Multivariate Analysis Techniques and Tolerance Indices for Detecting Bread Wheat Genotypes of Drought Tolerance

Diversity Pub Date : 2024-08-10 DOI:10.3390/d16080489
Ibrahim Al-Ashkar
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Abstract

Drought stress is one of the biggest hardships in wheat cultivation because of the strong negative relationship between water deficit and crop yields owing to a lower grain weight, a shorter grain-filling period, a slower grain-filling rate, and reduced grain quality. Genotype–environment interaction (GEN:ENV) generates hardships in selecting wheat genotypes and ideotypes due to biased genetic estimates. Diverse strategies have been proposed to respond to the urgent need for concurrent improvements in yield performance and stability. This study’s purpose was to appraise genetic variation and GEN:ENV effects on yield and yield components to discover drought-stress-tolerant genotypes and ideotypes. This study evaluated 20 genotypes in three consecutive seasons under non-stressful and drought-stress conditions in a total of six ENVs. The broad-sense heritability ranged from 0.54 to 0.82 based on expected mean squares and ranged from 0.60 to 0.90 based on plot mean, but in the other three ways, it was usually greater than 0.90. The high values of (σgen:env2) revealed the effect that broad-sense heritability has on the expression of traits. G01, G03, G06, G07, G08, G10, G12, G13, G16, G17, and G18 were stable genotypes for grain yield (GY), according to additive main effects and a multiplicative interaction biplot for the six ENVs. Based on scores in the weighted average of absolute scores biplot (WAASB), G02, G04, G05, G08, G10, and G18 were selected as stable and high-performance for GY, and they were all selected as the best genotype groups using the WAASB-GY superiority index. From the results obtained from principal component analysis and hierarchical clustering and from the tolerance discrimination indices, G02, G04, G05, G18, and G19 are genotypes that produce a suitable yield under non-stressful and drought-stress conditions. In essence, combining approaches that take into consideration stability and high performance can contribute significantly to enhancing the reliability of recommendations for novel wheat genotypes.
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检测面包小麦耐旱基因型的多元分析技术和耐旱指数
干旱胁迫是小麦种植中最大的困难之一,因为缺水与作物产量之间存在强烈的负相关关系,其原因是谷物重量降低、谷物灌浆期缩短、谷物灌浆速度减慢以及谷物品质下降。基因型与环境的交互作用(GEN:ENV)会导致遗传估计偏差,给小麦基因型和表意型的选择带来困难。为了满足同时提高产量性能和稳定性的迫切需要,人们提出了多种策略。本研究的目的是评估遗传变异和 GEN:ENV 对产量和产量成分的影响,以发现抗旱基因型和表意型。本研究在非胁迫和胁迫条件下连续三季对 20 个基因型进行了评估,共涉及 6 个 ENV。基于期望均方差的广义遗传力在 0.54 至 0.82 之间,基于小区均方差的广义遗传力在 0.60 至 0.90 之间,但在其他三种方法中,广义遗传力通常大于 0.90。(σgen:env2)的高值揭示了广义遗传力对性状表达的影响。根据六个 ENV 的加法主效应和乘法互作双图,G01、G03、G06、G07、G08、G10、G12、G13、G16、G17 和 G18 是谷物产量(GY)的稳定基因型。根据绝对得分加权平均双图(WAASB)的得分,G02、G04、G05、G08、G10 和 G18 被选为谷物产量稳定的高性能基因型,它们都被选为 WAASB-GY 优越性指数的最佳基因型组。从主成分分析和分层聚类的结果以及耐受性鉴别指数来看,G02、G04、G05、G18 和 G19 是在非胁迫和干旱胁迫条件下都能产生适宜产量的基因型。从本质上讲,将考虑稳定性和高性能的方法结合起来,可大大有助于提高小麦新基因型推荐的可靠性。
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