| Autor: |
D S Raj, Supritha, Patil, Rajesh S., Patil, Bhuvaneshwara R., Nayak, Spurthi N., Pawar, Kasu N. |
| Předmět: |
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| Zdroj: |
Journal of Cotton Research; 8/1/2024, Vol. 7 Issue 1, p1-13, 13p |
| Abstrakt: |
Background: Globally, the cultivation of cotton is constrained by its tendency for extended periods of growth. Early maturity plays a potential role in rainfed-based multiple cropping system especially in the current era of climate change. In the current study, a set of 20 diverse Gossypium hirsutum genotypes were evaluated in two crop seasons with three planting densities and assessed for 11 morphological traits related to early maturity. The study aimed to identify genotype(s) that mature rapidly and accomplish well under diverse environmental conditions based on the two robust multivariate techniques called multi-trait stability index (MTSI) and multi-trait genotype-ideotype distance index (MGIDI). Results: MTSI analysis revealed that out of the 20 genotypes, three genotypes, viz., NNDC-30, A-2, and S-32 accomplished well in terms of early maturity traits in two seasons. Furthermore, three genotypes were selected using MGIDI method for each planting densities with a selection intensity of 15%. The strengths and weaknesses of the genotypes selected based on MGIDI method highlighted that the breeders could focus on developing early-maturing genotypes with specific traits such as days to first flower and boll opening. The selected genotypes exhibited positive genetic gains for traits related to earliness and a successful harvest during the first and second pickings. However, there were negative gains for traits related to flowering and boll opening. Conclusion: The study identified three genotypes exhibiting early maturity and accomplished well under different planting densities. The multivariate methods (MTSI and MGIDI) serve as novel approaches for selecting desired genotypes in plant breeding programs, especially across various growing environments. These methods offer exclusive benefits and can easily construe and minimize multicollinearity issues. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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