Managing agricultural processes with significant logistics sub-processes is a challenge because coordinating a distributed fleet in a dynamic environment is difficult without proper oversight in terms of qualitative and quantitative process information. Digital assistance systems are thought to aid agricultural practitioners by providing process-related information and thus support operational decision-making or even control the logistic flow (semi-)automatically. However, their development is currently stifled by a lack of monitoring capabilities during process execution. This thesis concerns the topic of online process monitoring for ongoing agricultural logistic processes. It discusses how to extract process knowledge from the telemetry of agricultural machines by applying spatio-semantic reasoning techniques. Our method combines spatial analysis for identifying spatial relationships between machines and their environment with semantic inference to derive formal process knowledge through ontological and rule-based reasoning. To test our concepts, we implemented a domain-agnostic semantic mapping framework and applied it in the context of forage maize harvesting. We present custom-made ontological models and rules to represent agricultural environments and to reason about machine actors and their process states. Based on our prototype, we demonstrate how to implement automated process and service tracking in near-real-time. Finally, we discuss the role of online process analytics systems in the context of other agricultural assistance systems for farm and fleet management.