Genetic Diversity and Yield Traits Analysis Using Multivariate Approach in Bread Wheat Varieties
The present study was carried out at Nawabganj Farm, CSAUAT, Kanpur during Rabi season 2023-24. In this study, 24 wheat genotypes were tested using the complete randomized block design in five random plants per genotype for 19 traits. The harvest index had the highest coefficient of variability, whi...
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| Published in: | Journal of advances in biology & biotechnology Vol. 28; no. 4; pp. 819 - 833 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
SD Publisher group
21.04.2025
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| Subjects: | |
| ISSN: | 2394-1081, 2394-1081 |
| Online Access: | Get full text |
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| Summary: | The present study was carried out at Nawabganj Farm, CSAUAT, Kanpur during Rabi season 2023-24. In this study, 24 wheat genotypes were tested using the complete randomized block design in five random plants per genotype for 19 traits. The harvest index had the highest coefficient of variability, while ear-bearing tillers per plant had the lowest. Results showed that PCV and GCV were high for Leaf area index and Flag leaf length. Additionally, all of the traits exhibit high heritability and some traits exhibit high genetic advance per cent of mean thus confirming additive gene action. Highly positive correlation was observed between grain yield per plant and days to 50% heading (0.50**), days to maturity (0.52**), ear weight (0.70**), spikelets per spike (0.73**), grains per spike (0.67**), biological yield per plant (0.73**) and harvest index (0.68**) indicating that the selection for these traits will increase yield. Eleven traits, including biological yield (0.7899), leaf area index (0.1094), harvest index (0.714), and grain width (0.0949) exerted positive direct effect on grain yield. The genotypes were grouped into five clusters using the Tocher's method and the distance between clusters IV and V was the highest, note genetic diversity high among these clusters, proof that hybridization should be productive to take advantage of heterosis and transgression segregation between these clusters. Principal component analysis (PCA) produced six principal components with eigenvalues > 1.0, which accounted for 83.3% of the total variation. PC1 contributed 28.7% of the variation with the highest eigenvalue (5.46). Traits associated with PC1 and PC2 are ideal candidates for selection. |
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| ISSN: | 2394-1081 2394-1081 |
| DOI: | 10.9734/jabb/2025/v28i42238 |