Development of mathematical model for optimal rice production in Niger State, Nigeria

Abstract

Rice is a staple food and a critical crop for food security and economic stability in Niger State, Nigeria. However, achieving optimal production levels is challenged by various factors, including environmental variability, land use inefficiency, and rising production costs. Mathematical modeling offers a systematic approach to understanding and optimizing these factors to enhance yields and promote sustainable agricultural practices. A mathematical model to optimize rice production by integrating key agronomic, environmental, and economic factors were formulated. This research paper aims to predict optimal rice yields based on input variables such as rainfall, temperature, humidity, land area use and production cost using a multivariate linear regression (MLR) method. The developed model is validated with real-world data from agricultural research stations. It was observed from the analysis that predicted values were not significantly different from the observed values. The results show that R-square, Mean Square Error (MSE) and Root Mean Square Errors (RMSE) values were 0.96345, 0.0249 and 0.1578 respectively; indicating that approximately 96.35% of the variance in rice production can be explained by the independent variables. Due to its high level of accuracy in predicting rice yield; it can be concluded that the model can be used to determine optimum rice production in Niger state, Nigeria and provide a decision-support tool for farmers and policymakers.