Financing agricultural drought risk through ex-ante cash transfers

Climatic conditions and household food security: Evidence from Tanzania

Food security and adequate nutrition are critical for achieving progress toward sustainable development. Two billion people worldwide experience moderate to severe food insecurity, and rates of hunger have increased over the past several years after declining steadily for decades. The FAO attributes this increase in large part to climate change, though empirical evidence on the relationship between climatic conditions and food security remains limited. We examine this question by linking nationally representative longitudinal data from four rounds of the Tanzania National Panel Survey to high-resolution gridded climate data. We then estimate a set of fixed effects regression models to understand the linkages between recent rainy season precipitation and temperature and two indicators of household food security: Food Consumption Score (FCS) and reduced Coping Strategies Index (rCSI). We find that low rainfall-particularly dry and cool conditions-is negatively associated with food security. Moving from a typical rainfall year to a particularly dry one increases the risk of being food insecure on both measures simultaneously by 13-percentage points. This suggests that a lack of rainfall impedes households' ability to access food, likely through reduced agricultural production and increased food prices, leading to lower dietary diversity and food shortages. Vulnerability is higher among households with fewer working age members, suggesting that households with a greater supply of labor can better withstand droughts. As climate change alters precipitation and temperature patterns over the coming decades, policies to increase resilience will be critical for improving food security, particularly among populations heavily reliant on agriculture.

Forecasting transitions in the state of food security with machine learning using transferable features

Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.

Translating large-scale climate variability into crop production forecast in Europe

Studies show that climate variability drives interannual changes in meteorological variables in Europe, which directly or indirectly impacts crop production. However, there is no climate-based decision model that uses indices of atmospheric oscillation to predict agricultural production risks in Europe on multiple time-scales during the growing season. We used Fast-and-Frugal trees to predict sugar beet production, applying five large-scale indices of atmospheric oscillation: El Niño Southern Oscillation, North Atlantic Oscillation, Scandinavian Pattern, East Atlantic Pattern, and East Atlantic/West Russian pattern. We found that Fast-and-Frugal trees predicted high/low sugar beet production events in 77% of the investigated regions, corresponding to 81% of total European sugar beet production. For nearly half of these regions, high/low production could be predicted six or five months before the start of the sugar beet harvesting season, which represents approximately 44% of the mean annual sugar beet produced in all investigated areas. Providing early warning of crop production shortages/excess allows decision makers to prepare in advance. Therefore, the use of the indices of climate variability to forecast crop production is a promising tool to strengthen European agricultural climate resilience.