The relationship between events in the food supply chain can be modelled as a graph and can be used to perform some measurements (i.e. risk - a notion of a possible undesired outcome). However, performing such measurements without complete and relevant information increases uncertainty and leads to bad decisions. Meanwhile, provenance is a record of what actually occurs in a set of processes, including any aspect that contributes to those events. By integrating provenance, we gain benefits including increased relevance and better contextual decision-making. Capturing the provenance of something can be done with PROV, as a standard provenance language, and can also be expressed as a Provenance Graph (PG). Potentially, uncertainty will be reduced, allowing more reasonable and relevant decisions. In this work, we introduce the integration of provenance with uncertainty and risk in a set of processes to capture relevant and contextual information for better decision making.