## 3 Results and Discussion  
The median SOC values for the raw soil sampling datasets range from 19.9 to 23.0, indicating a high level of variability (Table 2). However, the coefficient of variation (CV) reveals a higher degree of dispersion in the entire LUCAS 2018 dataset (1.72) compared to the two study areas (0.90 and 1.07). The skewness and kurtosis values suggest that the distributions are positively skewed and leptokurtic, with the France (mainland) dataset exhibiting the most extreme values (skewness = 4.88, kurtosis = 37.72). Overall, the descriptive statistics indicate that the entire LUCAS 2018 dataset has a more dispersed and variable distribution compared to the two study areas used in this study. However, it should be considered that input samples for the two study areas were preprocessed, in contrast to the raw observations in the LUCAS 2018 dataset, achieving a notably lower CV, as well as a more normal SOC value distribution, as indicated by the skewness and kurtosis values. The accuracy assessment results suggest that RF and QRF perform with superior accuracy compared to the other evaluated methods on both datasets, with R\({}^{2}\) values of 0.411 and 0.409, respectively, for France, and 0.249 and 0.223, respectively, for the Czech Republic (Tables 3 and A1). There are several possible causes for their outperformance, one of them being their ability to effectively reduce overfitting by averaging the predictions from many trees. Since training on each tree was performed on different subsets of the data and features, this decreased the correlation between individual trees and enhanced its ability to generalize to unseen data [57]. Managing high-dimensional datasets with numerous features, typical for digital soil mapping studies [58], was automatically performed through feature selection by considering only a random subset of features for each split in the decision trees. Additionally, the ensemble nature of RF makes it resilient to noise and outliers in the data [59]. In contrast, GAM and CART exhibited a poor performance, with R\({}^{2}\) values of 0.373 and 0.307, respectively, for France, and 0.135 and 0.152, respectively, for the Czech Republic. Overall, the results suggest that ensemble methods (RF, QRF, and GBM) tend to outperform individual models (ANN, SVM, and MLR) on these datasets. This observation agrees with previous studies [10] but also suggests that methods like ANN and SVM tend to heavily depend on the quantity and properties of input soil sampling datasets [60]. The accuracy metrics were generally in agreement, with RF and QRF consistently ranking among the top three algorithms in both countries. However, there are some discrepancies between the metrics, with RMSE and NRMSE suggesting that KNN and GBM were also highly accurate, while R\({}^{2}\) and MAE indicate that SVM and PLS are among the top performers. The results suggest that ensemble methods, such as RF and GBM, tend to outperform traditional algorithms, such as MLR and GLM, in both study areas. Accuracy assessment metrics were also in slight disagreement regarding which algorithms are the most accurate; while \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Preprocessed** & \(\mathbf{n}\) & **Median** & **Min** & **Max** & **CV** & **Skewness** & **Kurtosis** \\ \hline France & No & 2731 & 23.0 & 3.2 & 473.0 & 1.07 & 4.88 & 37.72 \\ (mainland) & Yes & 2514 & 25.6 & 3.2 & 74.1 & 0.59 & 1.08 & 0.56 \\ \hline Czech & No & 445 & 19.9 & 3.2 & 208.6 & 0.90 & 3.46 & 15.60 \\ Republic & Yes & 400 & 21.2 & 3.2 & 51.2 & 0.44 & 1.19 & 1.03 \\ \hline Entire LUCAS 2018 & No & 18,984 & 21.8 & 2.1 & 723.9 & 1.72 & 3.97 & 16.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics of SOC values from the two study areas and the entire LUCAS 2018 dataset. QRF has the highest R\({}^{2}\) value in mainland France, it has a lower R\({}^{2}\) value than RF in the Czech Republic.