Document Type : Research Paper
Authors
1 PhD graduate
2 zanjan university
3 Dryland Agricultural Research Institute, Agricultural Research, Education and Extension Organization ،(AREEO), Maragheh, Iran.
Abstract
Introduction: Wheat is one of the most strategic staple crops globally, including Iran. Enhancing the production of this crop, considering its genetic potential and environment responses, plays a vital role in ensuring global food security. Understanding genetic diversity and population structure is essential for crop improvement, enabling the effective utilization of genetic resources to develop cultivars with high and stable yields and resistance to environmental stresses. Multivariate statistical methods are widely employed by plant breeders to estimate genetic diversity, as they allow for the simultaneous evaluation of multiple traits. The present study aimed to assess the the genetic diversity of advanced wheat lines and cultivars introduced for cold climates in Iran. Specifically, it focused on evaluating traits related to the performance of bread wheat cultivars and lines under rainfed conditions, grouping the genotypes based on these traits, and comparing the resulting clusters.
Research methodology: The experiment was conducted in a randomized complete block design with three replications under full rainfed conditions. The plant materials included 12 advanced lines, the WAZ line, and 11 autumn rainfed wheat cultivars recommended for the cold rainfed regions of Iran, namely Sardari, Homa, Azar2, Takab, Ohadi, Rasd, Hashtroud, Baran, Sain, Sadra and Praw. The trial was carried out at the research farm of the Faculty Agriculture of Zanjan University during the 2016-2017 cropping season. Cluster analysis and principal component analysis (PCA) were used to explore the genetic diversity among the genotypes and classify them accordingly.
Research findings: The analysis of variance revealed significant genetic diversity among the genotypes for most of the eavaluated traits. The number of days to physiological maturity exhibited the lowest phenotypic and genotypic coefficient of variation, whereas the highest values were recorded for peduncle extrusion and the number of seeds per spike, respectively. Cluster analysis grouped the genotypes into three clusters. Genotypes in the third group were characterized by a higher number of seeds per spike, seed yield and a higher harvest index. PCA identified five principal components explaining 80% of the total phenotypic variation. Based on the multivariate analysis, lines 9, 3, 11, 2, and 7, along with the cultivars Azar 2 and Hashtroud, were identified as promising genotypes with greater adaptability to rainfed conditions and desirable performance traits.
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