Impact of crop management practices on maize yield: Insights from farming in tropical regions and predictive modeling using machine learning

Understanding the influence of crop management practices on maize yield is crucial amidst the changing environmental conditions. Smallholder farming in tropical regions has long puzzled decision-makers in terms of maize management. Balancing alternative management practices, environmental factors, and economic outcomes is essential. In this study, we examine the relationships between maize yield and various factors including climate variables, soil quality parameters, cultivars, tillage practices, and fertilizer usage in Chiapas, Mexico. Pearson's correlation coefficient and T-test were employed to determine the statistical significance of the correlations. Our findings reveal strong positive associations between maize yields and factors such as planting, cultivar, vapor pressure, temperature, solar radiations and fertilizer inputs for nitrogen, phosphorus, and potassium in lower elevation farms. Conversely, there is a negative correlation with elevation, slope, and system. Extensive data analysis was conducted to investigate the impact of different crop management practices on yield, utilizing both data visualization and analytics. Additionally, maize yield prediction was performed using six hybrid machine learning (ML) models, employing Grid Search and Random Search optimization techniques. Based on evaluation, Grid Search-based XGBoost exhibited the most accurate prediction results. Furthermore, explainable artificial intelligence (XAI) was employed to assess the individual impact of each input parameter on the ML model output.