  International economics has a long history of improving our understanding of
factors causing trade, and the consequences of free flow of goods and services
across countries. The recent shocks to the free trade regime, especially trade
disputes among major economies, as well as black swan events, such as trade
wars and pandemics, raise the need for improved predictions to inform policy
decisions. AI methods are allowing economists to solve such prediction problems
in new ways. In this manuscript, we present novel methods that predict and
associate food and agricultural commodities traded internationally. Association
Rules (AR) analysis has been deployed successfully for economic scenarios at
the consumer or store level, such as for market basket analysis. In our work
however, we present analysis of imports and exports associations and their
effects on commodity trade flows. Moreover, Ensemble Machine Learning methods
are developed to provide improved agricultural trade predictions, outlier
events' implications, and quantitative pointers to policy makers.
