Climate change presents significant challenges to agriculture worldwide, leading to food insecurity and impacting rural livelihoods. Maize farming is especially vulnerable to extreme weather, such as heavy rainfall, high temperatures, soil acidity, humidity, and poor irrigation, which reduce crop yields and raise concerns about food security. The study aimed to develop a reliable and accurate machine learning method to predict maize crop yields using historical climate data to facilitate decision-making. This allows farmers and agronomists to forecast maize production based on past data for adaptation. A dataset from Meteo Rwanda and maize yield data from the Kayonza district, Rwanda, were used for training and testing. The weather data included annual mean temperature, maximum temperature, minimum temperature, rainfall, and soil temperature over the past thirteen years. The data were analyzed using machine learning techniques such as Random Forest regressor, Extreme Boost regressor, Gradient, Support Vector Machine, and LASSO (Least Absolute Shrinkage and Selection Operator). The results show that developing a high-yield crop depends on predicting and integrating climate variables, especially temperature and rainfall. Overall, Random Forest, Support Vector Machine, and Extreme Boost outperformed LASSO, with R2 values of 0.957, 0.955, and 0.953, compared to 0.256 for LASSO.
Lionel et al. (Mon,) studied this question.