countries that are in need; then helping to allocate finance toward the districts that face the highest risks. The empirical application uses 10 years of sub-annual assessments on district-level food insecurity outcomes in 21 developing countries together with monthly covariates that capture known drivers of food insecurity. The predictions are generated using a Random Forest and a novel approach is deployed to adjust probabilistic predictions to optimize the forecasting objectives of the paper. The forecasts are validated, up to a full year ahead, against holdout data and the accuracy is compared to historical non-model-based outlooks. The results indicate that, particularly at longer forecasting horizons, model-based predictions compare favorably. The paper concludes that statistical models demonstrate good ability to detect future crisis outbreaks for a set level of tolerance to false alarms and that using them may help increase lead time for action.

This research contributes to understanding the ability of statistical models to anticipate future food crisis in the particular context supporting early interventions. The paper builds on several earlier efforts.<sup>2</sup> Mellor (1986) discussed prevention strategies with an emphasis on economic weakness, crop failure, and subsequent price signals as leading indicators of famine. Price signals have been discussed further in particular by Seaman and Holt (1980), and their statistical significance has been investigated empirically in the context of the Ethiopian famine of 1972-1974 (Cutler, 1984) and the famine of 1984-1985 in Niger (Khan, 1994). Seaman (2000) explored a dynamic modeling approach to assess risks based on rapid household survey data and simulations of coping strategies under income and food supply shocks. More recently, researchers have begun to use machine learning techniques for prediction. For example, Mwebaze et al. (2010); Okori and Obua (2011) presented attempts at predicting household famine in Uganda between 2004 and 2005 using household data.

The importance of using systematic approaches to predicting food crises and allocating humanitarian resources effectively is likely to increase in coming years as key drivers of food insecurity are expected to worsen into the 21st century. Historical achievements in eradicating poverty have largely coincided with substantial degradation of environments (Stern et al., 1996; Andr ́ee et al., 2019). Degrading environments, climatic extremes, and growing populations will continue to put pressure on future agricultural systems (Grainger, 1990; Ingram et al., 2010; Myers et al., 2017; Diogo et al., 2017). These developments place obstacles in the path to zero hunger, specifically for the rural poor that rely on local natural assets for income and food consumption (Duraiappah, 1998; Barrett and Bevis, 2015; Barbier and Hochard, 2018). A further benefit of model-based predictions of food insecurity is that they offer an alternative to the allocating of scarce resources based on qualitative assessments, instead relying on readily-verifiable and openly available data in a systematic way to predict the deterioration in food security when action is delayed.

The paper is structured as follows. Section 2 introduces the data, section 3 develops a framework for the validation and calibration of probabilistic predictions, paying particular focus to balancing false positives and false negatives. It then details an application using a Random Forest algorithm. Section 4 presents key results, and section 5 concludes. Additional results are found in the supplementary appendices.

> 2Apart from the food insecurity literature mentioned here, the paper also relates to the work of Celiku and Kraay (2017) who explore predicting conflict outbreaks in the similar context of weighted prediction loss functions.

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