Genotype x environment interaction analysis in Wheat (Triticum aestivum L.)

D. Rawal, A. Tomar, Mahak Singh
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Abstract

The results of genotype x environment interaction analysis revealed highly significant mean squares due to genotypes as well as environments for all the characters which indicated presence of substantial differences among the genotypes as well as environments for all the eleven characters. The environment linear (E-L) component was also significant for all the characters indicating that the six environments can be graded linearly for their differences in influencing the expression of characters of wheat genotypes. The mean squares due to genotype x environment interaction were also highly significant for all the characters except days to heading, plant height, effective tillers per plant and total number of tillers per plant to suggest important role of g x e interaction in expression of most of the characters in wheat. The linear component of g x e interaction was significant for all the characters which indicated good possibility of predicting linear responses of genotypes for all the characters under study. Thus, there would be ample possibility of discriminating the genotypes for above average, below average and average linear responses to predict their performances in changing environments under study. The significance of non-linear component (pooled deviation) of g x e interactions for all characters except ear length suggested that considerable number of genotypes may exhibit unpredictable and unstable mean performance for different characters across environments even defying their prediction on the basis of linear sensitivity coefficient.
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小麦基因型与环境互作分析
基因型x环境互作分析结果显示,所有性状的基因型和环境均方差均显著,表明11个性状的基因型和环境均存在显著差异。环境线性(E-L)分量对所有性状的影响也显著,表明6种环境对小麦基因型性状表达的影响差异可以线性分级。除抽穗日、株高、单株有效分蘖数和单株总分蘖数外,基因型x环境互作对其他性状的均方差均极显著,说明基因型x环境互作对小麦大部分性状的表达有重要影响。g × e互作的线性分量在所有性状中均显著,表明预测所有性状基因型线性反应的可能性较大。因此,有足够的可能性区分高于平均水平、低于平均水平和平均线性响应的基因型,以预测它们在研究变化的环境中的表现。除穗长外,g × e相互作用非线性分量(汇总偏差)的显著性表明,相当数量的基因型在不同环境下可能表现出不可预测和不稳定的平均表现,甚至与基于线性敏感系数的预测相背离。
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