Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD

Character Associations and Correlation Networks for Yield-Related and Vegetative Traits in Open-Pollinated and Hybrid Maize ( Zea Mays L.) Genotypes in Contrasting Savannah Ecologies

Oluro Christopher O, S.Y Simon, Bashir, R.A, Goudjou, Ndjile. L.N, Corresponding Author

Abstract

Crop yield in maize (Zea mays L.) is a complex quantitative trait, making character associations crucial for developing indirect selection criteria. This study evaluated the phenotypic and genotypic associations among 22 growth, phenological, and yield traits across 10 elite maize genotypes. Field trials were conducted during the 2023 cropping season under contrasting environments in Gombe State, Nigeria: Dadin-Kowa (dry semi-arid, 350 mm annual rainfall) and Billiri (humid, 1,600 mm annual rainfall), using a Randomized Complete Block Design with three replications. Under dry semi-arid conditions at Dadin-Kowa, grain yield exhibited highly significant positive genotypic correlations with thousand-grain weight (rg = 0.865**) and field weight (rg = 0.931**). Conversely, under wet sub-humid conditions at Billiri, grain yield was strongly and positively correlated with vegetative traits, including number of leaves (rp = 0.890**; rg = 0.946**) and leaf area (rp = 0.925**; rg = 0.999**), indicating that canopy dimensions are primary yield drivers in high-moisture regimes. In the combined multi-environment analysis, grain yield was positively correlated with number of grains per row (rp = 0.790**; rg = 0.999**) and field weight (rp = 0.949**; rg = 0.999**). A notable physiological source-sink trade-off emerged, as harvest index was significantly negatively correlated with leaf area (rp = -0.795**) and above- ground biomass (rp = -0.814**). Maize selection must be environment-specific; breeders should prioritize thousand-grain weight for dry savannahs, leaf dimensions and ear diameter for humid zones, and grains per row for general regional cultivation.

Keywords

Zea mays L.; Phenotypic correlation; Genotypic correlation; Source-sink trade-offs; Dadin-Kowa; Billiri; Indirect selection index

References

Butu, A. W., Bilal, G. N., Emeribe, C. N., & Bichi, A. A. (2020). Physiochemical and microbial parameters in domestic water sources in Billiri Local Government Area Gombe State, Nigeria. Journal of Forestry, Environment and Sustainable Development, 6(1), 123-141 Echarte, L., Nagore, L., Di Matteo, J., Cambareri, M., Robles, M., & Maggiora, A. D. (2013). Grain Yield Determination and Resource Use Efficiency in Maize Hybrids Released in Different Decades. In Agricultural Chemistry (pp. 22-36). InTech. Fekadu, K., Obssi, D., Habtamu, E., & Yohannes, P. (2024). Genetic variability for yield and yield related traits in some maize (Zea mays L.) inbred lines in the central highlands of Ethiopia. Hindawi International Journal of Agronomy, 2024, Article ID 9721304, 1-13. Gopalakrishna, K. N., Hugar, R., Rajashekar, M. K., Jayant, S. B., Talekar, S. C., & Virupaxi, P. C. (2023). Simulated drought stress unravels differential response and different mechanisms of drought tolerance in newly developed tropical field corn inbreds. PLOS ONE, 18, e0283528. Khan, S., & Mahmud, F. (2021). Genetic variability and character association of yield components in maize (Zea mays L.). American Journal of Plant Sciences, 12, 1691-1704. Kowal, J. M., & Knabe, D. T. (1973). An Agro-Climatological Atlas of Northern States of Nigeria. A.B.U. Press. Kumsa, H., Zeleke, H., & Abakemal, D. (2020). Combining ability and standard heterosis of highland maize (Zea mays L.) inbred lines for yield and yield-related traits. EAS Journal of Biotechnology and Genetics, 2(6), 91-103. Magar, B. T., Acharya, S., Gyawali, B., Timilsena, K., Upadhayaya, J., & Shrestha, J. (2021). Genetic variability and trait association in maize (Zea mays L.) varieties for growth and yield traits. Heliyon, 7(2021), e07939. Mamud, A. (2021). Genetic Variability, Heritability and Association of Quantitative Traits in Maize (Zea mays L) Genotypes: Review Paper. EAS Journal of Biotechnology and Genetics, 3(2), 38-46. Maruthi, R. T., & Jhansi Rani, K. (2015). Genetic variability, heritability and genetic advance estimates in maize (Zea mays L.) inbred lines. Journal of Applied and Natural Science, 7(1), 149-154. Olaniyi, O. A., & Adewale, J. G. (2012). Information on maize production among rural youth: A solution for sustainable food security in Nigeria. Library Philosophy and Practice, 4(2), 105-120. Oluwagbenga, D. O., & Fayeun, L. S. (2020). Correlation and Path Coefficient Analysis for Yield and Yield Components in Late Maturing Pro-vitamin A Synthetic Maize (Zea mays L.) Breeding Lines. American Journal of Experimental Agriculture, 42(1), 64-72. Oluwagbenga, D. O., & Stephen, O. (2020). Path analysis and selection criteria for yield improvement in tropical maize (Zea mays L.). Journal of Agricultural Science, 12(4), 45- 53. Oyekola, O., & Fayeun, L. S. (2019). Evaluation of maize population for growth and yield performance. World Journal of Agricultural Sciences, 15(6), 396-401. PwC (PricewaterhouseCoopers). (2021). Positioning Nigeria as Africa's leader in maize production for AfCFTA: Assessment of maize production in Nigeria. PwC Nigeria. Richards, R. A. (2017). Selectable traits to increase crop photosynthesis and yield of grain crops. Journal of Experimental Botany, 51, 447-458.