2024/06/14 by Fernando Coello, Thomas Decorte, Coello, Fernando +11 · 2 citations
Agricultural and Biological Sciences · #Computational Engineering #Crop Yield and Soil Fertility #FOS: Computer and information sciences #Finance #Rice Cultivation and Yield Improvement #Soybean genetics and cultivation #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2406.10001
openalex publication_date 2024/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As global fertilizer application rates increase, high-quality datasets are paramount for comprehensive analyses to support informed decision-making and policy formulation in crucial areas such as food security or climate change. This study aims to fill existing data gaps by employing two machine learning models, eXtreme Gradient Boosting and HistGradientBoosting algorithms to produce precise country-level predictions of nitrogen (N), phosphorus pentoxide (P2O5), and potassium oxide (K2O) application rates. Subsequently, we created a comprehensive dataset of 5-arcmin resolution maps depicting the application rates of each fertilizer for 13 major crop groups from 1961 to 2019. The predictions were validated by both comparing with existing databases and by assessing the drivers of fertilizer application rates using the model's SHapley Additive exPlanations. This extensive dataset is poised to be a valuable resource for assessing fertilization trends, identifying the socioeconomic, agricultural, and environmental drivers of fertilizer application rates, and serving as an input for various applications, including environmental modeling, causal analysis, fertilizer price predictions, and forecasting.