadeh
    B
    Behroz Vaezi
    R
    Rahmatolah Mohammadi
    A
    Asghar Mehraban
    T
    Tahmaseb Hosseinpor
    K
    Kamal Shahbazi.
    1Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ardabil, Iran
    Cite article:- Khanzadeh Hassan, Vaezi Behroz, Mohammadi Rahmatolah, Mehraban Asghar, Hosseinpor Tahmaseb, Shahbazi. Kamal (2017). 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(1): 16-21. doi: 10.18805/IJARe.A-290.

    ABSTRACT

    The aim of this study was to assess the effect of GEI on grain yield of barley advanced lines and exploit the positive GEI effect using AMMI and SREG GGE biplot analysis. Therefore, 18 lines were evaluated at five research stations (Ghachsaran, Mogan, Lorestan, Gonbad and Ilam) of Dryland Agricultural Research Institute (DARI), in the semi-warm regions in Iran, in 2012, 2013 and 2014 cropping seasons under rain-fed conditions. Analysis of variance showed that grain yield variation due to the environments, genotypes and GE interaction were highly significant (p<0.01), which accounted for 68.9%, 9.3% and 22.7% of the treatment combination sum of squares, respectively. To determine the effects of GEI on yields, the data were subjected to AMMI and GGE biplot analysis. The first five AMMI model terms were highly significant (p<0.01) and the first two terms explained 59.56% of the GEI. There were two mega-environments according to the SREG GGE model. The best genotype in one location was not always the best in other test locations. According to AMMI1 biplot, G2, G4, G5 and G6 were better than all other genotypes across environments. G2 was the ideal genotype to plant in Gachsaran. It seems that Ghachsaran is the stable environment between the environments studied and next in rank was Gonbad. In finally, the ATC method indicated that G1, G3, G4 and G6 were more stable as well as high yielding. 

    REFERENCES

    1. Admassu, S., Nigussie, M. and Zelleke H., (2008). Genotype-environment interaction and stability analysis for grain yield of maize (Zea mays L.) in Ethiopia. Asian Journal of Plant Science. 7:163-169.
    2. Ahmadi, J., Vaezi, B. and Fotokian M.H. (2012). Graphical analysis of multi-environment trials for barley yield using AMMI and GGE-biplot under rain-fed conditions. Journal of Plant Physiology and Breeding. 2:43-54.
    3. Akcura, M., Kaya, Y. and Taner S., (2005). Genotype-environment interaction and phenotypic stability analysis for grain yield of durum wheat in the Central Anatolian Region. Turkish Journal of Agriculture and Forestry. 29: 369-375.
    4. Becker, H.C. and Leon J., (1988). Stability analysis in plant breeding. Plant Breeding. 101: 1-23.
    5. Baker, R.J., (1988). Tests for crossover genotype-environmental interactions. Canadian Journal of Plant Science. 68:405-410.
    6. 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 J. Agric. Res., 49(1):39-45.
    7. Ceccarelli, S. and Grando. S., (2000). Barley landraces from the fertile crescent: a lesson for plant breeders. In: Brush S.B., (eds). Genes in the field: On farm conservation of crop diversity. IPGRI Rome, IDRC Ottawa, Lewis Boca Raton.
    8. Cornelius, P.L., Seyed sadr M.S. and Crossa. J. (1992). Using the shift ed multiplicative model to search for “reparability” in crop cultivar trials. Theoretical Applied Generics. 84:161-172.
    9. Crossa J., Cornelius P. L. and Yan., W. (2002). Biplots of linear-bilinear models for studying crossover genotype×environment interaction. Crop Sciences. 42: 619-633.
    10. Dehghani, H., Ebadi, A. and Yousefi. A., (2008). Biplot analysis of genotype by environment interaction for barley yield in Iran. Agronomy Journal. 98:388-393.
    11. De Lacy, I.H., Basford, K.E., Cooper M., Bull, J.K. and Mclaren. C.G., (1996). Analysis of multi-environment trail an historical perspective. In: [Cooper, M. and G.L. Hammer, (eds)]. Plant Adaptation and Crop Improvement. CAB International, Wallingford, UK 39-124. 
    12. Gauch, H.G., (1988) Model selection and validation for yield trials with interaction. Biometrics. 44: 705-715.
    13. Gauch, H.G. and Zobel. R.W., (1997). Identifying mega-environments and targeting genotypes. Crop Science. 37:311-326.
    14. Gauch, H.G., (2006). Statistical analysis of yield trials by AMMI and GGE. Crop Science. 46:1488-1500. 
    15. 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.
    16. GenStat Release., (2010). VSN International, Hemel Hempstead
    17. Huehn, M., (1996). Nonparametric analysis of genotype × environment interactions by ranks. Genotype by environment interaction. CRC Press, Boca Raton. 213-228.
    18. Karimzadeh, R., Asghari A., Chinipardaz R., Sofalian O., and Ghaffari. A.A., (2016). Determining Yield Stability and Model Selection by AMMI Method in Rain-fed durum Wheat Genotypes. Turkish Journal of Field Crops. 21:174-183.
    19. Mladenov, V., Banjac, B. and Milosevic. M., (2012). Evaluation of Yield and Seed Requirements Stability of Bread Wheat (Triticum aestivum L.) Via AMMI Model. Turkish Journal of Field Crops. 7:203-207.
    20. Mohammadi, M., Karimizadeh R., Noorinia A.A., Ghojogh H., Hosseinpour T., Khalilzadeh G.R., Mehraban A., Roustaii M., and Hasanpor Hosni. M., (2013). Analysis of yield stability in multi-environment trials of barley (Hordeum vulgar L.) genotypes using AMMI model. Current Opinion in Agriculture. 2:20-24.
    21. Mohammadi, R. and Mahmodi. K.N., (2008). Stability analysis of grain yield in barley (Hordeum Vulgare L.). International Journal of Plant Breeding. 2:74-78.
    22. Mortazavian, S.M.M., Nikkhah H.R., Hassani F.A., Sharif-al-Hosseini M., Taheri M., and Mahlooji. M., (2014). GGE biplot and AMMI analysis of yield performance of barley genotypes across different environments in Iran. Journal of Agricultural Science and Technology. 16:609-622.
    23. Rodriguez, M., Rau D., Papa R., and Attene. G., (2008). Genotype by environment Interactions in barley (Hordeum vulgare L.): Different responses of landraces, recombinant inbred lines and varieties to Mediterranean environments. Euphytica. 163:231-247.
    24. Romagosa I and Fox PN (1993) Genotype×environment interaction and adaptation. In: [Hayward, M.D., Bosemark, N.O. and Romagosa I., (eds.)] Plant Breeding, Principles and Prospects. Chapman and Hall, London, UK. 374-390.
    25. Sahay G. and Sarma. B.K., (2005). Yield stability of some groundnut (Arachis Hypogea) genotypes in Meghalaya. Indian J. Agric. Res., 39(3):22-224.
    26. Sarkar, B., Sharma R.C., Verma R.P.S., Sarkar A., and Sharma. I., (2014). Identifying superior feed barley genotypes using GGE biplot for diverse environments in India. Indian Journal of Genetics. 74:26-33.
    27. SAS INSTITUTE .(1996). SAS/STAT user’s guide. v. 6, 4th ed. SAS Inst., Cary, NC.
    28. Vir, O. and Sultan. S.M., (2012). Identification of genotypes of wheat (Triticum aestivum L.) for specific adaptation using qualitative and quantitative genotype × environment interaction and regression analysis in the rainfed condition of Hymalyas . Indian J. Agric. Res., 46(2):178-83.
    29. Voltas, J., Van Eeuwijk F., Sombrero A., Lafarga A., Igartua E., and Romagosa. I., (1999). Integrating statistical and ecophysiological analyses of genotype by environment interaction for grain filling of barley.I. individual grain weight. Field Crops Research. 62:63-74.
    30. Yan, W. and Hunt. L.A., (1998). Genotype by environment interaction and crop yield. Plant Breeding Review. 16:135-178.
    31. Yan, W., (2001). GGE biplot–A windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal, 93:1111-1118. 
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