Studies on Character Expression for yield Components in Soybean
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1.Introduction Soybean (Glycine max (L.) Merrill) is a globally cultivated legume believed to have originated from East Asia, particularly the northern and central regions of China [15]. It is a diploidized allotetraploid species (2n = 40), predominantly self-pollinated [18], and valued for its edible seeds, which serve as major sources of vegetable oil and high-quality protein. Morphologically, soybean is an erect, bushy plant that thrives across tropical, subtropical, and temperate regions. Its exceptional nutritional composition—rich in protein, oil, and bioactive compounds such as isolavones—enhances its importance in food, feed, and industrial applications [16, 17]. Globally, soybean occupies about 124.9 million hectares, yielding approximately 348.7 million tonnes in 2018 (ref). The United States, Brazil, and Argentina accounted for most of this output, contributing about 87.2% of global production, followed by Asia (9.3%), Europe (2.7%), and Africa (0.8%). In Africa, total production was estimated at 3.56 million tonnes across 2.61 million hectares, with Nigeria leading in West Africa at about 393,860 metric tonnes [8, 9]. Yield in soybean is a complex quantitative trait inluenced by multiple genes and strongly modulated by environmental factors [4]. The variability observed in yield and its related components arises from the combined effects of genetic constitution and environmental conditions, making the evaluation of genetic variability essential for effective selection in breeding programs. Estimates of heritability and genetic advance provide critical information on the extent of genetic control over a trait and the potential for improvement through selection [14]. Key yield components such as the number of pods per plant, seeds per pod, and total number of seeds are important determinants of productivity and are relatively simple to assess [19]. Several studies have reported signiicant genetic variation among soybean genotypes for yield and associated traits. DOI:https://doi.org/10.51470/ER.2025.7.2.159Volume 7, Issue 2, 2025 |159to163 StudiesonCharacterExpressionforyieldComponentsinSoybean ABSTRACT Soybean(Glycinemax(L.)Merrill),aleguminouscropofEastAsianorigin,isgloballycultivatedforitsproteinandoil-richseeds. Understandingyield-relatedtraitsisvitalforimprovingproductivityandensuringfoodsecurity.Thisstudyassessedthevariability, heritability,andgeneticcontrolofyieldanditscomponenttraitsinivesoybeanaccessions.Theaccessionswereevaluatedusinga randomizedcompleteblockdesignwiththreereplications,anddataontenquantitativetraitswereanalyzedstatistically.Results revealedthatthenumberofpodsperplant(81.78%,98.80%),seedyieldperplant(76.14%,87.83%),and100-seedweight(69.73%, 63.45%)exhibitedhighheritabilityandhighgeneticadvanceasapercentageofthemean(GAM),indicatingadditivegeneeffectsand strongpotentialforselection.Plantheightcorrelatedpositivelywiththenumberofleaves(r=0.83),branches(r=0.79),podsperplant (r=0.66),andseedyield(r=0.69)suggestingthattallerplantswithgreaterfoliageandbranchingpotentialtendtoproducemorepods andhigherseedyield,makingplantheightavaluableindirectselectioncriterionforyieldimprovement.Traitsshowinghighvariability andfavorableassociationscanthereforeserveaskeyselectionindicesforenhancingsoybeanyieldinbreedingprograms. Keywords:Geneticadvance,geneticvariability,heritability,traitcorrelation. Citation: Osekita, O. S., Ajayi, A. T., Atimokhale, D. and Adeniyi, B. C. (2025). Studies on Character Expression for yield Components in Soybean. EnvironmentalReports;anInternationalJournal. DOI:https://doi.org/10.51470/ER.2025.7.2.159 Received 24 July 2025 | Revised 17 August 2025 | Accepted September 18 2025 | Available Online October 15 2025 Corresponding Author: Atimokhale,D | E-Mail: ([email protected]) OriginalResearchArticle ISSN: 3041-556X Journal homepage: https://er.researchfloor.org/ DepartmentofPlantScienceandBiotechnology,AdekunleAjasinUniversity,Akungba-Akoko,OndoState,Nigeria Osekita,O.S.,Ajayi,A.T.,Atimokhale,D.*andAdeniyi,B.C. For instance, [6] and [24] quantiied heritability, as well as phenotypic and genotypic coeficients of variation and genetic advance for major yield components, while [21] analyzed genetic variability, heritability, and genetic advance across 124 s o y b e a n g e r m p l a s m a c c e s s i o n s . B i o m e t r i c parameters—including the genotypic coeficient of variation (GCV), phenotypic coeficient of variation (PCV), heritability, and genetic advance (GA)—remain indispensable tools for quantifying genetic variability and predicting potential genetic gains in soybean breeding [1, 2]. Understanding the genetic expression of yield-related traits in soybean is crucial for improving productivity and enhancing its contribution to food security. Such insights can guide the development of superior cultivars, support sustainable agricultural practices, and strengthen breeding programs under changing environmental conditions. The present study was therefore undertaken to evaluate the variability in yield-related traits of soybean and to analyze the interactions between genetic and environmental factors inluencing these characteristics. 2.MaterialsandMethods Five soybean (Glycinemax L.) accessions used in this study were obtained from the National Centre for Genetic Resources and Biotechnology (NACGRAB), Ibadan, Nigeria (Table 1). The ield experiment was conducted at the Department of Plant Science and Biotechnology, Adekunle Ajasin University, AkungbaAkoko, Ondo State, Nigeria. The experimental site is located at latitude 7.20°N, longitude 5.44°E, and an altitude of 423 meters above sea level. A randomized complete block design (RCBD) with three replications was employed to ensure statistical precision and minimize environmental variation among treatments [22]. For each accession, three seeds were sown per planting hole. Each plot consisted of rows measuring 4 m in length, with a spacing of 60 cm between rows and 30 cm
Osekita,O.Setal., (2025)/EnvironmentalReports;anInternationalJournal https://er.researchloor.org/ 160. PCV = √Vp × 100 x GCV = √Vg × 100 x Where, Vp: Phenotypic variance Vg: Genotypic variance x: Grand mean The genetic advance as a percentage of mean (GAM)was determined by using the formula provided by [13] GAM = GA × 100 GM Where, GA: Genetic advance GM: Grand mean Genetic advance (GA) was calculated according to [10] as: 2 GA = H B x K x √Vp Where, 2 H B: Broad sense heritability K: Constant also known as intensity of selection (2.06) 3.ResultandDiscussion 3.1Analysisofvariance The analysis of variance (ANOVA) results (Table 2) revealed signiicant differences among the soybean accessions for all evaluated traits, indicating substantial genetic variability among them. These indings align with those of [12], who reported similar variability patterns among soybean lines, as well as with studies by [7], [20], and [23]. The highest coeficient of variation (CV) was observed for the number of leaves (29.33%), suggesting a greater environmental inluence on this trait, while days to maturity recorded the lowest CV (7.15%), consistent with the indings of [11] and [5]. Table1:ListofSoybeanaccessionsevaluatedforquantitativetraitsperformanceunder ieldconditions between plants within a row [22]. Irrigation commenced immediately after sowing and was applied twice daily during the early growth stages, then reduced to once daily approximately 45 days after planting. Weeding was carried out manually at regular intervals to minimize competition from weeds. A 10% cypermethrin solution was applied by spraying to control insect infestations. 2.1Datacollection Data were collected on ten quantitative traits: plant height (cm), internode length (cm), number of leaves, number of branches, days to lowering, number of pods per plant, seed yield per plant, pod weight (g), days to maturity, and 100-seed weight (g). Data were collected from three randomly selected plants from each accession in every plot, 14 days after the application of insecticide (cypermethrin). Measurements were taken using a ruler (cm), weighing balance, ield notebook, and pen. 2.2StatisticalAnalysis Data obtained from the experiment were subjected to analysis of variance (ANOVA) to determine signiicant differences among treatments. Duncan's Multiple Range Test (DMRT) was employed to separate the means at a 5% probability level (P ≤ 0.05). All statistical analyses were performed using SPSS software (version 20). The phenotypic coeficient of variation (PCV%) and genotypic coeficient of variation (GCV%) were estimated using the method suggested by [3] Table2:Meansquarevaluesfromanalysisofvariance(ANOVA)forquantitativetraitsamongsoybeanaccessionsevaluatedunderieldconditions *:signiicantatP≤0.05;ns:notsigniicant. Df: Degree of freedom; CV: Coeficient of variation; PH: Plant height: INL: Internode length; NL: Number of leaves; NB: Number of branches; DTF: Days to lowering; NPP: Number of pods per plant; SYP: Seed yield per plant; PWT: Pod weight; NDM: Days to maturity; SW:100-Seed weight. 3.2 Mean performance for quantitative traits among soybeanaccessionsevaluatedunderieldconditions Table 3 presents the mean performance of the evaluated soybean accessions for quantitative traits. Plant height ranged from 22.53 cm in NGBO3579 to 42.33 cm in NGBO3547, with NGBO2653 recording 34.40 cm. Internode length varied from 1.33 cm in NGBO3536 to 3.17 cm in NGBO3556. The number of leaves ranged from 35.67 in NGBO3579 to 54.33 in NGBO3556, while the number of branches varied between 14.67 in NGBO3579 and 29.67 in NGBO3547. The number of days to lowering ranged from 57.67 days in NGBO3547 (earliest) to 62.33 days in NGBO3536 (latest). The number of pods per plant ranged from 29.67 in NGBO3536 to 85.67 in NGBO3556. Seed yield per plant ranged from 87.33 g in NGBO3536 to 213.33 g in NGBO3579, while pod weight ranged from 1.80 g in NGBO3579 to 2.57 g in NGBO3556. The 100-seed weight varied from 7.00 g in NGBO3547 to 12.70 g in NGBO3556. The number of days to maturity ranged from 97.67 days in NGBO3536 (earliest) to 106.33 days in NGBO3579 (latest).
Osekita,O.Setal., (2025)/EnvironmentalReports;anInternationalJournal https://er.researchloor.org/ 161. Table3.Meanperformanceforquantitativetraitsamongsoybeanaccessionsevaluatedunderieldconditions Mean values followed by similar superscripts letter(s) within a column do not signiicantly different from one another at P≤ 0.05 according to Duncan's Multiple Range Test (DMRT). GM: Grand mean; PH: Plant height: INL: Internode length; NL: Number of leaves; NB: Number of branches; DTF: Days to lowering; NPP: Number of pods per plant; SYP: Seed yield per plant; PWT: Pod weight; NDM: Days to maturity; SW:100-Seed weight. 3.3EstimateofGeneticParameters Table 4 presents the estimates of genotypic and phenotypic variances, genotypic and phenotypic coeficients of variation (GCV and PCV), broad-sense heritability (H²B), and genetic advance as a percentage of the mean (GAM) for the traits evaluated. The results revealed high GAM values for most of the studied traits, indicating substantial potential for genetic improvement through selection. However, days to lowering and days to maturity exhibited moderate GAM values of 10.11% and 18.51%, respectively, suggesting that these traits are relatively less inluenced by additive gene effects and may respond more slowly to selection. Traits such as plant height (20.59%, 30.08%), internode length (33.78%, 44.50%), number of leaves (31.51%, 43.05%), number of branches (22.63%, 36.03%), number of pods per plant (42.12%, 46.57%), seed yield per plant (36.77%, 42.14%), and 100-seed weight (20.05%, 24.01%) exhibited high GCV and PCV values (>20%), indicating substantial genetic variability among accessions. Pod weight showed moderate GCV and PCV (11.07%, 19.44%), while days to lowering (1.25%, 4.79%) and days to maturity (4.90%, 8.66%) displayed relatively low values, suggesting limited variability for these traits. Table4:Estimatesofgeneticparametersofquantitativetraitsamongsoybeanaccessionsevaluatedunderieldconditions GM: General mean; GV: Genotypic variance; PV: Phenotypic variance; GCV: Genotypic coeficient of variation; PCV: 2 Phenotypic coeficient of variation; H B: Heritability; GA: Genetic advance; GAM: Genetic advance as percent over mean. PH: Plant height: INL: Internode length; NL: Number of leaf; NB: Number of branches; DTF: Days to lowering; NPP: Number of pods per plant; SYP: Seed yield per plant; PWT: Pod weight; NDM: Days to maturity; SW: 100-Seed weight. According to [26], the genotypic coeficient of variation (GCV) and phenotypic coeficient of variation (PCV) are classiied as low (0–10%), moderate (10–20%), and high (above 20%). The genetic parameter estimates from this study revealed considerable variability across all traits, with PCV values consistently higher than GCV, indicating environmental inluence on phenotypic expression. Similar observations were made by [27]. The largest difference between PCV and GCV occurred in internode length, suggesting a strong environmental effect on this trait, while pod weight showed moderate variability and days to lowering exhibited the lowest values for both parameters, in agreement with [6]. Heritability in the broad sense (H²B) quantiies the proportion of total phenotypic variance attributable to genetic factors, thereby indicating the degree of genetic control over a given trait. It serves as a critical parameter for identifying and selecting superior genotypes, particularly among homozygous lines. According to [25], heritability can be classiied as low (0–30%), moderate (30–60%), and high (>60%), while [13] categorized genetic advance as a percentage of the mean (GAM) as low (0–10%), moderate (10–20%), and high (>20%). In the present study, number of pods per plant, seed yield per plant, and 100seed weight exhibited high heritability coupled with high GAM, suggesting that these traits are largely governed by additive gene action and can be effectively improved through simple selection methods. Conversely, days to lowering exhibited the lowest heritability and GAM, indicating a stronger environmental inluence on its phenotypic expression and limited genetic gain through selection. 3.4.Pearson'sCorrelationAnalysis The Pearson correlation coeficients among the quantitative traits are presented in Table 5. Plant height exhibited highly signiicant positive correlations with the number of leaves (r = 0.83**), number of branches (r = 0.80**), number of pods per plant (r = 0.66**), and seed yield per plant (r = 0.69**), as well as a signiicant positive correlation with pod weight (r = 0.57*). In contrast, plant height showed a signiicant negative correlation with days to maturity (r = –0.53*).
Osekita,O.Setal., (2025)/EnvironmentalReports;anInternationalJournal https://er.researchloor.org/ 162. Table5:Pearson'scorrelationforquantitativetraitsamongsoybeanaccessionsevaluatedunderieldconditions Internode length was positively and signiicantly correlated with the number of pods per plant (r = 0.87**), seed yield per plant (r = 0.86**), and pod weight (r = 0.77**). The number of leaves also exhibited a strong positive correlation with the number of branches (r = 0.94**), while both traits were negatively associated with days to maturity (r = –0.65**). Furthermore, the number of pods per plant showed very strong positive associations with both seed yield per plant (r = 0.98**) and 100-seed weight (r = 0.88**), indicating that these traits are reliable predictors of yield performance among the soybean accessions.The Pearson correlation coeficients among the quantitative traits are presented in Table 5. Plant height exhibited highly signiicant positive correlations with the number of leaves (r = 0.83**), number of branches (r = 0.80**), number of pods per plant (r = 0.66**), and seed yield per plant (r = 0.69**), as well as a signiicant positive correlation with pod weight (r = 0.57*). In contrast, plant height showed a signiicant negative correlation with days to maturity (r = –0.53*). Internode length was positively and signiicantly correlated with the number of pods per plant (r = 0.87**), seed yield per plant (r = 0.86**), and pod weight (r = 0.77**). The number of leaves also exhibited a strong positive correlation with the number of branches (r = 0.94**), while both traits were negatively associated with days to maturity (r = –0.65**). Furthermore, the number of pods per plant showed very strong positive associations with both seed yield per plant (r = 0.98**) and 100-seed weight (r = 0.88**), indicating that these traits are reliable predictors of yield performance among the soybean accessions. The Pearson correlation coeficients among the quantitative traits are presented in Table 5. Plant height exhibited highly signiicant positive correlations with the number of leaves (r = 0.83**), number of branches (r = 0.80**), number of pods per plant (r = 0.66**), and seed yield per plant (r = 0.69**), as well as a signiicant positive correlation with pod weight (r = 0.57*). Conversely, plant height showed a signiicant negative correlation with days to maturity (r = –0.53*), suggesting that taller plants tend to mature earlier. Similarly, internode length exhibited strong positive and signiicant correlations with number of pods per plant (r = 0.87**), seed yield per plant (r = 0.86**), and pod weight (r = 0.77**), indicating its importance as a yield-contributing trait. The number of leaves was strongly and positively correlated with the number of branches (r = 0.94**), whereas both traits were negatively associated with days to maturity (r = –0.65**), implying that vigorous vegetative growth may shorten the crop's maturation period. Furthermore, the number of pods per plant displayed exceptionally strong positive associations with both seed yield per plant (r = 0.98**) and 100-seed weight (r = 0.88**). These relationships highlight that pod number, seed yield, and seed weight are reliable indicators of yield potential and can serve as effective selection criteria in soybean breeding programs. Correlation analysis reveals the nature and magnitude of associations among traits and serves as a valuable tool in breeding programs, as noted by [3]. Selection for positively correlated traits can result in simultaneous improvement of related traits, whereas selection for negatively correlated traits may hinder progress. In this study, plant height exhibited strong positive associations with the number of leaves, branches, and pods per plant, suggesting that taller plants generally produce more foliage and pods. Additionally, the number of pods per plant showed a strong positive correlation with seed yield per plant, indicating that increased pod production directly contributes to higher seed yield. Conversely, a negative correlation between the number of branches and days to lowering suggests that genotypes with more branches tend to lower earlier. 5.0Conclusion The results of this study revealed that the number of pods per plant, seed yield per plant, and 100-seed weight exhibited high heritability coupled with high genetic advance as a percentage of the mean (GAM). *:SigniicantatP≤0.05;**:SigniicantatP≤0.01. PH: Plant height: INL: Internode length; NL: Number of leaves; NB: Number of branches; DTF: Days to irst lowering; NPP: Number of pods per plant; SYP: Seed yield per plant; PWT: Pod weight; NDM: Days to maturity; SW: 100-Seed weight. This combination suggests that these traits are predominantly governed by additive gene effects, making them highly responsive to selection in breeding programs. The strong genetic control further implies minimal environmental inluence, thereby enhancing the reliability of selection based on phenotypic performance, the positive and signiicant correlation between the number of pods per plant and seed yield per plant underscores their importance as key selection criteria for yield improvement. Their responsiveness to selection provides soybean breeders with strategic opportunities to enhance productivity eficiently. Strengthening these yield-related traits could improve overall soybean performance and contribute to sustainable production systems, particularly amid the challenges of climate change and increasing global food demand. References 1. Aditya J.P., Pushpendra B. and Anuradha B. (2011). Genetic variability, heritability and character association for yield and component characters in soybean (Glycine max (L.) Merrill). JournalofCentralEuropeanAgriculture, 12(1):2734.
Osekita,O.Setal., (2025)/EnvironmentalReports;anInternationalJournal https://er.researchloor.org/ 163. 2. Agong, S.G., Ayiecho, P.O. and Arap Sang, W.K. (2001). Genetic variability and correlation studies in soybean (Glycinemax (L.) Merrill) germplasm. AfricanCropScienceJournal,9(4): 599-605. 3. Ajayi, A.T., Adesoye, A.I. and Adekoya, A.E. (2014). Genetic variability, heritability and genetic advance estimates in cowpea breeding lines (Vignaunguiculata L. Walp). Journalof AgriculturalSciences, 62(4): 279-293. 4. Aravin, A.A. (2006). Variability assessment in quantitative traits of soybean. PlantBreedingReview, 26(2): 89-107. 5. Azevedo, R.A., Lima, V.A. and Rombola, A.D. (2019). Variability and correlations among agronomic traits of soybean. Plant BreedingJournal, 150(1): 112-120. 6. Baraska, V.S., Reddy, K.R.B. and Ghosh, M.K. (2014). Genetic variability, heritability, and genetic advance in soybean. JournalofAgronomyandCropScience, 200(4): 249-258. 7. Danshiell, F. (1993). Genetic variability in soybean lines: A review. SoybeanResearchJournal, 15(3): 234-248. 8. FAO. (2012). The state of food and agriculture: Investing in food security. Food and Agriculture Organization of the U n i t e d N a t i o n s . http://www.fao.org/docrep/016/i3028e/i3028e.pdf. 9. FAO. (2020). Food and Agriculture Organization. Soybean production statistics2018. 10. Fehr, W. R., Caviness, C. E., Burmood, D. T., & Pennington, J. S. (1987). Stage of development descriptions for soybeans, Glycinemax (L.) Merrill. CropScience,17(6): 913-915. 11. Govindaraj, M., Vetriventhan, M. and Srinivasan, M. (2010). Nutritional and genetic variability in soybean varieties. JournalofFoodLegumes, 23(2): 106-111. 12. IITA. (2010). Annual report on soybean research. InternationalInstituteofTropicalAgriculture. 13. Johnson, H.W., Robinson, H.F. and Comstock, R.E. (1955). Estimate of genetic and environmental variability in soybean. AgronomyJournal, 47(7): 314-318. 14. Jandong, E. A., Uguru, M. I. and Okechukwu, E. C. (2020). Genetic variability, heritability and expected genetic advance in soybean (Glycinemax (L.) Merrill) accessions. Journalof PlantBreedingandCropScience,12(2), 45-53. 15. Laswai, H.S., Fuchs, J. and Manya, A. (2005). Soybean: Seed composition and its prospects in Tanzania. AfricanJournalof AgriculturalResearch, 1(1): 20-26. 16. Liu, K. (2017). Nutritional composition of soybeans: A comprehensive review. JournalofNutritionalScience, 6: 1-18. 17. Mahamood, Y., Adetunji, M.O. and Daramola, O.A. (2009). Soybean as a strategic crop for food security in developing countries: A review. AfricanJournalofFoodScience, 3(9): 234239. 18. Maletsema Alina Mofokeng 2021. Genetic variability, heritability and genetic advance of soybean (Glycine max) genotypes based on yield and yield-related traits. Australian JournalofCropScience, 15(12):1427-1434. 19. Malik M.F.A., Ashraf M., Qureshi A.S. and Khan, M.R 2011. Investigation and comparison of some morphological traits of the soybean populations using cluster analysis. Pakistan JournalofBotany, 43(2):1249-1255. 20. Maestri, M., Salerno, G.L. and Melchiorre, C. (1998). Agronomic performance and genetic variability in soybean. FieldCropsResearch, 57(1): 157-165. 21. Neelima G., Mehtre S.P, Narkhede G.W (2018) Genetic Variability, heritability and genetic advance in soybean. InternationalJournalofPureAppliedBioscience, 6(2):10111017. 22. Osekita, O.S. and Atimokhale D. Genetic diversity studies for assessment of variability in okra (Abelmoschusesculentus). InternationalJournalofScienceLetters. 6:2, 1-14. 23. Rao, P.S., Reddy, K. R. B. and Naik, B. (1998). Genetic assessment of soybean accessions. Journal of Oilseed Research, 15(1): 7-11. 24. Ravindra KJ, Arunabh J, Hem RC, Abhay D, Champa LK (2017) Study on genetic variability, heritability and genetic advance in soybean [Glycine max (L.) Merrill]. Legume Resources, (41):532-536 25. Robinson, H.F., Comstock, R.E. and Harvey, G.F. (1949). Estimation of heritability and the degrees of dominance in corn. AgronomyJournal, 41(7): 353-359. 26. Sivasubramanian, S. and Menon, M. (1973). Genetic variability in rice varieties. Indian Journal of Genetics and PlantBreeding, 33(2): 241-249. 27. Zida, A., Bognounou, F. and Gahouma, H. (2021). Evaluation of genetic variability in soybean genotypes. AfricanJournalof AgriculturalResearch, 16(3): 243-250.