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Genetic Variability and Heritability of M4 G16 Red Rice Mutants after 300 Gy Gamma Irradiation Baiq Susthia Sukma Anggraeni; A. A. Ketut Sudharmawan; I Wayan Sudika
Jurnal Biologi Tropis Vol. 25 No. 1 (2025): Januari - Maret
Publisher : Biology Education Study Program, Faculty of Teacher Training and Education, University of Mataram, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbt.v25i1.13088

Abstract

Gamma irradiation can broaden the genetic variability available for rice breeding, but selection in advanced mutant generations requires reliable estimates of trait variation and heritability. This study evaluated genetic variability and broad-sense heritability among M4 lines of G16 red rice (Oryza sativa L.) derived from 300 Gy gamma irradiation. A field experiment was conducted from May to November 2023 in Tatar Hamlet, Nyurlembang Village, Narmada, West Lombok, Indonesia. Ten M4 mutant lines were evaluated with four checks (G16, Inpari 32, IR64, and Ciliwung) using an augmented randomized block design. Twelve agro-morphological and yield traits were recorded from ten systematically sampled plants per entry. Data were subjected to analysis of variance at α = 0.05; significant genotype effects were followed by Tukey’s honestly significant difference test, and genotypic variance, genotypic coefficient of variation (GCV), and broad-sense heritability (H²) were estimated. Genotype effects were significant for longest panicle, filled grains per panicle, and empty grains per panicle. G30(2-12) had the highest filled-grain number, whereas G27(17-10) had the longest panicle. GCV was high for empty grains per panicle and 100-grain weight, and moderate for empty-grain weight. H² was high for longest panicle, filled grains per panicle, and empty grains per panicle, while several yield-related traits showed moderate H². These results indicate exploitable genetic variation in selected reproductive traits. G20(8-3) and G26(7-11) are promising for further selection because they combine relatively long panicles, high filled-grain numbers, and low empty-grain numbers. Multi-environment validation is recommended before advancing lines.