Application of Artificial Neural Networks to Optimize Nitrogen Supply to Meet Plant Needs for Soil Conservation: Case Study, M’Bahiakro Irrigated Perimeter (Central-East, Ivory Coast)

Abstract

The low nitrogen content of soils in the rice-growing area of M’Bahiakro requires optimized fertilization to improve yields while minimizing environmental impacts. This study proposes an intelligent model based on backpropagation neural networks (BPNN) to predict the nitrogen requirements of rice using seven physico-chemical soil parameters (K, P, Norg, OM, θ pf, CEC, and Kc). The model was trained using the Levenberg-Marquardt algorithm, with a sigmoid transfer function for the hidden layer and a linear function for the output layer. Model performance was evaluated using the coefficient of determination (R 2 = 0.98) and the mean squared error (MSE = 0.001), indicating high predictive accuracy. Results show that rice yield no longer improves significantly beyond 118 kg N∙ha − ¹, with R 2 and MSE values stabilizing around 98% and 0.007, respectively. This threshold therefore represents an optimal nitrogen dose, enabling a balance between agricultural productivity and the preservation of natural resources, particularly by reducing soil degradation and groundwater contamination. However, to strengthen the model’s robustness, further investigations are essential in the irrigated area of M’Bahiakro, especially during the dry season. Expanding the study to include other rice varieties, soil types, and cultivation practices would not only broaden the model’s applicability but also reinforce its role as a decision-support tool in sustainable nitrogen fertilization strategies.