Identifying agricultural disaster risk zones for future climate actions

events as reported in another research [40]. This individual forcing symbolizes the occurrence of an extreme weather event either over the US, EU, or China. Secondly, the agricultural sec- tors of two regions are equally simultaneously shocked such as the US and EU, US and China, and the EU and China representing the occurrence of concurrent extreme events over two breadbasket regions. Finally, the agricultural sectors of all three regions simultaneously are perturbed with same forcing strength. The simulation run time is 30 days signifying the dura- tion of the extreme events. For each case, the direct agricultural production and consumption value losses in the directly affected regions and globally are computed. Comparative studies of the impact of each forcing scenario are also carried out. This helps in assessing which forcing has the greatest economic repercussions. To uncover which regions are more vulnerable to production value losses, the production value losses are mapped to the quantity of agricultural production and the degree of connectivity in the global supply chain network by making of the bilateral and multilateral trade network data from the World Integrated Trade Solution (WITS) and the FAO data. Direct economic losses Since the agricultural sector is directly hit by extreme weather events such as heat stress and extreme precipitation, the effects of these events on crop growth and productivity, and the mental health of farmers are often severe. This sector will, therefore, experience direct eco-

nomic losses such as multiple harvest failures and farmers’ inefficiency due to poor mental health. These damages may also flow directly from insufficient product quality [41]. Hence, direct economic loss includes ordinary loss of bargain damages which is the difference between the actual value of the goods accepted and the value they would have had if they had been as warranted [41]. Regions that are directly hit by these disasters will experience direct Fig 2. A screenshot of the structure of the WITS total trade network for 2016. Which is the most recent on the site WITS. Link thickness is proportional to country export share while the node size (Weighted in degree) is proportional to the relevance of each country as a buyer in the selected sub-network. Country names are in ISO3 format. This figure is used for the purpose of illustration. https://doi.org/10.1371/journal.pone.0260430.g002 PLOS ONE Identifying agricultural disaster risk zones for future climate actions PLOS ONE | https://doi.org/10.1371/journal.pone.0260430 December 2, 2021 5 / 16