Soil organic carbon (SOC) concentration is an essential factor in biomass production and soil functioning. SOC concentration values are often obtained by prediction but the prediction accuracy depends much on the method used. Currently, there is a lack of evidence in the soil science literature as to the advantages and shortcomings of the different commonly used prediction methods. Therefore, we compared and evaluated the merits of the median approach, analysis of covariance, mixed models and random forests in the context of prediction of SOC concentrations of mineral soils under arable management in the A-horizon. Three soil properties were used in all of the developed models: soil type, physical clay content (particle size <0.01 mm) and A-horizon thickness. We found that the mixed model predicted SOC concentrations with the smallest mean squared error (0.05%2), suggesting that a mixed-model approach is appropriate if the study design has a hierarchical structure as in our scenario. We used the Estonian National Soil Monitoring data on arable lands to predict SOC concentrations of mineral soils. Subsequently, the model with the best prediction accuracy was applied to the Estonian digital soil map for the case study area of Tartu County where the SOC predictions ranged from 0.6 to 4.8%. Our study indicates that predictions using legacy soil maps can be used in national inventories and for up-scaling estimates of carbon concentrations from county to country scales.