TIME SERIES, MACHINE LEARNING AND ENSEMBLE MODELS TO FORECAST RICE CROP YIELD IN NIGERIA UNDER CLIMATIC VARIABILITY

Abstract

The rising demand for food globally due to unprecedented population growth has led to food insecurity in some populated regions, such as Africa. This leads to global agricultural production to be of increasing concern. The growing requirement for food in an era of a changing climate and scarce resources has inspired substantial crop yield prediction research. Another contributing factor to global food insecurity is climate change and its variability. The impact of increasing climate variability on rice crop yield is now evident. Predicting the potential effects of climate change on rice crops prompts the use of time series, machine learning, and ensemble models. The ensemble model was designed by assigning weights to the individual models based on their performance metrics, such as R-squared values and error rates. Results showed that the ensemble model achieved superior accuracy, with an R-squared value of 0.9523, surpassing the performance of the individual SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous Regressors) and LSTM (Long Short-Term Memory)models. The predicted yields aligned closely with observed values in both training and testing phases, demonstrating the model's ability to capture historical trends and generalise effectively to unseen data. This study shows the importance of integrating time-series forecasting and machine learning techniques to improve rice crop yield predictions.