Path analysis, Genetic variability and Correlation studies for Soybean (Glycine max (L.) Merill) for grain yield and Secondary traits at Asosa, Western Ethiopia

Mesfin, Hailemariam Habtegebriel

Ethiopian Institute of Agricultural Research (EIAR), P.O.BOX.2003

Jimma Agricultural Research Centre (JARC) P.O.BOX.192

1     INTRODUCTION

Soybean (Glycine max (L.) Merill) the ‘golden bean’, as it is an important crop in the words in terms of its uses in human food and animal feeds. It belongs to the family leguminosae and is self-pollinated crop having chromosome number of (2n=2x=40). It considered to be a miracle crop as it is extraordinary rich in proteins (~40) and is also second only to ground nut in terms of oil content (~20). Comprising 85% unsaturated fatty acids and is free from cholesterol, among food legumes, so it highly desirable in the human diet (Iqbal et al., 2008). According to central statics authority (CSA) 2016/17 in Ethiopia, 12,574,107.33 hectares is covered by the grain production among this about 804,752hectares are oil seeds. which is 6.40%. It is grown globally producing 329.40 million tone year in the world food basket. Ethiopia is experiencing the rise in the soybean production due to its widespread use in food and feed industry. 

In Ethiopia, so far around twenty-five soybean varieties were released. According central statics authority (CSA) of Ethiopia 2015/16 pulse grown covered 13.24% (1,652,844.19 hectares) of the grain crop area and 10.38 (about 27,692,743.11 quintals) of the grain production to oil seeds added 6.88 % (about 859,110.39 hectares) of the grain crop area and 2.94% (about 7,848,093.10 quintals) of the production to the national grain total. Sesame, Neug and linseed covered 3.11% (about 388,245.50 hectares),2.25% (about 281,036.36 hectares) and 0.68% (about 85,415.67 hectares) of the grain crop area and 1.03% (about 2,742,174.27 quintals), 0.96% (about 2,563,271.66 quintals) and 0.33% (about 885,511.44 quintals) of the grain production, respectively (CSA,2015/16). Due to the narrow genetic base of soybean in the country, the production and productivity is very low

The importance of genetic diversity in the improvement of soybean is a paramount importance. In this study was designed to estimate the genetic parameters GCV, PCV, heritability, expected genetic advance (GA), genetic advance as percent of mean(GAM) for yield and morphological traits of soybean that may be used for the selection tools for the for the future breeding programs and also useful biometrical tools for measuring genetic parameters (Aditya et al.,2011).

Keeping it in view, the present investigation was carried out to assess the nature and magnitude and revealing the genetic correlation among the traits and partition the genetic correlations into direct and indirect effects so that to estimate direct and indirect effects of various character on seed yield.  

2     MATERIALS AND METHODS

2.1. Experimental site - The experiment was carried out at experimental fields Asosa agricultural research centre (AsARC), Asosa, Ethiopia, during June to November 2015. Geographically, the place is located 10^o02'N34^o34'E. Agro-ecology’s of the area is characterized as hot-warm moist lowland plain tepid to cool humid sub humid lowland plain tepid to cool sub humid mountain. The annual Rain fall 1130mm and that of 15.9-29 ^oc. The area soil type is characterized as dystric nitisols (EIAR,)

2.2. Plant Materials - The experiment was conducted with twenty-four soybean genotypes laid out in randomized complete block design with three replications used as the experimental materials. The name of the twenty-four soybean genotypes was described in table 1.

2.3. The experimental design and setting the experiments - The experiments is laid out in randomized complete design with three replications.

2.4. Intercultural operations - Plots were made up of four rows 4m long and with 0.6 m inter row and 0.05m intra -row spacing.  Fertilizer DAP was applied at the rate of 100 kg ha^-1 at planting for all experiments. Sowing was conducted under rain-fed condition. The fields were kept free of weeds by hand weeding and other management practices were followed according to the recommendations of the specific area was followed for raising the crop.

2.5. Data Collections - Descriptor of soybean (Glycine max (L.) Merill) developed by International Plant Genetic Resources Institutes (IPGRI)/International Institutes for Tropical Agriculture (IITA) (1986) were used for data collection. (Table 2).

Among the descriptor developed in (IPGRI)/1986 the following eighteen quantitative date were measured. Data on days to flower, days to maturity, plant height(cm), 100 seed weight(g), branch per plant(cm), number of seeds per plant, number of pod per plant, harvest index, seed yield (Kg ha^-1),fresh biomass weight(g), root fresh weight (g), root volume (ml), tap root length(cm), plant dry weight (g), pod length (cm), internode length(cm), number of nodules(number), effective nodule weight(g), total nodule weight(g) were taken from ten competitive plants from each plot. Measurements were done according to the IPGRI descriptors lists of G.max L.

2.6. Statistical Analysis

Analysis of variance (ANOVA) using the generalized linear model (GLM) procedures of the SAS 9.3 Software for windows (SAS institute’s, 2011). Comparison among means were made using Least Significance Difference (LSD) at α =0.05) or less when ANOVA indicated that model and treatments were significant. Genetic parameters were estimated by the formula given by Burton, DeVane (1953) and Johanson using equation (1) - (5).

Environmental variance (σ²_E) = Ms_e--------------- (1)

Phenotypic variance (σ²_P) =σ²g +σ²e ------------- (2)

Genotypic variance (σ²_P) = (Ms_e- Ms_t)/r ---------- (3)

Where, Ms_e is the mean square error, Ms_t is the mean square treatment and r is the number of replications.

PCV=------------------------------------- (4)

GCV = --------------------------------- (5)

Estimation of heritability in broad sense: Broad sense heritability (h²) expressed as the percentage of the ratio of the genotypic variance (g) to the phenotypic variance (p) and was estimated on genotype mean basis as described by Robinson et al. (1949) as equation (6):

H²b= x100.  ------------------------------ (6)   

Where, h²B is the heritability in a broad sense, σ²p is the phenotypic variance and σ²g is the genotypic variance.

Heritability values are categorized as low, moderate and high (Robinson et al., 1949) and are given below,

0-30% - Low

30-60% - Moderate

60% and above - High

Genetic Advance (GA) and the percentage of the mean (GAM) assuming selection of the superior 5% of the genotypes was estimated in accordance with the methods illustrated by Johnson et al. (1955) and equations (7) and (8):

= -------------------------------------(7)

Where, GA is the expected genetic advance, K is the standardized selection differential at 5% selection intensity (K ¼ =2.063), σ²p is the phenotypic variance and σ²g is the genotypic variance.

GAM (%) = x100------------------------------------(8)

Where, GAM is the genetic advance as a percentage of the mean, GA is the expected genetic advance and x is the grand mean of a character. Genetic advance as percent of mean was classified as low, moderate and high (Johnson et al., 1955) and values are given below:

0-10% - Low

10-20% - Moderate and

20% and above - High

Phenotypic and genotypic correlation coefficients were estimated using the standard procedure suggested by Miller et al. (1958) using the corresponding variance and covariance components as shown in equations (9) and (10):                       

      ---------------------------------------(9)

and

= ------------------------------------------- (10)                               

Where,  is the phenotypic correlation coefficient,  is the genotypic correlation coefficient between the characters x and y, Pcovx.y is the phenotypic covariance and Gcovx. y is the genotypic covariance between the character’s x and y.

Path coefficient analysis was conducted as suggested by Dewey and Lu (1959) using the phenotypic and genotypic correlation coefficients to determine the direct and indirect effects of the yield component on the fruit yield based on equation (11):

 = + x--------------------------------------(11)

where, is the mutual association between the independent trait (i) and the dependent trait (j) as measured by the correlation coefficient,  is the component of direct effects of the independent trait (i) on the dependent variable (j) and r_ikp_kj is the assumption of components of the indirect effect of a given independent trait via all other independent traits.

The residual effect was obtained as per the formula given below:

R=    -------------------------------------- (12)

Where, = Direct effect of the character

=correlation coefficient of   character with  character.

 Path coefficient analysis was calculated using the SAS software package (Cosme, 2013). Genotypic and phenotypic coefficient variations are for the different character were calculated in all possible combinations following the formula.

Table 1. Details of genotypes used for the experiment

Source: EIAR/JARC          * AON =Advanced Observation Nursery

Table 2. List of different traits and their description measurement

3     RESULTS AND DISCUSSION

3.1     Variance Components and Coefficient of Variations

ANOVA showed that mean squares among genotypes were ranges from significant (P≥0.05) to very highly significant for significant (P≥0.001) for the traits, but non-significant difference for the traits like days to maturity, number of seed per pod, inter-node length and total nodules weights (Table 3). These results indicate that, there is a genotypic variation for among the genotypes for all the studied traits, except the four. This implied that the population of soybean genotypes would respond positively to selections. This indicates that substantial variability has been created and genetic base is broadened for most of the important characters among different genotypes developed through hybridization involving diverse parents. The exploitation of the present variability may lead to develop potential and suitable genotypes in future.

Similar results have been reported by Aditya et al. (2011), for plant height, pods per plant, branches per plant and 100 seed weight, Gupta and Punetha (2007) for pods per plant, Reni and Rao (2013) for pods per plant, branches per plant, biological yield, harvest index, yield per plant and Sureshrao et al. (2014) for number of pods per plant and seed yield per plant.

3.2     3.2. Genotypic and Phenotypic Coefficients of Variations (GCV and PCV)

Broad sense heritability (Hb), genetic advance (GA) and genetic advance as percent of mean were calculated for all traits (Table 4). In the present investigation, PCV was found higher than GCV for all traits under study. The highest observed for effective nodule weight recorded the highest PCV and GCV (94 and 43%, resp.) followed by total nodule weight (93 and 28%, resp.), plant height, branch per plant and the lowest PCV and GCV recorded for days to maturity (8 and 5%, resp.).In agreement with the present findings Chandda et al. (2013), Ghodrati (2013), Dilnesaw et al. (2013), Mahbub et al. (2015) and Pawae et al. (2014), Hakim et al. (2014), Pawar et al. (2014) and Malek et al.(2014) for days to maturity, Reni and Rao et al. (2013) and Chandel et al.(2013) for biological weight, Reni and Rao (2013), Chandel et al. (2013) and Pawar et al. (2014) for harvest index, Ghodrati (2013), Reni and Rao (2013),Chandel et al. (2013), Dilnesaw et al. (2013), Pawar et al. (2014), Malek et al.(2014) and Mahbub et al. (2014) for number of branches per plant, Aditya et al. (2011), Reni and Rao (2013), Chandel et al. (2013), Dilnesaw et al. (2013),Hakim et al. (2014), Pawar et al. (2014) and Sureshrao et al. (2014) for number of pods per plant, Chandel et al. (2013) for number of pod clusters per plant, Osekita and Ajayi (2013) for number of seeds per plant, Pawar et al (2014). for 100 seed weight, Aditya et al. (2011), Reni and Rao (2013), Osekita and Ajayi (2013), Chandel et al. (2013), Pawar et al. (2014), Sureshrao et al. (2014) and Mahbub et al. (2015) for seed yield per plant reported similar findings.

3.3     Heritability and Genetic Advance

Heritability in broad sense estimated for all the traits under study and presented in Table 4. The result indicates that estimate of heritability was high for plant height, grain yield and that of fresh weight. This result indicates that the preponderance of additive gene action. High heritability coupled with high genetic advance was recorded plant height, grain yield, and fresh biomass weight. In conformity of the results high heritability coupled with high genetic advance been reported plant height Hakim et al. (2014), Badkul et al. (2014) and Ghodrati (2013) for plant height, Mehta et al. (2016) for fresh biomass weight. So, the result suggests that selection may be effective because these traits may have governed by additive gene action.

3.4     Associations among Traits

The phenotypic and genotypic correlation of yield with yield components characters are indicated in table 6. Most of the genotypic correlation coefficients were higher than their corresponding phenotypic correlation coefficient indicating the masking of the efficiency of the environment which modified the expression of a character thereby reducing the phenotypic expression (Saha et al., 1992; Islam et al., 1993). All characters observed for quantitative data showed positive genotypic and phenotypic correlations with the fruit yield, except genotypic coefficient of variation for days to maturity (Table 6).The grain yield had a highly significant, positive, genotypic and phenotypic correlation with the days to maturity, plant height, number of seed per plant ,harvest index ,fresh biomass weight, root fresh weight , tap root length, pod dry weight ,number of nodules, effective nodule weight ,and total nodule weight, days to flower showed a negative correlation with the grain yield (Table 3).

3.5     Path Coefficient Analysis

Path coefficient analysis is known as standard partial regression coefficient analysis which is unitless. It is carried out using genotypic and phenotypic and taking the yield as the dependent variable in order to see the casual factor and to identify the best components, which is responsible for increasing the yield per plant. The genotypic path coefficient analysis of different morphological traits on seed yield per plant revealed that pod dry weight recorded very high estimates of positive direct effect. The high estimate of positive direct effect was recorded for days to maturity followed plant height plant, hundred seed weight, number of seed per plant, number of pod per plant, fresh biomass weight, and nodule number, while effective nodule weight recorded low value of positive direct effect on seed yield per plant. The very high negative direct effect on seed yield per plant was exhibited by tap root length and inter-node length.

Similar finding has also been obtained by Salimi and Moradi (2012) for fresh weight at full flowering, Badkul et al (2014), Chandel et al. (2014) and Jain et al. (2015) for biological weight, Abady et al. (2013), Badkul et al. (2014) and Chandel et al. (2014) for harvest index, Salimi and Moradi (2012), Badkul et al. (2014) and Silva et al. (2014) for number of seeds per plant, Salimi and Moradi (2012), Malek et al. (2014) and Jain et al. (2015) for 100 seed weight. However, Chandel et al. (2014) recorded low direct positive effect on seed yield per plant.

3.6     Diversity Analysis          

3.6.1    Cluster Analysis

Cluster analysis is based on 18 morphological traits group twenty-four soybean genotypes grouped into seven different clusters and indicated twenty-four soybean genotypes exhibited notable genetic divergence in terms of morphological traits (Figure 1). Formation of different number of clusters using morphological characters in diverse soybean genotypes was also reported. Reni et al. (2013) obtained 6 and 7 clusters by Mahalanobis’ D² statistic and cluster analyses.

Table 3. Mean square value for eighteen different phonological and morphological characters

NB:DM=Days to maturity, PH=Plant height, HSW=Hundred seed weight(g), NSPP=Number of seed per plant, BPP=Branch per plant, NPPP=Number of pod per plant, HI=Harvest index, GYLD=Grain yield, FBW=Fresh Biomass weight, RFW=Root fresh weight, RV=Root volume(ml), TRL=Tap root length(cm), PDW=pod dry weight, PODL=Pod length(cm), IL=Inter-node length, NN=Number of nodule, ENW=Effective nodule weight, TNW=Total nodule weight(gm), ** Significantly at 1% ,* Significantly at 5%

Table 4. Parameters of genetic variability for physiological and economic traits of soybean genotypes

NB:DM=Days to maturity, PH=Plant height, HSW=Hundred seed weight(g), NSPP=Number of seed per plant, BPP=Branch per plant, NPPP=Number of pod per plant,HI=Harvest index (%), GYLD=Grain yield, FBW=Fresh Biomass weight, RFW=Root fresh weight, RV=Root volume(ml), TRL=Tap root length(cm), PDW=pod dry weight, PODL=Pod length(cm), IL=Inter-node length, NN=Number of nodule, ENW=Effective nodule weight, TNW=Total nodule weight(gm), PCV=Phenotypic coefficient of variation; GCV=Genotypic coefficient of variation; h_b= broad sense heritability; GA=Genetic advance

Table 5. Path analysis showing direct (bold-diagonal) and indirect effects (Off-diagonal) effect at genotypic (G) and phenotypic (P) on yield component trait on yield in soybean genotypes (genotypic and phenotypic residual effect was 33.3 and 76.4% respectively).

NB:DM=Days to maturity, PH=Plant height, HSW=Hundred seed weight(g), BPP=Branch per plant, NSPP=Number of seed per plant, NPPP=Number of pod per plant, HI=Harvest index, GYLD=Grain yield, FBW=Fresh Biomass weight, RFW=Root fresh weight, RV=Root volume(ml), TRL=Tap root length(cm), PDW= pod dry weight, PODL= Pod length(cm), IL=Inter-node length, NN=Number of nodule, ENW=Effective nodule weight, TNW=Total nodule weight(gm),

Table 6. Genotypic (G) and Phenotypic (P) correlation coefficient among important quantitative traits in soybean genotypes

NB:DM=Days to maturity, PH=Plant height, HSW=Hundred seed weight(g), NSPP=Number of seed per plant, BPP=Branch per plant, NPPP=Number of pod per plant,HI=Harvest index, GYLD=Grain yield, FBW=Fresh Biomass weight, RFW=Root fresh weight, RV=Root volume(ml),TRL=Tap root length(cm),PDW=,PODL=,Pod length(cm),IL=Inter‑node length, NN=Number of nodule, ENW=Effective nodule  weight, TNW=Total nodule weight(gm), *, ** are significant at 1% and 5%  level of probability, respectively.

Figure 1. Dendrogram shows relation among the of 24 genotypes of soybean based on average linkage and Euclidean distance using the mean of 18 quantitative traits

4     CONCLUSION

From this study, it can be concluded that considerable genetic diversity was found at the genetic materials. Estimates of variance, genotypic and phenotypic coefficient of variation and range revealed large variability for seed yield per plant, number of pods per plant, biological yield per plant and number of nodules per plant over environments suggested that direct selection exercised for these traits can improve the seed yield.

    High estimates of heritability accompanied by high genetic advance were recorded for number of nodules per plant, number of pods per plant, days to

    maturity over environments suggested that direct selection on these traits can improve the seed yield

    The traits viz. number of primary branches per plant, plant height, number of nodules per plant, seed yield per plant and days to maturity have been identified as major yield contributing traits through association analysis.

    Therefore, there need to consider effort in making heterotic in hybridization, that may result good recombinant for improvement of quality traits.

Conflicting interests
 

The authors declare that they have no conflict of interests.

5     ACKNOWLEDGEMENTS

The authors are grateful to colleagues at EIAR for beneficial discussion on this manuscript. This work was supported by grants from the Ethiopian Institute of Agricultural Research (EIAR), EthiopiaThe authors also acknowledge Jimma Agricultural Research Centre (JARC) for providing the accessions of soybean and also is thanked Asosa Agricultural Research Centre (AsARC) pulses, oils and fiber crop research divisions staffs for execution of the field experiment.

6     REFERENCES

Abady S, Merkeb F and Dilnesaw Z. 2013. Heritability and path-coefficient analysis in soybean [Glycine max (L.) Merrill.] genotypes at Pawe, North Western Ethiopia. Journal of Environmental Science and Water Resources 2(8): 270-276.

Aditya JP, Bhartiya P and Bhartiya A. 2011. Genetic variability, heritability and character association for yield and component characters in soybean [Glycine max L. Merrill]. Journal of Central European Agriculture 12(1):27-34.

Advances of Quantitative Characters in F2 Progenies of Soybean Crosses. Indonesian Journal of Agricultural Sciences 15(1): 11-16

Allard R W. 1960. Principles of plant breeding. John Wiley and Sons Inc., U.S.A

Allard, R.W. 1960.  Principles of plant breeding.  John Wiley and Sons Inc., New York. 485pp.analysis. Indian Journal of Agricultural Sciences 84(4): 531–3.

 Badkul A, Shrivastava AN, Bisen R and Mishra S. 2014. Study of principal components analyses for yield contributing traits in fixed advanced generations of soybean [Glycine max (L.) Merrill]. Soybean Research 2: 44-50.

Bekele, A., Alemaw, G. and Zeleke, H., 2012. Genetic divergence among soybean (Glycine max (L) Merrill) introductions in Ethiopia based on agronomic traits. J. Biol., Agric. Healthcare, 2(6), pp.6-12.

Board J.E., Kang M.S., Harville B.G., 1999 - Path analyses of the yield formation process for late-planted soybean. Agron. J., 91:128-135.

Burton, G.W., 1952, August. Qualitative inheritance in grasses. Vol. 1. In Proceedings of the 6th International Grassland Congress, Pennsylvania State College (pp. 17-23).

Burton, G.W. and Devane, E.H., 1953. Estimating heritability in tall fescue (Festuca arundinacea) from replicated clonal material 1. Agronomy Journal, 45(10), pp.478-481.

Chandel KK, Patel NB and Patel JB. 2013. Genetic Variability Analysis in Soybean [Glycine max (L.) Merrill]. AGRES – An International e-Journal 2(3):318-325.

Dewey, D.R. and K.H. Lu. 1959. A correlation and path coefficient analysis of components      of crested wheatgrass seed production. Agron. J., 51: 515-518.

Dilnesaw Z, Abadi S and Getahun A. 2013. Genetic variability and heritability of soybean [Glycine max L. Merrill] genotypes in Pawe district, Metekel zone, Benishangule Gumuz regional state, Northwestern Ethiopia. Wudpecker Journal of Agricultural Research 2(9): 240-245.

Ghodrati GH. 2013. Study of genetic variation and broad sense heritability for some qualitative and quantitative traits in soybean ([Glycine max (L.) Merrill] genotypes. Current Opinion in Agriculture 2(1): 31-35.

Gupta AK and Punetha H. 2007. Genetic variability studied for quantitative traits in soybean [Glycine max (L.) Merrill]. Agricultural Science Digest 27(2):140-141.

H. W. Johonson, H. F. Robinson, and R. E. Comostock, “Genotypic and phenotypic correlations in soybeans and their implication in selection,” Agronomy Journal, vol. 47, pp. 477–483, 1955. International Journal of Plant, Animal and Environmental Science 3(4): 35-38.

Hakim LS and Eman P. 2014. Genetic Variability, Heritability and Expected Genetic International Journal of Food, Agriculture and Veterinary Sciences International Journal of Plant, Animal and Environmental Science 3(4):35-38.

Hakim LS and Eman P. 2014. Genetic Variability, Heritability and Expected Genetic Advances of Quantitative Characters in F2 Progenies of Soybean Crosses. Indonesian Journal of Agricultural Sciences 15(1): 11-16.

Iqbal, Z., Arshad, M., Ashraf, M., Mahmood, T. and Waheed, A., 2008. Evaluation of soybean [Glycine max (L.) Merrill] germplasm for some important morphological traits using multivariate analysis. Pakistan Journal of Botany, 40(6), pp.2323-2328.

Jain Sudhanshu, Srivastava SC, Singh SK, Indapurkar YM and Singh BK. 2015. Studies on genetic variability, character association and path analysis for yield and its contributing traits in soybean [Glycine max (L.) Merrill]. Legume Research 38(2): 182-184.

Johnson, H.W., H.F. Robinson and R.E. Comstock, 1955. Estimates of genetic and environmental variability in soybeans. Agron. J., 47: 314-318.

Joshi, D., Pushpendra, K.S. and Adhikari, S., 2018. Study of Genetic Parameters in Soybean Germplasm Based on Yield and Yield Contributing Traits. Int. J. Curr. Microbiol. App. Sci, 7(1), pp.700-709.

Joshi, D., Pushpendra, K.S. and Adhikari, S., 2018. Study of Genetic Parameters in Soybean Germplasm Based on Yield and Yield Contributing Traits. Int. J. Curr. Microbiol. App. Sci, 7(1), pp.700-709.

Kumar, S., Kumari, V. and Kumar, V., 2018. Assessment of genetic diversity in soybean [Glycine max (L.) Merrill] germplasm under North-Western Himalayas. Journal of Pharmacognosy and Phytochemistry, 7(2), pp.2567-2570.

Mahbub MM, Mamunur Rahman M, Hossain MS, Mahmud F and Mir Kabir MM. 2015. Genetic Variability, Correlation and Path Analysis for Yield and Yield Components in Soybean. American-Eurasian Journal of Agricultural & Environmental Sciences 15(2): 231-236.

 Malek MA, Mohd Y, Rafii, Afroz MSS, Nath UJ and Monjurul Alam Mondal M. 2014. Morphological Characterization and Assessment of Genetic Variability, Character Association, and Divergence in Soybean Mutants. The Scientific World Journal 14: 1-12.

Malek MA, Mohd Y, Rafii, Afroz MSS, Nath UJ and Monjurul Alam Mondal M. 2014.Morphological Characterization and Assessment of Genetic Variability, Character           Association, and Divergence in Soybean Mutants. The Scientific World Journal 14: 1-12.

Mehta, R.R., 2016. Assessment of genetic variability, coheritability and divergence in soybean (Doctoral dissertation, JNKVV). Thesis

Miller, P. A., J. C. Williams, H. F. Robinson, and R. E. Comstock. "Estimates of Genotypic and Environmental Variances and Covariances in Upland Cotton and Their Implications in Selection 1." Agronomy journal 50, no. 3 (1958): 126-131.

Osekita Oluwatoyin Sunday and Ajayi Abiola Toyin. 2013. Character expression and selection differential for yield and its components in soybean [Glycine max (L.) Merrill]. Academia Journal of Agricultural Research 1(9): 167-171.

Pawar KK, Rangare NR and Singh AK. 2014. Evaluation of soybean (Glycine max) germplasm for some important morphological traits using multivariate analysis. Indian Journal of Agricultural Sciences 84(4): 531–3.

 Reni YP and Rao YK. 2013. Genetic variability in soybean [Glycine max (L) Merrill]. International Journal of Plant, Animal and Environmental Science 3(4):35-38.

Reni YP and Rao YK. 2013. Genetic variability in soybean [Glycine max (L) Merrill]. International Journal of Plant, Animal and Environmental Science 3(4):35-38.

 SAS Institute Inc. (2004). SAS software release. SAS Institute, Inc., Cary, NC, USA.

Scientific World Journal 14: 1-12. Aditya JP, Bhartiya P and Bhartiya A. 2011. Genetic variability, heritability and character association for yield and component characters in soybean [Glycine max L. Merrill]. Journal of Central European Agriculture 12(1):27-3

Sureshrao SS, Singh VJ, Gampala Srihima and Rangare NR. 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.

Sureshrao, Sawale Swapnil, V. J. Singh, S. Gampala, and N. R. Rang are. "Assessment of genetic variability of the main yield related characters in soybean." International Journal of Food, Agriculture and Veterinary Sciences 4, no. 2 (2014): 69-74.