Validation of Telemetry System Indicators for Assessing the Operational Parameters of Mobile Aggregates

The purpose of the study is to evaluate the use of data from standard telemetry systems (onboard computers) to determine the operational and technological parameters of machine-tractor aggregates (MTAs), specifi cally productivity and fuel consumption, as well as to compare the accuracy of this data with results obtained through classical chronometry. Methods. Field studies were conducted with three tractors – a Claas Axion 930, a John Deere 8345R, and a Fendt 936 Vario – which were aggregated with an 8-furrow reversible plow. The work was performed on deep medium-loam chernozem soil while plowing at a depth of 30 cm and 35 cm. Operational parameters were recorded using two methods: 1) using the standard onboard computers (CEBIS, CommandCenter, Vario); 2) using a control method of continuous chronometry, which included direct time measurements with a stopwatch and determination of fuel consumption using an external calibrated measuring device. Results. A comparative analysis revealed discrepancies between the data from the onboard computers (OBCs) and the control measurements. Regarding productivity, the average deviation of the OBC data from the chronometry data was approximately ±8%. The largest deviation was recorded for the John Deere system (on average ~17%), while the Fendt system proved to be the most accurate (with deviations in the range of 0.4-4.5%). Regarding total fuel consumption, the average deviation was smaller, at approximately ±4.4%. The John Deere system showed the lowest error (1.6%), while the Claas system had the highest (6.8%). Conclusions. Standard telemetry systems are an important tool for monitoring MTAs; however, the data obtained can have signifi cant deviations from actual values. The results highlight the critical need for regular calibration and validation of OBC data before it is used for precise scientifi c tests, fi nancial accounting, or making critical management decisions. The use of uncalibrated data can lead to erroneous conclusions and a decrease in agricultural production effi ciency.