Estimation of narrow-sense heritability and genetic advance for yield and component traits in rice (Oryza sativa L.)

Introduction

Rice (Oryza sativa L.) is a major cereal crop belonging to the tribe Oryzeae of the family Gramineae. It is a diploid species (2n = 2x = 24), self-pollinated in nature, and chasmogamous in floral structure. Its inflorescence is a panicle made up of spikelets, and the grain is classified as a caryopsis. Rice is cultivated in 116 countries and serves as a staple food for more than half of the global population. In 2023, global milled rice production reached 535.8 million metric tonnes, with more than 90% used for direct human consumption. In India, rice is grown over 47.83 million hectares with a production of 137.8 million tonnes, while in Gujarat, it covers an area of 9.68 lakh hectares producing 2.49 million tonnes (Anonymous, 2023a, 2023b) [3,4] . Genetic improvement in rice depends on the presence of heritable variation and knowledge of gene action. Segregating generations, particularly F₂ and F₃, provide an opportunity to assess the variability created through recombination. Traits governed mainly by additive gene effects respond effectively to selection, especially when estimates of narrow-sense heritability and genetic advance are high. Narrow-sense heritability reflects the proportion of additive variance, while genetic advance as percent of mean (GA%) provides an estimate of the expected gain under selection pressure (Poehlman & Sleper, 1995; Johnson et al., 1955) [12, 8] . Evaluation of these parameters in early generations helps in identifying traits with high selection efficiency. The present investigation was therefore carried out to estimate narrowsense heritability and GA% for yield and its associated traits in F₂ and F₃ generations derived from four rice crosses.

Materials and Methods

The experiment was carried out during summer 2025 at the Regional Rice Research Station (RRRS), Vyara, under Navsari Agricultural University.

The experimental material comprised five generations (P₁, P₂, F₁, F₂, and F₃) from four rice crosses: NVSR 3169 × Gontra Bidhan 3, NVSR 3169 × Pusa 1509, Lal Kada Gold × DRR Dhan 62, and Lal Kada Gold × IR 55179-3B-11-3. However, only the F₂ and F₃ generations were utilized for estimating narrow-sense heritability and genetic advance, as these represent segregating populations where genetic variation is expressed. The experiment was laid out in a Compact Family Block Design with three replications. Each plot consisted of four rows per generation with 20 cm inter-row and 15 cm intrarow spacing. Standard agronomic practices were followed. Data were recorded on forty randomly selected competitive plants from each plot of F₂ and F₃ generations for thirteen quantitative traits: days to 50% flowering, days to maturity, plant height (cm), number of productive tillers per plant, panicle length (cm), number of grains per panicle, kernel length (mm), kernel breadth (mm), L:B ratio, 100-grain weight (g), grain yield per plant (g), straw yield per plant (g), and harvest index (%).

Statistical Analysis

The narrow sense heritability was calculated by using the formula suggested by Warner (1952) as follows:

Where, h 2 ns = Heritability in narrow sense VF 2 = Variance of F 2 generation D = Additive genotypic variance

Estimation of Expected Genetic Advance under Selection

The expected genetic advance at 5% selection intensity was estimated by using formula suggested by Allard (1960).

Expected genetic advance = h 2 (ns) × K ×σp

Where, h 2 (ns) = Heritability in narrow sense σp = Phenotypic standard Deviation K = Selection differential (K = 2.06 at 5% selection pressure) Expected genetic advance as per cent of mean was estimated by the following formula: Genetic advance Expected genetic advance as% of mean = ×100 X Where, X = Mean of the character under study

Results and Discussion

The estimates of narrow-sense heritability and genetic advance as percent of mean (GA%) were calculated for thirteen agronomic and grain quality traits across four rice crosses evaluated in F₂ and F₃ generations. The values varied notably among crosses and traits, reflecting differences in genetic control and environmental influence. In the cross NVSR 3169 × Gontra Bidhan 3, high heritability was recorded for plant height (64.18%), panicle length (61.19%), grains per panicle (60.11%), and 100-grain weight (83.18%), accompanied by high GA% for productive tillers per plant (35.18%), grain yield per plant (49.05%), and straw yield (80.9%). This suggests that additive gene action governs these traits and that selection can be highly effective. Conversely, low heritability and GA% for kernel length (20.44%, 2.58%) and harvest index (15.63%, 3.70%) indicate the influence of non-additive gene action or environmental effects. In NVSR 3169 × Pusa 1509, high heritability was observed for plant height (80.36%), days to maturity (79.01%), kernel breadth (79.52%), and kernel length (64.87%), with moderate to high GA% for kernel breadth (21.54%) and L/B ratio (18.52%). These results suggest that selection for grain size and shape traits could be rewarding in this cross. Other traits like panicle length and grain yield were not estimated in this cross, and some traits showed moderate heritability, indicating partial additive control. For the cross Lal Kada Gold × DRR Dhan 62, high heritability and GA% were observed for grain yield per plant (58.76%, 46.59%), 100-grain weight (84.94%, 19.77%), productive tillers per plant (59.47%, 45.77%), and harvest index (76.18%, 20.35%). These findings indicate that additive gene effects were predominant for these traits, and direct selection would be effective. Plant height and kernel length also showed favorable estimates, while traits like panicle length and grains per panicle had lower values, reducing selection efficiency. Evaluation of narrow-sense heritability and GA% across thirteen traits in the four rice crosses indicated varying degrees of additive genetic control. Traits such as plant height, productive tillers, 100-grain weight, and grain yield showed high h²ₙ and GA%, indicating predominance of additive gene action and scope for direct selection. A visual comparison of heritability and genetic advance across crosses for key traits is illustrated in Figure 1." In contrast, traits with low estimates may require recurrent selection or heterosis breeding to exploit non-additive gene effects. These results corroborate previous findings by Patel et al. (2022) [11] , Harijan et al. (2021) [7] , and Satasiya et al.

(2022) [14] , who reported similar genetic patterns for yield and its components in rice, confirming that additive gene action plays a significant role in governing key agronomic traits. The observed predominance of additive gene action for key traits such as plant height, productive tillers, 100grain weight, and grain yield aligns with the findings of these studies. In addition, similar patterns were also reported by Akinwale et al. (2011) [2] and Ahmed et al. (2016) [1] for kernel dimensions and yield attributes, supporting the efficacy of early generation selection. Traits like harvest index and panicle length, which showed lower heritability and GA% in the present study, also showed complex inheritance in previous reports by Divya et al. (2014) [6] and Lingaiah et al. (2023) [9] . These comparisons confirm that while several agronomic traits are governed by additive effects and suitable for direct selection, others may require delayed selection, recurrent breeding, or heterosis exploitation to capture their full genetic potential.

Heritability in the narrow sense (h²ₙ) provides an estimate of the proportion of total phenotypic variation that is attributed to additive genetic variance. This parameter is of particular importance in plant breeding, as it reflects the potential of a trait to respond to selection. However, heritability alone does not predict the genetic gain; hence, it is interpreted together with the genetic advance as percent of mean (GA%), which indicates the expected progress from selection in the following generation.

In the present investigation, h²ₙ and GA% were estimated for thirteen traits across F₂ and F₃ generations in four rice crosses. The magnitude of heritability estimates varied across traits and crosses, reflecting differential genetic control. According to the classification proposed by Robinson et al. (1949), traits were grouped into low (<30%), moderate (30-60%), and high (>60%) heritability, while GA% was interpreted based on Johnson et al. (1955) [8] as low (0-10%), moderate (10-20%), and high (>20%). Traits such as plant height, 100-grain weight, productive tillers per plant, and grain yield per plant frequently exhibited high h²ₙ coupled with high GA% across multiple crosses, suggesting a predominant role of additive gene action. Such traits are largely governed by fixable genetic effects, making them amenable to early generation selection and offering good scope for genetic improvement through direct phenotypic selection.

In contrast, traits such as days to flowering, days to maturity and panicle length often exhibited moderate heritability or lower GA% in certain crosses. These findings suggest a more complex genetic architecture involving non-additive gene effects or environmental interactions, where conventional selection methods may be less effective. For these traits, alternative strategies like delayed selection, recurrent selection, or biparental mating might be more suitable to accumulate favorable alleles.

Grain quality traits such as kernel length, kernel breadth, and L:B ratio exhibited a wide range of heritability and GA% values across crosses. In some combinations, moderate to high heritability accompanied by low GA% indicates the presence of additive effects partially masked by environmental influence or dominance effects. These traits may respond to selection but require more precise phenotyping or replicated testing to reduce environmental noise.

Evaluation of narrow-sense heritability and GA% across thirteen traits in the four rice crosses indicated varying degrees of additive genetic control. Traits such as plant height, productive tillers, 100-grain weight, and grain yield showed high h²ₙ and GA%, indicating predominance of additive gene action and scope for direct selection. Traits like panicle length, days to flowering, and kernel dimensions exhibited moderate to low h²ₙ or GA%, reflecting more complex inheritance. These traits may benefit from advanced breeding strategies like recurrent selection or selection in later generations to capture hidden variability.

Overall, traits with both high heritability and high genetic advance offer the greatest promise for efficient selection in rice improvement programs. Cross-specific patterns in narrow-sense heritability are further represented in a heatmap (Figure 2).

Conclusion

The present investigation demonstrated substantial variability in narrow-sense heritability (h²ₙ) and genetic advance as percent of mean (GA%) among thirteen quantitative traits across four diverse rice crosses. Traits including grain yield per plant, plant height, productive tillers per plant, straw yield per plant, and 100-grain weight consistently showed moderate to high estimates of narrowsense heritability coupled with high genetic advance, indicating a predominance of additive gene action. These findings suggest that such traits are primarily governed by fixable genetic effects and hence, direct selection can be effectively practiced during early segregating generations. The effectiveness of direct phenotypic selection for these traits is underscored by the substantial expected genetic gain, making them prime targets for immediate improvement through conventional breeding methods.

Conversely, traits such as kernel length, panicle length, harvest index, and certain quality parameters displayed comparatively lower estimates of heritability and genetic advance. This lower magnitude reflects the complexity of genetic inheritance involving significant non-additive gene effects, such as dominance and epistasis, and indicates a substantial influence of environmental factors. Consequently, direct selection in early generations for these traits would likely be less effective due to the masking effect of non-additive gene action and environmental fluctuations. Thus, these traits necessitate alternative breeding approaches to enhance genetic progress. Breeding strategies such as recurrent selection, biparental mating, population improvement schemes, or hybrid breeding are recommended to effectively exploit the non-additive genetic variance inherent in these traits. Furthermore, substantial cross-specific variability was observed in genetic parameters, reinforcing the necessity for trait-specific and cross-specific breeding strategies. For instance, the cross NVSR 3169 × Gontra Bidhan 3 exhibited high heritability and GA% for productive tillers, grain yield, and straw yield, making it particularly suitable for direct phenotypic selection. Similarly, crosses such as Lal Kada Gold × DRR Dhan 62 demonstrated favorable genetic parameters for traits like grain yield, productive tillers, 100grain weight, and harvest index, emphasizing their potential for significant genetic improvement through early generation selection. In contrast, the cross NVSR 3169 × Pusa 1509 displayed mixed results, with grain size traits exhibiting promising heritability but other traits showing moderate or low genetic parameters, suggesting a selective and more nuanced breeding strategy is required. The clear delineation of traits based on genetic parameters highlights the importance of interpreting heritability alongside genetic advance rather than in isolation. Traits combining both high heritability and high genetic advance consistently emerged as ideal candidates for simple and direct phenotypic selection, while traits with moderate or discordant values suggest partial additive control influenced by environment, thereby requiring more strategic selection approaches and rigorous evaluation methodologies to enhance selection efficiency. Overall, this comprehensive trait-wise and cross-specific genetic analysis provides critical and actionable insights for rice breeders. It outlines a clear framework for optimizing selection decisions, prioritizing breeding objectives, and effectively harnessing genetic variation to achieve substantial yield gains and agronomic improvements. By distinctly differentiating between additive and non-additive gene effects across diverse traits and genetic backgrounds, these findings significantly enhance the precision, efficiency, and success rate of rice improvement programs.

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