Assessment of genetic variability, heritability, genetic advance and trait associations for yield improvement in lentil (Lens culinaris Medik.)

Introduction

Lentil (Lens culinaris Medikus) is an important grain legume belonging to the family Fabaceae and genus Lens. It is one of the oldest domesticated food legumes and is considered one of the founder crops associated with the origin of agriculture in Southwest Asia (Zohary et al., 1973) [1] . Lentil is a predominantly self-pollinated, diploid crop (2𝑛 = 2𝑥 = 14) and is cultivated across diverse agro-ecological conditions, particularly in Mediterranean, subtropical, arid and semi-arid regions. Its adaptation to diverse production environments has resulted in considerable variation among genotypes for phenological, morphological, agronomic and quality attributes (Erskine et al., 2011; Erskine et al., 2016) [2,3] . The cultivated species, L. culinaris subsp. culinaris, is closely related to its wild progenitor, L. culinaris subsp. orientalis, and both possess substantial morphological and genetic similarity. The diploid nature and predominantly self-pollinated reproductive system of lentil are important considerations in the conservation, evaluation and utilization of its genetic variability for breeding purposes (Erskine et al., 2011; Erskine et al., 2016) [2,3] . The genus Lens contains considerable diversity, and cultivated lentil represents an important component of this genetic resource. Molecular studies have further contributed to the understanding of genetic structure and relationships within the genus Lens (Liber et al., 2021) [4] .

In India, lentil is an important rabi pulse crop and is predominantly cultivated under rainfed conditions, frequently utilizing residual soil moisture. It is grown in several agro-climatic regions and forms an important component of cereal-pulse and other cropping systems.

According to Agricultural Statistics at a Glance 2024-25, lentil occupied approximately 1.70 million hectares, with a production of 1.65 million tonnes and an average productivity of 973 kg ha⁻¹ during 2024-25 (DA&FW, 2025) [5] . Uttar Pradesh is one of the major lentilproducing states in India. During 2024-25, the crop was cultivated on approximately 0.60 million hectares in the state, producing about 0.63 million tonnes with an average productivity of 1,035 kg ha⁻¹. The state contributed approximately 37.8 per cent of India's total lentil production and recorded productivity higher than the national average (DA&FW, 2025) [5] . Assessment of genetic variability provides the first step towards effective selection. Genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) help quantify the magnitude of genetic and phenotypic variation for quantitative traits. Comparison of GCV and PCV provides an indication of the relative influence of environmental variation on trait expression. Significant genetic variation for growth, yield and yield-related traits has been reported in lentil, although the magnitude of variation differs among traits and genetic materials (Poudel et al., 2022; Tadesse et al., 2022) [6,7] . Heritability provides information about the proportion of phenotypic variation attributable to genetic differences. Broad-sense heritability includes additive, dominance and epistatic components. However, heritability alone does not indicate the expected magnitude of improvement through selection. Genetic advance, when considered together with heritability, provides additional information regarding the probable response to selection. High heritability associated with high genetic advance is generally indicative of a greater contribution of additive genetic effects (Johnson et al., 1955) [8] . In lentil, substantial heritability and genetic advance have been reported for several yield and yield-related traits, although their magnitude varies with genetic material and environment (Poudel et al., 2022; Anant et al., 2025) [6,9] . Correlation analysis is useful for determining the direction and magnitude of association among quantitative characters. Genotypic correlation provides information regarding the genetic association between traits, whereas phenotypic correlation represents the combined influence of genetic and environmental factors. Since seed yield is a complex character, understanding its association with component traits can help identify suitable secondary characters for indirect selection. However, correlation alone does not distinguish whether an association results from a direct influence or from an indirect relationship through other traits (Tadesse et al., 2022) [7] . Keeping the above facts in view, the present study was undertaken to estimate genetic variability, heritability, genetic advance and character associations among parents and segregating generations of lentil.

Materials and methods

The experimental material comprised fifteen lentil genotypes (12 lines and 3 testers). The genotypes were selected on the basis of their origin, adaptability and morpho-physiological characters viz., earliness, higher yield potential, disease resistance etc. These genotypes were obtained from the Legume Section, Department of Genetics and Plant Breeding, C. S. Azad University of Agriculture and Technology, Kanpur. Details of the genotypes used as parents are presented in Table 1.

Observations recorded

Observations were recorded on five randomly selected competitive plants for the following traits: days to 50% flowering (DFF), days to maturity (DM), plant height (PH, cm), number of primary branches per plant (NPB), number of secondary branches per plant (NSB), number of clusters per plant (NCP), number of pods per cluster (NPC), total number of pods per plant (TPP), seeds per pod (SPD), 100-seed weight (HSW, g), biological yield per plant (BY, g), seed yield per plant (SYP, g), harvest index (HI, %), protein content (PC, %) and methionine content (MC, %). Protein and methionine contents were estimated in the laboratory using standard biochemical assays.

Statistical analyses

Analysis of variance was performed as per standard procedure. Genotypic and phenotypic coefficients of variation were calculated according to Burton (1952) [10] . Broad-sense heritability was estimated following Hanson et al. (1956) [11] . Genetic advance as per cent of mean was computed as suggested by Johnson et al. (1955) [8] . Genotypic and phenotypic correlation coefficients were calculated according to the method of Al-Jibouri et al. (1958) [12] . Significance of correlation coefficients was tested against tabulated 'r' values at 5% and 1% levels of significance.

Results and discussion

Genetic variability, heritability and genetic advance

Estimates of genetic variability parameters for parents, F₁ and F₂ generations are presented in Table 2. The analysis revealed considerable range of variation for all the traits studied across generations. In general, phenotypic coefficient of variation (PCV) was higher than the corresponding genotypic coefficient of variation (GCV) for most of the traits, indicating the influence of environment on the expression of these characters. However, the difference between PCV and GCV was relatively small for several traits, suggesting that the traits were less influenced by environmental factors and selection based on phenotypic performance would be effective.

Among parents, high GCV (> 20%) was recorded for number of secondary branches per plant (22.29%), while moderate GCV (10-20%) was observed for number of primary branches per plant (18.00%), number of pods per cluster (16.66%), seeds per pod (15.36%), total pods per plant (14.50%), seed yield per plant (14.72%), number of clusters per plant (12.30%), methionine content (11.82%) and biological yield (10.41%). Low GCV (< 10%) was noted for days to 50% flowering, days to maturity, plant height, 100-seed weight, harvest index and protein content. Similar trends of high variability for yield-attributing traits have been reported by Poudel et al. (2022) [6] and Tadesse et al. (2022) [7] . High broad-sense heritability (> 80%) coupled with high genetic advance as per cent of mean (> 20%) was observed in parents for number of secondary branches per plant (h² = 98.70%, GA = 45.60%), number of primary branches per plant (h² = 82.80%, GA = 33.74%), number of pods per cluster (h² = 86.80%, GA = 31.97%), seed yield per plant (h² = 97.30%, GA = 29.90%), total pods per plant (h² = 77.50%, GA = 26.29%), seeds per pod (h² = 71.10%, GA = 26.67%), methionine content (h² = 97.32%, GA = 24.34%), number of clusters per plant (h² = 93.70%, GA = 24.52%) and biological yield (h² = 90.40%, GA = 20.39%). High heritability with moderate genetic advance was recorded for 100-seed weight, plant height, days to 50% flowering, harvest index and protein content. The combination of high heritability and high genetic advance indicates predominance of additive gene action and offers good scope for selection (Johnson et al., 1955) [8] . These findings are in agreement with Anant et al. (2025) [9] and Poudel et al. (2022) [6] . In F₁ generation, high heritability with high to moderate genetic advance was observed for number of primary branches, number of secondary branches, total pods per plant, biological yield and seed yield per plant. In F₂ generation, high heritability (> 80%) with moderate to high genetic advance was recorded for number of primary branches, number of secondary branches, total pods per plant, 100-seed weight, biological yield, harvest index and seed yield per plant. The persistence of high heritability and genetic advance in segregating generations for these traits indicates their reliable transmission and usefulness for selection in early generations.

Character associations

Genotypic and phenotypic correlation coefficients among different traits in parents, F₁ and F₂ generations are presented in Tables 3-6. In general, genotypic correlation coefficients were higher in magnitude than the corresponding phenotypic correlation coefficients, indicating strong inherent association among the characters. (2022) [7] and Poudel et al. (2022) [6] . Number of secondary branches showed positive association with total pods per plant and number of pods per cluster, while number of clusters per plant was positively associated with seed yield in parents. Plant height was positively correlated with number of primary branches and seeds per pod across generations. These interrelationships among component traits provide useful guidelines for simultaneous improvement of multiple yield attributes through correlated response to selection.

Path coefficient analysis

Path coefficient analysis was performed to partition the genotypic and phenotypic correlations of component traits with seed yield into direct and indirect effects (Tables 7 and3; Figs. 567). In parents, biological yield exerted the highest positive direct effect on seed yield (2.130), followed by harvest index (0.755), methionine content (0.643) and number of primary branches (0.424). Seeds per pod (-1.953), total pods (-0.752), 100-seed weight (-0.718), pods per cluster (-0.694) and secondary branches (-0.632) showed strong negative direct effects, although some of these traits exhibited positive total correlations through favourable indirect effects via biological yield and harvest index.

In the F₁ generation, biological yield again had the highest positive direct effect (0.736), followed by harvest index (0.426) and seeds per pod (0.144). In F₂, biological yield (0.711) and harvest index (0.525) continued to show the strongest positive direct effects. The residual effects were relatively low, indicating that the traits included in the path model accounted for a substantial proportion of the variation in seed yield. These results confirm that biological yield and harvest index are the most reliable traits for direct selection to improve seed yield in lentil.

Conclusion

The present study revealed substantial genetic variability among the lentil parents and their F₁ and F₂ generations for most of the quantitative and quality traits. High heritability coupled with high genetic advance as per cent of mean for number of secondary branches, number of primary branches, number of pods per cluster, total pods per plant, biological yield, seed yield per plant and methionine content indicated predominance of additive gene action and offered good scope for improvement through direct selection. Seed yield per plant was positively and significantly correlated with biological yield, harvest index, 100-seed weight, plant height and seeds per pod, while it was negatively associated with days to flowering and maturity. Protein content showed very strong positive association with methionine content. Therefore, selection based on biological yield, harvest index, 100seed weight, number of pods per plant and secondary branches, along with earliness, would be effective for developing high-yielding lentil genotypes with improved nutritional quality. The promising recombinants identified in F₂ generation may be advanced further for isolation of superior pure lines.

References

1. Zohary D, Hopf M. Domestication of Pulses in the Old World. Science. 1973;182:887-894.
2. Erskine W, Sarker A, Kumar S. Crops that feed the world 3. Investing in lentil improvement toward a food secure world. Food Security. 2011;3:127-139.
3. Erskine W, Sarker A, Kumar S. Lentil: breeding. In: Encyclopedia of Food Grains (Second Edition). Elsevier, Amsterdam. 2016.
4. Liber M, Duarte I, Maia AT, Oliveira HR. The history of lentil (Lens culinaris subsp. culinaris) domestication and spread as revealed by genotyping-by-sequencing of wild and landrace accessions. Frontiers in Plant Science. 2021;12:628439.
5. DA&FW. Agricultural Statistics at a Glance 2024-25. Department of Agriculture & Farmers Welfare, Ministry of Agriculture & Farmers Welfare, Government of India, New Delhi. 2025.
6. Poudel PP, Aryal L, Darai R, Sah R, Subedi K, Acharya B, Subedi S, Shrestha J. Genetic variability, heritability and genetic advance in Lentil (Lens culinaris Medik.). Journal of Agriculture and Natural Resources. 2022;5(1):130-137.
7. Tadesse T, Fikre A, Legesse T, Alemu D. Genetic variability and characters association for yield, yield attributing traits and protein content of lentil (Lens culinaris Medikus) genotype in Ethiopia. CABI Agriculture and Bioscience. 2022;3:9.
8. Johnson HW, Robinson HF, Comstock RE. Estimates of genetic and environmental variability in soybeans. Agronomy Journal. 1955;47:314-318.
9. Anant A et al. Genetic Variability, Heritability and Genetic Advance in Lentil [Lens culinaris Medik.] Genotypes for Seed Yield and Attributing Traits. Agricultural Research Communication Centre Journals. 2025.
10. Burton GW. Quantitative inheritance in grasses. Proceedings of the 6th International Grassland Congress. 1952;1:277-283.
11. Hanson CH, Robinson HF, Comstock RE. Biometrical studies of yield in segregating populations of Korean lespedeza. Agronomy Journal. 1956;48:268-272.
12. Al-Jibouri HA, Miller PA, Robinson HF. Genotypic and environmental variances and covariances in an upland cotton cross of interspecific origin. Agronomy Journal. 1958;50:633-636.
13. How to cite this article: Singh K, Yadav RK, Kumar S, Shweta, Singh L, Maurya CL, et al. Assessment of genetic variability, heritability, genetic advance and trait associations for yield improvement in lentil (Lens culinaris Medik.). International Journal of Advanced Biochemistry Research. 2026;SP10(9):2041-2052. doi: https://www.doi.org/10.33545/26174693.2 026.v10.i9Sz.10105