Association studies on soybean [Glycine max (L.)] Merrill] germplasm accessions for yield and yield attributing traits

R. C. Sivabharathi1 , A. Muthuswamy2 , K. Anandhi1* , L. Karthiba1

1Department of Pulses, Centre for Plant Breeding and Genetics, TNAU, Coimbatore-3, India

2Agricultural College and Research Institute, Karur-639001, India

Corresponding Author Email: anandhiagri@gmail.com

DOI : https://doi.org/10.5281/zenodo.8163358

Keywords

correlation, Path analysis, Soybean, Variability

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Abstract

The experiment was conducted to evaluate 135 soybean genotypes in order to determine the genetic variability, heritability, genetic advance, correlation, and path analysis for ten quantitative characters. Analysis of variance showed the existence of significant differences among all the traits except the number of branches per plant and the number of seeds per pod. High heritability accompanied by high genetic advance was observed in plant height, number of branches per plant, number of clusters per plant, number of pods per cluster, number of pods per plant, number of seeds per pod, hundred seed weight, and single plant yield suggested selection from these characters could be effective for future crop improvement program. Based on the association analysis characters like the number of pods per plant, plant height, number of clusters per plant, number of branches per plant, number of seeds per plant, and hundred seed weight were observed to be highly correlated with yield and hence these characters should be given more consideration in breeding for higher grain yield in soybean.

Introduction

Soybean (Glycine max L. Merrill) is a self-pollinated crop with a natural outcrossing rate of 0.03-6.32 percent [1].The soybean is indigenous to northeast Asia, particularly China.World soybean production was estimated as 385.527 million tonnes. Brazil ranks first in soybean production followed by the United States, Argentina, China and India [2]. Production in India accounts for 12.04 million tonnes cultivated under 11.45 million hectares with average productivity of 1051 kg/ha. Madhya Pradesh is the soybean bowl of India, contributing more than 89 percent of the country’s soybean production, followed by Maharashtra and Rajasthan [3]. The area and productivity of soybean is low when compared to other legume crops in India and therefore a step has to be taken to improve the seed yield. Increasing seed yield requires the presence of variability and hence variability is studied. Again, along with variability, the inheritance must be studied to assess the heritability of the characters. The heritability estimates provide a measure of the transmission of character from one generation to the next, as the consistency in the performance of the selection depends on the heritable portion of the variability. Thus, heritability and genetic advance as percent of the mean are estimated together to find the heritability nature of selected genotypes.

Seed yield is a complex character and is influenced by many environmental factors when cultivated hence it is not efficient for selection. It is therefore important for a plant breeder to identify the characteristics that are associated with the seed yield and that character could be used for the selection. Association between traits were crucial to breeding work as it helps to perform indirect selection for a quantitative trait especially yield, usually hard to be selected, by another directly correlated trait of higher genetic gain [4]. Hence, the present study was undertaken to analyze and to find the magnitude and nature of variation among 135 soybean genotypes with respect to various yield-contributing traits and the association between yield and its contributing traits in soybean by correlation and path coefficient analysis.

Materials and methods

            The research work was carried out during rabi, 2021-22 at the Department of Pulses, Tamil Nadu Agricultural University, Coimbatore. The experimental materials consisted of  135 soybean germplasm accession including the five check varieties viz., NRC 132, NRC 142, NRC 147, MACS 1460, and CO (Soy) 3 is given in (Table 1) and were sown in an augmented block design II with a 3m row length and spacing of 30cm between the rows and 10cm within the rows. The recommended fertilizers and cultural practices were followed to raise the crop. Observations were recorded on five randomly selected plants in each genotype for days to fifty percent flowering, days to maturity, plant height, number of branches per plant, number of clusters per plant, number of pods per cluster, number of pods per plant, number of seeds per pod, hundred seed weight and single plant yield. The mean data was used to estimate genetic variability, heritability and genetic advance. The phenotypic and genotypic coefficient of variability (PCV and GCV) was computed according to the method suggested [5] and heritability (h2) as suggested [6]. The mean data was also subjected to analyze correlation and path analysis using the TNAUSTAT software.

Result and discussion

Variability studies

            Genetic variability study is the basis for selection of genotypes for the future breeding program. Results from the analysis of variance (Table 2) showed that a highly significant difference exists among all genotypes in terms of all traits measured except the number of branches per plant and the number of seeds per pod. The variability parameters viz., phenotypic variation, genotypic variation, phenotypic coefficient of variation, genotypic coefficient variation, heritability, genetic advance, and genetic advance as percent of mean for ten quantitative traits is given in Table3.

Single plant yield ranges from 2.09 – 37.32g.  A maximum single plant yield (37.32g) was observed in PK 1146. In a previous study, it was reported that the genotypes MACS 1460, EC 18736 and PK 1038 showed high values for single plant yield [7]. The phenotypic coefficient of variation was higher than the genotypic coefficient of variance for all the ten traits. PCV and GCV for ten quantitative traits were represented in bar chart (Figure 1). Highest phenotypic coefficient of variation and genotypic coefficient of variance was observed in single plant yield as 45.36 and 42.42 respectively. The lowest phenotypic and genotypic variation was observed in days to maturity as 5.51 and 5.49 respectively. The maximum difference between phenotypic and genotypic variation was observed in single plant yield whereas the minimum difference was observed in days to maturity. High PCV and GCV was observed in plant height, number of branches per plant, number of clusters per plant, number of pods per cluster, number of pods per plant, number of seeds per pod and single plant yield. A similar result on plant height and number of pods per plant was reported [8, 9] for the number of branches per plant [10,11] for number of clusters per plant [12] and for hundred seed weight [13, 14]. High PCV and GCV in single plant yield were reported [15].  Moderate PCV and GCV were observed in hundred seed weights. Similar results were also reported [9], [16], and [17]. Low PCV and GCV were observed in days to fifty percent flowering and days to maturity. Similar results on days to fifty percent flowering and days to maturity were reported [14-15].

            Heritability and genetic advance as percent of mean expressed in percent for ten traits in 135 soybean germplasm accessions were depicted in bar chart in Figure 2. High heritability and high genetic advance as percent mean was observed in plant height, number of branches per plant, number of clusters per plant, number of pods per cluster, number of pods per plant, number of seeds per pod, hundred seed weight, and single plant yield. This showed that these traits exhibit additive gene action and could be used for selection. Similar results on plant height, number of branches per plant and number of pods per plant were reported [16] and along with the number of clusters per plant [11].The same results on a number of seeds per plant was reported [18]. A similar finding on hundred seed weight and single plant yield was reported [14] and [9]. Days to fifty percent flowering and days to maturity showed high heritability and moderate genetic advance as per cent mean indicates the presence of both additive and non-additive gene action for these traits. Similar findings on days to fifty percent flowering and days to maturity had been reported [19] and [20].

Correlation coefficient analysis

The phenotypic correlation for ten quantitative traits is given in Table 3. The phenotypic correlation coefficient of seed yield was positive and significant with days to fifty percent flowering, plant height, number of branches per plant, number of clusters per plant, number of pods per plant, and hundred seed weight at 1 percent level. A similar results were obtained on days to fifty percent flowering, plant height [4] and along with a number of pods per plant and a number of branches per plant [21] and along with a number of pods per plant [22]. However, single-plant yield showed positive and significant association with a number of seeds per plant.

Inter correlation among the yield attributing traits was given in the Table 3. Days to fifty per cent flowering showed a positive and significant correlation with days to maturity, plant height, number of branches per plant, number of clusters per plant, number of pods per cluster and number of pods per plant. Plant height showed a positive and significant association with number of branches per plant, number of clusters per plant, number of pods per plant. Positive and significant association between plant height and the number of pods per plant was reported [4], [23] and [24]. The number of branches per plant showed positive and significant association with number of clusters per plant and number of pods per plant. A positive and significant association of number of clusters per plant was observed with a number of pods per cluster and a number of pods per plant. Similar findings were reported [11].

The character with positive association can be raised through indirect selection for the corresponding trait in a breeding program. Intercorrelated traits contributing to yield can also be selected indirectly for breeding programme. In terms of the present study, days to fifty per cent flowering, plant height, number of branches per plant, number of clusters per plant, number of pods per plant and hundred seed weight were positively associated with single plant yield and therefore these traits except days to fifty percent could be used for yield improvement breeding work.

Path coefficient analysis

            The results of the path coefficient analysis for ten quantitative traits were given in Table 4 and Figure 3. The present study on path analysis revealed that a number of pods per plant had the highest and most direct effect on the single plant yield. Similar findings of high direct effect of number of pods per plant were reported [24-25]. A number of seeds per pod and hundred seed weight were observed to have moderate and positive direct effect on single plant yield. Previous studies reported same results of a number of seeds per pod on yield [26-27]. A number of clusters per plant had a positive and low direct effect on single plant yield. The remaining traits viz., days to fifty per cent flowering, days to maturity, plant height, number of branches per plant and number of pods per cluster were observed to have negligible direct effect on single plant yield.

            Days to fifty percent flowering have a moderate and positive indirect effect on single plant yield through a number of pods per plant and similar results were reported [27]. Plant height, number of branches per plant, number of clusters per plant and number of pods per cluster had positive and high indirect effects on single plant yield via the number of pods per plant. A similar result of the number of branches per plant on yield was reported [4]. Days to fifty percent flowering, plant height, number of branches per plant and number of pods per plant had low and positive indirect effects on single plant yield via number of clusters per plant.

Conclusion

            The present study showed the number of pods per plant had the maximum effect on single plant yield followed by number of clusters per plant and the number of seeds per pod. Therefore, these characters can be selected for future yield improvement breeding work.

Acknowledgement

The authors are thankful to the Professors of the Department of Pulses and Centre of Excellence for Molecular Breeding (CEMB), Tamil Nadu Agricultural University, Coimbatore for granting us the facilities and their support to complete the work successfully.

References

[1] Hao, Y, Zhang, J-y., Zhang, C-b., Bao, P.,Zhang, W-l., Wang, P-n., Ding, X-y., Liu, B-h., Feng, X-z. and Zhao,L-m. 2019. “Genetic effects and plant architecture influences on outcrossing rate in soybean.” Journal of Integrative Agriculture.18 (9):1971-1979.

[2] Anonymous. 2021. Oilseeds – World markets and Trade, a USDA Publications.

[3] Anonymous. 2022. Oilseeds – World markets and Trade, a USDA Publications.

[4] Humtsoe, ZB, PK Shah, and H Chaturvedi. 2017. “Correlation And Path Analysis Studies Among Soybean Genotypes Under Foothill Conditions Of Nagaland.” Indian Research Journal of Genetics and Biotechnology 9 (03):397–404-397–404.

[5] Burton, GW. 1952. “Quantitative inheritance in grasses.” Pro VI Int Grassl Cong, 1952:277-283.

[6] Johnson, HW., Robinson., H. and Comstock,R. 1955. “Estimates of genetic and environmental variability in soybeans 1.” Agronomy journal.47 (7):314-318.

[7] Sivabharathi, R.C., Muthuswamy, A., Anandhi, K. and Karthiba, L. 2023. “Genetic diversity studies in soybean (Glycine max L. Merrill) germplasm accessions using cluster and principal component analysis.” Legume Research – An International Journal.doi: 10.18805/LR-5071.

[8] Shilpashree, N., Devi, S. N., Manjunatha gowda,D. C., Muddappa, A., Abdelmohsen, S. A., Tamam, N., Elansary, H. O., El-Abedin, TKZ., Abdelbacki, A. M and Janhavi,V. 2021. “Morphological characterization, variability and diversity among vegetable soybean (Glycine max L.) genotypes.” Plants10 (4):671.

[9] Baria, A., Akabari, V. and Gohil, V. 2022. “Variability studies for seed yield and its components in soybean.” Journal of genetics and Plant Breeding.6(2): 54-59.

[10] Neelima, G., Mehtre, S. and Narkhede,G. 2018. “Genetic variability, heritability and genetic advance in soybean.” International Journal of Pure Applied Bioscience.6 (2):1011-1017.

[11] Kumar, S, V Kumari, and V Kumar. 2020. “Genetic variability and character association studies for seed yield and component characters in soybean [Glycine max (L.) Merrill] under North-western Himalayas.” Legume Research-An International Journal 43 (4):507-511.

[12] Baraskar, V., Kachhadia, V., Vachhan, J., Barad, H., Patel, M. and M Darwankar. 2014. “Genetic variability, heritability and genetic advance in soybean [Glycine max (L.) Merrill].” Electronic Journal of Plant Breeding.5 (4):802-806.

[13] Satpute, GK, C Gireesh, and M Shivakumar. 2016. “Genetic variability and association studies in new soybean germplasm accessions.” Soybean Research 14(2): 77-83.

[14] Banerjee, J. 2022. “Phenotypic and Yield associated Trait Characterization in Advanced Breeding lines of Soybean.” Electronic Journal of Plant Breeding.13 (2):597-607.

[15] Chandrawat, KS., Baig, K., Hashmi, S., Sarang, D., Kumar, A. and Dumai, P.K. 2017. “Study on genetic variability, heritability and genetic advance in soybean.” Int. J. Pure App. Bioscience.5 (1):57-63.

[16] Jandong, E., Uguru, M. and Okechukwu,E. 2020. “Estimates of genetic variability, heritability and genetic advance for agronomic and yield traits in soybean (Glycine max L.).” Afr J Biotechnoly.19 (4):201-206.

[17] Mofokeng, M. A. 2021. “Genetic variability, heritability and genetic advance of soybean'(Glycine max)’genotypes based on yield and yield-related traits.” Australian Journal of Crop Science.15 (12):1427-1434.

[18] Mahawar, R., Pawar, L., Koli, N., Chaudhary,H., Meena, D.and Ali,M. 2013. “Assessment of genetic variability, correlation and path analysis of quantitative traits in soybean [Glycine max (L.) Merrill].” Soybean Research:70.

[19] Sureshrao, S. S., Singh, V. J., Gampala, S and Rangare,N. 2014. “Assessment of genetic variability of the main yield related characters in soybean.” International Journal of Food, Agriculture and Veterinary Sciences.4 (2):69-74.

[20] Mahbub, M. M., and Shirazy,B. J. 2016. “Evaluation of genetic diversity in different genotypes of soybean (Glycine max (L.) Merrill).” American Journal of Plant Biology.1 (1):24-29.

[21] Machado, B, A Nogueira, O Hamawaki, G Rezende, G Jorge, I Silveira, L Medeiros, R Hamawaki, and C Hamawaki. 2017. “Phenotypic and genotypic correlations between soybean agronomic traits and path analysis.” Genetics and Molecular Research 16 (2).

[22] Chavan, B, D Dahat, H Rajput, M Deshmukh, and S Diwane. 2016. “Correlation and path analysis in soybean.” Studies 2 (9).

[23] Balla, MY, and SE Ibrahim. 2017. “Genotypic correlation and path coefficient analysis of soybean [Glycine max (L.) Merr.] for yield and its components.” Agric Res Tech 7 (3):1-5.

[24] Sileshi, Y. 2019. “Estimation of Variability, Correlation and Path Analysis in Soybean (Glycine max (L.) Merr.) Genotypes at Jimma, South Western Ethiopia.”Journal of Natural Science Research 9:22-29

[25] Badaya, VK, Gill, BS and Meenakshi Dhoot 2016. “Diversity and association analysis foryield contributing and morphological traits in soybean [Glycine max (L.) Merrill] “. Bioscan: An International Quaterly Journal of Life Sciences.

[26] Jain, RK, A Joshi, HR Chaudhary, A Dashora, and CL Khatik. 2018. “Study on genetic variability, heritability and genetic advance in soybean [Glycine max (L.) Merrill].” Legume Research 41 (4):532-536.

[27] Patil, S, Naik, MR, Patil, PP and Shinde, DA. 2011. “Genetic variability, correlation and path analysis in soybean.” Legume Research-An International Journal 34 (1):36-40.

Table 1. List of soybean genotypes used in the study

Sl. NoGenotypesSl. NoGenotypesSl. NoGenotypes
1.CLARK46.MACS 114891.NRC 43
2.CO 147.MACS 118892.VLS 53
3.CO 248.MACS 123893.VLS 69
4.CSB 080449.MACS 125494.PK 1158
5.CSB 080650.MACS 125995.PK 1223
6.CSB 080851.MACS 128196.PK 1243
7.CSB 080952.MACS 14597.MAUS 59
8.CSB 081053.MACS 56598.MAUS 60
9.CSB 081154.MACS 61099.MAUS 61
10.EC 1867855.MACS 629100.NRC 44
11.EC 1873656.MACS 693101.NRC 45
12.JS 20-0157.MACS 694102.NRC 46
13.JS 20-0958.MACS 715103.NRC 76
14.JS 7611959.MACS 798104.NRC 78
15.JS 76-119460.MACS 94-2105.NRC 79
16.JS 87-1261.MACS 985106.NRC 80-1
17.JS 89-2462.MAUS 109107.NRC 82
18.JS 90-2163.MAUS 144108.NRC 84
19.JS 90-2964.MAUS 17109.NRC 95-06-03
20.JS 92-2265.MAUS 2110.VLS 70
21.JS 95-6066.MAUS 20111.VLS 75
22.JS 95-9867.MAUS 311112.WC 37
23.JS 97-5268.MAUS 34113.WC 67
24.JS 98-2169.MAUS 39114.PK 1000
25.JS 98-6170.MAUS 414115.PK 1303
26.JS 98-6371.MAUS 417116.PK 25
27.JS 98-6872.MAUS 52-1117.PK 257
28.JS 99-1273.MAUS 55118.PK 258
29.JS 99-12874.JS(SH) 2001-04119.PK 727
30.JS 99-7275.JS(SH) 2002-14120.PK 768
31.JS 99-7676.JS(SH) 8554121.PK 1011
32.JS 99-7777.JS(SH) 89-2122.PK 1014
33.JS 99-8378.MAUS 65123.PK 1024
34.JS(SH)1860879.MAUS 68124.PK 1028
35.JS(SH)89-4980.MAUS 71-07125.PK 1038
36.JS(SH)90-9181.MAUS 81126.PK 1125
37.JS(SH)91-9382.NRC 2006-M-6127.PK 1146
38.JS(SH)92-4683.NRC 2007-G-1-13128.PK 1225
39.JS(SH)93-3784.NRC 2007-I-3129.PK 701
40.JS(SH)93-4485.NRC 2007-K-7-2130.PK 7247
41.JS(SH)99-1486.NRC 21131.NRC 132
42.MACS 103987.NRC 25132.NRC 142
43.MACS 112688.NRC 29133.NRC 147
44.MACS 113989.NRC 34134.MACS 1460
45.MACS 114090.NRC 42135.CO (Soy) 3

Table 2 . Analysis of variance for the quantitative traits among soybean germplasm accession

Source of varianceMean sum of squares
BlockTreatmentChecksGenotypesCheck vs GenotypesError
Degrees of freedom41344129116
Days to fifty per cent flowering42.0512.12**72.349.9846.960.22
Days to maturity69.1933.04**229.0426.5783.690.17
Plant height77.73149.96**377.82143.3098.070.48
Number of branches per plant2.161.662.761.631.280.16
Number of clusters per plant74.0188.87**105.3488.4675.260.84
Number of pods per cluster0.950.993.700.910.770.13
Number of pods per plant8.181074.43**2539.541037.330.018.49
Number of seeds per pod0.540.501.500.470.240.10
Hundred seed weight0.774.50**9.224.380.540.22
Single plant yield22.0084.07281.9978.546.879.87

Table 3. Correlation coefficient for yield and yield attributing traits in the soybean germplasm accessions

 DFFDMPHNBPNCPNPCNPPNSPHSWSPY
DFF1         
DM0.6126**1        
PH0.5672**0.3464**1       
NBP0.5435**0.2329**0.5594**1      
NCP0.5367**0.08110.6397**0.7758**1     
NPC0.1690*0.05970.16220.12980.2727**1    
NPP0.4670**0.04140.5802**0.6260**0.8375**0.5386**1   
NSP-0.0730-0.0482-0.11350.0276-0.0817-0.0579-0.16761  
HSW-0.0216-0.0551-0.1864*0.07540.02820.1079-0.0414-0.04161 
SPY0.3866**0.05240.3648**0.5855**0.6907**0.4146**0.7056**0.2192*0.3439**1

*Significance at 5% level **Significance at 1% level

DFFDays to fifty per cent floweringNPCNumber of pods per cluster
DM  Days to maturityNPPNumber of pods per plant
PH   Plant height (cm)NSPNumber of seeds per pod
NBP Number of branches per plantHSWHundred seed weight (g)
NCPNumber of clusters per plantSPYSingle plant yield (g)

Table 4. Path coefficient analysis for yield and yield attributing traits

  DFFDMPHNBPNCPNPCNPPNSPHSWSPY
1DFF0.00660.0348-0.02530.00680.10110.00060.2958-0.0259-0.00800.3866**
2DM0.00400.0569-0.01550.00290.01530.00020.0262-0.0171-0.02060.0524
3PH0.00370.0197-0.04460.00700.12060.00050.3677-0.0402-0.06960.3648**
4NBP0.00360.0132-0.02500.01260.14620.00040.39650.00980.02810.5855**
5NCP0.00350.0046-0.02850.00980.18850.00090.5305-0.02900.01050.6908**
6NPC0.00110.0034-0.00720.00160.05140.00340.3411-0.02050.04030.4146**
7NPP0.00310.0024-0.02590.00790.15780.00180.6334-0.0594-0.01550.7056**
8NSP-0.0005-0.00270.00510.0003-0.0154-0.0002-0.10610.3543-0.01550.2192**
9HSW-0.0001-0.00310.00830.00090.00530.0004-0.0262-0.01480.37120.3439**
 Residue=0.4678

*Significance at 5% level **Significance at 1% level

DFFDays to fifty per cent floweringNPCNumber of pods per cluster
DM  Days to maturityNPPNumber of pods per plant
PH   Plant height (cm)NSPNumber of seeds per pod
NBP Number of branches per plantHSWHundred seed weight (g)
NCPNumber of clusters per plantSPYSingle plant yield (g)

Figure 1. Bar chart depicting PCV and GCV for ten quantitative traits of soybean germplasm accessions

Figure 2. Bar chart depicting heritability and GAM for ten quantitative traits of soybean germplasm accessions

Figure 3. Path coefficient diagram for single plant yield

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