This paper demonstrates the application of a recently developed methodology, the combination of directed acyclic graphs (DAGs) with Bernanke structural vector autoregression (VAR) models, to model a system of U.S. commodity-related and value-added markets. As an example, the paper applies this methodology to a quarterly system of U.S. markets: the wheat market and a set of downstream milling and bakery markets that use wheat as an input. Analyses of the model's impulse response simulations and forecast error variance decompositions provide updated estimates of market elasticity parameters that drive these markets, and updated policy-relevant information on how these quarterly markets run and dynamically interact. Results suggest that movements in commodity-based markets strongly influence each other, although most of these effects occur in the long run beyond a single crop cycle. The paper illuminates how important U.S. food prices respond to wheat farm market shocks in price and quantity.