Estimation of Association among the Yield and Yield Attributing Traits in the Segregating Population of Rice

Ekka, Jenny P. and Kumari, Priyanka and Swapnil, . and Prasad, Krishna and Ahmad, Ekhlaque and Chakarbarty, Manigopa (2023) Estimation of Association among the Yield and Yield Attributing Traits in the Segregating Population of Rice. International Journal of Plant & Soil Science, 35 (19). pp. 1268-1273. ISSN 2320-7035

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Abstract

Indirect selection, using correlation coefficients, linear regression coefficients and path analysis, uses a variety of yield determinants that are more heritable than yield itself and less influenced by environmental factors and therefore are stable. Therefore, indirect selection based on the characters makes the selection process more efficient, especially in the early generations. Correlation coefficient analysis and the use of path coefficients are particularly important for economical and complex traits such as yield, where direct selection is less effective. Thus the present experiment was undertaken to decipher the character association between the characters in a segregating population (F2) obtained from a cross between Naveen x IR 64 Drt1rom the study, it was evident that the characters like leaf breadth, leaf area, plant height, panicle length, number of primary branches, number of secondary branches, number of filled grains, grain length and test weight had the maximum association with yield among the characters under the study. Correlation coefficient and path coefficient analysis, revealed that the characters viz., number of filled grains per plant followed bygrain breadth, total number of grains, number of secondary branches, plant height, number of panicles, leaf breadth, flag leaf area, 1000 seed weight, panicle lengthand number of primary branches had a highly significant and positive correlation with yield and positive direct effect on yield. Thus, emphasis may be given to these traits for the effective selection of promising lines under study.

Item Type: Article
Subjects: Academic Digital Library > Agricultural and Food Science
Depositing User: Unnamed user with email info@academicdigitallibrary.org
Date Deposited: 07 Oct 2023 05:57
Last Modified: 07 Oct 2023 05:57
URI: http://publications.article4sub.com/id/eprint/2328

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