6 Agriculture and Food Sciences Research Vol. 5, No. 1, 6-22, 2018 ISSN(E) 2411-6653 / ISSN(P) 2518-0193 DOI: 10.20448/journal.512.2018.51.6.22 Genetic Differentiation of ARC Soybean [Glycine Max (L.) Merrill] Accessions Based on Agronomic and Nutritional Quality Traits Mofokeng, Maletsema Alina1  Mashingaidze, Kingstone2 1,2Agricultural Research Council-Grain Crops, Potchefstroom, South Africa Abstract Soybean is one of the most important leguminous crops grown globally for food and feed. The study of genetic diversity is invaluable for efficient utilization, conservation and management of germplasm collections. The study aims at assessing genetic diversity present among the soybean genotypes using phenotypic markers. The restriction maximum likelihood revealed highly significant differences among the genotypes for eight quantitative traits. The principal component analysis revealed three most important PCs contributing 63.19%, 25.43% and 8.88% to the total variation of 97.5%, respectively. Seed yield was highly significant and highly correlated with seed number per plant, pod weight per plant, pod number per plant, and hundred seed weight but negatively correlated with seed number per pod. The hierarchical clustering revealed three major clusters with further sub-clusters. The accessions 2015/06/12, 69 S 10, PR 154-14, R 5-4-2 M, Hawkeye (USSR), and PR 145-2 were the most diverse. There were significant differences among the accessions based on nutritional quality traits such as oil, protein and stearic acid across the locations. The protein content varied from 29.1% to 35.6%, oil content varied from 10.6% to 20.7% whereas oleic acid and ash varied between 6.8% and 30.8%, and 4.3% and 8.2%, respectively. There was vast genetic diversity among the soybean genotypes. The presence of genetic diversity will aid breeders in selections and hybridization programmes for crop improvement. Keywords: Agro-morphology, Genetic diversity, Nutritional quality, Soybean. Citation | Mofokeng, Maletsema Alina; Mashingaidze, Kingstone, (2018). Genetic Differentiation of ARC Soybean [Glycine Max (L.) Merrill] Accessions Based on Agronomic and Nutritional Quality Traits. Agriculture and Food Sciences Research, 5(1): 6-22. History: Received: 10 October 2017 Revised: 17 January 2018 Accepted: 22 January 2018 Published: 25 January 2018 Licensed: This work is licensed under a Creative Commons Attribution 3.0 License Publisher: Asian Online Journal Publishing Group Contribution/Acknowledgement: The authors would like to thank the Department of Science and Technology for funding, Mr Paul Rantso, and soybean breeding team for technical help. Funding: This study received no specific financial support. Competing Interests: The authors declare that they have no conflict of interests. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study was reported; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. Ethical: This study follows all ethical practices during writing. Contents 1. Introduction ......................................................................................................................................................................................... 7 2. Materials and Methods ...................................................................................................................................................................... 7 3. Results ................................................................................................................................................................................................... 9 4. Discussion .......................................................................................................................................................................................... 15 5. Conclusion ......................................................................................................................................................................................... 16 References .............................................................................................................................................................................................. 21 http://crossmark.crossref.org/dialog/?doi=10.20448/journal.512.2018.51.6.22&domain=pdf&date_stamp=2017-01-14 http://creativecommons.org/licenses/by/3.0/ http://creativecommons.org/licenses/by/3.0/ https://orcid.org/orcid-search/quick-search?searchQuery=Mofokeng,%20Maletsema%20Alina https://orcid.org/orcid-search/quick-search?searchQuery=Mashingaidze,%20Kingstone Agriculture and Food Sciences Research, 2018, 5(1): 6-22 7 1. Introduction Soybean [Glycine max (L.) Merrill] (2n=2x=40) is one of the most important legumes produced worldwide. According to Food and Agriculture Organisation of the United Nations [1] the three major world-producing countries are U.S.A (90.6 million metric tonnes), Brazil (68.5 million metric tonnes) and Argentina (52.6 million metric tonnes). The total production in Africa was 1.5 million tonnes with West Africa producing 437,115 metric tonnes. Nigeria is the leading producer in West Africa with 393,860 metric tonnes [2]. In South Africa soybean is produced in almost all provinces with the Free State being the major producer. In 2014, South Africa produced 8851 metric tons in an area of 502900 hectares. Soybean is grown primarily for the production of seed and has several uses in the food and industrial sectors. It is the most important crop provider of proteins and oil used in animal nutrition and for human consumption. It contains 40 to 42% good quality protein and 18 to 22% oil comprising 85% unsaturated fatty acids and is free from cholesterol, it is highly desirable in the human diet [3]. Besides fixing the atmospheric nitrogen, this crop has the ability to grow in a wide range of environments, to reduce soil erosion, to suppress weeds and to suit inter and sequential cropping patterns. It is valued finding to understand genetic diversity and relationship for facilitating the transfer of useful genes among cultivated species and maximizing the use of available germplasm resources. The extent of genetic diversity in germplasm can be assessed through morphological characterization. The characterized material then helps the plant breeders to select the accessions to be utilized in hybridization programme [4]. An investigation of genetic relatedness at a broad level may provide important information about the historical relationship among different genotypes. It reveals genetic backgrounds and relationships of germplasm and also provides strategies to establish unitize and manage crop core collections [5]. Therefore the knowledge of the genetic variation within accessions from germplasm collections is essential to the choice of strategies to incorporate useful diversity into the program to facilitate the introgression of genes of interest into commercial cultivars, to understand the evolutionary relations among accessions, to better sample germplasm diversity and to increase conservation efficiency [6]. Morphological characters, both quantitative and qualitative have long been used to identify species, genera, to evaluate systematic relationships, and to discriminate between varieties [7]. In breeding practice and seed production, the role of morphological descriptor is very important, since the distinguishing between varieties can be done quickly and precisely. Qualitative traits are usually controlled by a few genes, thus easily observable and suitable for cultivar differentiation and identification. On the other hand, quantitative traits have more limitations in cultivar description, since they are affected by environmental effects, developmental stage of the plant and the generation of selfing of breeding material. According to Khalid, et al. [8] scientific classification of the plant still relies on morphological traits. They are easier to work with, cost effective and easy to score and requires less time and finally they do not need any technical knowledge. Kumar, et al. [9] evaluated genetic diversity and interrelationship of agro-morphological traits in soybean genotypes. Vesna, et al. [10] determined genetic relatedness of soybean genotypes based on agro-morphological traits and DNA markers and they found genetic differences among the genotypes. Malek, et al. [11] also assessed genetic variability and association of characters among the soybean mutants and reported a vast genetic variability. Khatab, et al. [12] reported the presence of genetic diversity among soybean genotypes assessed through agro-morphological descriptors. Soybean has nutritional quality attributes such as protein and oil that makes it an important food crop. It has 40–42% high quality protein and 18–22% oil comprising up of 85% unsaturated fatty acid as well as 12% carbohydrates [13]. Soybean not only contains high quality protein, but the protein content is also much higher than that of other plant foods. Soy protein is valued as a healthy protein due to containing a balanced proportion of all of the important and essential amino acids required by the human body [14]. It can provide two fold more proteins as compared to any other vegetable crop or grain [15]. Soy oil can serve as a good source of oleic and linoleic acid, even the partially hydrogenated soy oil contains 25% linoleic and 3% linolenic acid [16]. Soybean oil is also a good source of vitamin E [17]. Some recent studies revealed variations among the soybean accessions based on quality traits such as oil [18]. However, the nutritional quality traits vary depending on the varieties grown. There are limited studies on the analysis of genetic diversity among the soybean accessions based on nutritional quality traits in South Africa. There is still need to understand and record variations due to other nutritional quality traits that soybean cultivars possess. This will aid breeders to improve the quality of soybean cultivars and for selection of best accessions for both quality agronomic attributes. The objective of the study was to assess genetic diversity using agro- morphological and nutritional quality traits among soybean genotypes grown in South Africa. 2. Materials and Methods 2.1. Plant Material. Experimental Layout and Management Ninety-eight soybean genotypes maintained at the Agricultural Research Council-Grain Crops Institute were planted in Potchefstroom (26.7145° S. 27.0970° E) and Brits (25.6100° S. 27.7960° E) in alpha lattice design replicated two times. Each plot consisted of two 4 m length rows with a spacing of 75 cm between the rows and 10 cm between the plants. Fertilizer 2:3:4 was applied before planting. The plants were irrigated using sprinkler irrigation system. The pre-emergent weeds were controlled by herbicide Bateleur Gold and post emergent weeds were controlled by both Basagran EC and manually. Lime Ammonium Nitrate was top dressed 45 days after germination i.e. before the plants flower. The cultural practices were applied as per soybean planting recommendations. 2.2. Data Collection and Analysis The agro-morphological data recorded is indicated in Table 2. At harvest, five plants were randomly taken from each plot to measure days to 50% flowering; number of branches per plant; hundred seed weight (g); pod weight (g); seed number per pod; seed number per plant; pod Agriculture and Food Sciences Research, 2018, 5(1): 6-22 8 number per plant; and seed yield (g). Analysis of variance was performed for all traits in order to test the significance of variation among the genotypes. The data were further subjected to principal component analysis and correlations. The dendrogram was constructed to study the genetic relatedness among the tested accessions using hierarchical clustering in GenStat 18th version. Table-1. List of names of the soybean accessions used in the study. Serial Number GM number Accession Name Serial Number GM number Accession Name 1 220 69 S 10 50 673 1935/06/15 2 223 69 S 13 Seleksie 51 675 1971/06/06 3 231 69 S 19 52 678 105/5 4 266 Nim 53 679 2012/01/05 5 267 Yubelejuaja 54 681 2015/06/12 6 268 Hawkeye (USSR) 55 688 85/05/ 7 278 Shelby 56 691 1964/06/17 8 284 Gx Gous 57 699 Essex 9 290 Chippewa 63 58 707 DB 1601 10 291 R 5-4-2 M 59 715 Crawford 11 292 R 2-11-3M 60 724 D64-4636 12 297 B 66 S 365 61 725 D66-8666 13 304 B 66S 385 62 861 ORIBI 14 544 Columbia M 8 A 63 862 Impala 15 568 Grant 64 864 SSS 3 16 571 Hampton 266 A 65 873 ND 85 17 575 Hawkeye 66 1120 AGS 239 18 578 Hernon 67 1363 IPB 212-81 19 582 Jackson 68 1371 Coc Chum 20 592 Mack 6 69 1380 F 82-7824 21 593 Mandarin 70 1386 MTD 63 22 595 Maksura 71 1390 Jupiter 23 597 Mojiana 72 1403 F 82-7145 24 598 N69-2774 73 1409 F 82-7656 25 604 PI 170889/(R56-49) 74 1449 UFV-1 26 607 Pikett 75 1552 PR 118 (278) 27 617 S4-A.P.4 76 1554 PR 133 (484) 28 621 Santa Rosa 77 1555 PR 144-4 29 623 Soja (pautena) 78 1556 PR 144-9 30 624 Solar 12 79 1558 PR 145-2 31 626 SSS 2 80 1572 PR 154-13 32 630 Vaschadaka 81 1573 PR 154-14 33 631 Vicoja 82 1575 PR 154-22 34 637 Yeluanda 83 1578 PR 154-47 35 646 54 S 116 84 1590 PR 161-40 36 648 54 S 219 85 1594 PR 162-18 37 649 54 S 95 86 1595 PR 164-20 38 650 14/6/32 87 1596 PR 164-22 39 651 2017/06/11 88 1597 PR 165-3 40 652 2020/06/18 89 1598 PR 165-31 41 653 21/6/23/2 90 1599 PR 165-50 42 654 28/6/35 91 1600 PR 165-52 43 655 28/6/54 92 1659 TN 81-46 44 659 89/05/ 93 IBIS IBIS 45 660 104/5 94 EGRET EGRET 46 662 165/5 95 HERON HERON 47 664 Rhosa ligte hilum 96 NGUTHU NGUTHU 48 666 2017/06/26 97 DUNDEE DUNDEE 49 668 2020/06/25 98 JIMMY JIMMY Note: Agricultural Research Council-Grain Crops in South Africa Table-2. List of abbreviations Full name Abbreviation Hundred seed weight (g) HSW Number of branches per plant BNP Days to 50% flowering DFW Pod number per plant PNP Pod length (mm) PDL Pod weight/plant (g) PDW Seed number per pod SNP Seed number per plant SDP Seed yield (g) SDY Agriculture and Food Sciences Research, 2018, 5(1): 6-22 9 3. Results 3.1. Agro-Morphological Diversity 3.1.1. Analysis of Variance of Nine Agro-Morphological Traits There were significant differences (P ≤ 0.05) observed among the accessions grown in Potchefstroom based on seed number per plant and seed yield; and highly significant differences based on days to 50% flowering, pod weight per plant, and seed number per pod (P ≤ 0.001) (Table 3). Seed number per plant ranged between 46 and 47, and seed yield ranged between 5.58 and 156.3 g. Days to 50% flowering ranged between 55 and 121,5. Pod weight per plant ranged from 13.33 to 4.21 g. Seed number per pod ranged between 1 and 7 pods. In Brits, significant differences (P ≤ 0.05) were observed among the accessions based on number of branches per plant and pod number per plant (Table 4). Number of branches ranged between 3.8 and 8.3, and pod number per plant also ranged between 31.7 and 47.0. The genotypic effects were highly significantly different across the two locations based on number of branches per plant and seed number per plant; and there were significant differences on pod length, seed number per pod, and seed yield (Table 5). Hundred seed weight, days to flowering, pod number per plant and pod weight per plant were non-significant. Number of branches per plant ranged between 3.6 and 8.0. Seed number per plant ranged between 71.9 and 418.2, whereas pod length, seed number per pod, and seed yield ranged from 27.8 to 44.3 mm, 1 to 3, and 11.6 to 96.8 g, respectively. The two sites showed highly significant differences (P ≤ 0.001) in quantitative traits such as number of branches per plant, pod weight per plant, seed number per pod, seed number per plant, and seed yield and significant differences on days to 50% flowering. On the other hand, highly significant differences were observed on the genotype x site interaction for days to 50% flowering, seed number per plant and seed yield. The significant differences on genotype x site interaction could be attributed to the different reactions of the accessions to sites or due to differences between the sites. 3.1.2. Principal Component Analysis The agronomic data were subjected to principal component analysis (PCA), which revealed that the three most important PCs contributed 63.2%, 25.4% and 8.9% of the total variation, respectively (Table 6). Seed number per plant, pod number per plant, and pod weight were the traits that contributed the most variation in the first PC. Seed number per plant and pod number per plant were the traits that contributed the most variation in the second PC, whereas pod number per plant, and seed number per plant were the largest contributors to the variation observed in the third PC. The principal component biplot (Figure 1) grouped the tested soybean accessions into two major groups. The accessions exhibiting early flowering, high seed number per plant, high pod weight and high seed yield were grouped together. 3.1.3. Correlation Analysis among Phenotypic Traits The phenotypic traits were analysed using pair-wise rank correlations coefficients. The results and association of the traits are reported based on the significance levels of 5% (p < 0.05) and 1% (p < 0.001). Seed yield was highly significant and highly correlated with seed number per plant, pod weight per plant, pod number per plant, and hundred seed weight but negatively correlated with seed number per pod. However, was also positively and significantly associated with pod length and days to 50% flowering. Seed number per plant was highly significant and positively correlated with pod weight per plant, pod number per plant, days to 50% flowering but negatively correlated with seed number per pod. It was also significant and positively correlated with pod length. Seed number per pod was highly significant and negatively associated with pod weight per plant, days to 50% flowering, but positively correlated with number of branches. Pod weight per plant was highly and positively associated with pod length, pod number per plant, days to 50% flowering. Pod length was significant and positively correlated with pod number per plant, and number of branches. Pod number per plant was highly and significantly associated with branch number and days to 50% flowering, respectively. 3.1.4. Cluster Analysis The agro-morphological traits were analysed using agglomerative hierarchical clustering to construct a dendrogram (Figure 2). Three major clusters were formed among the soybean accessions. Cluster I was composed of two accessions, S4-A.P.4 (54) and Columbia M 8 A (41) which were genetically closely related. Cluster II consists of two accessions, 04-Apr-2000 (16) and 03-Apr-2000 (15) which were closely related. The third cluster was composed of 94 accessions, which were sub-clustered into three groups. The accessions within each sub cluster were closely related whereas the accessions between the clusters were unrelated. The accessions that were mainly distantly related from the other accessions were 2015/06/12 (54), 69 S 10 (1), PR 154-14 (81), R 5-4-2 M (10), Hawkeye (USSR) (6), and PR 145-2 (79). Table-3. Means of agro-morphological traits of 96 soybean accessions planted in Potchefstroom. GM HSW BNP DFW PNP PDL PDW SDP SNP SDY 1120 16.57 14.00 70.50 114.8 40.01 102.00 260.1 7.000 49.58 1363 12.07 19.00 109.50 164.0 35.01 89.50 231.5 6.500 33.08 1371 13.23 13.00 77.51 100.3 38.33 75.00 223.1 6.500 28.19 1380 12.57 16.50 97.00 239.7 37.51 67.00 435.6 7.000 61.25 1386 13.57 10.50 55.00 108.2 39.51 92.00 300.5 7.000 41.08 1390 14.45 20.50 81.50 145.5 35.23 86.00 232.0 5.500 32.29 1403 15.95 13.00 76.00 188.0 39.23 137.00 399.5 7.000 64.79 1409 12.57 9.50 98.00 107.8 26.67 65.50 138.8 4.500 17.91 1449 16.57 9.50 94.50 117.2 31.67 63.00 178.3 4.000 21.58 1552 9.00 20.50 105.51 131.7 0.00 28.50 176.3 4.000 20.00 1554 19.57 13.50 74.00 134.2 40.84 43.00 309.3 7.000 59.41 Agriculture and Food Sciences Research, 2018, 5(1): 6-22 10 1555 17.45 15.00 84.00 174.4 34.23 48.50 310.6 5.500 54.46 1556 18.23 17.00 83.01 140.1 43.00 122.50 243.6 7.000 45.52 1558 16.41 17.00 99.50 158.1 34.99 26.00 230.8 5.500 35.67 1572 13.00 23.00 116.00 47.9 39.99 39.50 68.5 7.000 7.17 1573 11.00 14.00 121.45 89.7 40.00 28.00 102.3 4.000 12.33 1575 12.00 18.00 97.04 74.3 34.00 23.00 91.3 4.000 10.67 1578 16.48 10.00 76.04 113.1 26.20 69.00 194.7 4.500 29.74 1590 10.00 16.50 96.54 38.6 37.87 71.00 46.4 7.000 5.58 1594 10.00 14.50 90.58 141.3 41.67 41.50 223.3 4.000 22.67 1595 14.33 10.50 103.00 157.6 39.41 69.50 181.5 6.000 24.40 1596 13.83 17.50 101.00 220.6 39.75 113.00 334.8 5.500 45.56 1597 12.98 23.00 104.54 174.7 40.37 66.00 331.1 7.000 51.08 1598 17.48 17.00 76.04 97.7 43.70 71.50 191.7 7.000 30.74 1599 14.26 11.00 77.45 172.1 44.08 101.00 274.8 7.000 43.09 1600 15.83 9.50 74.00 119.6 45.08 105.00 277.0 7.000 44.40 1659 13.66 19.00 107.58 176.4 39.23 124.00 360.1 7.000 52.56 220 16.00 15.00 73.48 136.4 26.99 55.00 294.4 3.500 48.61 223 22.03 13.50 69.48 154.4 39.99 60.00 323.2 7.000 67.61 231 23.45 13.50 67.97 110.8 42.54 61.50 202.8 7.000 54.54 266 17.00 17.00 68.97 165.7 40.33 4.50 374.7 4.000 74.00 267 20.03 13.50 79.98 189.3 39.82 122.50 210.7 7.000 54.77 268 18.03 16.00 74.48 241.6 39.32 35.00 570.4 7.000 126.44 278 19.90 18.50 63.49 159.0 40.83 49.00 343.5 7.000 59.54 284 18.53 21.50 75.48 207.1 39.99 17.00 436.4 7.000 81.77 290 18.45 15.00 74.47 169.2 42.54 71.50 375.7 7.000 72.87 291 20.40 17.00 70.49 141.7 41.67 34.50 309.7 7.000 63.04 292 19.45 14.00 77.47 180.5 38.38 57.00 318.3 6.500 71.04 297 21.45 12.50 72.97 131.5 39.38 111.00 272.0 7.000 55.20 304 18.95 21.00 78.47 221.7 41.71 85.50 434.3 7.000 83.04 544 15.95 19.50 77.47 216.0 43.38 35.00 517.2 7.000 79.04 568 23.53 19.50 70.98 141.4 40.65 130.50 258.5 7.000 61.11 571 24.95 11.50 66.47 79.2 36.71 82.50 153.5 7.000 37.87 575 17.90 18.50 107.99 81.3 40.00 64.50 135.0 7.000 20.04 578 14.95 8.50 89.47 90.0 45.04 103.00 135.0 7.000 20.20 582 18.53 21.00 107.48 125.3 43.32 89.00 191.0 7.000 29.27 592 13.53 14.00 61.98 179.1 41.65 135.00 457.0 7.000 65.77 593 18.29 19.50 75.97 82.9 36.64 94.50 150.8 6.000 24.28 595 20.59 13.00 76.96 130.6 31.70 109.00 239.0 5.500 48.94 597 23.09 16.50 75.96 166.6 39.20 21.50 328.7 7.000 75.78 598 19.79 17.50 73.97 113.7 40.81 94.00 216.3 6.500 43.78 604 20.05 13.50 72.98 141.6 39.17 51.50 330.8 7.000 60.94 607 78.55 13.00 94.48 107.1 37.00 48.50 158.5 6.000 84.27 617 22.05 23.50 74.48 309.6 40.00 40.00 688.0 7.000 156.27 621 17.55 14.50 70.98 92.2 36.67 99.50 217.3 7.000 35.77 623 59.09 17.50 76.46 134.4 31.03 79.00 205.0 4.000 60.28 624 15.00 23.50 83.47 157.0 36.67 52.50 248.0 2.500 37.33 626 17.09 15.00 91.96 127.4 43.36 87.00 201.3 6.500 35.11 630 22.00 9.00 85.48 201.7 31.67 3.50 310.7 2.500 65.67 631 16.59 14.00 90.46 90.7 45.86 69.00 170.3 7.000 26.94 637 16.55 12.50 72.98 179.2 40.83 136.00 373.0 7.000 60.77 646 18.59 12.00 71.96 106.1 30.03 78.50 185.3 4.000 33.44 648 18.05 11.00 73.98 114.1 36.67 91.50 226.5 5.000 59.61 649 16.59 10.00 71.46 104.4 41.86 84.00 190.2 6.500 34.61 650 16.79 15.00 78.47 234.1 36.64 71.00 432.0 5.500 72.78 651 20.42 13.00 76.98 123.5 49.21 44.50 246.3 7.000 54.58 652 14.92 9.00 78.98 89.2 27.55 69.00 202.0 4.500 30.08 653 19.23 14.50 73.98 143.7 36.56 57.50 241.2 7.000 50.08 654 14.04 14.00 68.49 117.5 37.51 87.50 251.5 6.500 39.05 655 15.04 15.00 71.99 255.2 41.34 73.00 608.5 6.500 101.21 659 17.00 12.50 79.98 72.7 13.33 30.00 148.7 1.500 25.33 660 18.98 18.50 77.47 126.4 39.09 121.50 247.1 7.000 51.40 662 61.23 13.50 74.48 144.4 37.40 54.00 311.7 6.000 73.25 664 17.04 20.50 72.99 165.2 36.67 129.50 291.3 6.500 60.38 666 17.54 15.50 65.49 134.4 40.01 139.50 341.1 7.000 64.71 668 20.54 15.50 72.99 158.9 40.01 82.50 350.0 7.000 73.38 673 20.06 11.00 67.98 90.0 37.37 94.00 191.8 5.500 40.33 675 19.56 14.50 76.48 160.3 37.54 53.00 313.9 7.000 55.83 678 0.00 15.00 75.49 0.0 0.00 1.00 0.0 1.000 0.00 679 18.04 18.00 77.99 137.2 41.67 43.50 302.5 7.000 53.88 681 22.23 15.50 77.48 137.9 27.40 65.50 333.6 4.500 72.08 688 21.23 16.00 71.98 114.2 39.06 66.50 240.6 7.000 49.42 691 15.56 16.00 75.98 192.8 42.37 28.50 391.3 7.000 89.16 699 6.98 21.50 104.97 87.4 25.75 39.00 136.6 4.000 8.30 707 18.93 16.00 90.99 225.8 39.11 19.50 464.0 6.500 90.14 715 18.54 18.00 70.99 141.5 41.67 52.50 291.0 7.000 59.38 724 15.23 14.00 83.48 124.2 42.40 93.00 265.2 7.000 37.75 Agriculture and Food Sciences Research, 2018, 5(1): 6-22 11 725 16.23 14.50 76.51 139.6 40.00 37.50 283.9 7.000 47.85 861 20.23 13.50 74.01 600.9 42.67 34.00 236.1 7.000 66.85 862 16.95 16.50 72.50 119.4 41.73 97.50 255.8 6.500 43.29 864 12.07 15.00 75.50 179.8 41.51 62.00 363.3 7.000 55.25 873 16.57 14.00 70.50 179.3 36.67 101.50 351.1 7.000 54.25 DUNDEE 17.98 18.33 65.01 149.4 39.87 33.33 344.7 5.000 62.58 DUNDEE* 20.95 16.00 66.02 152.5 43.84 128.00 314.9 7.000 57.89 EGRET 12.94 13.72 67.01 111.2 39.89 75.12 247.5 6.328 35.31 EGRET* 19.95 15.00 68.02 127.7 43.17 102.00 233.9 6.500 45.56 HERON 15.33 11.50 66.00 130.6 36.75 88.50 282.1 6.500 40.06 IBIS 16.83 17.50 66.50 153.1 40.58 122.50 328.1 7.000 56.90 F-prob 0.490 0.193 <.001 0.487 0.090 0.012 0.002 <.001 0.003 LSD 25.78 4.870 11.239 191.6 11.820 77.84 230.5 2.870 48.96 CV 66.61 31.83 7.19 63.77 15.25 55.11 40.70 23.69 47.05 SE 12.57 4.843 5.745 94.70 5.876 38.97 113.9 1.437 24.20 Table-4. Means of agro-morphological traits of 98 soybean accessions planted in Brits. Genotype HSW BNP DFW PDL PNP PDW SDP SDY 1120 24.02 4.135 58.49 39.51 80.2 62.00 114.8 31.30 1363 16.00 5.302 56.99 37.00 138.3 55.33 213.3 31.67 1371 16.61 6.838 68.98 38.32 156.5 61.57 210.3 34.66 1380 15.02 6.968 57.99 37.18 84.0 27.66 113.8 15.30 1386 21.02 3.635 66.99 37.51 82.8 49.33 118.8 31.97 1390 14.55 6.654 46.98 34.30 82.4 29.02 114.6 16.42 1403 19.00 5.654 57.48 42.13 111.6 47.52 142.1 31.25 1409 16.00 6.135 58.49 40.00 55.3 21.67 82.7 12.67 1449 17.52 7.635 67.99 34.18 114.0 39.50 152.3 22.80 1552 12.61 7.338 60.48 35.15 148.0 50.57 239.0 32.16 1554 17.52 5.333 60.99 47.01 94.0 57.50 172.8 37.30 1555 18.00 6.154 49.48 36.97 82.4 31.68 108.7 19.42 1556 18.11 8.338 69.98 41.65 161.0 67.24 219.8 39.83 1558 17.14 8.165 61.08 38.32 187.4 65.57 267.2 47.55 1572 11.64 6.832 76.08 41.82 81.8 34.40 147.7 31.39 1573 0.00 6.373 56.28 0.00 0.0 0.00 0.0 0.00 1575 15.00 4.844 62.26 41.67 128.0 51.67 192.7 31.67 1578 16.00 7.344 70.26 31.67 111.7 42.00 156.0 25.33 1590 16.00 6.510 60.26 37.33 91.3 43.33 124.3 21.67 1594 14.00 5.038 55.76 35.00 158.7 60.00 253.3 34.33 1595 14.10 7.647 64.91 39.28 158.9 51.92 223.3 31.88 1596 17.10 7.647 73.41 37.11 162.9 70.92 249.0 42.55 1597 13.61 6.177 47.26 35.09 131.3 55.69 209.1 34.10 1598 20.11 6.677 47.76 36.92 147.1 72.03 200.8 44.60 1599 19.65 5.206 49.78 39.20 125.8 64.72 211.3 41.26 1600 22.10 4.647 68.91 39.11 97.6 52.09 128.0 33.88 1659 14.29 6.705 44.76 39.25 102.3 44.05 174.7 26.10 220 21.43 5.012 46.49 39.33 107.2 51.35 171.2 33.50 223 22.43 3.845 63.99 41.00 119.2 65.52 180.5 39.50 231 25.00 3.848 47.50 40.11 38.8 23.40 53.0 12.89 266 25.00 5.181 55.50 43.28 64.4 32.74 76.9 19.89 267 26.00 7.012 67.49 36.67 67.2 48.68 101.4 23.50 268 21.93 6.512 44.99 38.67 105.5 60.18 168.5 36.67 278 21.87 5.348 59.50 41.55 86.6 44.88 124.9 28.42 284 25.00 5.345 60.99 36.67 46.5 27.52 68.7 16.50 290 19.00 7.348 51.50 41.45 75.1 42.07 119.9 70.89 291 17.37 5.181 53.00 39.30 85.0 36.88 116.9 23.50 292 22.00 7.015 59.00 41.33 55.3 33.00 90.3 20.33 297 25.00 6.681 63.50 40.78 59.6 40.24 91.5 26.39 304 13.41 5.515 57.50 42.95 65.6 28.74 104.2 16.89 544 13.91 8.015 47.00 41.61 163.4 71.74 240.4 42.39 568 0.00 4.512 48.99 38.00 49.7 22.02 58.7 11.50 571 19.91 5.515 68.00 39.61 135.3 71.90 205.5 47.39 575 20.37 5.514 63.50 41.97 129.2 74.72 197.7 36.17 578 15.41 6.681 56.50 39.95 198.8 85.40 272.2 47.39 582 11.43 7.345 65.49 39.17 172.0 79.85 233.4 42.84 592 17.43 4.345 69.99 40.50 56.0 26.02 86.9 15.00 593 20.00 5.163 50.01 40.67 77.3 34.67 98.7 19.33 595 23.01 6.666 60.50 42.42 102.1 48.93 140.7 31.83 597 20.51 6.166 56.50 38.42 117.8 65.76 186.3 36.83 598 24.10 6.496 55.01 41.34 113.1 60.10 161.8 38.12 604 23.55 6.499 53.99 40.81 111.1 62.33 185.8 41.89 607 21.00 5.332 54.99 32.00 106.7 47.33 146.3 30.00 617 23.55 6.832 53.49 37.47 247.6 50.50 141.8 35.23 621 20.55 5.832 50.49 41.14 101.2 60.00 180.8 39.56 623 20.00 6.333 59.50 35.08 72.0 32.43 89.3 18.33 624 17.60 6.996 55.51 37.51 137.3 55.60 177.8 31.12 Agriculture and Food Sciences Research, 2018, 5(1): 6-22 12 626 18.00 7.666 57.50 31.67 69.0 26.00 75.0 13.33 630 22.00 6.999 53.49 34.97 91.7 44.17 131.8 29.56 631 24.01 4.999 48.00 39.58 102.8 47.76 135.0 29.66 637 16.55 5.832 53.49 37.47 116.9 51.50 208.5 33.23 646 20.51 5.166 58.00 36.58 70.5 31.10 104.3 20.66 648 20.05 4.665 46.49 37.81 79.7 32.17 99.0 19.56 649 19.01 4.833 53.00 41.08 64.0 29.76 107.5 20.66 650 17.10 6.996 56.01 38.17 105.9 53.10 178.9 32.45 651 19.00 5.324 60.49 39.32 71.9 35.05 114.0 19.48 652 16.00 4.491 45.49 36.32 40.4 17.05 56.5 10.15 653 17.00 4.143 46.00 37.49 74.9 31.48 106.3 18.41 654 17.00 5.661 45.99 36.67 187.7 83.67 329.0 56.00 655 15.88 6.495 47.49 33.82 76.2 30.49 118.5 18.25 659 24.77 7.840 61.05 40.84 109.3 57.87 159.6 54.55 660 23.00 7.180 48.49 40.00 70.3 31.67 94.3 21.00 662 22.01 4.643 46.50 33.82 74.5 39.64 118.9 26.24 664 20.38 5.328 60.49 31.65 104.3 49.66 166.2 34.75 666 21.38 5.828 43.99 38.82 82.2 38.82 120.7 23.75 668 21.38 5.328 64.99 38.82 93.8 53.49 140.0 28.75 673 23.00 4.000 56.05 40.00 56.2 31.21 88.8 19.38 675 17.00 6.006 47.55 39.67 83.2 45.71 94.8 17.22 678 23.00 5.661 66.49 37.48 53.7 27.32 78.5 15.58 679 121.88 4.328 53.99 40.15 87.2 56.82 157.3 36.42 681 26.00 5.309 56.00 40.00 100.3 53.00 141.0 35.33 688 23.00 6.809 45.00 45.00 116.3 51.00 148.3 43.67 691 19.27 6.340 57.55 40.00 161.5 73.54 200.1 44.38 699 8.66 7.680 46.99 37.26 133.5 28.26 200.4 17.00 707 9.75 5.512 61.49 39.98 68.8 33.71 108.1 22.44 715 22.88 4.495 50.99 39.15 90.2 61.82 153.8 37.58 724 16.51 7.309 57.50 43.49 163.2 74.98 246.9 45.41 725 19.00 7.838 64.98 44.00 134.3 60.67 222.3 37.00 861 24.00 6.338 55.98 38.32 79.7 44.07 126.2 27.99 862 20.55 5.320 45.98 33.63 107.9 53.18 165.6 34.58 864 23.02 4.302 47.99 39.85 86.7 57.16 163.0 38.64 873 17.02 4.635 48.49 37.85 148.3 66.83 211.6 43.97 DUNDEE 21.65 5.552 47.50 39.62 111.5 57.70 172.9 38.74 DUNDEE* 18.87 5.029 45.38 37.90 95.0 49.06 122.1 26.39 EGRET 19.91 3.865 62.12 40.03 85.0 45.06 149.5 30.06 EGRET* 23.37 5.695 44.00 44.90 142.6 76.56 195.3 45.72 HERON 20.10 3.981 43.00 38.28 91.3 46.59 167.1 29.72 IBIS 21.60 4.647 60.41 40.61 130.3 69.09 218.1 43.22 F-prob 0.863 0.006 0.163 0.064 0.367 0.382 0.214 0.599 LSD 39.65 2.401 19.59 6.221 104.52 43.14 134.8 32.60 CV 86.77 20.60 17.32 7.67 47.97 42.02 42.07 50.54 SE 17.72 1.216 9.700 2.985 50.15 20.70 64.67 15.64 Table-5. Means and mean squares of combined analysis of variance of agro-morphological traits of 96 soybean accessions planted across two sites. Potchefstroom and Brits. 2016/17 Genotype HSW BNP DFW PNP PDL PDW SNP SDP SDY 1120 19.94 4.415 64.50 98.6 39.75 65.97 3,00 189.8 40.80 1363 13.22 5.833 83.31 156.0 35.67 58.40 2,83 226.0 32.67 1371 14.84 5.574 73.25 130.9 38.34 56.24 2,83 218.9 31.70 1380 13.60 6.243 77.54 163.9 37.34 66.61 3,00 278.5 38.86 1386 16.94 3.580 60.97 96.5 38.51 55.67 3,00 212.3 36.81 1390 14.67 6.747 64.28 114.9 34.76 42.21 2,50 174.8 24.51 1403 16.90 4.994 66.76 150.9 40.65 70.96 3,00 273.4 48.32 1409 13.48 4.823 78.29 91.5 31.00 35.75 2,00 121.3 16.25 1449 16.90 7.580 81.27 116.4 32.91 40.08 2,00 166.7 22.39 1552 11.15 7.079 83.05 142.7 35.17 44.84 3,00 218.1 28.07 1554 18.50 4.777 67.51 115.2 43.89 73.07 3,00 243.3 48.75 1555 17.60 5.577 66.78 129.6 35.57 59.28 2,50 211.9 37.25 1556 18.20 6.990 76.51 153.3 42.34 75.54 3,00 234.0 43.05 1558 16.67 6.907 80.29 173.4 36.65 66.01 2,50 250.0 41.53 1572 12.02 7.249 96.04 65.5 40.91 26.79 3,00 108.7 19.09 1573 11.00 8.005 89.07 89.7 40.00 32.67 3,00 102.3 12.33 1575 13.40 5.417 79.78 100.7 37.79 36.76 3,00 141.2 21.00 1578 16.30 5.320 73.25 111.7 27.77 43.39 2,00 181.6 28.28 1590 12.95 5.994 78.53 55.1 37.45 22.53 3,00 71.9 11.59 1594 11.87 4.913 73.28 149.9 38.37 57.29 3,00 238.1 28.40 1595 13.94 7.747 84.04 155.8 39.34 47.18 2,67 199.1 28.03 1596 15.07 6.742 87.27 189.7 38.43 73.13 2,50 289.7 44.03 1597 13.87 6.917 76.06 151.5 37.69 75.84 3,00 268.8 42.70 1598 19.30 6.161 62.02 120.2 40.29 59.31 3,00 193.9 37.53 1599 16.77 4.410 63.77 149.5 41.53 66.02 3,00 244.7 42.24 1600 18.47 3.910 71.49 106.3 42.12 61.24 3,00 200.8 39.17 Agriculture and Food Sciences Research, 2018, 5(1): 6-22 13 1659 13.94 6.496 76.32 142.4 39.17 63.49 3,00 266.8 39.57 220 19.40 4.997 60.02 121.8 33.10 60.05 1,67 233.1 41.12 223 22.17 4.165 66.75 136.8 40.50 85.40 3,00 252.3 53.73 231 23.90 4.165 57.76 75.9 41.35 59.31 3,00 130.0 33.99 266 20.73 5.415 62.26 100.7 42.31 59.74 3,00 182.4 38.33 267 21.85 5.741 73.75 129.0 38.27 66.36 3,00 156.2 39.34 268 19.80 5.911 59.78 174.4 39.01 115.21 3,00 372.0 82.23 278 20.87 5.749 61.49 123.9 41.21 66.75 3,00 234.9 44.19 284 20.50 6.252 68.26 127.9 38.35 76.62 3,00 254.8 49.62 290 18.62 6.158 63.02 123.4 42.01 74.92 3,00 250.7 71.84 291 19.04 5.415 61.76 114.4 40.51 56.30 3,00 213.8 43.55 292 20.19 5.824 68.26 139.6 39.31 77.06 2,83 243.5 53.99 297 22.56 5.407 68.25 96.6 40.08 57.85 3,00 184.1 40.97 304 16.37 6.251 68.02 145.4 42.33 79.46 3,00 272.8 50.44 544 15.00 7.243 62.28 190.6 42.51 117.80 3,00 381.9 60.95 568 23.44 5.502 60.02 96.0 39.35 55.54 3,00 159.5 36.66 571 22.60 4.659 67.24 107.2 38.15 64.27 3,00 179.9 42.49 575 19.10 5.832 85.79 105.4 40.99 57.97 3,00 164.8 27.93 578 15.17 6.915 73.03 144.0 42.53 78.11 3,00 203.3 33.51 582 15.17 7.163 86.54 148.0 41.27 68.40 3,00 211.1 35.89 592 15.30 4.498 65.98 118.3 41.09 59.62 3,00 274.2 40.75 593 18.93 5.834 63.02 80.6 38.00 53.01 2,67 132.9 22.57 595 21.87 5.491 68.76 116.2 37.03 61.27 2,50 190.9 40.54 597 22.04 5.829 66.26 142.2 38.84 102.84 3,00 258.9 56.64 598 21.80 6.162 64.51 113.2 41.08 62.20 2,83 189.3 40.99 604 21.84 5.492 63.51 126.7 39.99 76.98 3,00 258.4 51.59 607 61.92 4.828 74.79 107.2 35.38 79.17 2,67 152.7 66.72 617 22.90 7.333 64.02 279.2 38.77 122.06 3,00 418.2 96.75 621 19.10 5.328 60.76 96.8 38.89 63.30 3,00 198.3 37.65 623 47.03 6.079 68.01 103.3 33.06 42.91 2,00 148.3 39.66 624 16.58 7.416 69.52 144.1 37.22 60.09 2,00 201.8 33.24 626 17.60 6.323 74.78 107.8 39.54 48.11 2,83 159.9 27.90 630 22.00 5.154 69.53 129.7 33.86 65.11 2,00 194.0 42.17 631 20.20 4.829 69.29 96.3 42.79 46.05 3,00 153.2 28.30 637 16.70 4.993 63.26 148.7 39.19 72.58 3,00 291.0 47.24 646 19.64 4.577 65.01 88.2 33.30 42.00 2,00 145.7 27.18 648 19.14 4.161 60.27 97.3 37.25 47.49 2,33 162.7 39.93 649 17.87 4.076 62.26 84.1 41.51 42.36 2,83 149.7 27.77 650 16.94 5.992 67.27 170.8 37.41 85.35 2,50 307.4 52.94 651 19.99 4.828 68.76 97.5 44.31 59.39 3,00 180.4 37.32 652 15.27 3.911 62.28 64.5 31.87 31.57 2,00 129.6 20.28 653 18.37 4.499 60.02 109.4 37.08 60.46 3,00 175.0 34.52 654 14.90 5.161 57.27 141.0 37.22 67.44 2,83 277.7 44.84 655 15.64 5.743 59.77 167.2 37.63 68.78 2,83 367.8 60.45 659 21.96 6.155 70.51 96.6 31.43 51.49 1,00 156.1 44.22 660 20.13 6.661 63.03 110.0 39.44 66.11 3,00 200.8 41.93 662 42.74 4.580 60.52 109.6 35.69 62.87 2,67 217.0 50.14 664 18.80 6.084 66.75 135.3 34.20 69.15 2,83 230.1 47.81 666 19.54 5.495 54.77 108.7 39.43 67.71 3,00 233.0 44.60 668 21.14 5.246 69.00 126.9 39.43 91.36 3,00 247.0 51.47 673 20.84 3.776 62.00 74.2 38.74 47.68 2,50 142.4 30.04 675 18.67 5.411 62.03 123.2 38.66 67.26 3,00 207.5 36.85 678 23.00 5.329 71.00 53.4 37.50 27.25 - 78.3 15.52 679 66.70 5.168 66.02 112.6 40.93 70.81 3,00 231.4 45.33 681 23.13 5.246 66.77 125.1 31.56 88.01 2,00 271.3 60.11 688 21.54 6.076 58.52 114.2 41.06 86.23 3,00 210.5 47.50 691 17.30 5.828 66.76 178.2 41.27 116.26 3,00 298.6 67.15 699 7.64 7.412 76.06 111.9 31.52 25.02 2,00 171.3 12.86 707 14.74 5.413 76.28 151.0 39.58 83.71 2,83 290.2 57.03 715 20.77 5.251 61.01 116.3 40.43 74.54 3,00 223.9 48.70 724 15.64 5.990 70.52 143.0 43.00 68.90 3,00 256.4 41.53 725 17.19 6.323 70.75 141.5 41.30 72.60 3,00 266.2 44.69 861 21.56 5.409 65.01 347.5 39.78 60.76 3,00 184.2 48.07 862 18.80 5.414 59.27 114.2 37.71 59.01 2,83 212.0 39.03 864 17.07 4.665 61.77 134.8 40.68 76.72 3,00 266.0 47.29 873 16.67 4.664 59.52 164.9 37.25 68.18 3,00 283.7 49.40 DUNDEE 19.22 5.899 59.48 128.9 39.62 73.86 3,00 256.2 48.30 DUNDEE* 19.77 5.164 55.76 124.6 40.95 68.89 3,00 221.3 42.41 EGRET 16.44 4.232 64.66 95.6 40.01 48.16 2,88 197.1 32.73 EGRET* 21.34 5.329 60.06 135.4 44.08 73.43 2,83 216.1 45.65 HERON 17.27 3.913 58.31 108.8 37.49 52.94 2,83 222.7 34.92 IBIS 18.77 5.250 63.50 139.4 40.58 75.87 3,00 271.1 50.12 HSW BNP DFW PNP PDL PDW 1,00 SDP SDY Agriculture and Food Sciences Research, 2018, 5(1): 6-22 14 Genotype 236.7 3.851*** 278.09 5837 36.31** 1290.5 3,00 13538* ** 686.9* * Site 334.6 32.707** * 56339.34 *** 182894* ** 9.27 81648.5 *** 0.4953 *** 149129 0*** 39431. 5*** Genotype *Site 177.8 1.935 189.56** * 5384 23.93 1092.0 0.0000 14512* ** 631.2* ** Mean 19.10 5.49 67.68 123.79 37.82 62.68 0.0000 211.88 19.10 LSD 22.50 2.089 15.59 160.8 6.791 58.88 2,78 196.0 29.75 CV 75.53 27.04 11.62 62.26 12.04 44.93 0.7721 44.22 49.55 SE 14.80 1.516 7.900 78.99 4.661 28.93 13.77 96.31 20.49 R2 Table-6. Factor loadings of the three PCs based on agronomic traits Trait Factor Loadings 1 2 3 HSW -0.00436 0.00148 0.00187 BNP 0.00058 0.00631 -0.00191 DFW 0.03408 -0.10372 -0.05010 PNP 0.41799 0.26113 -0.86868 PDL 0.00519 0.00590 0.00401 PDW 0.20975 0.04508 0.07626 SNP -0.22688 0.95126 0.17318 SDP 0.83968 0.11403 0.45054 SDY 0.15344 0.03245 0.06380 Eigen values 6983426 2810062 981647 Percentage variation 63.19 25.43 8.88 Cumulative variation 63.19 88.62 97.50 Table-7. Correlations among nine agro-morphological traits of 96 soybean accessions SDY SDP SNP PDW PDL PNP DFW BNP HSW SDY - SDP 0.8805*** - SNP - 0.1863*** - 0.2436*** - PDW 0.9186*** 0.8634*** - 0.1831*** - PDL 0.1564** 0.1547** 0.0500 0.1946*** - PNP 0.6144*** 0.6451*** -0.0446 0.6164*** .1151** - DFW 0.1219** 0.2074*** - 0.6303*** 0.1749*** -0.0375 0.1422** - BNP 0.0423 0.0690 0.3093*** 0.0756 .1668** .1799*** 0.0334 - HSW 0.1808*** -0.0536 0.0212 0.0687 -0.0170 -0.0351 -0.0962 -0.0528 - Figure-1. A Principal Component Biplot of nine agro-morphological traits of 96 soybean accessions. Agriculture and Food Sciences Research, 2018, 5(1): 6-22 15 Figure-2. A dedrogram on agro-morphological traits of 96 soybean accessions. 3.2. Nutritional Quality Diversity The nutritional quality traits were analysed using analysis of variance. There were significant differences (P< 0.05) among the soybean accessions planted in Potchefstroom based on ash, moisture, and oil contents (Table 8). The ash content varied between 4.3 and 10.9%. Moisture content varied between 1.7 and 12.8% whereas oil content ranged between 7.1 and 23.9%. In Brits, highly significant differences were observed in fiber, moisture, oil, palmitic acid, and protein contents, and significant differences were observed for linolenic acid (Table 9). Fiber content varied between 4.5 and 5.5%. Moisture varied between 3.1 and 4.6%. Oil content varied between 12.0 and 21.8%. Palmitic acid ranged from 55.9 to 64.2% and protein content varied between 29.6 and 35.8% whereas linolenic acid ranged from 3.1 to 4.7%. When assessing the reaction of the soybean accessions across the two locations, highly significant differences were observed for ash, moisture, oil, oleic acid and stearic acid and significant differences were observed for protein content (Table 10). Ash content varied between 4.3% and 8.2%. Moisture content ranged from 2.8% to 12.8%. Oil content varied between 10.6% and 20.7%. Oleic acid varied between 6.8% and 30.8% and stearic acid ranged between 31.6% and 263.5% whereas protein content was ranging between 29.1% and 35.6%. The two sites showed highly significant differences among the genotypes based on linolenic acid, moisture content, oleic acid, stearic acid and significant differences were observed for linoleic acid and oil content. The genotype x site interaction was significant for ash and non-significant for all other quality traits. 4. Discussion The principal component analysis (PCA) is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background [19]. The goal is to evaluate the importance of each variable in relation to the total available variation among genotypes. The method provides an opportunity to exclude less important traits in the group studied [20] and simultaneously determine which traits are the most important. In this study, the traits that contributed to the most variation were seed number per pod, pod number per plant, seed number per plant and seed yield. The correlation information of the tested traits can be provided to the breeders in the direct and indirect selection programmes. The genotypes with significant correlations and desirable traits can be selected concurrently. Moreover, understanding the relationship between yield and its component traits is of great importance to a breeder for making the best use of these relationships in selecting desirable genotypes for yield improvement programs [21, 22]. Significant positive correlations of days to flowering, number of branches and pods per plant, pod length, seed number per pod with seed yield were reported by Malek, et al. [11] which concur with the results in this study. This means that in selecting high yielding genotypes these characters should be given more emphasis as the best selection criteria. Seed yield always showed a positive correlations with other desirable yield traits [23, 24] which indicates that the increase in one trait would result in the increase of the other; that is, simultaneous increase or decrease of both traits would be easy. The strong positive correlation of seed yield with other yield traits indicated that it would be very easy to identify a soybean genotype having higher seed yield simultaneously with higher number of pods per plant and but difficult with number of seeds per pod. Similar results were reported in other studies [25-28]. Traditional cluster analysis could provide an easy and effective way in determining the genetic diversity of germplasm collections [29]. It is commonly used to study genetic diversity and for forming core subset for Agriculture and Food Sciences Research, 2018, 5(1): 6-22 16 grouping accessions with similar characteristics into one homogenous category [30]. It is also used to summarize information on relationships between accessions by grouping similar units so that the relationship is easily understood. In this study, the accessions were clustered into three major groups of which two of them were further sub clustered into three groups based on the agro-morphological traits measured. The most diverse lines can be used as parents for hybridization for improvement of genes of novelty. Genetic diversity evaluation among germplasm is a necessity and a prerequisite in any hybridization program and would promote the efficient use of genetic variations [31, 32]. To improve an efficient crop, it is essential to obtain the information on genetic diversity and relationships among breeding materials for a plant breeder. This is because the assessment of genetic diversity is important not only for crop improvement but also for efficient management and conservation of germplasm resources. In this study, the nutritional quality traits showed a vast diversity among the accessions. The oil and protein content were within the range recorded in other studies [33]. The oil content ranged from 13.8% to 22.5% whereas protein ranged between 37.0% and 50.1%) with a mean of 19% and 42.9%, respectively. The vast divergence of the accessions will assist breeders in selecting accessions with good quality for hybridization and conservation. The analysis of genetic diversity plays a fundamental role in identification of parents [34] and it can help to achieve long-term selection gain [35]. As a traditional method, morphological traits used to assess genetic divergence and classify existing germplasm materials. However, this technique is a low level but powerful taxonomic tool and has been utilized for the preliminary grouping of germplasm prior to their characterization using more precise marker technologies. Although the genetic base of soybean cultivars is considered to be extremely narrow [36] studies of genetic diversity in soybean have been conducted using morphological characteristics [37, 38]. Recently, De Chavez, et al. [39] reported a vast phenotypic diversity among the soybean accessions in Phillipines. Khatab, et al. [12] reported the presence of genetic diversity among soybean genotypes assessed through agro-morphological descriptors. Hamzekhanlu, et al. [40] studied 34 mutant lines including one control cultivar and detected variability for ten quantitative traits in soybean. Iqbal, et al. [41] reported significant differences among all the assessed phenotypic traits. 5. Conclusion Most of the traits showed positive correlations between each other, which will assist in the combined improvement of these traits by selecting only highly and positively correlated and easily measurable phenotypic traits although most were highly significant and positively correlated with seed yield per plant. The accessions were clustered into three major groups with subgroups, which showed existence of a vast genetic diversity among the accessions. The most divergent accessions were 2015/06/12, 69 S 10, PR 154-14, R 5-4-2 M, Hawkeye (USSR), and PR 145-2. The nutritional quality traits also varied significantly among the accessions. The presence of genetic diversity can be useful for breeding and selection of parents for transgressive segregation. Table-8. Means of nutritional quality traits of 96 soybean accessions planted in Potchefstroom. GM Ash Fiber Linol eic acid Linole nic acid Moistu re Oil Oleic acid Palmi tic acid Protei n Steari c acid 1120 5.667 5.145 9.284 2.486 6.527 15.19 21.03 61.93 33.90 3.967 1363 5.952 5.215 9.489 3.886 6.272 16.10 23.59 60.36 33.70 4.232 1371 5.429 5.195 9.056 3.959 6.368 13.72 24.41 59.49 34.50 4.405 1380 5.552 5.000 9.974 4.111 8.677 13.90 22.87 60.02 33.77 4.007 1386 5.867 5.240 10.539 4.556 6.042 13.72 19.85 60.95 35.21 6.242 1390 4.983 4.932 10.179 4.318 7.569 14.11 25.29 58.55 33.76 4.595 1403 5.480 5.260 10.290 4.100 5.540 12.33 19.95 62.26 35.90 3.210 1409 5.040 4.870 8.360 3.750 12.450 17.26 30.02 57.60 31.86 5.590 1449 4.837 4.900 9.709 4.241 11.297 15.78 22.91 60.29 34.75 4.617 1552 6.190 5.220 10.110 4.410 7.130 17.15 23.15 60.14 31.80 4.920 1554 5.242 5.225 8.874 3.911 6.172 15.68 21.39 62.40 35.54 3.392 1555 5.123 5.207 9.129 4.098 6.484 14.87 24.51 61.01 35.49 2.225 1556 5.534 5.140 9.101 4.229 7.023 14.60 25.30 60.94 34.23 3.240 1558 5.051 5.030 8.378 3.566 9.278 17.17 29.86 56.36 32.98 5.793 1572 7.136 5.235 8.413 3.556 7.298 18.14 23.50 62.33 30.45 4.578 1573 5.456 4.819 8.846 3.892 12.784 17.46 26.54 59.14 31.34 7.295 1575 6.970 5.460 10.220 4.440 7.130 16.97 23.96 57.05 30.18 9.390 1578 5.299 5.348 9.399 4.193 5.395 14.88 25.29 61.33 35.70 3.672 1590 6.754 5.418 9.724 4.013 7.395 16.70 26.45 56.36 31.39 6.877 1594 10.938 2.350 29.234 15.461 -1.714 7.10 -33.03 55.74 28.55 10.460 1595 5.478 5.205 9.044 4.106 6.993 16.90 26.66 59.68 33.55 4.008 1596 5.108 5.335 10.424 3.966 6.468 16.37 21.65 60.71 33.61 3.693 1597 5.294 4.808 6.754 3.933 9.320 15.20 30.15 64.35 34.16 2.177 1598 4.469 5.153 9.529 4.093 8.230 16.61 23.57 60.18 34.48 4.442 1599 4.621 5.339 9.041 3.992 6.274 17.31 24.34 61.63 34.46 2.550 1600 5.008 5.365 9.689 4.036 6.133 16.79 22.29 61.73 34.66 2.493 1659 5.698 5.285 9.694 3.981 7.121 15.36 23.93 60.57 34.29 4.710 220 5.855 5.369 10.600 4.012 5.760 14.81 21.19 59.37 33.66 6.642 223 6.205 5.224 11.060 4.292 5.920 15.20 19.23 60.70 33.37 5.997 231 4.902 5.197 9.012 3.788 6.397 17.46 22.19 62.75 33.15 3.991 Agriculture and Food Sciences Research, 2018, 5(1): 6-22 17 266 5.072 5.152 9.687 4.258 5.942 14.11 23.09 59.86 34.70 4.866 267 4.950 5.234 9.710 3.922 5.965 14.28 19.94 61.29 35.58 4.992 268 5.140 5.164 9.680 4.252 6.340 14.61 23.31 59.86 35.46 4.317 278 5.342 5.152 9.396 4.084 5.757 13.56 30.40 59.59 34.93 1.981 284 4.770 5.204 9.405 3.912 6.320 16.76 20.86 61.25 33.94 4.932 290 4.782 5.107 9.047 4.133 6.087 14.95 21.95 60.73 34.10 5.741 291 4.662 5.212 9.016 4.089 6.207 15.83 22.22 60.86 34.77 5.021 292 5.192 5.187 10.017 4.073 5.857 14.07 21.79 59.79 34.88 4.331 297 4.857 5.142 8.892 3.938 6.092 15.51 21.81 60.86 33.99 5.366 304 4.587 5.162 8.782 3.973 5.932 16.74 21.69 61.92 33.99 5.291 544 5.107 5.132 10.362 4.448 5.937 13.78 22.94 59.23 34.65 4.891 568 5.000 5.164 10.230 3.917 5.965 13.43 23.38 55.52 35.76 3.497 571 5.842 4.302 16.497 9.988 5.912 22.58 15.15 63.07 26.23 5.891 575 5.137 5.272 9.836 4.239 7.212 17.13 23.89 60.13 33.36 2.721 578 5.482 5.177 8.937 3.783 6.012 18.28 26.01 60.20 32.48 4.026 582 5.000 5.229 8.920 4.102 7.115 17.11 25.44 60.06 33.53 1.987 592 5.345 5.159 9.425 4.132 6.095 12.84 23.94 59.64 35.59 4.352 593 4.304 5.104 7.879 3.956 8.242 16.44 27.93 59.13 34.27 2.980 595 5.177 5.201 9.463 4.026 6.669 15.76 23.28 60.71 35.51 3.263 597 5.102 5.221 9.033 3.881 6.354 15.54 23.78 60.49 35.40 2.818 598 5.159 5.224 8.529 3.916 6.597 16.17 23.63 61.31 34.98 2.590 604 5.153 5.314 9.553 3.918 5.920 15.50 22.87 60.50 35.05 3.008 607 5.088 5.169 8.213 3.718 6.370 16.44 24.43 62.18 34.76 2.273 617 4.878 5.234 9.528 4.003 5.860 14.48 20.01 61.45 35.75 5.613 621 4.743 5.164 8.638 4.143 6.020 15.38 24.89 59.69 34.62 5.428 623 5.112 5.181 8.743 4.056 6.274 17.93 22.89 62.27 32.44 4.563 624 5.054 5.159 9.219 4.221 6.402 16.49 23.73 60.77 33.90 3.795 626 5.087 5.011 9.943 4.036 9.414 17.59 24.72 59.85 34.66 4.443 630 4.370 5.060 8.380 3.830 8.650 18.39 28.17 58.57 32.17 3.360 631 5.542 3.941 13.968 9.856 6.344 23.90 16.71 46.62 24.46 2.373 637 5.103 5.169 9.518 4.103 6.845 15.07 22.32 60.43 34.12 4.918 646 5.157 5.131 9.113 4.151 6.394 17.02 22.83 59.95 33.41 7.483 648 5.938 5.369 7.493 6.868 6.475 16.08 13.38 41.59 33.73 33.723 649 4.387 5.126 5.118 4.916 5.399 18.87 20.04 61.29 33.25 3.568 650 5.134 5.069 8.774 4.246 5.682 14.45 22.78 62.33 34.89 5.160 651 4.831 5.153 9.445 4.110 6.015 17.04 23.79 59.84 33.18 4.519 652 5.941 5.128 9.805 4.445 5.875 14.44 21.66 59.92 32.98 6.599 653 5.293 5.229 10.036 4.036 6.031 15.45 12.09 61.07 34.62 4.009 654 5.184 5.195 9.086 4.249 5.785 15.57 22.68 61.96 33.80 4.522 655 4.869 5.180 9.071 4.139 5.395 14.32 22.28 62.96 34.82 3.577 659 5.720 4.920 9.260 4.050 5.240 10.83 22.18 60.25 35.27 8.500 660 4.619 5.228 8.759 3.764 6.234 17.25 22.03 62.23 33.75 4.525 662 5.083 5.184 8.866 4.006 6.431 14.58 23.94 60.78 34.19 2.099 664 5.054 5.145 9.086 3.914 6.285 15.43 23.44 59.93 34.11 3.772 666 4.809 5.150 9.021 3.979 6.220 16.37 21.66 61.51 34.33 4.497 668 5.004 5.045 9.356 3.994 6.195 14.28 22.88 59.44 33.90 4.572 673 5.532 5.276 9.296 3.895 5.642 15.31 22.64 61.62 34.37 5.398 675 5.177 5.251 9.551 3.985 6.012 15.29 22.99 60.59 34.77 3.373 678 4.894 5.195 9.626 3.884 6.080 16.71 22.18 60.25 33.61 4.907 679 5.474 5.130 10.371 4.384 5.575 13.30 23.11 59.60 34.39 3.247 681 4.748 5.064 7.856 4.106 6.421 17.04 27.41 59.14 33.78 4.844 688 4.898 5.104 9.986 4.086 6.036 14.81 21.40 60.03 34.15 4.814 691 5.327 5.226 10.631 3.970 6.707 15.88 21.00 60.60 34.35 3.913 699 6.404 5.318 8.919 4.309 6.794 20.25 26.43 60.59 30.60 4.705 707 5.418 5.244 9.576 3.934 6.617 14.95 23.45 60.24 34.72 3.763 715 5.289 5.185 9.136 3.809 5.945 13.93 21.78 61.84 35.65 3.602 724 5.158 5.019 8.686 4.151 5.326 14.32 24.83 61.34 32.45 4.229 725 5.190 5.080 9.120 4.650 6.180 15.18 26.75 58.94 31.94 5.470 861 5.144 5.265 9.466 3.964 6.253 16.93 21.56 61.14 33.62 4.635 862 5.103 5.102 9.704 4.308 5.599 14.66 21.66 60.26 34.39 5.625 864 5.002 5.185 9.464 3.866 5.927 15.94 20.59 60.99 33.72 5.617 873 5.077 5.130 10.029 4.041 6.187 13.79 19.55 61.08 34.45 4.967 DUNDEE 5.764 5.112 9.966 4.083 5.968 11.99 22.34 59.17 34.70 4.383 DUNDEE* 5.362 5.225 9.413 3.776 6.089 14.96 22.98 60.08 34.20 3.849 EGRET 5.521 5.053 10.072 4.244 6.261 15.13 20.19 59.91 33.16 4.892 EGRET* 5.137 5.235 9.133 3.796 6.114 14.28 21.85 61.57 35.17 3.814 HERON 5.273 5.175 9.399 3.791 5.588 14.34 21.16 60.75 34.45 4.278 IBIS 5.513 5.175 9.659 3.981 6.018 13.97 21.10 61.07 34.60 4.573 F-prob 0.02 0.42 0.622 0.492 0.038 0.047 0.802 0.771 0.396 0.512 Agriculture and Food Sciences Research, 2018, 5(1): 6-22 18 LSD 1.720 9626 7.039 4.214 3.524 4.509 20.47 8.438 4.649 9.342 CV 16.02 9.30 36.13 48.41 26.95 14.31 45.42 6.95 6.80 96.94 SE 0.8506 0.4762 3.482 2.085 1.743 2.231 10.12 4.174 2.300 4.621 Table-9. Means of nutritional quality traits of 96 soybean accessions planted in Brits Genotype Ash (%) Fiber (%) Linoleic acid (%) Linolenic acid (%) Moistu re (%) Oil (%) Oleic acid (%) Palmitic acid (%) Protein (%) Stearic acid (%) 1120 5.511 5.063 9.472 4.178 7.726 15.25 23.15 60.16 33.51 3.413 1363 4.621 4.733 8.207 3.618 11.121 15.84 30.18 55.87 32.49 5.443 1371 5.726 5.084 9.129 3.877 6.014 12.46 22.49 60.08 34.53 4.279 1380 4.766 4.903 8.517 4.053 8.676 16.28 27.32 59.20 33.35 3.553 1386 5.746 5.283 9.627 3.898 5.241 15.00 21.38 61.78 35.26 3.618 1390 5.679 5.112 9.662 4.138 5.570 13.35 21.43 61.74 33.77 3.039 1403 5.859 5.277 9.687 4.298 5.415 14.72 24.31 61.46 34.90 1.619 1409 5.046 4.753 8.732 4.008 11.141 14.63 24.91 61.78 35.04 4.673 1449 5.331 5.068 7.622 3.088 5.951 15.15 21.49 64.23 33.69 3.593 1552 5.761 5.069 8.939 4.007 6.599 12.34 20.89 61.43 34.32 4.124 1554 5.921 5.243 9.012 3.728 5.861 14.85 22.02 61.98 34.85 3.083 1555 5.634 5.092 8.512 4.193 7.485 12.01 27.41 61.85 34.48 3.599 1556 6.011 5.114 8.959 3.982 5.849 14.39 22.04 62.60 32.98 3.694 1558 3.920 4.660 9.350 4.060 11.300 16.29 25.66 57.51 33.71 6.810 1572 4.724 4.511 8.359 3.809 13.055 16.60 24.42 60.51 32.23 7.096 1573 4.763 4.641 9.899 3.836 12.802 16.77 21.92 60.20 32.75 7.536 1575 4.865 4.739 8.732 3.892 10.789 13.52 26.00 58.65 33.96 5.822 1578 5.020 4.954 8.957 3.977 10.129 17.18 25.38 59.46 32.74 4.577 1590 4.730 4.844 8.937 4.097 10.764 15.98 24.09 59.59 32.87 4.192 1594 5.263 4.991 9.644 4.168 7.704 14.71 21.73 60.45 33.31 3.963 1595 5.283 5.167 8.376 4.174 8.012 16.31 23.11 62.55 33.13 5.225 1596 5.028 5.072 9.066 3.914 8.742 18.04 22.83 61.20 32.44 4.700 1597 5.660 5.169 7.837 3.842 6.614 14.85 26.16 62.01 33.83 3.127 1598 4.215 5.194 7.627 3.897 7.564 18.84 29.36 58.55 32.99 2.277 1599 4.738 5.276 9.069 4.001 5.597 17.77 24.59 61.12 33.97 2.191 1600 4.653 5.177 9.086 4.064 7.522 17.50 25.75 57.49 33.39 2.925 1659 5.508 5.111 8.919 4.048 7.034 13.85 22.07 63.04 35.10 3.188 220 4.621 4.980 8.438 4.435 5.853 15.84 25.74 61.12 33.95 2.471 223 5.186 5.065 8.848 4.395 5.168 17.82 23.88 62.27 32.87 2.421 231 5.255 5.078 8.329 3.955 7.664 17.96 28.31 59.36 31.26 2.216 266 5.185 5.188 8.994 3.111 5.934 15.87 22.93 62.60 34.25 2.571 267 4.466 4.950 8.738 4.210 10.298 18.04 23.03 59.75 33.07 4.056 268 6.310 5.230 8.750 3.340 5.810 16.66 23.73 62.38 33.66 2.450 278 4.770 4.999 8.859 4.591 6.883 15.31 30.95 58.00 34.78 0.953 284 4.260 4.930 8.850 4.100 10.320 17.71 24.35 60.10 32.85 4.400 290 4.825 5.258 9.069 4.040 5.854 16.98 22.98 62.98 34.76 3.776 291 5.395 5.224 8.659 3.581 5.138 17.29 25.83 62.11 33.81 0.928 292 4.640 4.680 9.830 4.240 11.910 16.97 26.03 56.44 32.59 6.420 297 4.340 5.078 7.804 3.875 7.104 17.40 27.07 58.74 34.09 3.166 304 4.900 5.030 7.680 3.860 8.040 18.83 24.67 61.52 31.42 4.400 544 5.400 5.250 8.420 3.880 5.360 16.38 24.39 62.49 34.49 1.780 568 5.540 5.180 8.870 4.000 5.320 16.77 25.02 61.43 33.08 1.910 571 5.415 5.518 8.619 4.095 5.134 15.50 24.35 63.03 34.47 0.991 575 5.290 5.229 9.004 4.606 5.358 17.35 21.30 63.40 32.68 3.538 578 5.085 5.243 8.254 3.880 6.449 17.87 25.23 60.86 32.89 3.716 582 5.081 5.215 7.783 3.935 5.478 19.92 23.28 63.27 31.39 3.376 592 4.796 4.915 9.248 4.095 8.703 16.99 30.87 57.01 33.58 3.326 593 4.809 4.965 7.562 3.866 8.112 18.12 28.42 59.12 31.99 3.220 595 5.201 5.186 8.956 4.398 6.523 16.88 24.88 60.73 34.11 1.035 597 5.201 5.116 8.706 4.033 6.813 13.53 24.79 60.36 35.77 2.425 598 5.044 5.055 8.607 3.901 6.782 13.63 23.84 61.46 35.40 2.740 604 5.370 5.260 9.600 4.220 5.780 14.49 23.76 60.74 35.65 1.740 607 5.310 5.190 9.110 4.020 5.650 15.53 22.08 62.03 34.04 3.090 617 4.320 5.020 8.530 3.840 8.730 16.89 24.84 58.43 33.21 5.340 621 4.520 4.970 8.570 4.320 8.780 13.53 27.00 57.83 35.10 4.540 623 4.516 4.956 7.176 3.708 8.358 19.85 28.21 60.96 31.07 3.085 624 4.799 5.005 7.532 3.891 8.162 17.36 25.71 60.04 32.22 4.365 626 5.291 5.036 9.201 4.028 8.833 16.80 25.46 60.46 33.75 4.480 630 5.081 5.305 7.952 3.687 5.888 18.86 23.46 63.57 31.41 3.763 631 4.971 4.981 7.211 4.148 7.793 13.54 33.05 56.50 33.98 1.360 637 5.456 5.235 9.242 3.772 5.483 16.52 21.18 63.03 33.57 4.063 646 5.306 5.171 8.776 3.803 6.663 15.94 23.16 60.11 33.59 4.210 648 4.961 5.055 8.402 3.937 7.608 17.85 14.17 61.54 31.93 3.428 649 4.466 5.121 8.301 4.028 5.898 16.47 25.97 61.16 34.12 2.720 650 4.969 5.115 8.122 4.451 6.837 18.22 24.76 63.32 32.86 2.420 651 5.194 5.231 13.829 4.645 5.617 17.10 25.38 60.94 33.25 2.148 652 5.714 5.126 8.424 3.955 5.682 16.22 26.71 60.96 32.97 1.638 653 5.609 5.254 8.592 3.871 5.610 17.03 22.92 63.58 32.52 1.248 Agriculture and Food Sciences Research, 2018, 5(1): 6-22 19 654 4.921 5.049 8.714 4.234 8.584 15.89 31.67 60.53 33.66 3.780 655 5.626 5.129 8.809 4.029 6.554 17.58 22.07 62.74 34.75 2.995 659 4.642 4.853 7.912 3.935 8.086 14.24 26.63 60.17 35.29 2.756 660 5.714 5.248 9.547 4.052 6.912 18.19 22.18 60.74 32.34 5.453 662 5.640 5.200 9.630 3.970 5.580 13.18 23.75 60.25 34.89 1.860 664 4.186 4.934 7.704 4.224 6.489 15.72 26.12 60.17 33.87 3.505 666 5.306 5.229 9.314 3.949 5.769 15.55 21.39 61.71 34.25 3.115 668 4.430 4.790 8.310 4.310 9.880 14.89 30.60 56.22 33.40 4.700 673 5.357 5.168 7.767 3.615 5.961 17.50 24.28 63.57 33.68 2.436 675 5.497 5.253 8.372 3.990 5.591 18.07 24.66 63.44 32.95 1.351 678 5.356 5.114 9.439 4.019 6.874 14.52 21.87 60.76 33.42 4.285 679 4.781 5.009 9.349 4.179 7.534 14.38 27.25 58.13 34.66 2.235 681 4.659 4.869 8.202 3.631 8.825 17.01 27.35 59.37 32.43 4.498 688 4.399 5.089 8.232 4.061 7.475 18.24 27.97 59.05 33.14 3.323 691 4.790 5.100 8.690 4.070 7.300 15.37 24.31 60.73 33.77 4.020 699 5.870 5.310 8.700 3.510 5.930 21.75 26.34 62.21 29.58 4.040 707 4.730 4.740 9.350 4.300 11.520 14.62 28.05 57.34 34.40 3.800 715 5.031 5.159 8.614 3.874 7.839 17.28 26.45 59.85 33.73 2.135 724 5.449 5.164 8.877 4.536 5.580 16.80 26.04 61.12 32.94 1.113 725 5.510 5.240 9.190 4.070 5.540 17.92 24.15 61.46 32.20 2.230 861 5.510 5.280 9.880 4.050 5.840 17.39 23.23 60.94 33.62 2.340 862 5.939 5.202 9.892 3.998 5.410 13.68 20.99 60.66 33.86 4.224 864 5.131 5.193 8.082 4.113 6.686 15.71 27.48 60.53 33.89 -0.610 873 5.631 5.228 9.792 4.058 5.721 14.84 25.23 60.90 34.46 2.433 DUNDEE 5.763 5.160 9.701 4.137 5.509 14.29 24.18 59.75 34.30 2.896 DUNDEE* 5.745 5.238 9.254 4.009 5.664 16.58 24.84 60.19 33.25 2.894 EGRET 4.911 5.031 8.949 4.072 8.081 16.14 25.65 58.18 33.02 3.510 EGRET* 5.245 4.968 8.694 3.724 8.944 17.75 28.28 59.01 32.43 4.264 HERON 5.028 5.162 7.781 3.994 6.667 16.09 29.54 58.16 34.15 2.325 IBIS 5.383 5.092 8.151 3.819 6.972 13.85 25.89 60.42 35.55 2.775 F-prob 0.224 0.004 0.079 0.030 0.012 0.001 0.217 0.018 0.002 0.069 LSD 1.1530 0.3602 2.105 0.6042 4.113 3.218 7.386 4.319 2.199 3.300 CV 10.55 3.33 11.30 7.10 26.81 9.35 13.94 3.34 3.09 47.16 SE 0.5428 .1696 0.9908 0.2844 1.936 1.515 3.477 2.024 1.035 1.547 Table-10. Means of nutritional quality traits across the two sites. Potchefstroom and Brits in 2016/17. Genotype Ash (%) Fiber (%) Linoleic acid (%) Linolenic acid (%) Moisture (%) Oil (%) Oleic acid (%) Palmitic acid (%) Prote in (%) Stearic acid (%) 1120 5.605 5.114 9.359 3.292 7.049 15.24 22.07 61.09 33.70 134.3 1363 5.319 4.990 8.851 3.741 8.565 15.99 26.79 58.21 33.11 184.0 1371 5.594 5.132 9.164 3.955 6.157 13.08 23.28 59.84 34.50 171.6 1380 5.184 4.962 9.251 4.068 8.617 15.07 25.04 59.64 33.56 142.7 1386 5.821 5.270 10.080 4.222 5.594 14.35 20.60 61.37 35.23 191.8 1390 5.319 5.023 9.894 4.220 6.583 13.78 23.54 60.08 33.75 147.3 1403 5.726 5.273 9.906 4.229 5.450 13.90 22.78 61.77 35.24 59.7 1409 5.058 4.803 8.606 3.921 11.537 15.58 26.69 60.31 33.90 181.9 1449 5.090 4.990 8.680 3.667 8.644 15.49 22.23 62.21 34.23 163.9 1552 5.915 5.124 9.353 4.153 6.783 14.05 21.70 60.99 33.42 175.9 1554 5.585 5.243 8.925 3.807 5.962 15.29 21.70 62.21 35.20 109.8 1555 5.370 5.155 8.796 4.133 6.953 13.52 26.04 61.41 34.98 97.6 1556 5.786 5.118 9.105 4.145 6.415 14.48 23.52 61.81 33.61 121.2 1558 4.702 4.908 8.715 3.739 9.917 16.93 28.40 56.73 33.18 250.1 1572 5.984 4.886 8.401 3.689 10.060 17.37 23.88 61.43 31.31 227.1 1573 5.128 4.723 9.362 3.881 12.784 17.07 24.14 59.59 32.03 263.5 1575 5.605 4.994 9.257 4.086 9.523 14.80 25.31 58.07 32.59 252.4 1578 5.147 5.144 9.197 4.117 7.730 16.01 25.34 60.34 34.23 154.7 1590 5.755 5.126 9.355 4.082 9.067 16.37 25.31 57.84 32.08 199.7 1594 8.197 3.611 19.842 10.049 2.843 10.75 -6.76 58.03 30.83 110.5 1595 5.378 5.178 8.749 4.162 7.493 16.61 24.83 61.02 33.33 178.4 1596 5.064 5.199 9.794 3.964 7.577 17.18 22.12 60.90 33.02 158.0 1597 5.455 4.970 7.292 3.917 8.045 15.05 28.22 63.14 33.97 108.7 1598 4.329 5.160 8.619 4.026 7.945 17.71 26.38 59.31 33.73 117.3 1599 4.686 5.299 9.060 4.013 5.936 17.48 24.30 61.32 34.24 67.2 1600 4.830 5.266 9.425 4.073 6.813 17.13 23.86 59.63 34.02 83.3 1659 5.618 5.180 9.430 4.081 7.066 14.59 22.73 61.78 34.66 150.2 220 5.249 5.176 9.559 4.216 5.926 15.29 23.53 60.00 33.80 148.6 223 5.703 5.143 9.995 4.341 5.676 16.45 21.62 61.24 33.12 159.1 231 5.087 5.140 8.621 3.860 7.036 17.80 25.23 61.10 32.16 105.9 266 5.140 5.170 9.291 3.692 5.963 15.06 23.09 61.17 34.40 134.2 267 4.707 5.092 9.247 4.061 8.188 16.09 21.58 60.33 34.35 189.0 268 5.528 5.187 9.365 3.950 6.169 15.29 23.47 60.68 34.86 137.9 278 5.068 5.073 9.141 4.338 6.384 14.45 30.78 58.76 34.82 35.7 284 4.603 5.115 9.214 3.973 7.644 17.08 22.04 60.86 33.57 190.6 290 4.816 5.181 8.998 4.079 5.999 16.03 22.52 61.81 34.34 187.9 291 5.021 5.213 8.848 3.850 5.769 16.58 24.08 61.41 34.26 115.7 292 5.010 5.022 9.936 4.113 7.864 15.00 23.24 58.65 34.14 200.6 Agriculture and Food Sciences Research, 2018, 5(1): 6-22 20 297 4.620 5.112 8.305 3.898 6.608 16.53 24.43 59.82 33.96 166.4 304 4.691 5.121 8.399 3.920 6.637 17.41 22.73 61.75 33.16 209.1 544 5.204 5.173 9.701 4.245 5.756 14.62 23.48 60.26 34.62 150.5 568 5.180 5.170 9.773 3.943 5.756 14.54 23.95 57.45 34.87 96.5 571 5.648 4.893 12.615 7.120 5.559 19.24 19.69 63.04 30.15 31.6 575 5.215 5.246 9.438 4.425 6.393 17.28 22.74 61.65 32.99 109.7 578 5.303 5.210 8.546 3.821 6.249 18.18 25.70 60.51 32.60 146.0 582 5.032 5.218 8.376 4.020 6.441 18.45 24.53 61.40 32.49 83.5 592 5.071 5.037 9.347 4.113 7.481 14.83 27.44 58.15 34.61 153.6 593 4.550 5.040 7.756 3.901 8.185 17.20 28.22 59.08 33.20 103.8 595 5.199 5.201 9.217 4.179 6.559 16.26 24.09 60.73 34.87 65.3 597 5.161 5.177 8.874 3.927 6.538 14.53 24.31 60.44 35.62 74.8 598 5.104 5.146 8.598 3.897 6.693 14.89 23.78 61.33 35.22 78.6 604 5.223 5.295 9.598 4.017 5.867 15.17 23.09 60.62 35.24 74.4 607 5.160 5.173 8.522 3.817 6.137 16.13 23.65 62.18 34.54 71.3 617 4.697 5.161 9.229 3.954 6.798 15.25 21.55 60.50 34.92 228.2 621 4.672 5.098 8.646 4.206 6.921 14.75 25.53 59.12 34.78 214.0 623 4.833 5.079 7.982 3.859 7.246 18.82 25.51 61.65 31.81 134.0 624 4.931 5.088 8.432 4.050 7.262 16.86 24.74 60.37 33.12 153.3 626 5.197 5.030 9.583 4.004 9.093 17.17 25.12 60.15 34.26 168.9 630 4.828 5.218 8.115 3.740 6.875 18.68 25.15 61.78 31.69 129.4 631 5.276 4.453 10.689 7.059 7.008 18.84 24.68 51.41 29.12 57.0 637 5.277 5.200 9.415 3.948 6.178 15.75 21.74 61.71 33.86 171.4 646 5.240 5.158 8.949 3.954 6.486 16.46 23.03 60.04 33.54 219.3 648 5.467 5.215 7.965 5.451 7.018 16.91 13.73 51.27 32.86 184.3 649 4.436 5.131 6.662 4.457 5.602 17.67 22.95 61.24 33.71 102.5 650 5.055 5.095 8.489 4.334 6.248 16.22 23.79 62.76 33.94 134.3 651 4.999 5.195 11.578 4.347 5.836 17.09 24.72 60.35 33.22 121.1 652 5.823 5.131 9.141 4.184 5.796 15.32 24.27 60.40 32.99 148.6 653 5.443 5.248 9.314 3.950 5.779 16.28 17.31 62.33 33.59 85.3 654 5.041 5.125 8.878 4.238 7.149 15.81 27.11 61.30 33.71 162.6 655 5.221 5.156 8.917 4.082 5.964 15.98 22.24 62.88 34.76 112.7 659 5.011 4.879 8.374 3.976 7.064 12.96 25.05 60.19 35.33 149.9 660 5.154 5.243 9.085 3.896 6.498 17.73 22.03 61.61 33.07 169.5 662 5.263 5.190 9.109 3.987 6.163 14.14 23.87 60.61 34.42 47.2 664 4.618 5.044 8.388 4.061 6.390 15.65 24.81 60.08 33.97 133.5 666 5.035 5.189 9.136 3.961 6.008 16.05 21.59 61.63 34.27 142.7 668 4.818 4.966 9.012 4.089 7.349 14.49 25.32 58.43 33.74 192.1 673 5.441 5.225 8.551 3.767 5.794 16.35 23.38 62.55 34.05 143.6 675 5.326 5.253 8.976 3.995 5.804 16.62 23.75 61.96 33.90 70.1 678 5.103 5.157 9.508 3.946 6.472 15.72 22.10 60.53 33.50 187.7 679 5.123 5.072 9.848 4.281 6.532 13.90 25.19 58.91 34.50 83.0 681 4.701 4.976 8.003 3.870 7.539 17.09 27.35 59.30 33.11 189.4 688 4.652 5.103 9.114 4.068 6.686 16.53 24.56 59.60 33.65 156.9 691 5.168 5.187 10.044 4.011 6.893 15.76 21.95 60.64 34.14 149.1 699 6.233 5.317 8.843 4.033 6.520 20.73 26.37 61.08 30.28 183.0 707 5.197 5.072 9.543 4.076 8.228 14.84 24.84 59.32 34.61 141.2 715 5.149 5.173 8.855 3.837 6.870 15.64 24.11 60.91 34.70 94.0 724 5.296 5.096 8.758 4.332 5.401 15.59 25.39 61.28 32.67 93.1 725 5.353 5.161 9.156 4.355 5.855 16.57 25.43 60.21 32.07 144.5 861 5.284 5.256 9.696 4.036 6.078 17.05 21.82 61.14 33.60 150.5 862 5.507 5.154 9.761 4.147 5.491 14.22 21.46 60.44 34.12 197.4 864 5.078 5.198 8.777 3.971 6.236 15.84 23.94 60.84 33.79 165.0 873 5.359 5.187 9.898 4.034 5.901 14.32 22.31 61.01 34.45 136.1 DUNDEE 5.773 5.122 9.907 4.131 5.755 13.08 23.19 59.27 34.47 137.7 DUNDEE* 5.550 5.218 9.397 3.938 5.906 15.71 23.70 60.04 33.72 121.8 EGRET 5.212 5.100 9.277 3.975 7.107 15.77 23.55 58.98 33.15 170.7 EGRET* 5.192 5.092 8.981 3.810 7.510 15.92 24.79 60.24 33.83 159.0 HERON 5.149 5.160 8.643 3.913 6.118 15.19 25.12 59.44 34.28 118.0 IBIS 5.444 5.126 8.956 3.926 6.487 13.91 23.32 60.71 35.04 138.5 Genotype 0.8203** * .1472 0.93 2.282 6.577*** .766** * 55.09 12.59 4.906 * 8854** * Site 3.0554 0.067 0 9.68** 9.210* 9.696*** 37.343 ** 666.57 *** 25.12 7.737 250321 *** Genotype * Site 0.6848* 0.136 3 0.79 2.150 3.906 5.117 41.15 8.64 3.219 4359 Mean 5.12 5.00 9.03 4.07 6.70 15.56 23.11 59.15 33.02 139.23 LSD 1.480 0.780 4 5.479 3.208 3.865 4.281 15.93 7.018 3.798 129.4 CV 13.77 7.44 28.94 37.55 27.54 13.12 32.90 5.65 5.49 44.54 SE 0.7204 .3799 2.667 1.562 1.882 2.084 7.756 3.409 1.849 62.24 R2 Note: *** p<0.001, ** p< 0.025 and *p< 0.05 Agriculture and Food Sciences Research, 2018, 5(1): 6-22 21 References [1] Food and Agriculture Organisation of the United Nations, "FAO statistics division, FAOSTAT database." Retrieved: http://www.fao.org/docrep/016/i3027e/i3027e.pdf, 2012. [2] Food and Agriculture Oorganisation, "FAO statistics division, FAOSTAT database." Retrieved: http://www.fao.org/docrep/016/i3027e/i3027e.pdf, 2012. [3] M. Aslam, M. S. Mirza, S. M. Shah, S. Shafeeq, and U. Naeem, Crop production Bulletin No. 6. Islamabad, Pakistan: Pakistan Agricultural Research Council, 1995. [4] A. Ghafoor, A. Zahoor, A. S. Qureshi, and M. Bashir, "Genetic relationship in Vigna mungo (L.) Hepper and V. radiata (L.) R. Wilczek based on morphological traits and SDS-PAGE," Euphytica, vol. 123, pp. 367-378, 2002. View at Google Scholar [5] G. L. Brown-Guedira, J. A. Thompson, R. L. Nelson, and M. L. Warburton, "Evaluation of genetic diversity of soybean introductions and North American ancestors using RAPD and SSR markers," Crop Science, vol. 40, pp. 815-823, 2000. View at Google Scholar | View at Publisher [6] Y. B. Fu, "Applications of bulking in molecular characterization of plant germplasm: A critical review," Plant Genetic Resources, vol. 1, pp. 161-167, 2003. View at Google Scholar | View at Publisher [7] P. Smykal, J. Horacek, R. Dostalova, and M. Hybl, "Variety discrimination in pea (Pisum Sativum L.) by molecular, biochemical and morphological markers," Journal of Applied Genetics, vol. 49, pp. 155-166, 2008. View at Google Scholar | View at Publisher [8] M. Khalid, F. Farhatullah, N. U. Khan, R. Din, K. M. Yasir, M. Akmal, and N. Ali, "Linkage of morphological markers in Brassica," Pakistan Journal of Botany, vol. 42, pp. 2995-3000, 2010. View at Google Scholar [9] A. Kumar, A. Pandey, C. Aochen, and A. Pattanayak, "Evaluation of genetic diversity and interrelationships of agro-morphological characters in soybean (Glycine Max) genotypes," in Proceedings of the National Academy of Sciences. India Section B: Biological Sciences, 2015, pp. 397-405. [10] P. Vesna, N. Ana, B. Vojka, S. Aleksanda, M. S., D. Vuk, and M. D. Snežana, "Genetic relatedness of soybean genotypes based on agromorphological traits and RAPD markers," Genetika, vol. 46, pp. 839-854, 2014. View at Google Scholar | View at Publisher [11] M. A. Malek, M. Y. Raffi, M. S. S. Afroj, U. K. Nath, and M. M. A. Mondol, "Morphological characterization and assessment of genetic variability, character association, and divergence in soybean mutants," Scientific World Journal, pp. 1-12, 2014. View at Publisher [12] I. A. Khatab, A. R. Morsy, W. M. Fares, and T. Kumamaru, "Genetic diversity of soybean genotypes revealed by agro– morphological and SSR markers," Journal of the Faculty of Agriculture, Kyushu University, vol. 61, pp. 79-87, 2016. View at Google Scholar [13] R. W. Yaklich, B. Vinyard, M. Camp, and S. Douglass, "Analysis of seed protein and oil from soybean Northern and Southern Region uniform tests," Crop Science, vol. 42, pp. 1504-1515, 2002. View at Publisher [14] S. M. Potter, J. A. Baum, H. Y. Teng, R. J. Stillman, N. F. Shay, and J. W. Erdman, "Soy protein and isoflavones: Their effects on blood lipids and bone density in postmenopausal women," American Journal of Clinical Nutrition, vol. 68, pp. 1375S-1379S, 1998. View at Google Scholar | View at Publisher [15] Anonymous, "16 surprising sources of protein." Retrieved: http://www.fitday.com/fitness-articles/fitness/16-surprising-sources- of-protein.html, 2015. [16] M. N. Lokuruka, "Soybean nutritional properties: The good and the bad about soy foods consumption- a review," African Journal of Food Agriculture Nutrition and Development, vol. 10, pp. 2439-2458, 2010. View at Google Scholar | View at Publisher [17] K. J. Helzlsouer, H. Y. Huang, A. J. Alberg, S. Hoffman, A. Burke, E. P. Norkus, J. S. Morris, and G. W. Comstock, "Association between α-tocopherol, γ-tocopherol, selenium and subsequent prostate cancer," Journal of Natural Cancer Institute, vol. 92, pp. 2018- 2023, 2000. View at Google Scholar | View at Publisher [18] A. Farooq, M. K. Ghulam, N. Farooq, and S. Ghulam, "Variations of quality characteristics among oils of different soybean varieties," Journal of King Saud University – Science, vol. 28, pp. 332-338, 2016. View at Google Scholar | View at Publisher [19] G. McVean, "A genealogical interpretation of principal components analysis," PLoS Genetics, vol. 5, p. e1000686, 2009. View at Google Scholar | View at Publisher [20] C. D. Cruz and P. C. S. Carneiro, Biometric models applied to genetic improvement. Viçosa: UFV, 2003. [21] A. A. Ismail, M. A. Khalifa, and H. A. K., "Genetic studies on some yield traits of durum wheat," Asian Journal of Agricultural Science, vol. 32, pp. 103-129, 2001. [22] P. Kumar and R. S. Shukla, "Genetic analysis for yield and its attributed traits in bread wheat under various situations," Jawaharlal Nehru Krishi Vishwa Vidyalaya Research Journal, vol. 36, pp. 95-97, 2002. View at Google Scholar [23] S. C. Anand and J. H. Torrie, "Heritability of yield and other traits and interrelationship among traits in the F3 an F4 generations of three soybean crosses," Crop Science, vol. 3, pp. 508-511, 1963. View at Google Scholar | View at Publisher [24] M. Arashad, N. Ali, and A. Ghafoor, "Character correlation and path coefficient in soybean Glycine max (L.) Merrill," Pakistan Journal of Botany, vol. 38, pp. 121-130, 2006. View at Google Scholar [25] J. P. Aditya, P. Bhartiya, and A. Bhartiya, "Genetic variability, heritability and character association for yield and component characters in soybean (G. max (L.) Merrill)," Journal of Central European Agriculture, vol. 12, pp. 27-34, 2011. View at Google Scholar [26] T. Machikowa, A. Waranyuwat, and P. Laosuwan, "Relationships between seed yield and other characters of different maturity types of soybean grown in different environments and levels of fertilizer," Science Asia, vol. 31, pp. 37-41, 2005. View at Google Scholar [27] T. Machikowa and P. Laosuwan, "Path coefficient analysis for yield of early maturing soybean," Songklanakarin Journal of Science and Technology, vol. 33, pp. 365-368, 2011. View at Google Scholar [28] D. K. Ojo, A. A. O., and O. O. A., "Genetic relationships among soybean accessions based on morphological and RAPDs techniques," Pertanika Journal of Tropical Agricultural Science, vol. 35, pp. 237-248, 2012. View at Google Scholar [29] V. Belamkar, M. G. Selvaraj, J. L. Ayers, P. R. Payton, N. Puppala, and M. D. Burow, "A first insight into population structure and linkage disequilibrium in the US peanut minicore collection," Genetica, vol. 139, pp. 411-429, 2011. View at Google Scholar | View at Publisher [30] V. Ulaganathan and A. Nirmalakumari, "Finger millet germplasm characterization and evaluation using principal component analysis," Sabrao Journal of Breeding and Genetics, vol. 47, pp. 79-88, 2015. View at Google Scholar [31] S. L. Dwivedi, S. Gurtu, S. Chandra, W. Yuejin, and S. N. Nigam, "Assessment of genetic diversity among selected groundnut germplasm by RAPD analysis," Plant Breeding, vol. 120, pp. 345-350, 2001. View at Google Scholar | View at Publisher [32] A. H. Paterson, S. Damon, J. D. Hewitt., D. Zamir, H. D. Rabinowitch, L. S.E., L. E.S., and S. D. Tanksley, "Genetics. Mendelian factors underlying quantitative traits in tomato: Comparison across species, generations and environments," Genetics, vol. 127, pp. 181-197, 1991. View at Google Scholar [33] A. Shi, P. Chen, B. Zhang, and A. Hou, "Genetic diversity and association analysis of protein and oil content in foodgrade soybeans from Asia and the United States," Plant Breeding, vol. 129, pp. 250-256, 2010. View at Google Scholar | View at Publisher [34] M. S. Mazid, M. Y. Rafii, M. M. Hanafi, H. A. Rahim, M. Shabanimofrad, and M. A. Latif, "Agro-morphological characterization and assessment of variability, heritability, genetic advance and divergence in bacterial blight resistant rice genotypes," South African Journal of Botany, vol. 86, pp. 15-22, 2013. View at Google Scholar | View at Publisher [35] M. A. Chowdhury, B. Vandenberg, and T. Warkentin, "Cultivar identification and genetic relationship among selected breeding lines and cultivars in chickpea (Cicer Arietinum L.)," Euphytica, vol. 127, pp. 317-325, 2002. View at Google Scholar [36] T. Hymowitz, "On the domestication of the soybean," Economic Botany, vol. 24, pp. 408-421, 1970. View at Google Scholar | View at Publisher [37] R. D. Almeida, J. M. Peluzio, and F. S. Afférri, "Divergência genética entre cultivares de soja, sob condições de várzea irrigada, no sul do Estado Tocantins," Revista Ciência Agronômica, vol. 42, pp. 108-115, 2011. View at Publisher [38] M. C. G. Cunha, O. T. Hamawaki, and L. B. Sousa, "Genetic variability among 79 soybean progenies from ufu breeding program," Bioscience Journal, vol. 29, pp. 340-349, 2013. View at Google Scholar [39] H. De Chavez, T. Borromeo, B. N. Oswald, and R. Gentallan, "Phenotypic diversity of soybean (Glycine max (L.) Merr.) accessions in the Philippines for utilization," Legume Research-An International Journal, vol. 40, pp. 9-15, 2017. View at Google Scholar | View at Publisher http://www.fao.org/docrep/016/i3027e/i3027e.pdf, http://www.fao.org/docrep/016/i3027e/i3027e.pdf, https://scholar.google.com/scholar?hl=en&q=Genetic%20relationship%20in%20Vigna%20mungo%20(L.)%20Hepper%20and%20V.%20radiata%20(L.)%20R.%20Wilczek%20based%20on%20morphological%20traits%20and%20SDS-PAGE https://scholar.google.com/scholar?hl=en&q=Evaluation%20of%20genetic%20diversity%20of%20soybean%20introductions%20and%20North%20American%20ancestors%20using%20RAPD%20and%20SSR%20markers https://scholar.google.com/scholar?hl=en&q=Evaluation%20of%20genetic%20diversity%20of%20soybean%20introductions%20and%20North%20American%20ancestors%20using%20RAPD%20and%20SSR%20markers http://dx.doi.org/10.2135/cropsci2000.403815x https://scholar.google.com/scholar?hl=en&q=Applications%20of%20bulking%20in%20molecular%20characterization%20of%20plant%20germplasm:%20A%20critical%20review http://dx.doi.org/10.1079/pgr200324 https://scholar.google.com/scholar?hl=en&q=Variety%20discrimination%20in%20pea%20(Pisum%20Sativum%20L.)%20by%20molecular,%20biochemical%20and%20morphological%20markers http://dx.doi.org/10.1007/bf03195609 https://scholar.google.com/scholar?hl=en&q=Linkage%20of%20morphological%20markers%20in%20Brassica https://scholar.google.com/scholar?hl=en&q=Genetic%20relatedness%20of%20soybean%20genotypes%20based%20on%20agromorphological%20traits%20and%20RAPD%20markers http://dx.doi.org/10.2298/gensr1403839p http://dx.doi.org/10.1155/2014/968796 http://dx.doi.org/10.1155/2014/968796 https://scholar.google.com/scholar?hl=en&q=Genetic%20diversity%20of%20soybean%20genotypes%20revealed%20by%20agro–morphological%20and%20SSR%20markers http://dx.doi.org/10.2135/cropsci2002.1504 https://scholar.google.com/scholar?hl=en&q=Soy%20protein%20and%20isoflavones:%20Their%20effects%20on%20blood%20lipids%20and%20bone%20density%20in%20postmenopausal%20women http://dx.doi.org/10.1093/ajcn/68.6.1375s http://www.fitday.com/fitness-articles/fitness/16-surprising-sources-of-protein.html, http://www.fitday.com/fitness-articles/fitness/16-surprising-sources-of-protein.html, https://scholar.google.com/scholar?hl=en&q=Soybean%20nutritional%20properties:%20The%20good%20and%20the%20bad%20about%20soy%20foods%20consumption-%20a%20review http://dx.doi.org/10.4314/ajfand.v10i4.55335 https://scholar.google.com/scholar?hl=en&q=Association%20between%20α-tocopherol,%20γ-tocopherol,%20selenium%20and%20subsequent%20prostate%20cancer http://dx.doi.org/10.1093/jnci/92.24.2018 https://scholar.google.com/scholar?hl=en&q=Variations%20of%20quality%20characteristics%20among%20oils%20of%20different%20soybean%20varieties http://dx.doi.org/10.1016/j.jksus.2015.10.001 https://scholar.google.com/scholar?hl=en&q=A%20genealogical%20interpretation%20of%20principal%20components%20analysis https://scholar.google.com/scholar?hl=en&q=A%20genealogical%20interpretation%20of%20principal%20components%20analysis http://dx.doi.org/10.1371/journal.pgen.1000686 https://scholar.google.com/scholar?hl=en&q=Genetic%20analysis%20for%20yield%20and%20its%20attributed%20traits%20in%20bread%20wheat%20under%20various%20situations https://scholar.google.com/scholar?hl=en&q=Heritability%20of%20yield%20and%20other%20traits%20and%20interrelationship%20among%20traits%20in%20the%20F3%20an%20F4%20generations%20of%20three%20soybean%20crosses http://dx.doi.org/10.2135/cropsci1963.0011183x000300060015x https://scholar.google.com/scholar?hl=en&q=Character%20correlation%20and%20path%20coefficient%20in%20soybean%20Glycine%20max%20(L.)%20Merrill https://scholar.google.com/scholar?hl=en&q=Genetic%20variability,%20heritability%20and%20character%20association%20for%20yield%20and%20component%20characters%20in%20soybean%20(G.%20max%20(L.)%20Merrill) https://scholar.google.com/scholar?hl=en&q=Relationships%20between%20seed%20yield%20and%20other%20characters%20of%20different%20maturity%20types%20of%20soybean%20grown%20in%20different%20environments%20and%20levels%20of%20fertilizer https://scholar.google.com/scholar?hl=en&q=Path%20coefficient%20analysis%20for%20yield%20of%20early%20maturing%20soybean https://scholar.google.com/scholar?hl=en&q=Genetic%20relationships%20among%20soybean%20accessions%20based%20on%20morphological%20and%20RAPDs%20techniques https://scholar.google.com/scholar?hl=en&q=A%20first%20insight%20into%20population%20structure%20and%20linkage%20disequilibrium%20in%20the%20US%20peanut%20minicore%20collection http://dx.doi.org/10.1007/s10709-011-9556-2 http://dx.doi.org/10.1007/s10709-011-9556-2 https://scholar.google.com/scholar?hl=en&q=Finger%20millet%20germplasm%20characterization%20and%20evaluation%20using%20principal%20component%20analysis https://scholar.google.com/scholar?hl=en&q=Assessment%20of%20genetic%20diversity%20among%20selected%20groundnut%20germplasm%20by%20RAPD%20analysis http://dx.doi.org/10.1046/j.1439-0523.2001.00613.x https://scholar.google.com/scholar?hl=en&q=Genetics.%20Mendelian%20factors%20underlying%20quantitative%20traits%20in%20tomato:%20Comparison%20across%20species,%20generations%20and%20environments https://scholar.google.com/scholar?hl=en&q=Genetic%20diversity%20and%20association%20analysis%20of%20protein%20and%20oil%20content%20in%20foodgrade%20soybeans%20from%20Asia%20and%20the%20United%20States http://dx.doi.org/10.1111/j.1439-0523.2010.01766.x https://scholar.google.com/scholar?hl=en&q=Agro-morphological%20characterization%20and%20assessment%20of%20variability,%20heritability,%20genetic%20advance%20and%20divergence%20in%20bacterial%20blight%20resistant%20rice%20genotypes http://dx.doi.org/10.1016/j.sajb.2013.01.004 https://scholar.google.com/scholar?hl=en&q=Cultivar%20identification%20and%20genetic%20relationship%20among%20selected%20breeding%20lines%20and%20cultivars%20in%20chickpea%20(Cicer%20Arietinum%20L.) https://scholar.google.com/scholar?hl=en&q=On%20the%20domestication%20of%20the%20soybean http://dx.doi.org/10.1007/bf02860745 http://dx.doi.org/10.1007/bf02860745 http://dx.doi.org/10.1590/s1806-66902011000100014 https://scholar.google.com/scholar?hl=en&q=Genetic%20variability%20among%2079%20soybean%20progenies%20from%20ufu%20breeding%20program https://scholar.google.com/scholar?hl=en&q=Phenotypic%20diversity%20of%20soybean%20(Glycine%20max%20(L.)%20Merr.)%20accessions%20in%20the%20Philippines%20for%20utilization http://dx.doi.org/10.18805/lr.v0iof.3769 http://dx.doi.org/10.18805/lr.v0iof.3769 Agriculture and Food Sciences Research, 2018, 5(1): 6-22 22 [40] M. Y. Hamzekhanlu, A. I. Darbandi, P. Beiranvand, M. T. Hallajian, and A. Majdabadi, "Phenotypic and molecular analysis of M7 generation of soybean mutant lines through random amplified polymorphic DNA (RAPD) marker and some morphological traits," African Journal of Agricultural Research, vol. 6, pp. 1779-1785, 2011. View at Google Scholar [41] Z. Iqbal, M. Arshad, M. Ashraf, T. Mahmood, and A. Waheed, "Evaluation of soybean [Glycine Max (L.) Merrill] germplasm for some important morphological traits using multivariate analysis," Pakistan Journal of Botany, vol. 40, pp. 2323-2328, 2008. View at Google Scholar Asian Online Journal Publishing Group is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use of the content. Any queries should be directed to the corresponding author of the article. https://scholar.google.com/scholar?hl=en&q=Phenotypic%20and%20molecular%20analysis%20of%20M7%20generation%20of%20soybean%20mutant%20lines%20through%20random%20amplified%20polymorphic%20DNA%20(RAPD)%20marker%20and%20some%20morphological%20traits https://scholar.google.com/scholar?hl=en&q=Evaluation%20of%20soybean%20%5bGlycine%20Max%20(L.)%20Merrill%5d%20germplasm%20for%20some%20important%20morphological%20traits%20using%20multivariate%20analysis https://scholar.google.com/scholar?hl=en&q=Evaluation%20of%20soybean%20%5bGlycine%20Max%20(L.)%20Merrill%5d%20germplasm%20for%20some%20important%20morphological%20traits%20using%20multivariate%20analysis