Genotype - Environment Interaction for Grain Yield In Wheat

N. Ali
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Abstract

Three field experiments were carried out at the Experimental Farm Station of the Faculty of Agriculture Moshtohor, Benha University, Kalubia Gavernorate, Egypt, during two successive winter seasons of 2017/2018 and 2018/2019 to investigate the effect of some folair application materials: [ascorbic acid (ASA) and potassium (K) on and genetic stability of wheat plants grown under different water stress levels. The treatments included the combination between three water treatments and 4 treatments of folair application spray with control. The treatments were arranged in split-split plot design with three replicates, the main plots were assigned to water stress levels, while five treatments of folair application spray were located in subplots and six varieties were arranged in sub-sub plot. Stability analysis of the 6 wheat genotypes was carried out for grain yield/plant across all studied environments. The effect of the interaction becomes more complex with the increase of number of factors with the same magnitude that have impact on genotype. Very often one prevalent environmental factor influences the genotype. In such cases linear regression models can comprise a good part of the sum of squares of the interaction and thus explain the stability of the genotype. With regard to AMMI analysis of grain yield/ m 2 , Results showed highly significant due to treatments, genotypes and environments this pointed out that all sources of variance are important in analysis, however genotypes contributed with (5.77%) in treatments variances, the environment contributed with (89.63%) in treatments variance also interaction principal component axis (IPCA) Pc1 and Pc2 accounted for (39.36% and 28.62%) respectively, were found to be highly significant, the (IPCA1 and IPCA2) together with had a total (67.98%) variances of the interaction. The genotype G5 is suitable to E3, E5, E11, E23 and E29. The genotype G6 is suitable to E12, E14 and E26. The polygon reflects that G2, G1 and G3 are high grain yielding and suitable to either of the environments. An important feature of the AMMI was also predicted. In mega-environment identification process, furthest genotypes are connected together to form a polygon, and perpendicular genotypes are drawn to form sectors which will make it easy to visualize the mega-environments.
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小麦籽粒产量的基因型-环境互作
在2017/2018和2018/2019两个冬季,在埃及Kalubia省Benha大学Moshtohor农业学院实验农场站进行了3项田间试验,研究了抗坏血酸(ASA)和钾(K)对不同水分胁迫水平下小麦植株遗传稳定性的影响。处理包括3个水处理和4个喷施与对照处理的组合。试验采用3个重复的分割小区设计,主小区按不同的水分胁迫水平进行处理,5个喷施叶叶酸的处理布置在小小区,6个品种布置在小小区。对6个小麦基因型在不同环境下的单株产量进行了稳定性分析。随着影响基因型的同等量级因子数量的增加,相互作用的影响变得更加复杂。通常一个普遍的环境因素会影响基因型。在这种情况下,线性回归模型可以包含相互作用平方和的很大一部分,从而解释基因型的稳定性。在籽粒产量/ m2的AMMI分析中,由于处理、基因型和环境的影响,结果均显示为极显著,说明所有方差来源在分析中都很重要,但基因型对处理方差的贡献(5.77%),环境对处理方差的贡献(89.63%),互作主成分轴(IPCA) Pc1和Pc2分别占(39.36%和28.62%),具有极显著性。IPCA1和IPCA2基因的相互作用总方差为67.98%。基因型G5适用于E3、E5、E11、E23和E29。基因型G6与E12、E14和E26最适宜。多边形反映G2、G1和G3是高产品种,适合于任何一种环境。还预测了AMMI的一个重要特征。在大环境识别过程中,将最远的基因型连接在一起形成一个多边形,并绘制垂直的基因型形成扇形,以便于大环境的可视化。
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