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

Abstract

Multiple regression was employed to model the relationship between growth and yield components of okra (Abelmoschus esculentus (L) Moench) and ayoyo (Corchorus olitorius (L)), with the aim of generating a predictive model. Data on growth parameters and yields of okra and ayoyo crops were collected and analysed using IBM SPSS Statistics 21. Ten (10) plants were tagged in each stream, and a metre rule was used to measure their heights at two-week intervals (2, 4, 6, 8, and 10 weeks). The mean was calculated to obtain the average height per plot/experiment/stream. Data were also collected on the number of leaves per plot, leaf area index per experiment, leaf spread per experiment, and fresh and dry weight per plot. Total nitrogen content was determined using the Kjeldahl method, while phosphorus (P) levels were analyzed using the Bray-P solution method. Additionally, potassium (K) concentrations were measured using the flame photometer method. Results showed an average infiltration rate of 160.25 mm/h, suggesting that the site’s soils belong to hydrologic soil group A/B.  Group A consists of sand, loamy sand, or sandy loam soil types. Group B comprises silt loam or loam with moderate infiltration rates, low runoff potential, and high infiltration rates when thoroughly wetted. Due to their moderate to high water transmission rates, these soils are suitable for drip and sprinkler irrigation systems. Notably, there is a strong positive correlation between plant height and leaf area (0.889), and between leaf area and leaf area index (0.981). This suggests that as plant height increases, so does the leaf area, and a similar positive relationship exists between leaf area and the leaf area index. Furthermore, the correlation between the number of leaves and other growth parameters such as leaf area (0.966) and leaf area index (0.988) is also strongly positive. This indicates that an increase in the number of leaves is associated with increases in both leaf area and leaf area index. These correlations, ranging from 0.582 to 0.807, indicate a high degree of association. The high R2 value suggests a strong correlation between predicted and observed yields of okra, indicating reliable predictive capability when the growth parameters of okra are provided. Similarly, for ayoyo, a model equation was developed through regression analysis, yielding an R2 value of 0.941. The yield of ayoyo can thus be predicted during cultivation, provided its growth parameters are known. Hence, this study is focused on establishing a multiple regression model for the growth and yield of okra and ayoyo under different irrigation stream systems