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Pattern analysis of genotype-by-environment interaction for grain yield in durum wheat

Published online by Cambridge University Press:  03 June 2009

Dryland Agricultural Research Institute (DARI), P. O. Box 67145-1164, Kermanshah, Iran
International Center for Agricultural Research in the Dry Areas (ICARDA), Aleppo, Syria
Dryland Agricultural Research Institute (DARI), P. O. Box 67145-1164, Kermanshah, Iran
Dryland Agricultural Research Institute (DARI), Maragheh, Iran
Center of Agricultural Research and Natural Resources, Ilam, Iran
Center of Agricultural Research and Natural Resources, Shiravan, North Khorasan, Iran
*To whom all correspondence should be addressed. Email: and


Pattern analysis, cluster and ordination techniques, was applied to grain yield data of 20 durum wheat genotypes grown in 19 diversified environments during 2005–07 to identify patterns of genotype (G), environment (E) and genotype-by-environment (G×E) interaction in durum multi-environment trials (METs). Main effects due to E, G and G×E interaction were highly significant, and 0·85 of the total sum of squares (SS) was accounted for by E. Of the remaining SS, the G×E interaction was almost 12 times the contribution of G alone. The knowledge of environmental and genotype classification helped to reveal several patterns of G×E interaction. This was verified by ordination analysis of the G×E interaction matrix. Grouping of environments, based on genotype performance, resulted in the separation of different types of environments. Pattern analysis confirmed the cold and warm mega-environments, and allowed the discrimination and characterization of adaptation of genotypes. However, several patterns of G×E interaction in Iran's regional durum yield trials were further discerned within these mega-environments. The warm environments tended to be closer to one another, suggesting that they discriminate among durum genotypes similarly, whereas cold environments tended to diverge more. The dwarf and early maturing breeding lines from ICARDA with low to medium yields and high contribution to G×E interaction were highly adapted to warm environments, whereas the tall and later maturing genotypes with low to high yields were highly adapted to the cold environments of Iran.

Crops and Soils
Copyright © Cambridge University Press 2009

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Abdalla, O. S., Crossa, J. J., Autrique, E. & DeLacy, I. H. (1996). Relationships among international testing sites of spring durum wheat. Crop Science 36, 3340.Google Scholar
Alagarswamy, G. & Chandra, S. (1998). Pattern analysis of international sorghum multi-environment trials for grain-yield adaptation. Theoretical and Applied Genetics 96, 397405.Google Scholar
Baker, R. J. (1988). Test for crossover genotype-environmental interactions. Canadian Journal of Plant Science 68, 405410.Google Scholar
Brennan, P. S. & Sheppard, J. A. (1985). Retrospective assessment of environments in the determination of an objective strategy for the evaluation of the relative yield of wheat cultivars. Euphytica 34, 397408.Google Scholar
Byth, D. E., Eisemann, R. L. & DeLacy, I. H. (1976). Two-way pattern analysis of a large data set to evaluate genotypic adaptation. Heredity 37, 215230.Google Scholar
dos, S.Dias, C. T. & Krzanowski, W. J. (2003). Model selection and cross validation in additive main effect and multiplicative interaction models. Crop Science 43, 865873.Google Scholar
Cooper, M. & DeLacy, I. H. (1994). Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments. Theoretical and Applied Genetics 88, 561572.Google Scholar
Cooper, M., Byth, D. E. & Woodruff, D. R. (1994). A preliminary investigation of the grain yield adaptation of advanced CIMMYT wheat lines to water stress environments in Queensland 2. Classification analysis. Australian Journal of Agricultural Research 45, 985–1002.Google Scholar
Cooper, M., DeLacy, I. H. & Basford, K. E. (1996 a). Relationships among analytical methods used to analyze genotypic adaptation in multi-environment trials. In Plant Adaptation and Crop Improvement (Eds Cooper, M. & Hammer, G. L.), pp. 191224. Wallingford, UK: CAB International.Google Scholar
Cooper, M., Brennan, P. S. & Sheppard, J. A. (1996 b). A strategy for yield improvement of wheat which accommodates large genotype-by-environment interactions. In Plant Adaptation and Crop Improvement (Eds Cooper, M. & Hammer, G. L.), pp. 487511. Wallingford, UK: CAB International.Google Scholar
DeLacy, I. H., Basford, K. E., Cooper, M., Bull, J. K. & McLaren, C. G. (1996 a). Analysis of multi-environment trials – an historical perspective. In Plant Adaptation and Crop Improvement (Eds Cooper, M. & Hammer, G. L.), pp. 39–124. Wallingford, UK: CAB International.Google Scholar
DeLacy, I. H., Cooper, M. & Basford, K. E. (1996 b). Relationships among analytical methods used to study genotype-by-environment interactions and evaluation of their impact on response to selection. In Genotype-by-Environment Interaction (Eds Kang, M. S. & Gauch, H. G.), pp. 5184. Boca Raton, FL : CRC Press.Google Scholar
DeLacy, I. H., Eisemann, R. L. & Cooper, M. (1990). The importance of genotype-by-environment interaction in regional variety trials. In Genotype-by-Environment Interaction and Plant Breeding (Ed. Kang, M. S.), pp. 287300. Baton Rouge, Louisiana: Louisiana State University.Google Scholar
Eberhart, S. A. & Russell, W. A. (1966). Stability parameters for comparing varieties. Crop Science 6, 3640.Google Scholar
Finlay, K. W. & Wilkinson, G. N. (1963). The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research 14, 742754.Google Scholar
Fox, P. N. & Rosielle, A. A. (1982). Reducing the influence of environmental main-effects on pattern analysis of plant-breeding environments. Euphytica 31, 645656.Google Scholar
Gabriel, K. R. (1971). The biplot-graphical display of matrices with application to principal component analysis. Biometrika 58, 453467.Google Scholar
Gauch, H. G. (1992). Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Amsterdam: Elsevier.Google Scholar
Gauch, H. G. & Zobel, R. W. (1997). Identifying mega-environments and targeting genotypes. Crop Science 37, 311326.Google Scholar
Haussmann, B. I. G., Hess, D. E., Reddy, B. V. S., Mukuru, S. Z., Kayentao, M., Welz, H. G. & Geiger, H. H. (2001). Pattern analysis of genotype×environment interaction for striga resistance and grain yield in African sorghum trials. Euphytica 122, 297308.Google Scholar
International Rice Research Institute (IRRI) (2005). IRRISTAT software 5.0 for Windows. Manila, Philippines: IRRI.Google Scholar
Kang, M. S. (1998). Using genotype-by-environment interaction for crop cultivar development. Advances in Agronomy 62, 199252.Google Scholar
Kempton, R. A. (1984). The use of bi-plots in interpreting variety-by-environment interactions. Journal of Agricultural Science, Cambridge 103, 123135.Google Scholar
Lillemo, M., van Ginkel, M., Trethowan, R. M., Hernandez, E. & Rajaram, S. (2004). Associations among international CIMMYT bread wheat yield testing locations in high rainfall areas and their implications for wheat breeding. Crop Science 44, 11631169.Google Scholar
Mohammadi, R. & Amri, A. (2008). Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159, 419432.Google Scholar
Mungomery, V. E., Shorter, R. & Byth, D. E. (1974). Genotype×environment interactions and environmental adaptation. I. Pattern analysis-application to soya bean populations. Australian Journal of Agricultural Research 25, 5972.Google Scholar
Nachit, M. M., Baum, M., Poreciddu, E., Monneveux, P. & Picard, E. (1998). Proceedings of the SEWANA Durum Network Workshop, 20–23 March 1995. Aleppo, Syria: ICARDA.Google Scholar
Perkins, J. M. & Jinks, J. L. (1968). Environmental and genotype-environmental components of variability. III. Multiple lines and crosses. Heredity 23, 339356.Google Scholar
Rattunde, H. F. W., Obilana, A. B., Haussmann, B. I. G., Reddy, B. V. S. & Hess, D. E. (2000). Breeding sorghum for striga resistance at ICRISAT: progress and perspectives. In Breeding for Striga Resistance in Cereals: Proceedings of a Workshop, IITA, Ibadan, Nigeria, 18–20 August 1999 (Eds Haussmann, B. I. G., Hess, D. E., Koyama, M. L., Grivet, L., Rattunde, H. F. W. & Geiger, H. H.), pp. 8593. Weikersheim, Germany: Margraf Verlag.Google Scholar
Redden, R. J., DeLacy, I. H., Butler, D. G. & Usher, T. (2000). Analysis of line by environment interactions for yield in navy beans. 2. Pattern analysis of lines and environment within years. Australian Journal of Agricultural Research 51, 607617.Google Scholar
Trethowan, R. M., Crossa, J., van Ginkel, M. & Rajaram, S. (2001). Relationships among bread wheat international yield testing locations in dry areas. Crop Science 41, 14611469.Google Scholar
Trethowan, R. M., van Ginkel, M., Ammar, K., Crossa, J., Cukadar, B., Rajaram, S. & Hernandez, E. (2003). Relationships among twenty years of high yielding international bread wheat yield evaluation environments. Crop Science 43, 16981711.Google Scholar
Trethowan, R. M., van Ginkel, M. & Rajaram, S. (2002). Progress in breeding wheat for yield and adaptation in global drought affected environments. Crop Science 42, 14411446.Google Scholar
Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58, 236244.Google Scholar
Williams, W. T. (1976). Pattern Analysis in Agricultural Science. Amsterdam: Elsevier Scientific Publishing Company.Google Scholar
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, 643653.Google Scholar
Zhang, Y., He, Z., Zhang, A., van Ginkel, M. & Ye, G. (2006). Pattern analysis on grain yield performance of Chinese and CIMMYT spring wheat cultivars sown in China and CIMMYT. Euphytica 147, 409420.Google Scholar