Genetic diversity studies in bread wheat (Triticum aestivum L.) genotypes under timely sown irrigated condition

Introduction

Wheat (Triticum aestivum L.) is a hexaploid cereal crop (2n = 6x = 42; AABBDD genomes), annual in nature, self-pollinated, and primarily grown as a cool-season rabi crop. It belongs to the tribe Triticeae under the family Poaceae. The complex genetic structure of wheat has endowed it with wide adaptability and substantial genetic diversity, enabling tolerance to diverse abiotic and biotic stresses arising from environmental fluctuations. Genetic diversity plays a crucial role in understanding patterns of variation within and among populations, which is essential for yield improvement and stress management. Estimating the extent and pattern of genetic divergence provides valuable insights for breeding and selection of desirable traits (Maniee et al., 2009) [5] . Genetic divergence analysis is particularly important in breeding programs aimed at developing high-yielding, timely sown varieties that are resilient and perform consistently across environments. Crop improvement fundamentally relies on the availability of genetic diversity in plant genetic resources, which provides opportunities to develop new and improved cultivars with desirable attributes such as higher yield potential, larger seed size, and resistance to pests and diseases. The presence of variability within wheat genotypes allows breeders to identify superior lines, either for direct release as new varieties or as potential parents in hybridization programs. Genetic diversity between parents is essential to exploit heterosis and generate transgressive segregants. Furthermore, it enables breeders to develop varieties tailored for specific traits, including quality enhancement and tolerance to both biotic and abiotic stresses.

Materials and Methods

The present investigation was carried out during rabi season of 2024, at BTC college of Agriculture and Research Station, Bilaspur. Twenty six wheat genotypes with four checks viz, GW322 (C), HI8737(d) (C), HI8713(d) (C) and MACS6768 (C) were used. Experimental material were planted in randomized block design (RBD) with four replications. Each genotype was grown in a plot size of 8 m 2 for each entry in each replication. The observations were recorded on five randomly selected plants from each plot in all the four replications of all the twelve characters viz, days to heading, days to maturity, plant height (cm), number of effective tillers/plant, spike length (cm), spike weight (g), total number of spikelet's/spike, peduncle length (cm), biological yield/plot (kg), grain yield/plot (kg), harvest index (%) and 1000 grains weight (g). Genetic divergence was estimated by Mahalanobis (1928) [6] , clustering was done by clustering was done by using Tocher method. Percent contribution towards total divergence was calculated by Mahalanobis (1928) [6] .

Results and Discussion

Genetic divergence analysis

Genetic divergence analysis of 26 wheat genotypes were grouped in 6 clusters. cluster Ⅴ was the largest cluster (8 genotypes) followed by cluster I (6 genotypes), cluster IV (5 genotype), cluster VI (3 genotype), cluster II and III had 2 genotypes. Cluster IV had the maximum intra-cluster distance, followed by cluster III. Cluster IV and VI had maximum inter cluster distances. This revealed that using parents from these clusters in a hybridization programme will result in a higher frequency of better segregants, which is important for the production of desired varieties. Similar results were observed by Lakra et al., (2020) [4] , Hazra et al.

(2019) [1] , Singh et al., (2018) [8] and Yadav et al., (2014) [9] .

Contribution of Traits to Divergence

The contribution identifies the traits driving genetic divergence. For each pair of treatments, the trait contributing most to their D² distance was found. 'Times Ranked 1st' is a count of how many times each trait was the top contributor. 'Contribution (%)' in this count as a percentage of total pair wise comparisons. The trait contributing most to divergence was greatest for 1000 grains weight(g), grain yield/plot (kg) and biological yield/plot (kg). Similar results were observed by Jagdev (1993) [2] for 1000 grains weight (g) and grain yield/plot (kg), Nimbalkar et al., (2002) [7] for 1000 grains weight (g) and Kumar et al., (2017) [3] for grain yield/plot (kg). The charts below summarize these contributions. For clarity, traits with a contribution below 1% have been excluded. The pie chart shows the proportional share, while the bar chart allows for easier comparison of magnitudes.

Conclusion

Genetic divergence analysis of 26 wheat genotypes classified them into six distinct clusters, with Cluster V being the largest, comprising eight genotypes. The clustering pattern suggests that selecting parents from divergent clusters in hybridization programs would increase the likelihood of generating superior segregants, thereby facilitating the development of desirable varieties. Trait contribution analysis further highlighted the characters driving genetic divergence. For each pair of genotypes, the trait contributing most to the Mahalanobis D² distance was identified. The parameter 'Times Ranked 1st' denoted how often a particular trait ranked as the highest contributor, while 'Contribution (%)' represented its relative importance across all pairwise comparisons. The traits contributing most substantially to overall divergence were 1000-grain weight (g), grain yield per plot (kg), and biological yield per plot (kg), indicating their pivotal role in differentiating the wheat genotypes.

Acknowledgement

Conflict of StudyThe authors declare that there is no conflict of interest associated with this study or its publication.

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