The growing global population, diminishing biodiversity, limited agricultural resources, poverty and the formidable climatic conditions have significant impact on worldwide food and nutritional security. Pulses play a crucial role in sustainability of agriculture and can provide effective solution to address these concerns as they contribute significantly to environmental, economic and social aspects of farming systems (Singh, 2017). Mungbean [Vigna radiata (L.) Wilczek], is one of the oldest cultivated and most important pulse crops. It is a diploid (2n= 2x =22), belongs to family Leguminosae; subfamily papilionoideae and tribe Fabeae and it is believed to be originated in the Indian subcontinent, with evidence of its cultivation dating back over 4,000 years. It was an important crop in ancient India and spread to Southeast Asia and other regions through trade and agricultural expansion (Pratap  et al., 2021). Mungbean being a pulse crop plays a key role in crop rotation by interrupting the conventional sequence of cereal crops (Rice-Wheat) and can be utilized as a green manure also (Sharma et al., 2023). It is highly valued for its nutritional quality as it is rich in protein (25.10%), carbohydrates (59-65%), fiber (13.40%), vitamin A (83 mg/100 g), thiamine (0.72 mg/100 g) and riboflavin (0.15 mg/100 g) (Vairam et al., 2016). Therefore, enhancing mungbean production and productivity is imperative to meet the escalating global demand for vegan protein-rich diets, address food security challenges and optimize resource use in sustainable agriculture.
       
Mungbean breeding faces significant bottlenecks due to limited genetic diversity, low harvest index and the complex interplay of biotic and abiotic stress, which hinders the development of robust, high-yielding cultivars. Consequently, to develop mungbean cultivars that are both locally adapted and high-yielding, breeding programs must incorporate a diverse array of genotypes. This approach is essential for integrating various traits that address the needs of farmers and end consumers (Neupane et al., 2021). Genetic diversity among the genotypes serves as a crucial resource for breeding programs, aiding in the development of innovative farming systems, enhancing production diversity and generating new high-quality products (Jing et al., 2010). As yield is the most important agronomic and economic feature in crops which is a outcome of numerous interacting component characteristics that have relatively low heritability and are influenced by multiple loci with mostly uncertain genetic foundations, hence, to develop a plant genotype with an appropriate blend of attributes, complete information on the relationship of these traits with yield, as well as detailed a priori knowledge of the genetic mechanism governing these traits, are required for any successful breeding program. Furthermore, insights into the genetic relationships between yield and its characteristics will assist in defining selection criteria and pinpointing beneficial secondary traits. This will ultimately lead to improved yields and will broaden the potential for success in breeding programs (Evans and Fischer, 1999). In addition, as for two traits, correlation doesn’t exist among them but it involves complicated pathways including many other traits also. As a result, selection for seed yield will be more effective when path coefficient partitioning the relationship into direct and indirect effects, which demonstrates the relative relevance of each of the causal components (Dewey and Lu, 1959), are computed. Hence, the current investigation was undertaken to estimate the genetic variability, trait associations and diversity among 142 mungbean genotypes for seed yield and its allied traits.

The experimental material consisted of 142 diverse mungbean genotypes including released varieties from different agro-climatic zones of India and advanced genotypes developed at CCS HAU, Hisar. These genotypes were evaluated during kharif 2021 and 2022 in Randomized Block Design with two replications. Each genotype was planted in three rows, each of 4m length with spacing of 45cm between the rows and 10 cm between plants. The experiment was planted at Pulses Research Area, Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, Haryana. This site is geographically positioned at coordinates 29.15^oN, 75.75^oE and is characterized by a semi-arid climate with high temperatures in summer and low temperatures in winter. It is situated on the edge of the southwest monsoon region and has a tropical dry climate receiving about 350-400 mm average yearly rainfall. All suggested agronomic practices were implemented to raise a high-quality crop. The 142 mungbean genotypes were evaluated based on nine quantitative traits viz., days to 50% flowering, days to maturity, plant height, number of branches per plant, number of pods per plant, number of seeds per pod, pod length, 100-seed weight and seed yield per plant. Genetic variability was assessed using genotypic coefficients of variation (GCV) and phenotypic coefficients of variation (PCV), as outlined by Burton and de Vane (1953). Heritability in the broad sense for each trait was calculated as per Singh and Chaudhary (1985), while the expected genetic advance at a 5% selection intensity was calculated as per Johnson  et al. (1955). The city block (Manhattan) distance was employed to construct a dendrogram through hierarchical clustering of genotypes, using the Unweighted Pair-Group Method with Arithmetic Averages (UPGMA). Statistical analysis was conducted using R Studio version 2023.06.2+561. The list of genotypes and their pedigree used in the study are described in Table 1.

Analysis of variance and yield performance of genotypes
 
The pooled mean squares for each of the nine traits under study were highly significant (P<0.01) for genotypes, indicating a substantial amount of variability in the experimental material. All through analysis of means, coefficient of variation and critical differences (CD) further revealed significant difference among the genotypes for all traits, confirming the presence of considerable genetic variability (Table 2). The box plot distribution in Fig 1 illustrates the mean performance of 142 mungbean genotypes pertaining to seed yield and its attributes. Seed yield per plant showed significant variation among genotypes, ranging from 4.21 (g) to 18.37 (g). Out of the 142 genotypes, 69 genotypes displayed seed yields above the mean (10.46 g) and highest seed yield per plant was observed in MH 1762 (18.37 g) followed by MH 1871 (17.08 g), MH 1703 (16.66 g) and IPM 604-16 (16.16 g), while the lowest yield was recorded in genotype VGG 16-036 (4.21 g). Similar reports were found in the investigations of Govardhan  et al. (2017); Muthuswamy  et al. (2019); Talukdar  et al. (2020); Afroz  et al. (2022); Samita  et al. (2022); Thonta  et al. (2023) and Kumar  et al. (2024). The wide spectrum of variation observed for studied traits among these genotypes provides plant breeders with ample opportunities to select superior and desired genotypes for crop improvement programmes.




 
Genetic variability parameters
 
The estimates of characteristics revealed that the phenotypic coefficients of variance (PCV) for almost all the qualitative characters was marginally higher than their corresponding genotypic coefficient of variance (GCV), indicating that the expression of the trait was significantly influenced by genetic factors rather than environmental variables (Fig 2). The genotypic coefficients of variation (GCV) and phenotypic coefficients of variation were categorized as low (<20%), moderate (20-30%) and high (>30%) as suggested by Sivasubramanian, J. and Menon, P.M. (1973). Among all the traits studied, seed yield per plant, number of branches per plant and plant height expressed high GCV and PCV, while moderate genotypic and phenotypic coefficient of variation were observed for 100-seed weight, number of pods per plant, number of seeds per pod and pod length. Low estimates of GCV and PCV were recorded in days to 50% flowering and days to maturity. Our findings are in accordance to the studies of Hemavathy  et al. (2015), Nalajala et al., (2022) and Sofia  et al. (2023).


       
According to Johnson  et al. (1955), heritability and the anticipated genetic advance as a percentage of the mean are directly linked to the extent of additive gene effects. Consequently, straightforward selection would prove effective for traits exhibiting high heritability and high genetic advance. High heritability (>60%) estimates were recorded for all the traits viz., 100-seed weight, number of branches per plant, days to 50% flowering, days to maturity, pod length, seed yield per plant, plant height, number of pods per plant and number of seeds per pod. In the present investigation, high magnitude of genetic advance as per cent mean (>20%) along with high heritability was recorded for seed yield per plant and number of branches per plant. Earlier studies of Nalajala  et al. (2022) for seed yield per plant while that of Yoseph  et al. (2022) for days to maturity and days to 50% flowering are also in agreement with the findings of the present study. The remaining traits exhibited high heritability (>60%) with moderate (10-20%) or low (<10%) genetic advance revealing control of non-additive gene action and high influence of environment on all these characters and therefore, intermating in early generations of the hybridization programme can be helpful. Talukdar  et al. (2020); Harini  et al. (2022) and Sofia  et al. (2023) recorded similar results for number of branches per plant and number of pods per plant.

Association among seed yield contributing traits
 
Since seed yield is complex trait influenced by many factors and have a low genetic inheritance, therefore, relying solely on heritability and genetic advance for this trait selection could lead to suboptimal choices, whereas incorporating both of these along with association of yield with other traits may offer greater benefits. So, inferential understanding of the association between yield and its related causal factors is crucial for developing effective plant selection guidelines. A thorough critical perusal of the scatter plot (Fig 3) revealed significant phenotypic correlation coefficients between the traits. In the present investigation number of seeds per pod, number of pods per plant, hundred seed weight, pod length and number of branches per plant exhibited positive significant association with seed yield per plant. Furthermore, correlation coefficients among different yield related traits were assessed in mungbean and results are presented in Table 3. Hence, opting for these traits for selection could lead to a noteworthy increase in yield. To determine yield related characters affecting seed yield directly or indirectly, analysis of path coefficient is helpful. Path coefficient usually lies between -1 and +1, where values closer to-1 represent strong negative effect and those closer to +1 indicate strong positive effect. The highest positive direct effect on seed yield per plant was exhibited by number of seeds per pod (0.4645) followed by number of pods per plant (0.3920), 100-seed weight (0.2245), days to maturity (0.0743) and plant height (0.0360), while number of branches per plant (-0.0636), pod length (-0.1114) and days to 50% flowering (-0.1886) exhibited negative direct effect on seed yield per plant (Table 4). In order to enhance the seed yield, the characters that have high direct effects on seed yield per plant are considered as the most important traits for selection. These results of correlation and path were in accordance with the findings of Dhunde  et al. (2021); Parsaniya  et al. (2022); Rahevar  et al. (2023); Aravinth  et al. (2023) and Srivastava  et al. (2024).






 
Cluster analysis
 
Hierarchical cluster analysis using Unweighted Pair Group Method using Arithmetic Averages (UPGMA) method was used to classify the 142 mungbean genotypes on the basis of nine yield related quantitative traits. The 142 genotypes of mungbean were grouped into eight clusters and maximum genotypes were grouped in cluster V (29) followed by cluster II (27), cluster I (22), cluster III (17), cluster VI and VII (16 each), cluster VIII (10) and cluster IV (5) (Fig 4). Cluster analysis by Abna et al. (2012) resulted in three major clusters in 20 greengram genotypes. Similarily, Gokulakrishnan  et al. (2012); Rahangdale  et al. (2023) and Chauhan  et al. (2024) also assessed genetic divergence in mungbean and reported four, seven and nine clusters, respectively.


       
Maximum intra-cluster distance was observed in cluster I while minimum was recorded in cluster IV, close relatedness among the genotypes of cluster IV. Maximum inter-cluster distance was observed between cluster II and cluster V followed by cluster I and cluster V and cluster I and cluster II whereas, minimum inter-cluster distance was recorded in cluster III and cluster VII (Table 5). This suggested that genotypes from the most distant clusters can be used as parents to obtain more heterotic combinations in hybridization programme. Similar findings were reported by Rahangdale  et al. (2023) and Chauhan  et al. (2024).


               
The cluster IV consisted of genotypes that had maximum mean values for almost all the yield contributing traits viz., number of branches per plant, number of pods per plant, number of seeds per pod and seed yield per plant. Cluster VI showed highest mean value for 100-seed weight whereas cluster VII exhibited highest mean value for pod length. Early flowering and maturing genotypes were observed in cluster IV, cluster VII and cluster II (Table 6). Largely, better performing genotypes with high values of seed yield and yield influencing traits from cluster IV and cluster VIII could be used in the breeding programmes to combine yield contributing traits. Genotypes MH 1468, MH 1871, MH 1762, IPM 604-16 and IPM 205-7 were found early maturing along with high mean values of yield related characters such as number of seeds per pod, number of pods per plant, 100-seed weight and seed yield per plant. These genotypes can further be used in breeding programmes for mungbean improvement. 

The understanding of variability present among the genotypes of a crop species is of utmost importance for their genetic improvement to produce high yielding cultivars. In this study, high magnitude of variability was observed for number of seed yield per plant followed by number of branches per plant, plant height and 100-seed weight implying adequate scope for selection. Significant association and positive direct effect of number of seeds per pod, number of pods per plant and 100-seed weight on seed yield suggested that for yield improvement in mungbean, selection for these traits would be beneficial. Broad range of diversity among the genotypes was also observed through cluster analysis which categorized the mungbean genotypes into eight distinct clusters. The diversity studies suggested hybridizing genotypes from Cluster II and Cluster V to produce superior transgressive segregants. Overall, genotypes MH 1468, MH 1871, MH 1762, IPM 604-16 and IPM 205-7 were found superior regarding early maturity and yield related characters, therefore, these can further used be in breeding programmes for mungbean improvement. The findings of this study will aid in identifying heterotic clusters and superior parent lines, which are crucial for designing effective breeding strategies to develop enhanced mungbean cultivars.
 

The authors gratefully acknowledge Chaudhary Charan Singh Haryana Agricultural University, Hisar, India, for providing all field facilities for conducting this study. The authors acknowledge the station managers and technical staff of the Pulses Section, Department of Genetics and Plant Breeding, CCS HAU, for technical assistance and overall support.
 
Data availability statement
 
All the data associated with the manuscript is mentioned in the text of the manuscript

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.