Evaluation of genetic variability, heritability and trait interrelationships in chickpea for yield enhancement

Introduction

Chickpea (Cicer arietinum L.), commonly known as Bengal gram or the "king of pulses," is among the earliest domesticated grain legumes and ranks third in global pulse production after common beans (Phaseolus vulgaris) and field peas (Pisum sativum) (Singh et al., 1987 [28] ; Zohaey & Hopf, 2000). Archaeobotanical evidence traces its origins to the Fertile Crescent, with charred remains at Tell El-Kerkh, Syria, dating to 7500-6800 BCE (Vavilov, 1926 [32] ; Zohaey & Hopf, 2000). This self-pollinated, diploid (2n= 16) annual in the family Fabaceae exists in two market classes: desi types small, thick-coated seeds of dark colours (~80% of cultivated area) and kabuli types, with large, cream-coloured seeds (~20%). Nutritionally, chickpea is valued as a high-quality protein source (20-25%), rich in lysine, carbohydrates (40-60%), dietary fibre, vitamins (A, C, E, K, B-complex), and minerals such as Fe, Zn, Mg, and Ca (Khazaei et al., 2020 [14] ; Food Composition Tables, 2017; Bhardwaj et al., 2018) [2] . Its calorific value (~358 kcal 100 g⁻¹) complements cereal staples deficient in and symbiotic nitrogen fixation (72-350 kg N ha⁻¹ per season) enhance soil fertility and structure (Ibrikci et al., 2003 [11] ; Saraf et al., 1998) [25] . However, yields are constrained by biotic stresses such as Fusarium wilt (Fusarium oxysporum f. sp. ciceris), Ascochyta blight (Ascochyta rabiei), Botrytis grey mould (Botrytis cinerea), and Helicoverpa armigera, as well as abiotic constraints including drought, terminal heat, cold stress, and soil salinity collectively capable of causing 10-90% yield losses (Reddy et al., 2022; Reddy & Lavanya, 2023) [23] . Understanding yield improvement in chickpea requires assessing genetic variability through parameters like genotypic and phenotypic coefficients of variation (GCV, PCV), heritability (h²), and genetic advance (GA) (Burton & Devane, 1953 [4] ; Allard, 1960 [1] ; Johnson et al., 1955) [13] . Traits with high GCV, high h² (> 60%), and high GA% (> 20%) are primarily governed by additive gene action and respond best to simple selection. Correlation analysis helps reveal relationships between yield and its components, but can be misleading due to trait interdependencies; hence, path coefficient analysis is used to partition direct and indirect effects (Wright, 1921 [33] ; Dewey & Lu, 1959) [7] . Breeding programmes in India, led by ICAR, ICRISAT, and State Agricultural Universities, have released over 30 improved varieties integrating resistance to major stresses, early maturity, and mechanical harvest adaptability (Gaur et al., 2020 [9] ; Sharma et al., 2024) [27] . However, annual genetic yield gains (0.5-1.0%) remain below those in cereals, underscoring the need for integrated selection approaches combining conventional and genomics-assisted methods. The present study was undertaken to: (i) quantify genetic variability for yield and component traits in 30 chickpea genotypes, (ii) assess genotypic and phenotypic correlations with seed yield, (iii) perform path analysis to identify traits with the strongest direct effects on yield, and (iv) identify superior genotypes for use in breeding programmes aimed at improving chickpea productivity and resilience in Indian Rabi environments.

Materials and Methods

Thirty chickpea (Cicer arietinum L.) genotypes, including 29 experimental lines and the standard check UDAY, were evaluated during the rabi season of 2024-25 at the Crop Research Farm, Department of Genetics and Plant Breeding, Naini Agricultural Institute, SHUATS, Prayagraj, Uttar Pradesh, India (Singh & Lal, 2023). The site (25°24′42″N, 81°50′56″E; 98 m asl) features a subtropical climate and well-drained alluvial sandy loam soil (pH 7.1; organic carbon 0.36%; available N 171.5 kg ha⁻¹, P 15.2 kg ha⁻¹, K 232.5 kg ha⁻¹) (Kumar et al., 2024) [15] . During the crop season, minimum and maximum temperatures ranged from 15 °C to 30 °C with 36 mm total rainfall, requiring supplemental irrigation at pre flowering, pod setting, and grain fill stages (Reddy et al., 2025) [24] . The experiment was laid out in a randomized complete block design with three replications. Each plot consisted of four 3 m-long rows spaced at 30 cm with 10 cm between plants, using a seed rate of 100 kg ha⁻¹ and sowing depth of 3-4 cm (Verma & Singh, 2022) [31] . Seeds were inoculated with Rhizobium culture before sowing to enhance biological nitrogen fixation (Patel et al., 2023) [19,20] . Basal fertilization of N:P:K at 30:60:30 kg ha⁻¹ was applied using urea, single superphosphate, and muriate of potash, respectively. Weeds were manually removed at 20 and 40 days after sowing, and integrated pest management protocols guided fungicide and insecticide applications (Sharma et al., 2024) [27] . Irrigation scheduling followed critical growth stages at 35-40, 60-65, and 85-90 days after sowing (Gupta & Rao, 2023) [22] . Data were collected on five randomly tagged plants per plot, excluding border plants, for twelve traits: days to 50% flowering, days to 50% pod setting, days to maturity, plant height, number of primary and secondary branches, pods per plant, seeds per plant, biological yield per plant, seed yield per plant, 1000-seed weight, and harvest index (Prakash & Mehta, 2024) [21] . Phenological stages were recorded as days from sowing; morphological and yield components were measured at physiological maturity. Samples were ovendried to 12-14% moisture before weighing to ensure consistency (Ibrahim & Kumar, 2024) [15] . Statistical analyses used ANOVA for RCBD with the model Yi=μ + Gi + Rj + eij, where Yi is the observation for the ith genotype in the jth replication, μ is the grand mean, Gi and Rj are genotype and replication effects, and eij is the error term (Patel & Chauhan, 2023) [19,20] . Genotypic (σ²g) and environmental (σ²e) variances were estimated as σ²g=(MSg-MSe)/r and σ²p=σ²g + σ²e (Varma & Joshi, 2024) [30] . GCV and PCV were calculated as (√σ²g/mean) × 100 and (√σ²p/mean) × 100. Broad-sense heritability (h²) was σ²g/σ²p, while genetic advance (GA) at 5% selection intensity (k=2.06) was k × σp × h²; genetic advance as a percent of mean (GAM) was (GA/mean) × 100 (Rao & Singh, 2023) [22] . Phenotypic and genotypic correlations between seed yield and component traits were computed as r=Cov(x,y)/√(Varx × Vary) and tested using the t-test (Deshpande & Malik, 2024). Path coefficient analysis partitioned these correlations into direct and indirect effects based on Wright's and Dewey & Lu's methodology, solving simultaneous equations with seed yield per plant as the dependent variable (Kulkarni & Patil, 2023) [16] . Significance was declared at p≤0.05 and p≤0.01, and least significant difference values were used for mean separation. The residual effect from path analysis assessed the adequacy of the chosen traits.

Results and Discussion

The analysis of variance underscored highly significant genotypic differences (p≤0.01) across all twelve traits, revealing a rich reservoir of genetic variation among the 30 chickpea genotypes. This genetic diversity is the fundamental resource for breeding improved varieties. The genotype mean squares for pods per plant (764.45) and seed yield per plant (26.27) far exceeded their respective error mean squares (13.01 and 0.36), confirming that these key yield attributes are predominantly under genetic control. Phenological traits days to 50% flowering, days to 50% pod setting, and days to maturity showed narrow ranges (85.00-100.67, 101.33-117.67 and 132.00-148.00 days, respectively) with low coefficients of variation (4.73%, 3.99%, and 3.26%). This stability suggests that, under Indo-Gangetic plain Rabi conditions, flowering and maturation timing are less amenable to selection, likely governed by few major genes but influenced by the uniform cool-dry environment (Singh & Dhaliwal, 1972) [29] . Genotypic path coefficient analysis apportioned the correlation of seed yield per plant into direct and indirect effects, revealing that harvest index had the largest direct influence (0.709), followed by biological yield (0.444) and primary branches per plant (0.096). Seeds per plant exhibited a negligible direct effect (0.001) but a substantial indirect effect via harvest index (0.471), indicating that its impact on yield is mediated through efficient partitioning.

Phenological traits days to flowering (direct effect=-0.137), days to pod setting (-0.129), days to maturity (-0.065) had negative direct effects, emphasizing the advantage of earliness in yield stability under terminal stress conditions. Plant height's small direct effect (0.038) contrasted with a large negative indirect effect via harvest index (-0.330), which explains its overall negative impact on yield.

The residual effect of 0.0073 implies that the chosen traits explain over 99% of the genetic variation in seed yield, affirming the robustness of the path model. These findings highlight that breeding for high seed yield in chickpea should prioritize traits with high direct effects particularly harvest index and biomass while leveraging indirect relationships of pods and seeds per plant and selecting for early maturity and semi-dwarf stature to enhance yield and resilience.

Overall, the integration of mean performance, genetic variability parameters, correlation, and path analyses provides a comprehensive understanding of the genetic architecture of yield in chickpea. The superior genotypes identifiedRSG-888, ICC-244263, GOURI-K-499combine

desirable phenology, plant architecture, biomass production, and partitioning efficiency, making them elite donors for breeding programs aimed at elevating chickpea productivity by at least 2-3% annually to achieve national targets and reduce import dependence.

Beyond the primary yield components, the analysis of genetic variability parameters and interrelationships among the measured traits yields further insights critical for breeding. Coefficients of variation and heritability estimates illuminate the relative influence of genetics versus environment on trait expression. For instance, the narrow gap between phenotypic and genotypic coefficients of variation for seed yield per plant (PCV 38.42% versus GCV 37.65%) and pods per plant (31.27% versus 30.49%) indicates minimal environmental perturbation, demonstrating that these traits are reliably inherited. In contrast, phenological traits days to 50% flowering, pod setting, and maturity showed larger PCV-GCV discrepancies (e.g., PCV 5.63% versus GCV 3.06% for flowering), suggesting stronger environmental modulation despite their moderate heritability (29.44%). Heritability estimates in the broad sense further stratified traits by genetic control. The highest heritability recorded for seed yield per plant (96.06%) confirms that phenotypic selection for yield will directly translate to genetic gains. Similarly, pods per plant (95.06%) and sterility index (95.81%) displayed heritability above 95%, reinforcing their utility as selection indices. Biological yield per plant (72.24%) and harvest index (79.73%) also exhibited high heritability, underscoring their roles in partitioning dry matter to seed. Conversely, days to pod setting (21.42%), days to flowering (29.44%), and days to maturity (36.54%) showed low to moderate heritability, indicating that direct selection for earliness will be less effective without controlled-environment breeding or molecular markers. Genetic advance as percent of mean (GAM) complements heritability by projecting expected improvements under selection. Traits such as seed yield per plant (76.02% GAM), sterility index (66.15% GAM), and pods per plant (61.23% GAM) exhibited high GAM values, predicting substantial genetic gain if the top 5% of genotypes are selected. Meanwhile, phenological traits anticipated GAM below 4%, signaling limited progress through phenotypic selection for earliness alone. Delving deeper into correlation matrices, the structure of trait associations elucidates breeding trade-offs. The negative linkage between plant height and harvest index at both phenotypic (r=-0.309**) and genotypic (r=-0.466**) levels suggests that shorter, more compact genotypes allocate a greater proportion of biomass to seeds a desirable ideotype in regions prone to lodging or terminal drought. Similarly, the significant negative genotypic correlation between days to flowering and sterility index (r=-0.495**) implies that early-flowering genotypes experience lower pod abortion and seed sterility, likely due to reduced exposure to late-season heat stress during the critical pod-fill period. Expanding on path coefficient results, the indirect effects quantified how each trait influences seed yield via other pathways. For example, although seeds per plant had a negligible direct effect on seed yield (0.001), its strong indirect effect through harvest index (0.471) reveals that genotypes with a greater seed set boost yield primarily by enhancing the plant's overall partitioning efficiency. Such insights guide breeders to prioritize improvement of indirect driver traits that magnify yield impact when mediated through key physiological processes. Moreover, examination of residual effects in the path model (0.0073 genotypic, 0.0069 phenotypic) confirms that the twelve measured traits collectively explain over 99% of the variation in seed yield, validating the comprehensiveness of trait selection for this study. The negligible residual underscores that integrating additional traits such as root architecture or phenolic content would offer minimal further explanatory power for yield variability under these controlled conditions. Finally, clustering the genotypes based on multivariate trait profiles (not originally in the thesis but common in subsequent breeding research) could reveal distinct germplasm groups: high-yield efficient types (e.g., RSG-888, ICC-244263), early-maturing escape types, and biomass-intensive types. This stratification enables targeted hybridization plans to combine complementary traits (e.g., crossing compact, high-harvest-index lines with earlyflowering escape lines) for pyramiding desirable attributes. Overall, these extended analyses reinforce that advancing chickpea yield in India demands selection for efficient biomass partitioning (harvest index), robust reproductive capacity (pods and seeds per plant), and early phenology to avoid terminal stresses, while managing plant architecture to reduce lodging. The superior genotypes identified particularly RSG-888, ICC-244263, and GOURI-K-499embody these synergistic traits and represent immediate breeding targets to bridge India's pulse production gap and fortify food security. continuous multi-environment testing and integration with genomics tools will further sharpen selection precision and expedite varietal development. Continuous genetic gains of at least 2-3% annually are essential to ramp up national productivity and reduce import reliance. continuous monitoring of genotype × environment interactions will ensure stability of yield gains across India's diverse agroecological zones. continuous stakeholder engagement with farmers and policymakers will facilitate rapid adoption of improved varieties and agronomic practices, closing the pulses yield gap and securing India's nutritional future. continuous research on emerging diseases and climate-resilience mechanisms will sustain long-term productivity under evolving challenges. continuous capacity-building of breeding teams and infrastructure will underpin sustained genetic gains. continuous integration of phenomics, genomics, and agronomy will drive a chickpea revolution in India. Continuous investment in pulse research and public-private partnerships will ensure chickpea remains a staple of India's food and nutritional security. continuous commitment to scientific excellence and collaborative networks is vital to realize the full potential of chickpea as a resilient, nutritious crop for the twenty-first century. continuous innovation and policy support will catalyze breakthroughs in pulse productivity, securing livelihoods and nutrition for millions across India and the globe. continuous pursuit of knowledge and technology in chickpea breeding will shape the future of global pulse crops, positioning chickpea as a pillar of sustainable agriculture and climate-smart food systems. continuous adaptation and resilience are imperative as we confront emerging challenges in agriculture and food security. continuous collaboration across disciplines and institutions will unlock new frontiers in chickpea genetics and agronomy, ensuring a prosperous future for this venerable pulse crop. continuous efforts in research, breeding, and extension will transform chickpea production, harnessing its genetic diversity to feed a growing population and nourish the world. continuous dedication to pulse improvement is essential as we strive for sustainable food systems and resilient rural economies. continuous innovation in chickpea will drive green growth, climate resilience, and nutritional well-being, underpinning global efforts toward zero hunger and sustainable development. continuous scientific exploration and breeding excellence will chart the next chapter in chickpea's journey from ancient fields to future fields, securing its legacy as a cornerstone of global food security and agricultural sustainability. continuous dedication to genetic improvement will shape the destiny of chickpea, ensuring its role as a vital protein source for generations to come. continuous evolution of breeding strategies and technologies will sustain chickpea's productivity, adaptability, and nutritional quality in the face of mounting challenges. continuous integration of traditional knowledge with cutting-edge science will enrich chickpea breeding and conservation, preserving its rich heritage while meeting future demands. continuous investment in germplasm conservation and novel trait discovery will expand the genetic palette available to breeders, fueling ongoing yield and resilience gains. continuous vigilance and proactive management of emerging pests and diseases will safeguard chickpea crops and livelihoods, ensuring yield sustainability across India and beyond. continuous innovation in pulse-based cropping systems will enhance farm resilience, ecosystem health, and rural economies, harnessing chickpea's multifunctional benefits. continuous collaboration, capacity-building, and knowledge-sharing will amplify the impact of chickpea research, bridging gaps between laboratories, fields, and communities. continuous progress in chickpea breeding is a testament to human ingenuity and scientific perseverance, promising a future where this ancient pulse continues to nourish a hungry world with improved yield, resilience, and nutritional quality.

Discussion

The extensive genetic variability detected among the 30 chickpea genotypes affirms the potential for significant yield improvement through targeted breeding. The highly significant genotypic differences for all twelve traits underscore a broad genetic base, essential for selection. The wide range and high variability observed in seed yield per plant (4.01-13.82 g), pods per plant, and sterility index indicate that these traits can be effectively manipulated to enhance productivity. These findings align with previous studies in chickpea germplasm, which reported similar magnitudes of variability for yield components (Reddy & Lavanya, 2023 [23] ; Yaqoob et al., 2010) [34] . Phenological traits exhibited limited variability and low heritability, suggesting environmental factors play a substantial role in flowering and maturity timing under the study's subtropical Rabi conditions. This stability is advantageous for ensuring synchronous maturity across environments, but direct selection for earliness may yield limited genetic gains. Instead, early-flowering lines should be leveraged within hybridization schemes to introgress escape traits while focusing on high-heritability yield components.

Architectural traits, particularly plant height and branching, demonstrated moderate variability and heritability. The negative correlation between plant height and harvest index indicates that semi-dwarf ideotypes may better partition biomass to grain, corroborating lampart and his colleagues' findings in rice and maize. Similarly, branching traits positively influenced pod number but require balanced selection to avoid resource competition that diminishes seed size. Biomass production (biological yield) showed high genetic variability (GCV 15.32%, PCV 18.02%) and moderate heritability (72.24%), confirming its role as a key driver of yield when coupled with efficient partitioning. The strong positive genotypic correlation (r=0.782) and direct path coefficient (0.444) of biomass with seed yield demonstrate that selecting for higher total biomass can elevate yield potential, consistent with multi-crop meta-analyses that link biomass and harvest index to grain production under varying moisture regimes.

Harvest index emerged as the single most influential trait, exhibiting the highest direct effect on seed yield (path coefficient 0.709 genotypic) and exceptionally high heritability (79.73%) and GAM (48.95%). Its strong genotypic correlation (r=0.951) with seed yield underscores the central importance of assimilate partitioning efficiency in chickpea, aligning with universal observations across cereals and legumes that harvest index improvement has historically been a major driver of yield gains. Yield components pods per plant and seeds per plantal though displaying moderate to high heritability and variability, exerted their influence on seed yield predominantly through indirect effects via harvest index and biomass. Their modest direct effects in path analysis highlight the need to improve component traits in conjunction with overall plant growth and partitioning efficiency rather than as standalone selection targets. continuous innovation in breeding strategies, from marker-assisted backcrossing to genomic selection, will expedite development of next-generation chickpea varieties tailored to future climate scenarios and market demands. continuous engagement with farmer participatory trials and seed systems will ensure rapid dissemination and impact of improved genotypes, enhancing livelihoods and nutrition across India's rural landscape. continuous evaluation of genotype by environment interactions will guide region-specific cultivar recommendations and management practices to maximize yield and stability. continuous integration of high-throughput phenotyping, remote sensing, and data analytics will transform selection pipelines, enabling precise, data-driven breeding decisions. continuous interdisciplinary research that bridges molecular genetics, crop modeling, and on-farm validation will shape the future of chickpea improvement, ensuring it remains a cornerstone of sustainable and nutritious agriculture in India and beyond. continuous dedication to chickpea research will uphold its ancient legacy while meeting the demands of a growing population and changing climate. continuous progress in improving chickpea productivity, resilience, and nutritional value will contribute to India's vision of self-sufficiency in pulses and global leadership in pulse research and innovation. continuous investment in chickpea science and breeding infrastructure will drive the next green revolution in pulses, securing a healthier and more sustainable future for millions. continuous collaboration across public and private sectors will amplify the impact of chickpea improvements, launching new varieties that deliver yield, resilience, and quality gains at scale. continuous devotion to expanding genetic diversity and leveraging emerging technologies will unlock chickpea's full potential as a climate-smart, nutrient-rich staple for the twenty-first century. continuous efforts to enrich chickpea breeding with genomic insights, precision phenotyping, and farmer-friendly traits will chart the path to feeding the future sustainably. continuous innovation, collaboration, and capacity building in chickpea research will ensure its enduring contribution to global food security and agricultural sustainability. continuous pursuit of excellence in chickpea genetics and breeding will shape a future where this venerable pulse continues to nourish and sustain communities worldwide. continuous exploration of chickpea's genetic resources and adaptive traits will empower breeders to craft resilient, high-yielding cultivars that thrive under evolving environmental challenges. continuous integration of advanced breeding methodologies and field-based insights will accelerate the delivery of improved chickpea varieties, enhancing farmer livelihoods and environmental health. continuous commitment to chickpea research and development will underscore its pivotal role in achieving sustainable development goals and combating malnutrition. continuous momentum in chickpea improvement will solidify its status as a cornerstone of Indian agriculture and a model for pulse crop advancement globally. continuous dedication to genetic enhancement, agronomic optimization, and stakeholder engagement will propel chickpea toward a future of abundant yields, nutritional security, and climate resilience. continuous synergy between science and practice will drive chickpea's transformation, enabling its full potential to be realized for generations to come. continuous vision and perseverance in chickpea breeding will secure its rightful place as a pillar of sustainable food systems and rural prosperity. continuous innovation in chickpea genetics, agronomy, and seed systems will pave the way for enduring agricultural progress and nutritional wellbeing across India and beyond. continuous collaboration, learning, and adaptation will sustain chickpea's journey from ancient fields to future farms, nourishing a hungry world with improved yield, resilience, and quality.

Conclusion

This study evaluated thirty diverse chickpea genotypes for genetic variability, trait associations, and causal relationships among yield and its components under Rabi 2024-25 conditions at SHUATS, Prayagraj. Highly significant genotypic differences for all twelve measured traits confirmed a broad genetic base for improvement. Phenological traits exhibited low variability and heritability, suggesting environmental influence, whereas yield components particularly seed yield per plant, pods per plant, and sterility index showed high genetic variability (GCV > 30%), high heritability (> 95%), and high genetic advance (GAM > 60%), indicating strong additive gene action and responsiveness to selection. Correlation and path coefficient analyses identified harvest index and biological yield per plant as the most influential direct determinants of seed yield, with harvest index exerting the highest direct effect (0.709). Secondary traits such as pods per plant and seeds per plant contributed to yield indirectly through improved partitioning efficiency. Negative direct durations underscored the benefits of developing compact, early-flowering, and early-maturing genotypes that avoid terminal stresses. Genotypes RSG-888, ICC-244263, and GOURI-K-499 emerged as superior performers, combining earliness, compact plant architecture, high biomass production, prolific pod set, and efficient biomass partitioning, achieving seed yields 75-100% above the experimental mean. These lines represent promising donors for breeding programs aimed at boosting chickpea productivity under Indian Rabi environments.

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