Agronomic performance of okra (Abelmoschus esculentus (L.) Moench) accessions and trait association partitioning using path analysis
Article Sidebar
Main Article Content
Abstract
Background and Objective: Yield improvement in okra (Abelmoschus esculentus (L.) Moench) is challenging due to the complexity of its genetic inheritance. Understanding the relationships between yield and its component traits is essential for effective selection in breeding programs. This study aimed to assess the genetic variability among okra accessions, identify high-yielding and early-maturing varieties, and develop appropriate selection indices for yield improvement.
Methodology: Thirty okra accessions were evaluated across two locations in Nigeria over two growing seasons. Quantitative data were collected on ten agronomic and yield-related traits from five representative plants per plot. The data were analyzed using analysis of variance (ANOVA), correlation analysis, and path coefficient analysis to identify traits influencing yield performance.
Main Results: Significant genotypic variation was observed among the accessions, with NHOKO179, NHOKO158, and NHOKO593 demonstrating the highest yield potential. Correlation analysis showed strong positive relationships between yield and the number of pods per plant (r = 0.90; P < 0.01) and pod weight per plant (r = 0.90; P < 0.01). In contrast, negative correlations were found between yield and days to first flowering, days to first picking, and days to first pod appearance (r = -0.50; P < 0.01 for all). Path coefficient analysis revealed that days to first picking (7.68) had the highest positive direct effect on yield, followed by days to first flowering (4.55) and pod weight per plant (1.23). Conversely, days to first pod appearance (-12.25) exhibited the most significant negative direct effect on yield.
Conclusions: The study identified pod weight per plant, early flowering, and early picking times as effective selection indices for enhancing okra yield. NHOKO179, NHOKO158, and NHOKO593 emerged as promising candidates for breeding programs aimed at developing high-yielding okra cultivars with improved adaptability and productivity across diverse environments.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Abd-allah, S.A.M. 2015. Path coefficient analysis for some characters on fruit and seed yield of okra. J. Horticulture. 2(2): 140. http://doi.org/10.4172/2376-0354.1000140.
Aboderin, O.S., F.A. Bankole, M. Oyekunle, G. Olaoye and Z. Saminu. 2023. Performance of maize inbred lines of different maturity groups under different soil nitrogen levels. Abuja Journal of Agriculture and Environment. 3(2): 225–245.
Adekoya, M.A., O.J. Ariyo, O.B. Kehinde and A.E. Adegbite. 2014. Correlation and path analyses of seed yield in okra (Abelmoschus esculentus (L.) Moench) grown under different cropping seasons. Pertanika J. Trop. Agric. Sci. 37(1): 39–49.
Akinyele, B.O. and O.S. Osekita. 2006. Correlation and path coefficient analyses of seed yield attributes in okra (Abelmoschus esculentus (L.) Moench). Afr. J. Biotechnol. 5(14): 1330–1336.
Aminu, D., O.B. Bello, B.A. Gambo, A.H. Azeez, J.O. Agbolade, U.A. Abdulhamid and A. Iliyasu. 2016. Varietal performance and correlation of okra pod yield and yield components. Bangladesh J. Pl. Breed. Genet. 29(1): 11–20. http://doi.org/10.3329/bjpbg.v29i1.33703.
Ashraf, A.T.M.H., M.M. Rahman, M.M. Hossain and U. Sarker. 2020. Study of correlation and path analysis in the selected okra genotypes. Asian Res. J. Agric. 12(4): 1–11. http://doi.org/10.9734/ARJA/2020/v12i430087.
Bankole, F.A., A. Menkir, G. Olaoye, O. Olakojo and G. Melaku. 2019. Association studies between grain yield and agronomic traits of a MARS maize (Zea mays L.) population under drought and non-stress conditions. Acta Agric. Slov. 114(1): 75–84. http://doi.org/10.14720/aas.2019.114.1.9.
Bankole, F.A. and O.S. Aboderin. 2024. Yield performance and adaptability of maize hybrids of different maturity groups in multiple environments in Nigeria. Thai J. Agric. Sci. 57(3): 164–180.
Belsley, D.A., E. Kuh and R.E. Welsch. 2005. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons, Inc., New Jersey, USA.
Chowdhury, S. and S. Kumar. 2019. Exploitation of heterosis for yield and yield attributes in okra [Abelmoschus esculentus (L.) Moench]. Int. J. Chem. Stud. 7(4): 853–857.
Cudeck, R. and M.W. Browne. 1992. Constructing a covariance matrix that yields a specified minimizer and a specified minimum discrepancy function value. Psychometrika. 57(3): 357–369. http://doi.org/10.1007/BF02295424.
Davis, T.W. 2022. Grower’s guide: A review for sustainable production of okra (Abelmoschus esculentus) in West Africa and other regions. Int. J. Res. Appl. Sci. Eng. Technol. 10: 128–138. http://doi.org/10.22214/ijraset.2022.46950.
Epskamp, S., S. Stuber, J. Nak, M. Veenman and T.D. Jorgensen. 2019. Path diagrams and visual analysis of various SEM packages’ output, vol. 34. Available Source: https://github.com/SachaEpskamp/semPlot. February 3, 2023.
Hu, L.T. and P.M. Bentler. 1998. Fit indices in covariance structure modeling: Sensitivity to under-parameterized model misspecification. Psychol. Methods. 3(4): 424–453. http://doi.org/10.1037/1082-989X.3.4.424.
Hu, L.T. and P.M. Bentler. 1999. Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Modeling. 6(1): 1–55. http://doi.org/10.1080/10705519909540118.
Ibitoye, D.O. and A.O. Kolawole. 2022. Farmers’ appraisal on okra [Abelmoschus esculentus (L.)] production and phenotypic characterization: A synergistic approach for improvement. Front. Plant Sci. 13: 787577. http://doi.org/10.3389/fpls.2022.787577.
Jöreskog, K.G. and D. Sörbom. 1993. LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Scientific Software International, Lawrence Erlbaum Associates, Inc., Mahwah, New Jersey, USA.
Kugbe, J.X., W. Mawiya, A.M. Hafiz and C. Maganoba. 2019. Increase in the use of organic fertilizers in the maintenance of soil fertility and environmental sustainability. World J. Agri. & Soil Sci. 4(1). http://doi.org/10.33552/WJASS.2019.04.000577.
Kumar, S., S. Dagnoko, A. Haougui, A. Ratnadass, D. Pasternak and C. Kouame. 2010. Okra (Abelmoschus spp.) in West and Central Africa: Potential and progress on its improvement. Afr. J. Agric. Res. 5(25): 3590–3598. https://doi.org/10.5897/AJAR10.839.
Kumari, M., A.K. Dubey, K. Kumar, D.P. Singh and S. Tomar. 2019. Assessment of direct and indirect relationships among fruit yield and yield components in okra (Abelmoschus esculentus L. Moench). J. Plant Dev. Sci. 11(7): 427–430.
Mamud, A. 2021. Genetic variability, heritability, and association of quantitative traits in maize (Zea mays L.) genotypes: Review paper. EAS J. Biotechnol. Genet. 3(2): 38–46. http://dx.doi.org/10.36349/easjbg.2021.v03i02.002.
Nkongho, R.N., J.T. Efouba-Mbong, L.M. Ndam, G.A. Ketchem, E.G. Etchu-Takang and D.T. Agbor. 2022. Seed production system and adaptability of okra (Abelmoschus esculentus L.) cultivars in Buea, Cameroon. PLoS ONE. 17(12): e0278771. http://doi.org/10.1371/journal.pone.0278771.
Nnamezie, A.A., A.A. Famuwagun and S.O. Gbadamosi. 2021. Characterization of okra seed flour, protein concentrate, protein isolate, and enzymatic hydrolysates. Food Prod. Process. Nutr. 3(1): 14. http://doi.org/10.1186/s43014-021-00059-9.
Oyekunle, M., A. Haruna, B. Badu-Apraku, I.S. Usman, H. Mani, S.G. Ado, G. Olaoye, K. Obeng-Antwi, R.O. Abdulmalik and H.O. Ahmed. 2017. Assessment of early-maturing maize hybrids and testing sites using GGE biplot analysis. Crop Sci. 57(6): 2942–2950. https://doi.org/10.2135/cropsci2016.12.1014.
Pugesek, B.H., A. Tomer and A. Von Eye. 2003. Structural Equation Modeling: Applications in Ecological and Evolutionary Biology. Cambridge University Press, Cambridge, UK.
R Core Team. 2022. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
Rambabu, B., D.P. Waskar and V.S. Khandare. 2019. Correlation and path coefficient analysis of fruits yield and yield attributes in okra [Abelmoschus esculentus (L.) Moench]. Int. J. Curr. Microbiol. App. Sci. 8(4): 764–774. http://doi.org/10.20546/ijcmas.2019.804.084.
Rosseel, Y. 2012. Lavaan: An R package for structural equation modelling. J. Stat. Softw. 48(2): 1–36. http://doi.org/10.18637/jss.v048.i02.
Rynjah, S., T. Arumugam, K.N. Ganesan and P.R. Kamalkumaran. 2020. Correlation and path coefficient analysis studies in okra (Abelmoschus esculentus (L.) Moench). J. Pharmacogn. Phytochem. 9(3): 1423–1427.
Shipley, B. 2000. Cause and Correlation in Biology: A User’s Guide to Path Analysis, Structural Equations, and Causal Inference. Cambridge University Press, Cambridge, UK.
Shuirkar, G., A.K. Naidu, B.R. Pandey, A.K. Mehta and S.K. Dwivedi. 2018. Correlation coefficient analysis in okra. The Pharma Innovation Journal. 7(6): 644–647.
Sugri, I., M.S. Abdulai, A. Larbi, I. Hoeschle-Zeledon, F. Kusi and R.Y. Agyare. 2015. Participatory variety selection of okra (Abelmoschus esculentus L.) genotypes for adaptation to the semi-arid agroecology of Northern Ghana. Afr. J. Plant Sci. 9(12): 466–475. http://doi.org/10.5897/ajps2015.1340.
Vrunda, R., A.I. Patel, J.M. Vashi and B.N. Chaudhari. 2019. Correlation and path analysis studies in okra (Abelmoschus esculentus (L.) Moench). Acta Sci. Agric. 3(2): 65–70.
Wright, S. 1921. Correlation and causation. J. Agric. Res. 20: 557–587.
Yang, J., Y. Li, H. Cao, H. Yao, W. Han and S. Sun. 2019. Yield-maturity relationships of summer maize from 2003 to 2017 in Huanghuaihai Plain of China. Sci. Rep. 9: 11417. http://doi.org/10.1038/s41598-019-47561-2.
