Assessment of genetic variation in soybean (Glycine max (L.) Merrill) using Mahalanobis D² analysis

Introduction

The Soybean (Glycine max (L.) Merrill) is one of the most important oilseed crops not only in India but in the entire world. Soybean belongs to genus Glycine wild, which is a family Leguminosae, subfamily Papilionoideae and tribe Phaseoleae. The cultivated soybean-Glycine max and the wild annual soybean-G. soja, comes under subgenus Soja. (Hymowitz, 2004) [5] . It has 20 chromosomes (2n = 40) and primary center of origin of soybean is China. Global soybean production for 2023-2024 is 394.71 million metric tons (United States Department of Agriculture). Brazil produced 153 million metric tons of soybean in 2023-2024, surpassing the United States as the world's largest producer. The United States of America stands second with 113.34 million metric tons, followed by Argentina (48.1 million metric tons), China (20.8 million metric tons), India (11.9 million metric tons) .The total sowing area under soybean crop in India during Kharif 2023 was 118.547 lakh ha with production and expected yield of 118.747 lakh metric ton and 1002 kg per ha respectively. (Anonymous 2023) [1] .

Genetic Divergence Analysis or Cluster Analysis

Quantification of genetic diversity existing within and between groups of germplasm is important and particularly useful in proper choice of parents for realizing higher heterosis and obtaining useful recombinants. The quantitative assessment of genetic divergence was made by adopting Mahalanobis D2 statistics for yield and its contributing characters. D2 statistics, a concept developed by Mahalanobis (1936) [7] , is important tool to plant breeder to classify the genotypes into different groups based on genetic divergence between them. It is also useful in quantifying degree of divergence between biological populations at genotypic level and assessing the relative contribution of different component traits to the total divergence at both intra and inter-cluster level. Rao (1952) [11] suggested the application of this technique for the assessment of genetic diversity in plant breeding. Genetic divergence was estimated for 25 finger millet germplasm lines. These genotypes were grouped following Mahalanobis D2 as described by Rao (1952) [11] .

Materials and Methods

The study was conducted in Kharif 2023 at Research cum instruction farm College of Agriculture IGKV Raipur (C.G.). The experimental material 27 genotypes were planted in Randomized Complete Block Design with three replications fulfilling all recommended agronomical requirements. Each plant entry was planted in a single row plot. The distance maintained between plant to plant and row to row was 10 and 45 following observations were recorded on five randomly selected competitive plants, from each entry and in each replication.

Results and Discussion

The values for heritability estimates were different for different traits and classified as high (>70%), moderate (50-70%) and low (<50%) (Robinson 1966). The highest heritability was recorded for the number of seeds per plant (93.89), seed yield per plant (g) (90.56), number of pods per plant (90.40), number of primary branches per plant (83.32), pod bearing length (cm) (82.44) and plant height (cm) (81.70). Similar findings had been also reported by Tigga et al. (2021) for seed yield per plant (g), number of pods per plant, number of primary branches per plant, pod bearing length (cm) and plant height (cm). Banerjee et al. (2023) [3] for number of pods per plant. Neelima et al. (2018) [10] for plant height (cm) and number of pods per plant. Results was similar with Karchal et al. (2024) [6] for number of pods per plant and pod bearing length. Nag et al. (2020) [8] for number of primary branches per plant. The highest genetic advance as percentage of mean was recorded for seed yield per plant (g) (59.90%) followed by number of seeds per plant (57.27%), number of pods per plant (45.05%), pod bearing length (cm) (28.65%), plant height (cm) (25.36%) and number of primary branches per plant (25.19%). These results were in accordance with the findings of Banerjee et al. (2023) [3] for seed yield per plant and number of pods per plant High heritability coupled with high genetic advance as percentage of mean was observed for seed yield per plant (g) (90.56%) (59.90%), number of seeds per plant (93.89%) (57.27%) and number of pods per plant (90.40%) (45.05%), pod bearing length (cm) (82.44%) (28.65%), plant height (cm) (81.70%) (25.36%) and number of primary branches per plant (83.32%) (25.19%). Similar results were reported by Nag et al. (2007) [9] for plant height, pod bearing length, number of pods per plant, seed yield per plant and number of seeds per plant. Upadhyay et al. (2020) [16] for the number of seeds per plant and seed yield per plant. Satpute et al. (2016) [13] for plant height and Chandel et al. (2017) [4] for the number of primary branches per plant. With a set of 27 genotypes of soybean, D2 analysis was done for 12 features. Five groups were formed using the D2 values. These demonstrated the considerable variability seen in the soybean gene pool that is available for use. The largest Cluster-I (23 genotypes) was found using cluster analysis and Cluster-II, Cluster-III, Cluster-IV and V consisting only one genotype each. The highest inter-cluster distance was measured between cluster II and III (200.33), followed by between cluster II and V (198.01), cluster II and IV (121.99), cluster I and III (98.47), cluster I and V (93.45), cluster I and IV (68.16), cluster I and II (62.72), cluster III and V (37.14), and cluster IV and V (31.79). The lowest inter cluster distance was noticed between cluster III and IV (27.69). The maximum intra cluster distances was observed in cluster I (35.76) and remaining cluster is (0.00) because of these cluster contained single genotype. The cluster mean for the days to maturity was the highest in Cluster IV (106.67) and lowest in the Cluster III (90.00). High cluster mean is found by Thakur et al. (2015) for days to maturity in cluster IV. Cluster V has had the highest cluster mean for the plant height (86.33), while Cluster II had the lowest (52.33). The cluster mean for the number of primary branches per plant was the highest in Cluster II with (4.13) and lowest in the Cluster III (3.33). Cluster II had the highest cluster mean for the number of pods per plant (63.47) whereas Cluster III had the lowest (36.13). Cluster V has had the highest cluster mean for the pod bearing length (53.26), while Cluster II had the lowest (34.13). Cluster V has had the highest cluster mean for the number of seeds per pod (2.80), while Cluster III and IV had the lowest (2.53). Cluster II showed the highest mean value for the number of seeds per plant (164.67), while Cluster III showed the lowest (36.47). Cluster V has had the highest cluster mean for the 100 seed weight (12.77), while Cluster II had the lowest (8.10). Cluster III showed the highest mean value for the oil content % (19.83), while Cluster V had the lowest (19.07). Cluster II showed the highest mean value for the seed yield per plant (12.01), while Cluster III showed the lowest (3.05). Cluster V has had the highest cluster mean for the protein content % (40.22), while Cluster II had the lowest (37.93).

Conclusion

Overall, high heritability coupled with high GA as % of mean was observed for seed yield per plant (g), number of seeds per plant, number of pods per plant, pod bearing length (cm), plant height (cm) and number of primary branches per plant. Suggesting that these traits are governed by additive gene effects, which is fixable, in such situation direct selection for seed yield may be effective.

Cluster II and III have the highest inter cluster distance (200.33), followed by cluster II and V (198.01), cluster II and IV (121.99), cluster I and III (98.47), cluster I and V (93.45), cluster I and IV (68.16). Indicating the presence of greater diversity between genotypes of these groups. Hence, crossing between genotypes belonging to these clusters may result in high heterosis, which could be exploited in crop improvement.

Acknowledgement

AcknowledgementThe authors are thankful to the Department of Genetics and Plant Breeding, College of Agriculture, Indira Gandhi Krishi Vishwavidyalaya, Raipur, (C.G.) for its valuable support during entire research.

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