Genetic variability and heritability in advanced rice (Oryza sativa L.) strains under heterogeneous environments

Introduction 1.1 Global Importance of Rice

Rice is a cornerstone of food security and agricultural livelihoods, particularly in Asia, where over 90% of global rice production and consumption occur. India alone produces over 120 million tonnes annually, contributing nearly 23% of global output. In regions like Uttar Pradesh, which cultivates over 5.87 million hectares of rice, yield enhancement is essential to meet future demand and reduce reliance on imports.

Need for Genetic Improvement

Despite technological advancements, productivity remains constrained by the lack of genetically diverse, high-yielding, and climate-resilient varieties. The assessment of genetic variability and heritability is a foundational step toward developing such cultivars. Heritability offers insight into the proportion of trait variation due to genetic causes, while genetic advance reflects the expected improvement under selection.

Role of Multi-Trait and Multi-Environment Analysis

Agronomic traits such as productive tillers, grain size, panicle length, and harvest index directly impact yield. Their genetic parameters must be studied under real field conditions across varied environments to ensure selection effectiveness. Path coefficient and correlation analyses help breeders identify yield-contributing traits and prioritize them for selection.

Literature Review

Numerous studies affirm the value of GCV, heritability, and path analysis in rice breeding:  Manjunatha et al. (2019) found high GCV and PCV in spikelet number and panicle traits, suggesting potential for direct selection.  Basavaraja et al. (2013) [1] emphasized that traits with high heritability and genetic advance (e.g., grain yield and productive tillers) respond well to selection.

 Zahid et al. (2006) [11] noted strong direct effects of productive tillers and spikelet fertility on yield, highlighting their importance in breeding indices.  Islam et al. (2019) recorded heritability estimates ranging from 71% to 99%, reinforcing the reliability of phenotypic selection for traits like filled grains and plant height.  Rathod et al. (2020) [8] also reported that indirect effects via panicle length and spikelet number significantly impact grain yield.

These findings underscore the value of robust genetic parameter studies for sustainable rice improvement.

Materials and Methods

Experimental Design

Statistical Analysis

 GCV/PCV: Burton and de Vane (1953) [2][3]  Heritability: Hanson et al. (1956) [5]  Genetic Advance: Robinson et al. (1949) [9]  Correlation and Path Analysis: Dewey and Lu (1959) [4]  ANOVA: Panse and Sukhatme (1967) [7]  Cluster analysis: Euclidean non-hierarchical method

Results and Interpretation (Expanded)

This section delves into the core findings from the study, interpreting the performance of rice genotypes across multiple genetic parameters.

Analysis of Variance (ANOVA)

The ANOVA results indicated highly significant differences (p < 0.01) among the genotypes for all measured traits across locations. This suggests that a broad spectrum of genetic variability exists in the studied material, which is a prerequisite for effective selection. These results confirm substantial genotypic influence over the phenotypic expression of these traits.

Genetic Parameters: GCV, PCV, Heritability, and Genetic Advance

Genetic parameters are crucial for estimating the extent of environmental and genetic contribution to phenotypic traits.  Genotypic Coefficient of Variation (GCV) and Phenotypic Coefficient of Variation (PCV) help determine variability.  Heritability (broad sense) indicates the proportion of phenotypic variance attributable to genetics.  Genetic Advance as Percent of Mean (GAM) signifies the expected improvement through selection.

Correlation Coefficient Analysis

This analysis identifies relationships between traits and helps breeders determine which characteristics indirectly improve yield.

Key Takeaways:

Grain yield was positively and significantly correlated with productive tillers, spikelet fertility, and biological yield. negative correlations with plant height and days to flowering suggest that short, early genotypes are more yield-efficient under the studied conditions.

Path Coefficient Analysis

To dissect cause-effect relationships, the correlation coefficients were decomposed using path analysis.

Interpretation:

 Biological yield had the highest direct positive effect, confirming its central role in yield enhancement.  Spikelet fertility and tillers contributed substantially both directly and indirectly.  Traits with high indirect effects via biological yield should be considered for indirect selection.

Discussion

The extensive analysis of variance, genetic variability, correlation, and path coefficients reveals several key insights regarding the nature of yield-related traits and their potential utility in breeding.

Genetic Variability and Trait Expression

The high degree of variability observed for traits such as grain yield, biological yield, and spikelet fertility underscores the rich genetic diversity within the advanced rice genotypes studied. These results are consistent with earlier findings by Singh et al. (2011) [10] and Basavaraja et al. (2013) [1] , who emphasized that variability in spikelet number, tillering capacity, and grain weight serves as a vital resource for trait improvement. The proximity of GCV and PCV values for most traits further suggests that environmental influence is minimal, indicating the reliability of phenotypic selection. For instance, grain yield exhibited a GCV of 18.3% and PCV of 19.1%, denoting stable genotypic performance across locations. [6] , such traits are governed largely by fixable genetic factors and thus can be improved effectively through simple selection methods.

Heritability and Genetic

In contrast, traits like panicle length and 1000-grain weight, which had moderate heritability and lower GAM, suggest the involvement of non-additive gene actions. These would require breeding strategies like recurrent selection or hybridization followed by selection in segregating generations.

Trait Associations and Yield Improvement Strategy

The strong positive correlations of yield with productive tillers (r = 0.79), biological yield (r = 0.83), and spikelet fertility (r = 0.71) imply that multi-trait selection will enhance yield gain. This agrees with Zahid et al. (2006) [11] , who reported that yield is a complex polygenic trait, and its improvement is more effective when indirect selection is based on highly associated contributing traits.

The negative correlation with plant height and days to flowering implies that shorter, early-maturing varieties may be more efficient under current environmental stressors such as drought or heat late in the growing season.

Path Analysis: Decoding Cause-Effect Relations

While correlation analysis indicates associations, path coefficient analysis clarifies causal relationships:  Biological yield had the highest direct effect (0.643), confirming its status as a major yield component.  Productive tillers and spikelet fertility also had substantial direct effects, indicating they directly enhance grain yield.  Panicle length and leaf area index, although showing weak correlation, had significant indirect effects, particularly through biological yield.

These findings are comparable to those of Rathod et al.

(2020) [8] , who demonstrated the role of biological yield and spikelet fertility in determining rice yield through indirect contribution via harvest index.

Breeding Implications and

Integration with Molecular Breeding

Genotypes identified with high trait stability and performance can be used in marker-assisted selection (MAS) and QTL mapping programs. For example, QTLs linked to productive tillers (e.g., qPT1.1, qPT2.1) or biological yield (qBY4.1) could be targeted in future genomics-assisted breeding.

Conclusion

This research demonstrated substantial genetic variability, high heritability, and strong trait associations among advanced rice genotypes evaluated under three contrasting environments. Traits such as biological yield, productive tillers, and spikelet fertility emerged as key contributors to yield, supported by both correlation and path analysis.

The study emphasizes the feasibility of simple phenotypic selection for these traits due to their high additive genetic control. These findings serve as a guiding framework for varietal development and ideotype breeding in rice, contributing to increased productivity and sustainability under climate-affected production systems.

Recommendations

1. Select for high biological yield and tillering capacity to drive grain yield gains. 2. Integrate early maturity and short plant stature into breeding pipelines for stress-prone regions. 3. Use high-performing genotypes as parents for hybridization and trait introgression. 4. Validate these genotypes across multiple years and agroecologies to confirm stability. 5. Couple phenotypic screening with molecular markerbased selection for efficiency.

References

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