Clustering durum wheat genotypes in multi-environmental trials of rain-fed conditions
Naser Sabaghnia
sabaghnia@maragheh.ac.irDepartment of Agronomy and Plant Breeding, Faculty of Agriculture, University of Maragheh, Maragheh, Iran (Iran, Islamic Republic of)
Mohtasham Mohammadi
Dryland Agricultural Research Institute (DARI), Gachsaran, Iran (Iran, Islamic Republic of)
Rahmatollah Karimizadeh
Dryland Agricultural Research Institute (DARI), Gachsaran, Iran. (Iran, Islamic Republic of)
Abstract
For durum wheat genotypes evaluation in multi-environmental trials (MET), measured seed yield is the combined result of effects of genotype (G), environment (E) and genotype by environment GE interaction. The GE interaction structure can be identified if the data are stratified into homogeneous subsets through cluster analysis. A combined analysis to assess GE interactions of 20 durum wheat genotypes across 14 environments was undertaken. The combined analysis of variance for E, G and GE interaction was significant, suggesting differential responses of the genotypes in various environments. Four cluster methods, which differ in the dissimilarity indices depending on the regression model or ANOVA model, were used. According to dendograms of regression methods there were 10 different genotypic groups based on G (intercept) and GE (line slope) sources and 3 different genotypic groups based on GE (line slope) sources. Also, the dendograms of ANOVA methods indicated 11 different genotypic groups based on G and GE sources and 13 different genotypic groups based on GE sources. The above mentioned genotypic groups were determined via F-test as an empirical stopping criterion for clustering. Due to the high values of regression’s determination coefficient which ranged from 92.6 to 99.4, using of the linear regression-based clustering was more practical. The genotypes clustering based on similarity of linear regression parameters or ANOVA model indicated that there were considerable variations among durum wheat genotypes and there are different with each other in response to environmental changes. Such an outcome could be regularly applied in the future to clattering durum wheat genotypes and other crops based on regression or ANOVA models in the Middle East and other areas of the world .
Keywords:
GE interaction, genotypes grouping, Triticum durum, seed yieldReferences
Abou-El-Fittouh, H.A., Rawlings, J.O., Miller, P.A., 1969. Classification of environments to control genotype by environment interactions with an application to cotton. Crop Sci. 9, 135–140.
Google Scholar
Allard, R.W., Bradshaw, A.D., 1964. Implications of genotype-environment interactions in applied plant breeding. Crop Sci. 4, 503–508.
Google Scholar
Baril, C.P., Denis, J.B., Brabant, P., 1994. Selection of environments using simultaneous clustering based on genotype × environment interaction. Can. J. Plant Sci. 74, 311–317.
Google Scholar
Becker, H.C., 1981. Correlations among some statistical measures of phenotypic stability. Euphytica 30, 835–840.
Google Scholar
Becker, H.C., Leon, J., 1988. Stability analysis in plant breeding. Plant Breeding 101, 1–23.
Google Scholar
Bertero, H.D., de la Vega, A.J., Correa, G., Jacobsen, S.E., Mujic, A., 2004. Genotype and genotype-by- environment interaction effects for grain yield and grain size of quinoa (Chenopodium quinoa Willd.) as revealed by pattern analysis of international multi-environment trials. Field Crops Res. 89, 299–318.
Google Scholar
Brandle, J.E., Brule-Bable, A.L., 1991. An integrated approach to oilseed rape cultivar selection using phenotypic stability. Theor. Appl. Genet. 81, 679-684.
Google Scholar
Calinski, T., Corsten, L.C.A., 1985. Clustering means in ANOVA by simultaneously testing, Biometrics 41, 39–4
Google Scholar
Cooper, M., Rajatasereekul, S., Immark, S., Fukai, S., Basnayake, J., 1999. Rainfed lowland rice breeding strategies for Northeast Thailand. I. Genotypic variation and genotype × environment interactions for grain yield. Field Crops Res. 64, 131–151.
Google Scholar
Corsten, L.C.A., Denis, J.B., 1990. Structuring interaction in two way tables by clustering. Biometrics 46, 207–215.
Google Scholar
Edwards, A.W., Cavalli-Sforza, L.L., 1965. A method for cluster analysis. Biometrics 21, 362–375.
Google Scholar
Finlay, K.W., Wilkinson, G.N., 1963. The analysis of adaptation in a plant breeding programme. Aust. J.Agric. Res. 14, 742–754.
Google Scholar
Francis, T.R., Kannenberg, L.W., 1978. Yield stability studies in short-season maize: I. A descriptive method for grouping genotypes. Can. J. Plant Sci. 58, 1029–1034.
Google Scholar
Hill, J., 1975. Genotype–environment interaction, a challenge for plant breeding. J. Agric. Sci. 85, 477–493.
Google Scholar
Huehn, M, Leon, J., 1985. Phenotypic yield stability depending on plant density and on mean yield per plant of winter rapeseed varieties and of their F1 and F2-generations. J. Agron. Crop Sci. 162,172–179.
Google Scholar
Kang, M.S., 1998. Using genotype-by-environment interaction for crop cultivar development. Adv. Agron. 62, 199–252.
Google Scholar
Karimizadeh, R., Dehghani, H., Dehghanpour, Z., 2006. Using Cluster Analysis for Stability of Maize Hybrids 2, 2006; J. Crop Produc. Process.10, 337–348.
Google Scholar
Lin, C.S., 1982. Grouping genotypes by a cluster method directly related to genotype–environment interaction mean square. Theor. Appl. Genet. 62, 277–280.
Google Scholar
Lin, C.S., Binns, M.R., 1991. Genetic properties of four types of stability parameters. Theor. Appl. Genet. 82,505–509.
Google Scholar
Lin, C.S., Binns, M.R., Lefkovitch, L.P., 1986. Stability analysis: where do we stand?. Crop Sci. 26, 894–900.
Google Scholar
Lin, C.S., Butler, G., 1990. Cluster analyses for analyzing two-way classification data. Agron. J. 82, 344–348.
Google Scholar
Lin, C.S., Butler, G., Hall, I., Nault, C., 1992. Program for investigating genotype-environment interaction. Agron. J. 84, 121–124.
Google Scholar
Lin, C.S., Thompson, B., 1975. An empirical method of grouping genotypes based on a linear function of the genotype environment interaction. Heredity 34, 255–263.
Google Scholar
Lin, C.S., Williams, C.J., Binns, M.R., 1984. Investigation of interchromosomal interaction among three major chromosomes of Drosophiln melangaster in response to environments and the relationship between multi-line and two-line analyses: Reexamination of Caligari and Mather data. Heredity 52, 403–
Google Scholar
Lin, C.Y. Lin, C.S., 1994. Investigation of genotype-environment interaction by cluster analysis in animal experiments. Can. J. Animal Sci. 74, 607–612.
Google Scholar
Mandel, J., 1961. Non-additivity in two-way analysis of variance. J. Am. Statist. Ass. 56, 878–888.
Google Scholar
Mohebodini, M., Dehghani, H., Sabaghpour, S.H., 2006. Stability of performance in lentil (Lens culinaris Medik) genotypes in Iran. Euphytica 149, 343–352.
Google Scholar
Mungomery, V.E., Shorter, R., Byth, D.E., 1974. Genotype environment interactions and environmental adaptation. 1 Pattern analysis - application to soya bean populations. Aust. J. Agric. Res. 25, 59–72.
Google Scholar
Perkins, J.M., 1972. The principal component analysis of genotype-environmental interactions and physical measures of the environment. Heredity 29, 51–70.
Google Scholar
Pinthus, J.M., 1973. Estimate of genotype value: a proposed method. Euphytica 22, 121–123.
Google Scholar
Robert, N., 1997. Structuring genotype × environment interaction for quality traits in bread wheat, in two multi-location series of trials Euphytica 97, 53–66.
Google Scholar
Sabaghnia, N., Dehghani, H., Sabaghpour, S.H., 2008. Graphic analysis of genotype × environment interac- tion of lentil yield in Iran. Agron. J. 100, 760–764.
Google Scholar
SAS Institute., 1996. SAS/STAT user’s guide. v. 6, 4th ed. SAS Inst., Cary, NC.
Google Scholar
Shukla, G.K., 1972. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29, 237–245.
Google Scholar
Signor, C.E., Dousse, S., Lorgeous, J., Denis, J.B., Bonhomme, R., Carolo, P., Charcosset, A., 2001. Interpre- tation of genotype × environment interactions for early maize hybrids over 12 years. Crop Sci. 41, 663–669.
Google Scholar
Wricke, G., 1962. Über eine methode zur erfassung der ökologischen streubreite in feldversuchen. Z. Pflanzenzüchtung 47, 92–96.
Google Scholar
Yan, W., Kang, M.S., 2003. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists.CRC Press. Boca Raton, FL.
Google Scholar
Yates, F., Cochran, W.G., 1938. The analysis of groups of experiments. J. Agr. Sci. 28, 556–580.
Google Scholar
Yau, S.K., 1995. Regression and AMMI analyses of genotype × environment interactions: An empirical comparison. Agron. J. 87, 121–126.
Google Scholar
Authors
Naser Sabaghniasabaghnia@maragheh.ac.ir
Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Maragheh, Maragheh, Iran Iran, Islamic Republic of
Authors
Mohtasham MohammadiDryland Agricultural Research Institute (DARI), Gachsaran, Iran Iran, Islamic Republic of
Authors
Rahmatollah KarimizadehDryland Agricultural Research Institute (DARI), Gachsaran, Iran. Iran, Islamic Republic of
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