Introduction: Wheat's economic significance and contribution to human and animal nutrition are indisputable. This makes it the third most important crop in terms of global production. The rising global demand for wheat is due to its ability to produce specialized foods. In particular, the unique properties of the gluten protein allow wheat to be processed into bread. Wheat contains and consists of numerous healthful components. Therefore, plant breeders should be able to select for both increased crop yield and improved health benefits. Wheat landrace genotypes are more genetically diverse than the majority of breeding programs, and this diversity includes adaptation to a variety of local conditions. Wheat breeders face the challenge of maximizing genetic productivity gains while minimizing yield gaps and ensuring environmental sustainability.
Wheat's efficiency and utility in plant breeding programs are determined by its genetic diversity. Improving grain yield is regarded as the most important objective of wheat breeding and the most efficient method of increasing production. The estimation of genetic variation in crops is indispensable for breeding programs and the conservation of genetic resources. Hybridization and subsequent selection is one of the most essential wheat breeding techniques. Selecting the parents is the first step in a hybridization-based plant breeding program. The purpose of this research is to identify wheat genotypes with superior agronomic traits, classify them using cluster analysis, and reduce the measured traits using principal component analysis.
Materials and Methods: To evaluation of genetic diversity of rain-fed wheat genotypes, an experiment was carried out in a randomized complete block design with 24 genotypes and four replications in Research Station of Dryland Agricultural Research Institute (Maragheh) at 2015-2016. This study evaluated plant height, grain filling period, days to physiological maturity, days to spike emergence, vigor, grain yield, straw yield, number of spikes per m², weight per 1000 grains, number of grains per spike, spike weight, number of spikelet per spike, spike length, biological yield, and harvest index. Before conducting an analysis of variance, assumptions were examined. Analysis of variance and comparison of means (Least Significant Difference) was performed. The relationship between the studied traits was determined using Pearson's coefficient of correlation. Principal component analysis (PCA) was utilized to reduce the data, and cluster analysis based on the Euclidean distance coefficient and Ward's algorithm was employed to classify the genotypes under study. The SPSS software was utilized for data analysis.
Results and Discussion: Difference between rain-fed wheat genotypes were significant for the majority of traits, indicating a high degree of genetic diversity. The genotypes 1 and 23 have the highest and lowest grain yield values, respectively. Positive and significant correlation exists between grain yield and vigor, straw yield, harvest index, number of spikelets per spike, spike weight, and number of grains per spike. Cluster analysis categorizes 24 genotypes into four groups based on their evaluated traits. The first cluster contains genotypes 22, 24, 1, 15, 7, 13, 3, 21 and 17. The second group included genotypes 2, 6, 20, 11, and 19. The third group consisted of the genotypes 9, 18, 14, 10, 8, and 16. The fourth group consists of extra genotypes. In principal components analysis, five main components account for 83.80% of the variation. High positive coefficients were observed for grain yield (0.702), harvest index (0.714), and vigor (0.797) in the first component. The initial component can be identified as the grain yield component.
Conclusion: Based on the results, the yield component was determined to be the first principal component. These genotypes are appropriate for selection and breeding programs and objectives in rain-fed environments, and can be used to boost wheat grain yield.