We do not attribute likelihoods to those events but use them to explore further

We do not attribute likelihoods to those events but

This preview shows page 35 - 36 out of 182 pages.

identify extreme events for further analysis. We do not attribute likelihoods to those events but use them to explore further dynamics of the particular country production losses and how they impact on global food systems. To calculate probabilities of such events and the social consequences will require further analysis of the underlying distribution as well as assigning probabilities to the social responses to those events. This is out of the scope of the current paper. Figure 3 shows the distribution of percentage anomalies for country level production as a contribution to global production away from trend. Due to the very small standard deviation compared to the furthest outliers in Figure 3, we use a logarithmic scale to represent the shape of the fat-tailed distribution. Although at global level (Figure 3) we see outliers where production deviating from a country trend line amounts to nearly ´2% off global production, in fact the largest shock here is ´3.45%, but these outliers are not displayed due to the logarithmic scale. Therefore, we see that a few events have contributed to significant shocks. We then classify the particular countries that fall outside the 3-sigma event and the year in which the shock occurs. This classification allows us to produce a list of countries that we highlight as having experienced a production shock themselves or those that contributed to a shock in global production. The years that particular countries experienced production shocks given the two criteria are listed in Tables 2 and 3. Table 2 shows the countries experiencing significant shocks relative to their own production are usually not major producers. It instead highlights countries that experience the most variability in their annual food production are more likely to be smaller producers with less infrastructure and processes to manage major weather or political events that impact food production. These countries are less secure in their food availability and are more likely to need to increase their dependency on imports during these times of shock. Under usual conditions this may not represent a significant challenge however, if finance was a problem or their shocks coincided with a global shocks or import restrictions then this could represent a major potential impact on those countries. Furthermore, many of the nations identified are African and Middle Eastern states, arguably some of the most unstable parts of the world. There are production shocks in these nations in 1995, 1997, 1998, 1999, 2000, 2002, 2005, 2007 and 2008. If a major producer experienced a food production shock of 58% it would have catastrophic impacts on the global food supply system. Table 3 shows the countries that experience the largest shocks relative to global production and are, as expected, the biggest producers.
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