Nagy Dániel, Meszlényi Tamás, Boda Krisztina, Lantos Csaba, Pauk János
Cereal Research Non-Profit Ltd., P.O. Box 391, H-6701 Szeged, Hungary.
Department of Medical Physics and Informatics, University of Szeged, Korányi fasor 9, 6720 Szeged, Hungary.
Plants (Basel). 2025 Aug 6;14(15):2435. doi: 10.3390/plants14152435.
Drought stress is a major environmental constraint that significantly reduces wheat productivity worldwide. In this study, seventeen wheat genotypes were evaluated under well-watered and drought-stressed conditions across two consecutive years (2023-2024) in a controlled greenhouse experiment. Twenty morphological and agronomic traits were recorded, and their responses to prolonged water limitation were assessed using multivariate statistical methods, including three-way ANOVA, principal component analysis (PCA), and cluster analysis. Drought stress significantly decreased all traits except the harvest index (HI), with the most severe reductions observed in traits related to secondary spikes (e.g., grain weight reduced by 95%). The ANOVA results confirmed significant genotype × treatment (G × T) interactions for key agronomic traits, with the strongest effect observed for total grain weight (F = 7064.30, < 0.001). A PCA reduced the 20 original variables to five principal components, explaining 87.2% of the total variance. These components reflected distinct trait groups associated with productivity, spike architecture, and development in phenology. Cluster analysis based on PCA scores grouped genotypes into three clusters with contrasting drought response profiles. A yield-based evaluation confirmed the cluster structure, distinguishing genotypes with a stable performance (average yield loss 58%) from highly sensitive ones (70% loss). Overall, the findings demonstrate that drought tolerance in wheat is governed by complex trait interactions. Integrating a trait-based multivariate analysis with a yield stability assessment enables the identification of genotypes with superior adaptation to water-limited environments, providing an excellent genotype background for future breeding efforts.
干旱胁迫是一种主要的环境限制因素,显著降低了全球小麦的产量。在本研究中,在可控温室实验中,连续两年(2023 - 2024年)对17个小麦基因型在充分浇水和干旱胁迫条件下进行了评估。记录了20个形态和农艺性状,并使用多变量统计方法评估了它们对长期水分限制的反应,包括三因素方差分析、主成分分析(PCA)和聚类分析。干旱胁迫显著降低了除收获指数(HI)之外的所有性状,与二级穗相关的性状降幅最大(例如,粒重降低了95%)。方差分析结果证实了关键农艺性状存在显著的基因型×处理(G×T)相互作用,总粒重的效应最强(F = 7064.30,< 0.001)。主成分分析将20个原始变量减少到5个主成分,解释了总方差的87.2%。这些成分反映了与生产力、穗结构和物候发育相关的不同性状组。基于主成分分析得分的聚类分析将基因型分为三个具有不同干旱反应特征的聚类。基于产量的评估证实了聚类结构,区分了表现稳定(平均产量损失约58%)和高度敏感(损失约70%)的基因型。总体而言,研究结果表明小麦的耐旱性受复杂的性状相互作用控制。将基于性状的多变量分析与产量稳定性评估相结合,能够识别出对水分有限环境具有卓越适应性的基因型,为未来的育种工作提供了优良的基因型背景。