2.5.3.1 Food security

Systematic early warning for food security applications has been in existence since at least the 1980s (Funk et al., 2019). These systems have helped avert catastrophic food crises, such as during the 2017 drought in Kenya. In this particular case, the drought was analogous to the crisis of 2011, but sufficient early warning and early action reduced humanitarian needs for 500,000 people – demonstrating the potential of early warning systems to trigger response (Funk et al., 2018). The most simple systems focused on translating climate parameters such as rainfall anomalies into predictions of crop production (and, indirectly, impacts on food security). Food security early warning systems have developed to include other considerations in forecasting food insecurity, such as political instability, fluctuations in food prices, labour availability and violent conflict. As technologies and methods to predict different triggers of food insecurity become increasingly available, predictions of food crises and famine will also improve.

Food security can change seasonally. As such, it does not exhibit traditional bifurcation in the sense of irreversibility. A permanent change towards a state of food insecurity would be catastrophic, representing a permanent food crisis. Krishnamurthy, Choularton and Kareiva 2020 offer a framework to identify “transitions” as prolonged periods of food insecurity using the Integrated Food Security Phase Classification (IPC), the leading global metric for standardised food security assessment which combines data on agricultural production, food prices, nutrition rates, weather patterns and other variables to determine the general food security situation in a given location based on five classes (1: minimal food insecurity, 2: stress, 3: crisis, 4: emergency, 5: famine) (Figure 2.5.3). With these metrics, a tipping point in a food system can be thought of as a shift between periods with low food insecurity (IPC 1 or 2) to periods of sustained food crisis (IPC 3 or higher) (see Figure 2.5.3 for an illustration of this concept).

An example of a potential tipping point using the IPC categories is found in East Africa after the 2015/2016 El Niño episode. Usually El Niño events yield extended autumn rains in East Africa, which is beneficial for livestock grazing (Korecha and Barnston, 2007). This was not the case for the 2015/2016 event, which saw anomalously low rainfall in both the summer and autumn. This trend, combined with insufficient drought preparedness, resulted in crop failures and livestock mortality – and consequently a depletion of livelihood assets, food stocks and overall food security in northern and eastern regions of Ethiopia (Figure 2.5.3).

Building on this approach, Krishnamurthy et al., (2022) were able to detect transitions in food security states by integrating lag-1 autocorrelation statistics into remotely sensed observations from the SMAP mission with food prices. The research reported dramatic improvements in anticipating the timing and intensity of food crises across arid, semi-arid and tropical regions, suggesting universality in the approach. The analysis highlights the potential to use elements of tipping point theory in social systems. In this particular context, the approach showed improvements in predictions of impending food crises, with a lead time of up to three to six months – a sufficient period to mount a humanitarian operation. The trigger based on lag-1 autocorrelation of soil moisture anticipates the timing of the transition and the magnitude of the food security change among small to large transitions, both into and out of crises (Figure 2.5.4).