Scaling Spatiotemporal Transformers for Regional Food Security: A Tri-Stream Latent-Dynamic Approach to Pre-Harvest Yield and Price Forecasting

Pre-harvest yield forecasting and price stability prediction are critical for food security planning in climate-vulnerable regions, yet existing approaches struggle to integrate heterogeneous data streams spanning satellite imagery, historical agricultural statistics, and socioeconomic indicators. We present the Tri-Stream Latent-Dynamic Transformer (TLDT), a novel deep learning architecture that unifies spatial-spectral features from Sentinel-1/2 satellite image time series, temporal phenological dynamics, and exogenous economic signals through a purpose-designed cross-attention mechanism. Unlike conventional fusion strategies that treat data streams independently, our Tri-Stream Cross-Attention module employs spatial features as queries, economic context as keys, and temporal phenology as values, enabling the model to learn how market conditions modulate the relationship between observed crop growth and final yields. We validate TLDT on a comprehensive dataset covering 24 locations across Cameroon, Chad, and Nigeria in the Lake Chad Basin from 2018–2023, incorporating Sentinel-1/2 imagery from Google Earth Engine, CHIRPS rainfall data, FAO yield statistics, and WFP market prices. On held-out test data, TLDT achieves an RMSE of 0.244 t/ha for yield prediction (representing a 2.9% improvement over LSTM baselines and 9.3% over standard Transformers) with an R2 of 0.197, while simultaneously attaining 93.2% accuracy and 0.933 weighted F1-score on three-class price stability classification (Stable, Vulnerable, Crisis). Our Adaptive Positional Encoding component effectively handles the irregular temporal sampling inherent in cloud-affected optical satellite data. The integrated Harvest-Expectation Index provides interpretable 90-day pre-harvest forecasts suitable for early warning systems. These results demonstrate that tri-stream fusion with economic context awareness substantially improves predictive performance in data-sparse, conflict-affected agricultural regions, offering a scalable framework for operational food security monitoring.