Mehdi Kakaei - Department of Agriculture (Plant Breeding and Genetics), Payame Noor University, Tehran, Iran
Seyed Iman Mirmazloum - Department of Plant Physiology and Plant Ecology, Institute of Agronomy, Hungarian University of Agriculture and Life Sciences (MATE), Budapest, Hungary

Postanthesis drought stress in wheat cultivation typically occurs in semiarid regions due to limited irrigation water availability and yield loss during cereal production. The present study was designed and implemented to investigate the agronomical traits of bread wheat varieties in response to post-flowering drought stress in a randomized complete block design in Hamedan Province (Asadabad), Iran. The results of the trait variance analysis under both stress and control conditions indicated that there were significant differences (p≤۰.۰۵) among the cultivars in terms of most studied traits. The cluster analysis classified the studied varieties into three groups, and the correctness of the groupings was confirmed by the analysis of the discriminant function. According to the results of the principal component analysis, four components explained ۷۹.۶ and ۷۹% of the variance in the total data under the control and stress conditions, respectively. Factor analysis based on principal component analysis of the control plants revealed that three factors accounted for ۶۴.۶۸% of the total changes, including the first factor (yield), the second factor (characteristics related to height) and the third factor (harvest index), whereas the same analysis of the stressed plants at the end of the season indicated that three factors were behind ۷۰.۳۶۱% of the changes (yield and greenness, traits related to height, and plant moisture content). Based on all statistical analyses, (both univariate and multivariate of ۱۴ studied genotypes), three winter-type varieties (Pishgam, Zare, and Mehan) were found to have significantly better yield under the drought stress conditions.

Triticum aestivum, Postanthesis drought stress, Discriminant function, Principal component analysis, Factor Analysis