Evolution and Resilience of agri-food trade network

In the complex global food system, the dynamics associated to international food trade became key determinants of foodsecurity. In this paper, we combine a community detection approach with a supervised learning technique in order to describeboth the evolution and the resilience of agri-food trade network. Using a large amount of data, we detect similarities betweencountries and we identify the main drivers that affect trade dynamics over time. The analysis is completed also including thevariability on food production and trading due to the climate conditions’ evolution. Results emphasize how the topologicalstructure of the network is evolving over time and how it is modifying the clusters composition. Interestingly, we identifysustainability, climate and food nutrition aspects between the emerging factors in the explanation of trade relationships.