Identifying agricultural disaster risk zones for future climate actions

while Willner et al [25] showed that the total economic losses due to fluvial floods will increase in the next 20 years globally by 17% despite partial compensation through market adjustment within the global trade network. Kuhla et al [38] equally using Acclimate, recently showed that output losses due to heat stress alone are expected to increase by about 24% within the next 20 years if no additional adaptation measures are taken. The concepts of agricultural losses in a telecoupled world [20, 26, 35–37, 39] and making use of Acclimate [23–25, 38] for simulating shock propagation in the global supply chain net- work are not new. But combining data sets from the FAO, WITS, and Acclimate to identify agricultural vulnerable regions before climate-related disasters occur is what is unique in this work. To adapt this phenomenon to a real-life scenario, the economic impacts of the 2018 European heatwave are considered. These extreme weather events in early summer 2018 were found to be connected by a recurrent hemispheric wave-7 pattern [2]. These heatwaves cov- ered North America, Western Europe, and the Caspian Sea region, and there were also a lot of rainfall extremes in South-East Europe and Japan that occurred near-simultaneously. Researchers equally found that two or more weeks per summer spent in these waves events have been found to associate with a 4% reduction in crop production when averaged across the affected mid-latitude regions, with regional decreases of up to 11% [40]. To model the eco-

nomic impact of this climate disaster scenario, a special case where the extreme weather events induce a 4% reduction in agricultural production per month spent under the extreme events when averaged over the affected breadbasket mid-latitude regions (Fig 1) is considered. To assess and identify regions at risk, the production and consumption value losses for seven dif- ferent forcing scenarios including three single extreme events(USA, EU, and CHN) and four concurrent extreme events (EU-US, EU-CHN, US-CHN, and ALL (EU-US-CHN)) are com- puted and compared. Where USA is the single extreme event over the USA and USA-CHN represents concurrent extreme event over the USA and China. The degree of connectivity of a particular region in the global supply chain network is associated to its share of production value losses by making use of the bilateral and multilateral trade network data WITS and the Fig 1. The major breadbasket regions under study and regions affected by the defined events. This figure was generated with the Mapping package in python. https://doi.org/10.1371/journal.pone.0260430.g001 PLOS ONE Identifying agricultural disaster risk zones for future climate actions PLOS ONE | https://doi.org/10.1371/journal.pone.0260430 December 2, 2021 3 / 16