Achieving precise, real-time localization for autonomous robots in large-scale, remote outdoor environments remains a significant challenge. Traditional Real-Time Kinematic (RTK) Global Navigation Satellite Systems (GNSS) rely heavily on continuous internet or cellular connections to transmit correction data for accurate localization. This dependence makes them power-intensive, expensive, and often infeasible in rural or remote regions with limited digital infrastructure. To overcome these limitations, we present a novel, unified network architecture that integrates RTK GNSS correction services with Long Range Wide Area Network (LoRaWAN) technology, enabling scalable, secure, and energy-efficient robot navigation. The proposed Integrated RTK-LoRaWAN Network is designed to broadcast centimeter-accurate RTK correction messages over long distances using custom LoRaWAN gateways. These gateways support both high-frequency RTK data transmission and low-frequency IoT telemetry, eliminating the need for costly or unavailable internet backhaul for robot navigation. By leveraging LoRaWAN’s long-range and low-power properties, this system significantly reduces energy consumption while maintaining high real-time localization accuracy, which is critical for autonomous robotic operations in agriculture, construction, and other field applications. Field validation demonstrates a centimeter-level accuracy on par with WiFi-based RTK systems, while achieving a communication range exceeding 2 km, significantly longer than typical WiFi coverage. The proposed architecture emphasizes scalability, supporting dense networks of robots and sensors, with security addressed through LoRaWAN's native encryption and authentication. This work positions RTK-LoRaWAN as a viable foundation for a next-generation digital agriculture infrastructure, supporting precision automation while bridging connectivity gaps in underserved rural areas. By uniting two traditionally distinct technologies (RTK and LoRaWAN) in different applications, this system lays the groundwork for more inclusive, cost-effective, and intelligent field robotics.