FORECASTING HUNGER BEFORE THE HARVEST: A SOVEREIGN, EARLY, AND UNCERTAINTY AWARE CROP YIELD AND FOOD SECURITY EARLY WARNING FRAMEWORK FOR CLIMATE EXPOSED IRRIGATED SYSTEMS, WITH AN ORIGINAL HYBRID OBSERVATION AND SIMULATION ENGINE (HOSIL)

Abstract

In 2025 the world received an unwelcome demonstration of how fragile its hunger warning infrastructure had become. The Famine Early Warning Systems Network, the gold standard that had for four decades forecast food crises months in advance, was abruptly taken offline in January when its single donor was dismantled, and although it resumed later in the year, the episode exposed a structural dependence that no food insecure country should accept. This paper argues that the appropriate response is not merely to restore the old arrangement but to build sovereign, low cost, scientifically rigorous national capacity for crop yield and food security forecasting that can survive donor shocks, that warns early enough to enable action rather than only to record disaster, and that is honest about its own uncertainty. We synthesize the established discipline of agricultural early warning and crop yield forecasting with the current methodological frontier, namely the fusion of process-based crop simulation models, machine learning, and Earth observation data assimilation, and with the operational practice of anticipatory action, in which pre-arranged financing is released automatically when forecast triggers are met. As the central contribution we propose an original system, not yet implemented, which we name HOSIL, a Hybrid Observation and Simulation Intelligence for Livelihoods. HOSIL assimilates Earth observation state variables derived from geospatial foundation model embeddings and multitemporal radar and optical data into a calibrated crop model, couples that mechanistic core with a label efficient machine learning correction trained partly on synthetic data, forecasts yield progressively through the season with shrinking and quantified uncertainty, translates the forecast distribution into a national food balance, and converts that balance into costed anticipatory action triggers. We specify the architecture and the rationale for each design choice, set out a validation design that could refute the approach, and analyze its failure modes without flinching, including the cost of false alarms, the blindness of crop models to conflict driven hunger, the capacity required for sovereign operation, and the moral hazard of automated triggers. We close with prioritized recommendations and a phased, buildable roadmap. The contribution is a defensible architecture for turning open data and open models into timely, accountable, and nationally owned warnings that are followed by action.