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Research Article Open access CC BY 4.0

Assessment of Genetic Variability for Yield and Yield Attributing Traits in F2 and F3 Population of Rice (Oryza sativa L.) Cross

A. K. Choudhary, Z. A. Haider, S. B. Mishra, K. Prasad, Shanti Bhushan, Sanjay Kumar, V. K. Choudhary, Mani Bhushan, J. P. Prasad

Current Journal of Applied Science and Technology · pp. 1–5 · Published 8 Dec 2018

10.9734/CJAST/2018/45875

Abstract

The present investigation was carried out during Kharif 2013 and 2014 in Rice experimental area, Birsa Agricultural University, Kanke, Ranchi, Jharkhand. One thousand F2 progeny from BG 102/Naveen cross were randomly tagged and selected 200 plants based on four groups viz; high yielder (HY), low yielder (LY), MTL (Multi trait limit) and RPS (Random plant selection). These F2 selected plants were grown during Kharif, 2014 in RBD with two replication and two methods of sowing, such as, direct seeded and transplanted at twenty days interval; each plot measuring 2.7 x 0.4-meter size. All F3 progenies exhibited high h2 coupled with high GA as per cent of mean for traits viz; PH, PPP, GPP TW and GYP plant in Ist date of DS whereas in Ist and 2nd date of TP; GYP plant also exhibited high h2 coupled with high GA as per cent of mean indicating the preponderance of additive gene action suggesting that selection may be effective for these characters in early generation. Leptokurtic and positively skewed distribution suggested the involvement of dominance based complementary gene interaction and involving a fewer number of genes having an increasing effect in the inheritance of PPP and GYP plant, whereas in GPP any gene interaction was not found due to negative kurtosis. Maximising the genetic gain in respect of these traits with positively skewed distribution requires intense selection from the existing variability.

Rice crop heritability GA Kurtosis and Skewness.

Cited by 2

Genetic variability, correlation and path coefficient analysis in segregating population of rice

K. Bhargava, D. Shivani, S. Pushpavalli · Electronic Journal of Plant Breeding · 2021

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