Genetic Diversity Analysis in Green Gram (Vigna radiata (L.) Wilczek)

Introduction

Green gram (Vigna radiata (L.) Wilczek), commonly known as mung bean, is a selfpollinated diploid (2n = 22) grain legume of the family Fabaceae, subgenus Ceratotropis of the genus Vigna. It is one of the most important pulse crops of tropical and subtropical regions, valued for its short duration, wide adaptability, high protein content (20-25%) and ability to fix atmospheric nitrogen, making it an important component of sustainable cropping systems. India is the largest producer and consumer of green gram, cultivating the crop across kharif, rabi and summer seasons. Despite its nutritional and agronomic importance, green gram productivity remains low relative to its genetic potential, owing to biotic stresses such as yellow mosaic virus, powdery mildew and Cercospora leaf spot, and abiotic stresses including drought, heat and salinity. Development of high-yielding, stresstolerant cultivars requires a thorough understanding of the genetic variability and divergence present within available germplasm. Multivariate techniques, particularly Mahalanobis' D² statistics (Mahalanobis, 1936; Rao, 1952) [7,10] , allow simultaneous evaluation of divergence across several correlated traits and enable grouping of genotypes into genetically homogeneous clusters, thereby guiding the choice of divergent parents for hybridization. Complementary approaches such as correlation and path coefficient analysis (Dewey and Lu, 1959) [3] and principal component analysis further help identify the traits contributing most to yield and total genetic divergence.

The present investigation was therefore undertaken with the objectives of (i) assessing morphological diversity among seven green gram genotypes, (ii) estimating growth, yield and earliness parameters, and (iii) measuring the degree of genetic divergence among the genotypes to identify promising parents for future breeding programmes.

Materials and Methods

Experimental site

The experiment was conducted during Kharif 2025 at the Research Farm, School of Agricultural Sciences, G H Raisoni University, Saikheda, District Pandhurna, Madhya Pradesh, India (21.90° N, 78.78° E; altitude ~650-700 m amsl). The site falls in the Central Plateau and Hills agroclimatic zone with medium to deep black cotton soil (Vertisol) and a sub-tropical climate receiving 900-1100 mm annual rainfall, mostly during June-September.

Plant material and experimental design

Seven green gram genotypes -BM-4, Vaibhav, Utkarsha, AKM-8802, AKM-9904, IPM-02-03 and Samratrepresenting a range of maturity duration (60-80 days) and diverse breeding sources (Mahatma Phule Krishi Vidyapeeth, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Indian Institute of Pulses Research and Jawaharlal Nehru Krishi Vishwa Vidyalaya) were evaluated in a Randomized Block Design (RBD) with three replications, following recommended agronomic package of practices including a basal dose of 20 kg N and 40 kg P₂O₅ ha⁻¹ and two manual weedings at 20 and 35 days after sowing.

Observations recorded

Data were recorded on five randomly selected competitive plants per genotype per replication (days to 50% flowering and days to maturity on a plot basis) for twelve yield and yield-contributing traits: days to 50% flowering, days to maturity, plant height, primary branches per plant, clusters per plant, pods per plant, pod length, seeds per pod, 100seed weight, biological yield per plant, harvest index and seed yield per plant.

Statistical analysis

Analysis of variance was performed following Panse and Sukhatme (1985) [9] . Genotypic (GCV) and phenotypic (PCV) coefficients of variation, broad-sense heritability and genetic advance as percent of mean were estimated as per Burton (1952) [1] , Johnson et al. (1955) [5] and Lush (1949). Genotypic and phenotypic correlation coefficients were computed, and path coefficient analysis was carried out following Dewey and Lu (1959) [3] to partition correlations into direct and indirect effects on seed yield. Genetic divergence was assessed using Mahalanobis' D² statistic (Mahalanobis, 1936) [7] , with genotypes grouped into clusters by Tocher's method (Rao, 1952) [10] . Principal component analysis (PCA) was performed on standardized trait values, and genotype stability across environments was estimated following the Eberhart and Russell (1966) [4] model using mean performance, regression coefficient (bi) and deviation from regression (S²di).

Results and Discussion

Analysis of variance and mean performance

Analysis of variance revealed highly significant (P ≤ 0.01) differences among the seven genotypes for all twelve characters studied, confirming the presence of substantial genetic variability available for selection (Table 1). Mean seed yield per plant ranged from 10.24 g (IPM-02-03) to 12.48 g (AKM-8802), with an overall mean of 11.51 g plant⁻¹ (14.72 q ha⁻¹). AKM-8802 recorded the highest values for plant height, branches, clusters, pods per plant, pod length, seeds per pod, 100-seed weight and seed yield, followed closely by AKM-9904 and Utkarsha, whereas IPM-02-03 and BM-4 were the earliest to flower and mature.

Genetic variability parameters

Phenotypic coefficients of variation were consistently slightly higher than the corresponding genotypic coefficients of variation across traits, indicating comparatively limited environmental influence on trait expression. High heritability coupled with high genetic advance as percent of mean was observed for seed yield per plant, number of pods per plant, biological yield and 100-seed weight, indicating predominance of additive gene action and suggesting that these traits would respond effectively to direct phenotypic selection. In contrast, traits such as days to flowering and days to maturity, though highly heritable, showed comparatively lower genetic advance.

Correlation and path coefficient analysis

Genotypic correlations were generally higher than phenotypic correlations, indicating that environmental effects marginally reduced observed associations. Seed yield per plant exhibited strong positive correlation with pods per plant (r = 0.91), clusters per plant (r = 0.82), branches per plant (r = 0.69), 100-seed weight (r = 0.66) and seeds per pod (r = 0.61), while days to flowering (r = -0.32) and days to maturity (r = -0.27) were negatively associated with yield, indicating a yield advantage for relatively earlier genotypes ( [6,11] .

Genetic divergence and cluster analysis

Mahalanobis D² analysis grouped the seven genotypes into five clusters using Tocher's method (Table 3). Cluster I (BM-4, Vaibhav) and Cluster II (Utkarsha, AKM-8802) each contained two genotypes with comparable morphological and yield attributes, whereas AKM-9904, IPM-02-03 and Samrat each formed solitary clusters, reflecting their unique genetic constitution. The maximum inter-cluster distance was recorded between Cluster V (Samrat) and Cluster III (AKM-9904), indicating that these genotypes are the most genetically divergent among those evaluated and would be expected to yield superior transgressive segregants upon hybridization. This finding is consistent with previous divergence studies in green gram, which similarly report that clustering does not necessarily correspond to geographic origin (Murty and Arunachalam, 1966; Charles Wesly et al., 2020) [8,2] .

Stability of genotypes across environments

Stability analysis following Eberhart and Russell (1966) [4] showed that Utkarsha (bi = 0.98), BM-4 (bi = 1.01) and IPM-02-03 (bi = 1.00) had regression coefficients close to unity with non-significant deviation from regression, indicating broad adaptability and consistent performance across environments. Vaibhav (bi = 1.12) and AKM-9904 (bi = 1.15) were more responsive to favourable, high-input environments, while AKM-8802 (bi = 0.89) and Samrat (bi = 0.91) showed comparatively better adaptation to moisturelimited or resource-poor conditions. These results suggest that Utkarsha, BM-4 and IPM-02-03 are best suited for general recommendation, whereas the remaining genotypes can be targeted to specific production environments.

Conclusion

The present study demonstrates the existence of substantial genetic variability and divergence among seven green gram genotypes evaluated at G H Raisoni University, Saikheda. High heritability coupled with high genetic advance for seed yield, biological yield, pods per plant and 100-seed weight indicates that these traits are governed largely by additive gene action and can be improved through direct selection.

Pods per plant, clusters per plant, 100-seed weight and branches per plant emerged as the most reliable yieldselection criteria based on correlation, path and PCA results. Mahalanobis D² analysis identified Samrat and AKM-9904 as the most genetically divergent genotypes, while AKM-8802, AKM-9904 and Utkarsha combined high mean yield with, in the case of Utkarsha, superior stability across environments. These genetically diverse and agronomically superior genotypesparticularly in the combinations BM-4 × AKM-8802, BM-4 × Samrat, Vaibhav × IPM-02-03 and Utkarsha × AKM-9904are recommended as parental material for hybridization programmes aimed at developing high-yielding, widely adapted green gram cultivars. Further multi-location evaluation of these genotypes under varied agronomic conditions is suggested before their use in largescale breeding and varietal recommendation.

References

1. Burton GW. Quantitative inheritance in grasses. In: Proceedings of the 6th International Grassland Congress. 1952;1:277-283.
2. Charles Wesly E, Nagaraju R, Roopa Lavanya G. Genetic divergence studies in green gram germplasm. 2020. [As cited in the parent thesis].
3. Dewey DR, Lu KH. A correlation and path coefficient analysis of components of crested wheatgrass seed production. Agron J. 1959;51(9):515-518.
4. Eberhart SA, Russell WA. Stability parameters for comparing varieties. Crop Sci. 1966;6:36-40.
5. Johnson HW, Robinson HF, Comstock RE. Estimates of genetic and environmental variability in soybean. Agron J. 1955;47:314-318.
6. Kumar R, Singh AK, Verma P. Genetic diversity assessment in mungbean (Vigna radiata L.) using multivariate statistical analysis. Legume Res. 2018/2021;44(8):905-912.
7. Mahalanobis PC. On the generalized distance in statistics. Proc Natl Inst Sci India. 1936;2:49-55.
8. Murty BR, Arunachalam V. The nature of divergence in relation to breeding system in some crop plants. Indian J Genet Plant Breed. 1966;26A:188-198.
9. Panse VG, Sukhatme PV. Statistical methods for agricultural workers. New Delhi: Indian Council of Agricultural Research; 1985.
10. Rao CR. Advanced statistical methods in biometric research. New York: John Wiley & Sons; 1952.
11. Sharma V, Singh G, Kumar P. Principal component analysis and genetic divergence in mungbean germplasm for yield improvement. Indian J Agric Res. 2014/2022;48(5):376-381.
12. Singh RK, Chaudhary BD. Biometrical methods in quantitative genetic analysis. New Delhi: Kalyani Publishers; 1985.
13. Waghmare YV. Genetic diversity analysis in green gram (Vigna radiata) [MSc thesis]. Saikheda, Pandhurna (MP): School of Agricultural Sciences, G H Raisoni University; 2026.