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Exploring Morpho-Physiological Relationships among Drought Resistance Related Traits in Wheat Genotypes Using Multivariate Techniques

Saed-Moucheshi, Armin, Hasheminasab, Hojat, Khaledian, Zahed, Pessarakli, Mohammad
Journal of plant nutrition 2015 v.38 no.13 pp. 2077-2095
Triticum aestivum, Triticum turgidum, canopy, cell membranes, cluster analysis, drought tolerance, durum wheat, electrolytes, factor analysis, field experimentation, genotype, reactive oxygen species, temperature
Eighteen bread wheat (Triticum aestivum L.) and two durum wheat genotypes (Triticum turgidum L.) were used to explore relationship among their morpho-physiological traits. Two separate field experiments were conducted at the Experimental Station, College of Agricultural, Shiraz University. Each experiment was designed as a randomized completed block with three replications. Twenty-five morpho-physiological drought resistance related traits were measured and multivariate statistical techniques were used to consider these relationships. Based on the results of the factor analysis, electrolyte leakage (EL) had the highest positive correlation, while membrane stability index (MSI) had the highest negative correlation with the first factor, canopy temperature depression (CTD). It could be clearly detected that these two variables had high effect on canopy temperature and the genotype with lower EL and higher MSI resulted in lower canopy temperature. This factor also showed that reactive oxygen species (ROS) related traits and plant water relationship related traits had significant effect on CTD. Since yield stability (YS) had the highest coefficient in factor 3, this factor was the most important one showing effective variables on this trait, thus this factor's suggested name was YS. The significant positive correlations with factor 3 were relative water protected (RWP), which is a new index, and cellular membrane stability (CMS). This result clearly shows that new index of RWP can be an efficient indirect criterion to screen higher YS genotypes together with CMS. Cluster analysis grouped variables into three clusters. These results were confirmed by the results of principal and discriminate function analysis. In addition, the results of the factor analysis for considering relationships among measured traits were confirmed through the cluster analysis.