## 3 Results and Discussion
Also, PLS had a high accuracy assessment rank and a moderate WoSCC ranking, resulting in a positive rank difference of 5 in the Czech Republic. The findings of this study on digital SOC mapping must be interpreted within the context of the specific limitations associated with the research design. The analysis was conducted in two geographical regions--France (mainland) and the Czech Republic--employing soil samples from the LUCAS 2018 dataset as input data. Notably, the sample size in the two administrative units used as study areas, France (mainland) and the Czech Republic, may not sufficiently capture the broader variability of soil types across these regions, potentially impacting the generalizability of the results. Furthermore, the selected study areas may not adequately represent the full spectrum of soil characteristics and environmental conditions present in either country, thereby limiting the applicability of the findings to other regions. The restricted sample sets utilized in this study are a critical factor that may skew the results, as these were related to national borders and not natural phenomena [63]. A limited dataset can lead to an inadequate generalization of the findings, possibly underrepresenting the variability found in larger populations [64]. Given the heterogeneous nature of soil properties, geographic diversity, and climate influences, the results may differ substantially with a larger and more diverse sample. This raises the concern that the performance of the employed algorithms may not be fully reflective of their efficacy in varied contexts. Moreover, data collection from two distinct countries introduces an additional layer of complexity. Different geographical areas can exhibit distinct soil formation processes, types, and land use practices, which can inherently influence the SOC levels. Consequently, the algorithms developed and validated in this study may exhibit biases when applied to other settings, particularly if trained solely within a single national context. Despite these limitations, the study's insights into the various machine learning algorithms are valuable. The findings suggest that no universally superior algorithm exists for SOC mapping; instead, the effectiveness of each algorithm is contingent upon specific data characteristics and contextual factors. This multiplicity of performance emphasizes the importance of employing an ensemble approach or a suite of algorithms tailored to distinct scenarios in future research. Further studies should prioritize expanding the dataset both in size and geographical representation. Incorporating data from a broader array of countries would enhance the robustness of the results and allow for the identification of global trends in SOC mapping accuracy. Additionally, employing rigorous methodologies that encompass diverse soil types, climates, and land use practices will yield more reliable outcomes. Future research should also focus on the intrinsic factors influencing machine learning algorithm performance, as well as their interpretability [65]. Future studies will also explore the effects of the selection of individual machine learning models in ensemble machine learning, which, in previous studies, had the potential of producing a superior prediction accuracy to individual methods [66; 67].