Genetic diversity assessment of soybean (Glycine max (L.) Merrill) genotypes in Summer season

Introduction

Recognized globally as a foundational crop of the modern agricultural framework, soybean (Glycine max (L.) Merrill) provides immense economic utility and plays a vital role in global food security. Belonging to the Papilionoideae subfamily of the Fabaceae family, it functions as the world's dominant oilseed crop, contributing to roughly 25% of the international edible oil supply and commanding over 50% of aggregate oilseed production. The unparalleled agricultural value of this legume arises from its highly complementary seed composition, which averages 40-42% premium protein and 18-22% oil. This biological profile makes it an indispensable source of both vegetable protein for global livestock industries and edible oil for human consumption (Verma et al., 2023) [1] . Nutritionally, the protein profile is exceptionally balanced, supplying all eight essential amino acids-including lysine and tryptophan, which are often deficient in standard cereal grains. Furthermore, soybeans are rich in vitamins, essential minerals, and health-promoting phytoestrogens, specifically isoflavones, which help mitigate cardiovascular and bone density ailments. Botanically, the cultivated soybean is a diploidized ancient polyploid with a chromosomal configuration of 2𝑛 = 2𝑥 = 40. In India, organized breeding efforts during the 1970s triggered a "yellow revolution," establishing the crop as a crucial kharif-season commodity. Recent production estimates for the 2024-25 season place India's total output at a record 151.80 lakh metric tonnes (LMT) across 126.90 lakh hectares. Maharashtra stands as the premier cultivating state, recording a production of 52.69 LMT across 50.72 lakh hectares during 2023-24. Despite this expansive acreage, national productivity remains constrained at approximately 1.0 to 1.2 tonnes per hectare, severely lagging behind the global average of 2.8 tonnes per hectare. This stagnant productivity is fundamentally driven by cultivation in marginal, rainfed environments prone to drought, which can slash yields by up to 40% during critical pod-filling stages (Marinho et al., 2022) [2] . Additionally, the genetic base of current Indian cultivars is exceptionally narrow, increasing vulnerability to environmental stresses. Consequently, evaluating available germplasm to understand genetic variability and trait associations is a prerequisite for formulating strategic breeding programs aimed at breaking the current yield plateau.

Materials and methods

A comprehensive field experiment was executed to evaluate the genetic diversity among 50 distinct droughttolerant soybean genotypes. The trials were conducted at the Agril. Botany Research Farm, affiliated with the Post Graduate Institute at MPKV, Rahuri, during the Summer season of 2026. The genotypes were sys tematically assessed across fourteen quantitative parameters covering phenological, morphological, biochemical, and physiological traits. Standard statistical procedures were utilized to perform an analysis of variance (ANOVA), compute genetic variability indices, and execute correlation, path coefficient, and D 2 genetic divergence analyses.

Results and discussion

Analysis of variance

The analysis of variance confirmed that the mean sum of squares for the genotype treatments was highly significant at the 1% probability level for all fourteen traits, definitively establishing the presence of extensive genetic diversity within the test population.

Parameters of genetic variability

To evaluate the true genetic potential of the traits, variability indices were calculated. The phenotypic coefficient of variation (PCV) consistently marginally exceeded the genotypic coefficient of variation (GCV) for all characters, affirming that environmental factors influenced final trait expression. However, the magnitude of variability was highly promising. Traits such as primary branches per plant, seed yield per plant, pods per plant, and plant height exhibited high GCV (above 20%) and PCV values, indicating a robust inherent variance that is highly responsive to direct selection. The characters, primary branches per plant, seed yield per plant, pods per plant, and plant height marked matching results with Sureshrao et al. (2014) [3] and Pawar et al. (2020) [4] in terms of high GCV and PCV. Broad-sense heritability serves as a predictive metric for trait transmission. The study documented exceptionally high heritability estimates (greater than 60%) for pods per plant (94.18%), primary branches per plant (94.10%), seed yield (91.08%), and 100-seed weight (89.98%). The characters, pods per plant, primary branches per plant, seed yield per plant, and plant height marked matching results with Sureshrao et al. (2014) [3] in terms of exceptionally high heritability. Furthermore, these same parameters demonstrated high genetic advance as a percentage of the mean (GAM), with primary branches reaching 66.31% and seed yield registering 61.49%. Neelima et al. (2018) [5] and Kumari et al. (2022) [6] reported high amount of genetic advance as percent of mean for 5 traits, viz. primary branches per plant, seed yield per plant, pods per plant, plant height, and 100seed weight. The concurrence of high heritability and high GAM firmly indicates that these structural yield components are governed by additive gene action, ensuring that conventional breeding methodologies will yield rapid genetic gains.

Correlation analysis

Correlation coefficient analysis was utilized to quantify trait associations, providing vital insight for indirect selection criteria in crop improvement programs. At the genotypic level, single-plant seed yield exhibited a highly significant positive correlation with the number of pods per plant (0.583), pods per cluster (0.430), 100-seed weight (0.429), the Normalized Difference Vegetation Index (NDVI) (0.411), seeds per pod (0.395), and days to maturity (0.340). It maintained non-significant positive associations with primary branches per plant, days to 50% flowering, protein content, canopy temperature, and plant height. Conversely, chlorophyll content exhibited a significant negative correlation with seed yield (-0.373), indicating a physiological trade-off between prolonged vegetative maintenance and reproductive sink filling. At the phenotypic level, the trend remained largely consistent, with seed yield registering a significant positive correlation with pods per plant (0.549), 100-seed weight (0.424), and pods per cluster (0.356). These associations confirm that pods per plant, cluster density, and individual seed weight act as the dominant yield-contributing characters. Therefore, localized yield improvements in rainfed soybean populations can be effectively achieved by selecting for denser pod clusters and heavier seeds. Furthermore, significant negative correlations were observed between antagonistic biochemical traits, such as protein and oil content (-0.948 genotypically), presenting standard bioenergetic trade-offs that breeders must balance. The data confirms that selecting for robust structural traits like pods per plant will concurrently drive the genetic advancement of complex polygenic traits like seed yield. Jagtap et al. (2024) [7] and Tyagi et al. (2025) [8] also found a significant positive relationship between seed yield per plant and primary yield-attributing traits such as the number of pods per plant, pods per cluster, seeds per pod and 100-seed weight.

Path coefficient analysis

Path coefficient analysis was employed to dissect genotypic correlation coefficients into direct and indirect causal effects, explaining 84.40% of the total variation in seed yield (residual effect = 0.3950). The standardized component regression revealed that 100-seed weight exerted the highest positive direct effect on single-plant seed yield (0.7707). This was sequentially followed by strong positive direct contributions from seeds per pod (0.6249), pods per plant (0.6169), plant height (0.3617), canopy temperature (0.3436), days to maturity (0.1397), and primary branches per plant (0.0049). Conversely, phenological and physiological maintenance traits displayed substantial negative direct impacts on seed yield, most notably days to 50% flowering (-1.5863), chlorophyll content (-0.8578), and NDVI (-0.6059). This dynamic indicates a clear biological trade-off: while prolonged canopy greenness and delayed flowering support initial vegetative endurance under moisture stress, they can restrict the direct remobilization of assimilates into developing seed sinks during reproductive phases. However, traits with negative direct effects often compensated through positive indirect pathways; for example, NDVI channeled positive indirect contributions to yield via 100-seed weight (0.4716) and seeds per pod (0.3670). Similarly, the negative direct effect of oil content (-0.1070) was mitigated by positive indirect contributions through days to 50% flowering. Ultimately, the path analysis firmly validates that direct phenotypic selection for heavier seeds (100-seed weight), increased seed packing per pod, and total pod load are the most reliable strategies for securing yield enhancements in drought-stressed soybean populations. Similar results showing a positive direct effect on seed yield per plant and 100-seed weight, seeds per pod, pods per plant, plant height, days to maturity, and primary branches per plant were reported by Shete et al. (2023) [9] and Barpanda et al. (2024) [10] . This biological dynamic confirms that while vegetative vigor is necessary for early establishment, the direct mobilization of resources into structural pod and seed parameters is the ultimate driver of soybean productivity.

No. of genotypes

Name of Genotypes in each cluster I 25 G33, G34, G16, G40, G39, G14, G47, G27, G45, G43, G23, G7, G18, G30, G10, Phule Durva, G32, G13, G35, G26, G41, Phule Kimaya, G37, G22, G9 II 13 G3, G4, G11, G12, G5, G24, G28, G6, G36, G17, G20, G42, G8 III 1 G38

Cluster

No. of genotypes

Name of Genotypes in each cluster

Genetic divergence (D 2 analysis)

Maximizing hybrid vigor requires parents selected from genetically distant backgrounds. The multivariate D 2 analysis grouped the 50 evaluated soybean genotypes into 12 distinct clusters, minimizing intra-cluster variation while maximizing inter-cluster diversity. Cluster I was the largest, comprising 25 genotypes, followed by Cluster II with 13 genotypes and Cluster V with 3 genotypes. The remaining nine clusters (III, IV, VI, VII, VIII, IX, X, XI, and XII) were solitary, containing only a single genotype each. The maximum intra-cluster distance was recorded in Cluster II (D = 9.30). The highest inter-cluster distance was observed between Cluster XI and Cluster XII (D = 21.62), signifying the maximum genetic divergence within the test population. Conversely, the lowest inter-cluster distance occurred between Cluster III and Cluster X (D = 7.40). Evaluating the percent contribution of individual parameters revealed that the number of pods per plant exerted the most substantial influence, responsible for 22.78% of the total genetic divergence. This was closely trailed by seed yield per plant (19.84%), number of primary branches (18.53%), plant height (17.14%), and 100-seed weight (14.78%). Conversely, physiological traits like canopy temperature and NDVI contributed nothing (0.00%) to the overall divergence. Based on these maximum inter-cluster spatial distances and superior mean performance, specific cross combinations are recommended to generate elite transgressive segregants. Hybridizing genotype G21 from Cluster VIII with genotype G46 from Cluster XI is strategically recommended to maximize overall seed yield, while crossing G8 with G44 is advised for improving plant architecture and total primary branches.

Conclusion

The rigorous evaluation of the fifty drought-tolerant soybean lines established the presence of profound genetic variability across critical agro-morphological and physiological profiles. Traits heavily influencing ultimate productivity, notably primary branches, pod counts, and seed yield, showcased high heritability and additive gene action, rendering them ideal candidates for immediate phenotypic selection. Both correlation matrices and path coefficient analyses conclusively validated that genetic improvement in soybean yield is most effectively achieved by directly selecting for heavier seeds, increased pods per plant, and higher seed packing per pod. Utilizing the established D 2 clustering data to inform parent selection-particularly by hybridizing the highly divergent G21 [JS 2098 × EC333901 (F6-25)] and G46 [KDS 1173 × EC 538828 (F6-24)] lines-provides an optimized, strategic pathway for breeders to develop climate-resilient cultivars capable of breaking the productivity barriers currently affecting rainfed agricultural systems.

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11. How to cite this article: Patwe M, Chimote V, Ranade G, VG B, Rasal P, Niranjan. Genetic diversity assessment of soybean (Glycine max (L.) Merrill) genotypes in Summer season. International Journal of Advanced Biochemistry Research. 2026;SP-10(9):2445-2451. doi: https://www.doi.org/10.33545/26 174693.2026.v10.i9Sad.10196