This study proposes a data-efficient surrogate modeling approach for predicting hydrostatic transmission (HST) system efficiency in tractors using minimal data. Only 27 samples were selected from a dataset of 5092 measurements based on the minimum, mean, and maximum values of the input variables (input shaft speed, HST ratio, and load), which were used as the training data. A hybrid prediction model combining deep kernel learning and a residual radial basis function surrogate was developed with hyperparameters opt…