Using the Global Agro-Ecological Zones (GAEZ), machine learning, and agent-based modeling approaches, this research simulated the trade network topologies of four major global staple food, i.e. wheat, corn, soy, and rice, for the years 2020 (the average of 2010-2040), 2050 (the average of 2040-2060), and 2080 (the average of 2070-2090) under multiple climatic and economic scenarios (see Methods). This entails utilizing the Global Agro-Ecological Zones (GAEZ) framework to simulate the production, machine learning to project the consumption, and an agent-based model to generate the trade networks of the aforementioned staple food commodities. For each of the four major staple food commodities, we constructed 60 trade networks which include 1) three years, i.e. 2020 2050, and 2080; 2) four representative concentration pathways (RCPs) scenarios, i.e. RCP 2.6 RCP 4.5 RCP 6.0 and RCP 8.5; and 3) five shared social-economic pathways (SSPs) scenarios, i.e. SSP1, SSP2, SSP3, SSP4, and SSP5. Each of the networks encompasses 179 world major countries, and the links of the network are the physical trade value measured in tones among these countries. The trade network topologies under each scenario is examined through the Ecological Network Analysis (ENA) approach (see Methods) to evaluate the agro-economic implications of the resilience and security of staple foods under future climate change scenarios. These future networks are also compared with historical network topologies from 1986 to 2019.
As changes in both the food production and consumption are affected by the aforementioned climate change scenarios, to meet the food demand of each country, the food trade network needs to be adjusted. Our results reveal that in comparison to 2019 the network resilience of all four staple foods in all scenarios are increasing for wheat (1.9%~4.2%), corn (0.4%~3.2%) and soy (27.3%~27.5%), while spanning a wide range for rice (4.7%~-6.9%). Contrary to other SSP scenarios, the SSP4 scenario will see an increase in the trade network resilience. These results indicate that under the extreme RCP scenarios, the food trade network needs to be more resilient to meet the food demand of all countries. Compared with wheat and corn, the current soy trade network needs to be adjusted the most to improve its resilience, while the future trade network for rice is still quite uncertain. These findings can enable new strategies inspired by network science for public policies relevant to the security and resilience of staple foods under future climate change.
