CANBUS to drawbar load estimation: Mapping real-world tractor loads for mission profiling

With increasingly stringent requirements for greenhouse gas reduction, assessing current tractor drivetrains and developing cleaner alternatives are of growing importance. Designing such systems requires extensive field-load data, which remain difficult to obtain due to the absence of scalable, long-term measurement methods. This study presents a non-invasive methodology for evaluating tractor field loads using only controller area network (CANBUS) messages, universally available on modern tractors. A static mathematical model of a hydromechanical continuously variable transmission (IHMCVT) is developed, validated through experimental tests, and applied to map drawbar forces under real-world conditions. The model employs convergence-based calculations with multiple feedback loops and shallow neural networks to determine hydrostatic unit efficiencies. Validation against power-based measurements from road and field tests demonstrates accuracy within ±10%, suitable for practical applications. Applied to one year of operational data, the method reveals that tractors mainly operate at low speeds (<10 km h⁻¹) under heavy soil tillage and transport conditions. Specific fuel consumption mapping further highlights inefficiencies due to engine–transmission interactions. Overall, the proposed CANBUS-based model provides a reliable, scalable, and low-complexity approach for real-world mission profiling and future drivetrain optimization.