Assessment of genetic variability, heritability and genetic gain for agro-morphological traits in lentil (Lens culinaris Medik.)

Introduction

Lentil (Lens culinaris L. Medik.) is a self-pollinated, annual diploid legume crop (2n = 14) belonging to the family Fabaceae, valued for its distinctive lens-shaped seeds and high nutritional content. With a haploid genome size of about 4, 063 Mbp (Kumar et al., 2014) [13] , lentil is one of the earliest domesticated pulse crops and continues to play a vital role in global food systems. Its seeds contain 60-67% carbohydrates, 20-36% protein, and around 4% fat (Johnson et al., 2020) [11] , making it a rich source of plant-based protein and micronutrients. In South Asian countries such particularly in India, Bangladesh, Nepal, and Sri Lanka, lentils are an integral component of daily diets, featuring prominently in traditional dishes such as "dahl" and soups.

On a global scale, lentil ranks fifth among pulse crops, following chickpea, pigeon pea, common bean, and mung bean occupying around 5.0 million hectares. And the top 5 countries are Canada, India, Australia, Türkiye and Nepal (FAO, 2020) [9] . In Nepal, lentil is the principal pulse crop, accounting 62.64% of the total legume cultivation area and 64.35% of total production (Darai et al., 2017) [7] . In India, lentil is the second most important rabi pulse after chickpea, cultivated during the winter season from November to March. The country accounts for nearly 40% of the global lentil cultivation area and about 28% of global output. The country contributes nearly 40% of the world's lentil area and about 28% of total production, with a cultivated area of approximately 1.94 million hectares and an annual output of 1.65 million tons (FAO, 2024) [8] . The bulk of production comes from Madhya Pradesh, Uttar Pradesh, West Bengal, and Bihar, which together account for almost 90% of national output. Despite it's prominence, India's average lentil productivity (766 kg/ha) remains below the global mean (1, 015 kg/ha). Major productivity constraints include the use of low-yielding varieties with narrow genetic bases, high susceptibility to diseases and insect pests, sensitivity to drought and temperature extremes, and cultivation on marginal lands with limited input use. Chemical-based stress management strategies are often uneconomical and environmentally unsustainable, underscoring the need for genetic improvement as a sustainable alternative. Harnessing the existing genetic variability within lentil germplasm offers a viable pathway to developing high-yielding and stress-resilient cultivars. Genetic variability forms the foundation of any crop improvement program. Effective crop improvement hinges on the selection of genetically diverse parents for hybridization, guided by an understanding of genetic parameters such as variance, heritability, coefficient of variation, and genetic advance (Idris, 2012) [10] . Yield, a complex phenotypic trait, is influenced by multiple contributing traits, (e.g., branches per plant, pods per plant, seeds per pod, 100-seed weight, plant height), and knowledge of their genetic architecture is essential for designing efficient breeding strategies. Biometrical analyses, including variance, standard error, and heritability estimates, provide insights into the extent of genetic variability and environmental influence within a population. High heritability combined with substantial genetic advance typically indicates additive gene action, suggesting that traits can be improved effectively through simple selection and enable breeders to predict genetic gains through selection by identifying traits with high heritability for targeted improvement. Well-characterized Germplasm repositories, rich in diverse genetic resources, are invaluable for crop improvement, provided the extent of genetic variability is well-characterized. This study aims to evaluate the genetic variability and interrelationships among morphological traits and yield in lentil to identify key attributes contributing significantly to yield variation. By characterizing these traits, the research seeks to pinpoint those with the greatest potential for enhancement through targeted breeding programs, thereby facilitating the development of high-yielding lentil varieties.

Materials and Methods Experimental Site Design and Material

The present study was conducted during the rabi seasons of 2024-25 at the N.E. Borlaug Crop Research Centre, G.B. Pant University of Agriculture and Technology, Pantnagar, Udham Singh Nagar, Uttarakhand. The experimental material consisted of 50 lentil genotypes mentioned in Table .1 provided by G.B. Pant University of Agriculture and Technology, Pantnagar. The genotypes that were employed varied in terms of various morphological and quantitative traits. Two replications of each of the 50 genotypes of lentil were planted in a randomized block design. Each of the genotype was planted in a 2-meter-long row with a 30-centimeter gap between rows and a 10centimeter gap between plants.

Data on Yield and Yield Contributing Components

For recording observations five randomly selected plants from each genotype are selected in each replication for ten quantitative traits viz., Days to 50% flowering (DFF), Days to maturity (DM), Plant height(cm) (PH), No. of primary branches/plant (NPB), No. of pods/plant (NPP), No. of seeds/pod (NSP), 100-seed weight(g) (HSW), Seed yield/ plant(g) (SY/P), Biological yield/plant(g) (BY/P), Harvest index (%) (HI).

Statistical analysis

MS Excel 2007 was used to process experimental data and R-stat 3.5.was used to analyze it. The PCV and GCV of (Singh and Chaudhary, 1985) [18] , Heritability (in the broad sense) (Johnson et al., 1955) [12] , genetic advance (Burton, 1952) [5] , and genetic advances as percentage of the mean (Johnson et al., 1955) [12] were all calculated by using the following formulas:

Were, 𝑋̅ = General mean of the character σ 𝟐 𝒈 = Genotypic variance σ 𝟐 p = Phenotypic variance

Estimation of heritability

Broad sense heritability was calculated by using the formula given by Weber and Moorthy (1952) [19] Heritability (h 2 ) = 𝜎 2 𝑔𝑖/𝜎 2 𝑝𝑖×100

Estimation of genetic advance

The expected genetic advance under selection for various characters was calculated according to the formula given by Allard et al. 1960 [1] .

G.A. (S

Results and Discussion

Analysis of Variance

The analysis of variance (ANOVA) demonstrated that all evaluated traits exhibited highly significant differences (p≤0.01) among the genotypes studied for all the traits viz., DFF, DM, PH, NPB, NPP, NSP, HSW, SY/P, BY/P, HI (Table .2), indicating plentiful genetic variation in the material studied. Such diversity is necessary for effective breeding and selection as reported in previous crop improvement research (Brady, 2015) [15] . The coefficient of variation (CV%) values indicated the precision and stability of trait measurement. Phenological traits such as DM (2.35%), DFF (5.01%), and NSP (8.67%) showed notably low CVs, indicating minimal environmental influence and high measurement reliability. These genetically controlled traits are well suited for early-generation selection (Moore, 2023) [16] . Moderate CV values were recorded for morphological traits like plant height (8.74%) and primary branches per plant (12.33%), reflecting acceptable stability. These traits contribute to plant architecture and biomass formation, which are important considerations in ideotype breeding (Araújo, 2003) [2] . Yield-related traits including NPP (15.44%), BY/P (16.33%), HI (17.60%), and particularly SY/P (21.84%) exhibited higher CVs, revealing greater environmental sensitivity. Despite this, significant genotypic effects for these traits confirm the presence of exploitable genetic variability, underscoring the necessity of multienvironment testing to identify stable, high-yielding genotypes (Brady, 2015; FAO, 2024) [15,8] .

Genetic Variability

The evaluation of genetic variability parameters including GCV, PCV, ECV, heritability in the broad sense, genetic advance (GA), and GAM provides critical insights into the inheritability and improvement potential of traits in a breeding program. In this study, the traits exhibited a range of variability and heritability estimates that reflect their genetic control and environmental influence. Phenological traits such as DFF and DM showed moderate GCVs (5.38% and 3.36%, respectively) and relatively low PCVs, resulting in narrow differences between GCV and PCV, indicating limited environmental effect on these traits. The heritability estimates for these traits were moderate to high (53.54% for DFF and 67.00% for DM), and the genetic advance as percentage of mean ranged from 5.67% to 8.11%, suggesting that selection for these traits would be moderately effective and reliable in early generations (Bhagasara et al., 2017; Kumar et al., 2018) [3,14] . Morphological traits such as PH and PB demonstrated higher GCVs (12.64% and 18.90%) and PCVs (15.37% and 22.59%), with moderate environmental coefficients of variation, illustrating moderate environmental influence. Heritability was high for both traits (>67%), and GAM was substantial (21.43% and 32.58%), indicating these traits are primarily governed by additive gene effects and can be effectively improved through selection (Chacko et al., 2023; Meena et al., 2017) [6,3] . Yield-related traits including PPP, SPP, HSW, BY, SY, and HI displayed high GCV and PCV values, reflecting considerable genetic variability but also notable environmental influence as seen by relatively high ECV values. Heritability estimates for these traits ranged from approximately 66% to 80%, indicating good genetic control. GAM was particularly high for SY/P (81.18%) and HI (61.10%), signifying that these traits hold significant potential for improvement via selection. The high magnitude of GA coupled with high heritability suggests additive gene action predominates, which is essential in breeding for quantitative traits like yield (Meena et al., 2017; Bhagasara et al., 2017; Chacko et al., 2023) [3,6] . Overall, the close proximity of GCV and PCV values in many traits suggests minor environmental influence, underscoring the genetic basis of variability. Traits with high heritability and GA, especially seed yield and its components, are promising candidates for selection in breeding programs aimed at improving productivity. This aligns with prior studies highlighting the importance of combining genetic variability and heritability measures to predict the efficiency of selection strategies (Bhagasara et al., 2017) [3] .

Conclusion

The present investigation evaluated 50 genotypes for ten agro-morphological and yield-related traits to assess variability, heritability, and genetic advance. Phenological traits (DFF, DM) exhibited low coefficients of variation (CV = 2.36-5.01%), suggesting stable expression and high experimental precision. In contrast, yield and yield-contributing traits, especially SY/P (CV = 21.84%), recorded higher variability, implying greater environmental influence.

GCV were highest for SY/P (43.99%), followed by HI (33.51%) and BY/P (24.90%), indicating wide genetic variation. High heritability coupled with high GA as a percentage of mean was observed for SY/P (80.22%, GA = 81.18%), HI (78.37%, GA = 61.10%), and BY/P (69.92%, GA = 42.89%), suggesting the predominance of additive gene action and scope for improvement through direct selection. Moderately high heritability coupled with moderate GAM was observed in traits like NPP, HSW, and NSP, which also recorded moderate GCV and PCV values. These traits can respond to selection, but progress may be slower compared to traits with higher genetic variability and heritability. The findings highlight SY/P, HI, NSP, and BY/P as key traits for selection in breeding programs aimed at yield enhancement

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