## 4 Conclusions  
While the evaluation of machine learning algorithms for digital SOC mapping has been a focus of several previous studies, it is essential to recognize that these studies often arrived at a predominant conclusion that the optimal machine learning algorithm and its hyperparameters are heavily dependent on the specific properties of the input soil samples. This dependency highlights the necessity for the improved evaluation of various algorithms in each study to ensure the most effective predictive approaches are employed. The multitude of available machine learning algorithms can lead to computational inefficiencies, particularly when conducting national-scale digital SOC mapping, a process that \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{
\begin{tabular}{c} **Rank per** \\ **WoSCC** \\ **Indexing** \\ \end{tabular} } & \multicolumn{2}{c}{**Accuracy Assessment Rank**} & \multicolumn{2}{c}{**Rank Difference**} \\ \cline{3-6}  & & **France** & **Czech** & **France** & **Czech** \\  & & **(Mainland)** & **Republic** & **(Mainland)** & **Republic** \\ \hline RF & 1 & 1 & 2 & 0 & \(-1\) \\ CUB & 2 & 11 & 12 & \(-9\) & \(-10\) \\ MLR & 3 & 10 & 10 & \(-7\) & \(-7\) \\ SVM & 4 & 13 & 7 & \(-9\) & \(-3\) \\ ANN & 5 & 5 & 6 & 0 & \(-1\) \\ PLS & 6 & 8 & 1 & \(-2\) & 5 \\ CART & 7 & 15 & 14 & \(-8\) & \(-7\) \\ GBM & 8 & 3 & 5 & 5 & 3 \\ QRF & 9 & 4 & 9 & 5 & 0 \\ XGB & 10 & 14 & 13 & \(-4\) & \(-3\) \\ KNN & 11 & 2 & 8 & 9 & 3 \\ GLM & 12 & 9 & 11 & 3 & 1 \\ GAM & 13 & 12 & 15 & 1 & \(-2\) \\ LASSO & 14 & 7 & 3 & 7 & 11 \\ LARS & 15 & 6 & 4 & 9 & 11 \\ \hline \hline \end{tabular} RMSE was used as a metric for ranking machine learning algorithms per accuracy assessment results.  
\end{table} 
Table 4: Relative accuracy assessment of the 15 evaluated machine learning regression methods for digital SOC mapping according to their frequency in previous studies indexed in the WoSCC. is crucial for informed land management decisions. This study aimed to address this gap by systematically assessing the prediction accuracy of 15 frequently used machine learning algorithms. To provide an extensive evaluation based on a robust dataset derived from diverse geographical regions, two independent study areas were used, including mainland France and the Czech Republic. The key conclusions from the study are as follows:  
* RF ranked first in France (mainland) and second in the Czech Republic for prediction accuracy, confirming its prominence in previous studies. RF should be prioritized for future evaluations in national-scale digital SOC mapping. * KNN and PLS achieved high prediction accuracy in France and the Czech Republic, respectively, but performed near average in other study areas. Their effectiveness depends on the quantity and distribution of input soil sampling data, warranting situational evaluation. * The ranking of GBM and KNN suggests they are underrated and should be more frequently considered in future digital SOC studies. * In contrast, CUB, MLR, and SVM were highly ranked in previous studies but did not justify their popularity based on this study's findings. * While France and the Czech Republic serve as representative European countries, the study's observations are limited. Future research should explore areas beyond human-made borders and consider more interpretable machine learning approaches. Conceptualization, D.R. ; methodology, D.R. ; software, D.R. ; validation, D.R. ; formal analysis, M.J.; investigation, D.R. ; resources, D.R. ; data curation, D.R. ; writing--original draft preparation, D.R. ; writing--review and editing, D.R., D.J., I.J. and M.J.; visualization, D.R. ; supervision, D.J., I.J. and M.J.; project administration, D.R. ; funding acquisition, D.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author.