The density of soil observations is a major determinant of digital soil mapping (DSM) prediction accuracy. In this study, we investigated the effect of soil sampling density on the performance of DSM to predict topsoil particle-size distribution in the Mayenne region of France. We tested two prediction algorithms, namely ordinary kriging (OK) and quantile random forest (QRF). The study area is a region of ~5000 km2 with the highest density of field soil observations in France (1 profile per 0.64 km2). The number of training sites was progressively reduced (from n = 7500 to n = 400, corresponding to 1 profile per 0.7 km2 to 1 profile per 13 km2) to simulate the different density of observations. For OK and QRF, we tested random subsampling for splitting the data into training and testing datasets using k-fold cross validation. For QRF we also tested conditioned Latin hypercube sampling based on the point coordinates or the covariates. The results indicated that, with increasing density of observations, OK performed as well or even better than QRF, depending on the particle-size fraction. For silt prediction, OK was systematically better than QRF. However, the prediction intervals were much larger for OK than for QRF, and OK did not seem to estimate uncertainty correctly. Overall, the performance indicators increased with the density of observations with a threshold at about 1 profile per 2 km2 which suggests that the main limitation of DSM prediction accuracy using QRF is the amount of data collected in the field, not the type of calibration sampling strategy. Future DSM activities should focus on gathering more field observations.