Chickpea (Cicer arietimum L.) is commonly known as gram or Bengal gram, is an important food legume crop belong to the Fabaceae family. It is widely grown across South Asia, Mediterranean basin, Australia, North America  and East Africa. Chickpea seeds are rich in protein (18-24%), carbohydrates, vitamins and essential minerals, making them an important component of human nutrition. Based on seed characteristics, cultivated chickpea is classified into two major market types, namely desi and kabuli. Desi chickpea possesses coloured and angular seeds, whereas kabuli chickpea is characterized by larger cream-coloured seeds with a smooth seed coat.
       
Despite its economic importance, the productivity of chickpea remains below its genetic potential due to limited utilization of the complicated inheritance of yield along with related characteristics, as well as the restricted use of available genetic variability. Yield is a multigenic characteristic greatly influenced by environmental conditions and interactions among component characters.
       
The low genetic composition of the available cultivars is the main cause of this. In order for a plant breeder to select optimum yielding genotypes and genetic diversity is essential for crop development and a requirement for any breeding program. Estimates of a population’s heritability and genetic advancement provide insight into the anticipated gain in subsequent generations. Plant breeders can choose a particular genotype from various genetic populations with the use of heritability estimation.
       
Information regarding the interconnection between seed yield and its related character is require for any successful selection. By using the correlation along with path coefficient analysis, one can statistically estimate the association of one or more characteristics. Correlation analysis is employed in plant breeding to quantify the degree (strength) and the direction of an association among several variables, as well as to identify the component traits that may be utilized in selection for chickpea improvement.
       
The correlation coefficients were partitioned into direct and indirect effects for different independent traits through path coefficient analysis. Genetic divergence analysis provides valuable information regarding the extent of diversity among genotypes and facilitates identification of genetically distinct parents for hybridization programme. Crosses involving parents with diverse genetic backgrounds have a greater likelihood of generating transgressive segregants and therefore increase the diversity among populations. Therefore, the current research was conducted to assess heritability, correlation, genetic variability, path coefficient analysis, genetic divergence among forty-one chickpea genotypes for identifying important traits and superior genotypes useful in chickpea breeding programmes.

The experimental material utilized in the current investigation consist of forty-one chickpea genotypes. These genotypes were procured from IIPR Kanpur, CSA Kanpur and SVPUAT Meerut (Table 1).


 
Experimental site and experimental design
 
The experimental material was examined in randomized block design with three replications at research farm of the Dept. of Genetics  and P. Breeding, CCS University Meerut, Uttar Pradesh, during the rabi season of 2021-2022. The genotypes were planted by dibbling seeds in single rows of 4 m long, maintaining 30 cm spacing between rows and between plants 10 cm. To ensure optimal crop growth and development, all necessary agronomic practices were implemented.
 
Statistical analysis
 
The statistical analysis was performed using the mean values of each genotype for all characters across replications. Analysis of variance was conducted according to the procedure described by Panse and Sukhatme (1967). Phenotypic and genotypic coefficients of variation (PCV and GCV) were estimated following Burton and Devane (1953). Broad-sense heritability and genetic advance were calculated as suggested by Johnson et al., (1955). Correlation coefficients were determined using the method of Al-Jibouri et al. (1958), while path coefficient analysis was performed following Dewey and Lu (1959). Cluster analysis was carried out using squared Euclidean distance and genetic divergence was estimated through the Mahalanobis D² statistic (1936). As the experiment was conducted at a single site during one crop season, calculation of variability, genetic advance and heritability should be interpreted under the prevailing environmental conditions. Further validation through multi-location and multi-season testing is necessary for broader applicability of the observed genetic parameters. Statistical computations were performed using SPAR software developed by the IASRI, New Delhi.

Analysis of variance
 
The mean of square (Table 2) attributable to treatment for all traits under study were highly significant at 1% level of probability (P≤0.01), reveal the existence of substantial genetic variation among the evaluated genotypes for all characters examined. The observed variability provides ample scope for the selection of promising genotypes that may either be released as varieties or utilized as donor parents in future chickpea breeding and hybridization programmes. The variation detected among these genotypes may be attributed to differences in their genetic constitution along with the influence of environmental factors. Comparable finding has also been documented by Thapa et al. (2023); Yadav et al. (2024); Kumar et al., (2025).


 
Phenotypic and genotypic coefficient of variation
 
A high magnitude of genetic variability among genotypes offers greater scope for efficient selection and genetic enhancement in crop improvement programmes. Hence, PCV and GCV are widely applied to study the significance of the variability existing among genotypes. The reason behind using GCV is that it indicates the heritable part of the total variance. In the present investigation, PCV estimates were greater than the corresponding GCV values for all studied characters (Table 3 and Fig 1), suggesting that the observed variability was governed by both genetic factors and environmental influences affecting the expression of the traits.




       
GCV  and PCV were noticed highest for seeds/plant, then by total pods/plant, 100 seed weight, seed yield/plant, biological yield, primary branches, seeds/ pod, plant height. This specify the existence of substantial variability for these traits and suggests considerable scope for their improvement through suitable breeding strategies. The value of GCV  and PCV ranged from 0.42% and 0.68% for days to maturity 43.2% and 44.50% for no. of seeds plant, respectively. Similar observations were reported by Thapa et al. (2022); Jain et al. (2023); Prathyusha et al. (2024); Patel et al. (2025).
 
Heritability and genetic advance
 
The proportion of overall phenotypic variation that can be attributed to genetic variables is known as heritability and it is an essential measure for determining how characteristics are passed from parents to progeny. Genetic advance is the expected enhancement brought about by selection and it expressed as the deviation between mean genotypic value of chosen individuals and that of the original population prior to selection and magnitude of genetic advance depends on heritability, selection intensity and genetic variability. Thus, genetic advance as well as heritability are considered major criteria for selecting and improving desirable traits. The prevalence of additive gene expression in the inheritance of a characteristic is typically indicated by high heritability combined with significant genetic advance, which creates favourable conditions for efficient selection. Conversely, moderate or high heritability associated with low genetic advance demonstrated that the predominant of non-additive gene effect, thereby reducing the efficiency of selection.
       
The highest heritability estimated were recorded for no. of seeds/plant (93.44%), then by total pods/plant (91.47%), hundred seed weight (90.85%), biomass/plant (88.51%), seed yield (82.58%), no. of seeds/pod (77.97%), reflecting a strong genetic control over these traits. The seeds/plant, biomass/plant and total pods/plant revealed the greatest genetic advance. The characters with greatest GA as % of mean ware seeds/plant (85.66%), next to it by pods/plant (61.43%), hundred seed weight (54.71%), biomass/plant (43.34%), seed yield (42.39%), seeds/pod (33.08%). The combination of high heritability  and GA as % of mean was reported for seeds/plant, pods/plant, hundred seed weight, biomass/plant, seed yield, seeds/pod, revel that these characters are primarily determined by additive gene action and can be successfully enhanced by direct selection. The results are presented in Table 3 and Fig 2. Similar finding has also been reported by Verma et al. (2023); Jain et al., (2023); Itana et al. (2024); Patel et al. (2025).


 
Correlation coefficient analysis
 
Successful selection programmes largely depend on understanding the relationship among different traits and these association are statistically evaluated through correlation and path analysis. Correlation examine the interdependence among traits and provides information regarding both the direction and degree (magnitude) of association between two or more variables. Additionally, it helps discover significant characteristics that can be considered as selection parameters for genetic enhancement of seed yield. In this investigation, seed yield/plant revealed significant positive relationship with pods/plant, seeds/pod, seeds/plant, harvest index, biological yield/plant (Table 4).


       
Among the inter trait association, no. of seeds/plant reported significant positive relationship with pods/plant, seeds/pod, primary branches/plant, biomass/plant, whereas exhibiting negative relationship with hundred seed weight. Primary branches displayed positive significant relationship with pods/plant, seeds/plant, biomass/plant. Hundred seed weight was positively significantly associated with days to maturity and flowering, plant height, whereas it demonstrates negative correlation with seeds/plant, seeds/pod, pods/plant. Biological yield with maturity days, plant height, seeds/pods, pods/plant, seeds/ plant, branches/plant. These imply that the characteristics mentioned above should be considered as essential selection criteria in order to increase chickpea seed yield. Similar finding has been reported by Ningwal et al. (2023); Jain et al., (2023); Kumar et al., (2025).
 
Path coefficient analysis 
 
Path coefficient, commonly known as standardized partial regression coefficients, are valuable statistical measures that partition correlation in both direct as well as indirect effect of various independent traits on dependent trait. This tool offers valuable information for determining crucial traits to consider into account in selection programs meant to increase yield and is therefore widely used in indirect selection. The interrelationship among component traits may differ in both magnitude and direction, which can mask the actual association between related characters and seed yield. Hence, to evaluate each trait’s unique contribution, it is essential to divided correlation into direct and indirect effect.
       
All of the investigated characters’ direct and indirect influences on seed yield were calculated (Tables 5 and 6). Path analysis revealed, seeds/pod, biomass/plant, plant height, harvest index exerted strong positive direct effect towards seed yield. In contrast hundred seed weight, seeds/plant, branches/plant, pods /plant revealed negative direct impact on seed yield, suggesting that their relationship with yield was mainly result of indirect influences through other contributing traits rather than their own direct effect. Maturity and flowering days, plant height, hundred seed weight, seeds/pod, pods/plant, seed/plant, branches/plant, exabited indirect positive effect towards seed yield by biological yield. Similarly, pods/plant, flowering days, seeds/pod, seeds/plant contributed indirect positive effect towards seed yield through harvest index. Comparable findings have also been reported by Ningwal et al. (2023); Jain et al., (2023); Kakaei et al. (2025).




 
Genetic divergence
 
The degree of genetic variability present in a crop population plays an essential role in determining the efficacy of any breeding and improvement programs. Genetic diversity is a crucial factor in choosing distinct parents for hybridization programs. It can be efficiently quantified using biometrical approaches such as Mahalanobis D² statistics (Mahalanobis, 1936) which is widely employed for identifying genetically diverse parents suitable for planned hybridization. Genetic divergence analysis based on Mahalanobis D² statistics and grouping by Tocher’s method (Rao, 1952) classified the 41 chickpea genotypes into three distinct clusters (Table 7  and Fig 3). The majority of genotypes were found in Clusters I as well as II, with sixteen genotypes each, whereas Cluster III comprised only nine genotypes.




       
An examination of Table 8 revealed that Cluster III (D=2.918) had the largest intra-cluster distance, in comparison to Cluster II and I (D=2.529  and D=2.261), respectively. These values confirm that the genotypes categorized inside each cluster exhibit adequate genetic variation.


       
The inter cluster distance revealed that cluster II  andIII were the most divergent, exhibiting the highest distance (D=4.432) then between Cluster I and III (D=3.510) and Cluster I and II (D=3.262). These clusters’ genotypes were genetically distinct and might be considered potential progenitors in hybridization programs for improving yield and other desirable traits.
       
Cluster III recorded maximum values of cluster mean for primary branches, plant height, 100 seed weight. Cluster II exhibited superior for total pods/plant, seed yield, biomass/plant, seeds/pods, seeds/plant. Cluster I found best for harvest index and minimum mean value for maturity and flowering days (Table 9). Minimum cluster means for flowering and maturity indicate the suitability of this cluster for developing early flowering and early maturing chickpea genotypes.


       
Genotypes exhibiting superior mean performance within a cluster may either be directly utilized as pureline varieties after evaluation or employed as promising parents in hybridization programmes for chickpea improvement. In cluster II, the genotypes RDH-52, BG-372, JG-2001-115 and K-1065 were identified as suitable for early flowering and maturity. Genotypes JG-226, DC-18-1107, ICC-8948, JG-2001-115, ICC-7549 and P-1106 were found superior for seed yield. Likewise, DC-18-1107, ICC-8948, 6954-2, ICC-15926, ICC-7549 and P-1106 were suitable for total seeds/plant and no. of pods/plant. In cluster III, the genotypes JG-141, DAHODYLIAL and SC-3 were identified as desirable for 100 seed weight, whereas RVG-203 and C-911 from cluster I were found promising for biological yield.
 
Character contribution towards genetic divergence
 
The relative contribution of eleven attributes in relation to genetic divergence is illustrated in Fig 4. Among all the evaluated characters, the greatest contribution to genetic diversity was recorded for plant height (11.75%) then by days to maturity (11.25%), primary branches/ plant (11.00%), days to flowering (10.45%) and harvest index (10.06%). Similar discovery has been earlier discovered by: Patel et al. (2025); Swetha et al. (2024); Yadav et al. (2023).

Based on the current investigation, it can be stated that chickpea genotypes evaluated revealed sufficient genetic variability for all characters, indicating considerable scope for selection, development off transgressive segregants and genetic enhancement in chickpea. The consistently higher PCV values for 11 characters than correspond GCV, suggest the observed variation was governed by environment and genetic variation among genotypes influenced trait expression. Genetic improvement may not always be achieved through selection based only on heredity. Therefore, Heritability and genetic advancement performed together give a more trustworthy basis for identifying superior genotypes in crop improvement programmes. For all trait combinations, correlation at genotypic level were greater than corresponding phenotypic correlation, demonstrated the associations between these characters were predominantly genetically in nature and could be effectively exploited through indirect selection.
       
The calculations of intra and inter-cluster distances demonstrated the presence of adequate genetic diversity both within  and between clusters. Between clusters II  and III, found greater inter cluster distance, this reveal the greater amount of diversity between the genotypes grouped into both clusters. Hybridization involving genotypes from these divergent clusters may generate optimum variability and widespread heterosis in segregating generations, thereby facilitating chickpea improvement. The current study’s findings showed that there was a significant amount of genetic heterogeneity among the assessed chickpea genotypes, offering ample opportunities for effective selection and breeding advancement. The superior performance of genotypes such as JG-226, DC-18-1107, ICC-8948, ICC-7549, P-1106 and JG-2001-115 for seed yield and associated traits suggests their potential utility as promising parents in future chickpea breeding programmes. However, their performance should be validated across multiple locations and seasons before their broader utilization in breeding programmes.

The authors sincerely acknowledge C.C.S. University, Meerut for providing the research facilities and necessary support to carry out this study.
 
Disclaimers
 
The views expressed in this article are solely of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided.

The authors declare that there are no conflicts of interest regarding the publication of this article. No funds were available for supporting the manuscript.