Identifying agricultural disaster risk zones for future climate actions

FAO. This work is new as a strong dependency is found between the agricultural production value losses of a region to its connectivity in the global trade network. The rest of the section is as follows, in section 2, the source of the FAO data used, the bilateral global trade network data, the EORA economic network, and the agent-based economic model Acclimate are pre- sented. The method of computing economic production and consumption value losses from their baseline production/consumption is presented and discussed. In section 3 and 4, the results of the numerical experiment are equally presented and discussed and a conclusion in section 5. 2 Materials and methods Data The agricultural data set used in this study are from the Food and Agriculture Organization of the United Nations (FAO), freely available online at https://www.fao.org/faostat/en/#data/ QCL. This data set covers all crops and livestock primary production quantities in tonnes for the year 2018. The year 2018 was selected to model the global impact of the 2018 heatwave. The list of Crops and Livestock primary used in this study and covered by the FAO are pre- sented in Tables 4 and 5 in S1 File respectively while the data visualization is presented in Fig 3 in the form of a bubble map and bar charts. The bilateral and multilateral global trade network data used are from the World Integrated Trade Solution (WITS) freely available online at WITS with the United States as the Reporter for the year 2016 (most recent on the website) aggregated over all products. Here, export is

considered for trade flow at a threshold of 0.01 with the buyer as a viewpoint. The Buyer’s viewpoint shows the role of each country as a source of demand in the selected sub-network. The node size (Weighted in degree) is proportional to the relevance of each country as a buyer in the selected sub-network which we also consider here as the degree of entanglement in the global supply chain network. A sample structure of the WITS network is shown in Fig 2. Acclimate model Acclimate is an agent-based economic model that simulates the propagation of production losses induced by local demand, supply, or price shocks in the global supply network. Its global economy is assumed to be demand-driven with nodes in a complex network of trade and sup- ply relations representative of firms (or regional sectors) and consumers as economic agents. Being based on local optimization principles, the model accounts for local price effects such as demand surges which are important for a comprehensive assessment of the total costs of disas- ters. The full description of the model is found in the paper by Otto et al [23]. This model is made up of highly interconnected regional sectors with regions representing each country in the world and the sectors are the various economic sectors that make up the economy of a country such as the agricultural sector, Food, Hotels, and Restaurants, Wholesale trade, Oil and Gas, Wood, Transport, Finance, Mining, and quarrying, etc. The economic network used in this study is the Eora26 2013 economic network which consists of 15,909 sectors across 188

countries. The multi-regional Input-Output data describe annual monetary flows between 26 major sectors and final demand in 188 countries. More about the Eora global supply chain database can be read here https://worldmrio.com/. To simulate the spreading of economic losses caused by concurrent extreme weather events in the agricultural sector, a particular case where the extreme weather events occur near-simul- taneously across very important agricultural regions of the world is assumed. The agricultural sectors of the US, EU, and China individually are shocked with a 4% forcing strength corre- sponding to a regional average reduction in crop production of 4% under these blocking PLOS ONE Identifying agricultural disaster risk zones for future climate actions PLOS ONE | https://doi.org/10.1371/journal.pone.0260430 December 2, 2021 4 / 16