A scalable, end-to-end IoT and remote sensing platform for precision rangeland and livestock management

• Scalable end-to-end data pipeline for precision livestock and grazing management on extensive rangelands. • Cloud based platform fusing diverse IoT sensors with satellite remote sensing data. • Machine learning models classify cattle behavior and events from movement and location patterns. • Decoupled architecture supports independent scaling of ingestion, analytics, and AI. • Empirical validation performed at multiple sites: 134 M + records across 202,000 + acres of rangeland. Efficient livestock grazing management across vast, arid rangelands is challenging due to resource scarcity, environmental variability, and labor-intensive monitoring. Precision agriculture has successfully integrated Internet of Things (IoT) devices and remote sensing technologies for farm management; however, precision ranching, the counterpart for extensive livestock production on rangelands, remains underdeveloped. This gap is largely due to the large spatial extent and harshness of these environments, which limit connectivity, impose strict power constraints on distributed IoT devices, and generate high-volume, heterogeneous data streams that are difficult to collect, transmit, and analyze at scale. To address this gap, this study aimed to design and operationally validate a scalable, modular, and vendor-agnostic system architecture for precision ranching that integrates IoT sensors, satellite imagery, and artificial intelligence (AI)-driven analytics to support rangeland and livestock management across large-scale ranching operations. The resulting platform employs a multi-tier architecture designed to collect, process, and present data from distributed IoT devices and satellite imagery efficiently. Its core functionalities include near real-time livestock tracking, vegetation assessment, and environmental monitoring. Predictive analytics are used to optimize resource allocation, detect potential animal health issues, assess environmental risks, and support day-to-day decision-making. By addressing the scalability and computational challenges inherent to extensive operations, the platform establishes a practical framework for data-driven and sustainable rangeland and livestock management. The system was deployed across beef cattle operations spanning more than half a million acres in four states. The platform demonstrated potential to improve operational efficiency, reduce labor and associated costs, and support animal welfare.