Modelling Crop Evapotranspiration and Water Use Efficiency of Maize Using Artificial Neural Network and Linear Regression Models in Biochar and Inorganic Fertilizer-Amended Soil under Varying Water Applications
2023
Oluwaseun Temitope Faloye | Ayodele Ebenezer Ajayi | Toju Babalola | Oluwayomi Omotehinse Omotehinse | Oluwafemi Ebenezer Adeyeri | Bolaji Adelanke Adabembe | Akinwale Tope Ogunrinde | Abiodun Okunola | Abayomi Fashina
The deficit irrigation strategy is a well-known approach to optimize crop water use through the estimation of crop water use efficiency (CWUE). However, studies that comprehensively reported the prediction of crop evapotranspiration (ETc) and CWUE under deficit irrigation for improved water resources planning are scarce. The objective of the study is to predict seasonal ETc and CWUE of maize using multiple linear regression (MLR) and artificial neural network (ANN) models under two scenarios, i.e., (1) when only climatic parameters are considered and (2) when combining crop parameter(s) with climatic data in amended soil. Three consecutive field experimentations were carried out with biochar applied at rates of 0, 3, 6, 10 and 20 t/ha, while inorganic fertilizer was applied at rates of 0 and 300 Kg/ha, under three water regimes: 100% Full Irrigation Treatment (FIT), 80% and 60% FIT. Seasonal ETc was determined using the soil water balance method, while growth data were monitored weekly. The CWUE under each treatment was also estimated and modelled. The MLR and ANN models were developed, and their evaluations showed that the ANN model was satisfactory for the predictions of both ETc and CWUE under all soil water conditions and scenarios. However, the MLR model without crop data was poor in predicting CWUE under extreme soil water conditions (60% FIT). The coefficient of determination (R2) increased from 0.03 to 0.67, while root mean-square error (RMSE) decreased from 4.07 to 1.98 mm after the inclusion of crop data. The model evaluation suggests that using a simple model such as MLR, crop water productivity could be accurately predicted under different soil and water management conditions.
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