Identifying agricultural disaster risk zones for future climate actions

economic losses this also includes regions that import more from the affected countries than they export and produce locally such is the case in Sub-Saharan Africa while other countries will experience indirect economic losses due to trade relations with the affected countries. The total economic losses are the sum of the direct and indirect economic losses. For each single extreme event scenario, I investigate its global economic impact by comput- ing its production value and consumption value losses by using the expression, PVL ¼ BPV PVF ð1Þ and CVL ¼ BCV CVF; ð2Þ where PVL = Production value losses, BPV = Baseline production value, PVF = Production value under forcing, CVL = Consumption value losses, BCV = Baseline consumption value, CVF = Consumption value under forcing. We should note that, since we are computing losses, negative values imply production value under force is higher than baseline production value. Signifying a rise in production/consumption value while positive values imply a drop. 3 Results Let us begin this section by looking at the top ten (10) agricultural producing regions of the world in 2018 in terms of the total crop, livestock, and total agricultural output as shown in Fig 3. Here, the share of aggregated agricultural production for the year 2018 is presented. This fig- ure shows top agricultural producing countries. (a), (c), and (e) are bubble maps showing top crops, livestock, and total (crop + livestock) producing regions while (b), (d), and (f) are bar

charts indicating the top ten crop, livestock, and total (crop + livestock) producing regions. Countries such as China, India, Brazil, the USA, Indonesia, Thailand, Russia, Nigeria, Argen- tina, and Vietnam lead in crop production while China, India, the USA, Brazil, Russia, Mexico, Pakistan, Japan, Germany, and Indonesia lead in livestock production. Climate-related disas- ters that affect any of these breadbasket regions, will have significant regional and global reper- cussions. To assess and identify the most vulnerable regions to climate disasters, the median production value losses when concurrent extreme weather events hit the agricultural sectors of the EU, the USA, and China are computed. Comparing these production losses to the degree of connectivity of a particular region in the global supply chain network (Table 1), we see that India with smaller production output and degree of connectivity compared to China, has a much larger share of the production losses even when it is not directly hit by the extreme weather event. This might be due to their greater import of agricultural products from these directly affected breadbasket regions than China. A similar scenario is observed between the United States and Brazil as Brazil turns to import more losses than the United States that is directly affected. Similarly, Nigeria with a much larger production output but with a smaller degree of connectivity in the global supply chain network suffers a lesser share of losses when compared to South Africa and Canada which are both highly interconnected regions. In gen-

eral, this table shows that regions with a larger share of production output and a degree of con- nectivity above 1.0, suffer a greater share of the production losses. The case of countries such as India, Brazil, Russia showing a higher share of production value losses than the directly affected USA and China, tells us that, regions that depend more on the import of agricultural products from these directly hit breadbasket regions to meet their food demand such as the Sub-Saharan African countries are the most vulnerable regions to climate-related disasters. The Median production and consumption value losses over all concurrent forcing scenarios are presented in Fig 4. Here, the top 10 countries with the most production value losses (a) and PLOS ONE Identifying agricultural disaster risk zones for future climate actions PLOS ONE | https://doi.org/10.1371/journal.pone.0260430 December 2, 2021 6 / 16