High-Resolution Maize Yield Mapping across Africa using Earth Observation and Machine Learning, Deep Learning, and Foundation Model

Africa’s food security is increasingly threatened by climate change and population growth. High-resolution yield data are vital for precision agriculture and climate adaptation, yet much of the continent lacks sufficient monitoring due to limited ground data.This study presents first high-resolution (250 m), continent-wide maize yield prediction framework for 42 African countries and a novel yield disaggregation method using Net Primary Productivity (NPP) to spatially downscale national-level FAO yield statistics, creating fine-scale training data for supervised learning. A comprehensive feature set of 296 variables was constructed by integrating multi-source Earth observation, climate, and soil data. The framework evaluates multiple machine learning and deep learning models- including XGBoost, LightGBM, a hybrid deep neural network (HDNN), and, for the first time in this context, the Tabular Prior-data Fitted Network (TabPFN), a tabular foundation model. Using an expanding-window temporal cross-validation strategy, XGBoost achieved the highest temporal R² (0.78), while TabPFN demonstrated superior spatial generalization and the lowest mean absolute percentage error (MAPE ≈ 25%). Causal inference and ablation analyses underscored the predictive importance of vegetation indices (e.g., NDVI, NDWI), drought metrics, and soil properties. Model outputs showed strong alignment with FAOSTAT-reported national yields (R² > 0.75; MAPE ≈ 26–28%), highlighting the reliability of the proposed approach. Despite known limitations- such as reliance on proxy-based disaggregation and the use of coarse-resolution climate inputs- this work provides a novel and scalable framework for yield monitoring in data-scarce regions. It also marks the first application of tabular foundation models in continental-scale agricultural prediction, opening new directions for high-resolution, data-efficient crop yield prediction.