Modelling Growth and Yield Components of Okra (Abelmoschus esculentus L. Moench) and Ayoyo (Corchorus olitorius) Using Multiple Regression

Abstract

Multiple regression was used to analyzed the relationship between growth and yield components of Okra and Ayoyo crops with the aim of generating a predictive model. Ten (10) plants were tagged in each stream, and the average height per plot/experiment/stream, number of leaves per plot, and leaf area index. Results show an average infiltration rate of 160.25 mm/h, suggesting that the soils of the site belong to hydrologic soil group A/B.  Group A is sand, loamy sand or sandy loam types of soils while Group B is silt loam or loam. Based on regression analysis, a model equation was formulated to predict the yield of okra, exhibiting a coefficient of determination (R²) of 0.832. This high R² value indicates a strong correlation between predicted and observed yields of okra, suggesting reliable predictive capability when the growth parameters of okra are provided. Similarly, the model equation for ayoyo crop had R² value of 0.941, suggesting a close match between predicted and observed yields of ayoyo, indicating the potential for accurate yield prediction during cultivation, given knowledge of the growth parameters of ayoyo.