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Clustering Durum Wheat Genotypes in Multi-Environmental Trials of Rain-Fed Conditions

机译:雨养条件多环境试验中聚类硬粒小麦基因型

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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.
机译:对于在多环境试验(MET)中进行的硬粒小麦基因型评估,测得的种子产量是基因型(G),环境(E)和基因型受环境GE相互作用影响的综合结果。如果通过聚类分析将数据分层为同质子集,则可以确定GE交互结构。进行了一项综合分析,以评估在14个环境中20个硬粒小麦基因型的GE相互作用。对E,G和GE相互作用的方差的综合分析非常重要,表明在不同环境中基因型的差异反应。使用了四种聚类方法,根据回归模型或ANOVA模型,它们的相异指数不同。根据回归方法的树状图,基于G(截距)和GE(线斜率)源有10个不同的基因型组,基于GE(线斜率)源有3个不同的基因型组。此外,ANOVA方法的树状图显示了基于G和GE来源的11个不同基因型组和基于GE来源的13个不同的基因型组。上述基因型组通过F检验确定为聚类的经验终止标准。由于回归的确定系数值较高,介于92.6至99.4之间,因此使用基于线性回归的聚类更为实用。基于线性回归参数或ANOVA模型相似性的基因型聚类表明硬粒小麦基因型之间存在相当大的差异,并且对环境变化的响应互不相同。根据中东或世界其他地区的回归或ANOVA模型,将来可以定期将这种结果应用于硬粒小麦基因型和其他农作物。

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