As the complexity of our food systems increases, they also become susceptible to unanticipated natural and human-initiated events. Commodity trade networks are a critical component of our food systems in ensuring food availability. We develop a generic data-driven framework to construct realistic agricultural commodity trade networks. Our work is motivated by the need to study food flows in the context of biological invasions. These networks are derived by fusing gridded, administrative- level, and survey datasets on production, trade, and consumption. Further, they are periodic temporal networks reflecting seasonal variations in production and trade of the crop. We apply this approach to create networks of tomato flow for two regions – Senegal and Nepal. Using statistical methods and network analysis, we gain insights into spatiotemporal dynamics of production and trade. Our results suggest that agricultural systems are increasingly vulnerable to attacks through trade of commodities due to their vicinity to regions of high demand and seasonal variations in production and flows.