Integrating Environmental Variables into Geostatistical Interpolation: Enhancing Soil Mapping for the MEDALUS Model in Montenegro

Geostatistical methods are important in analyzing natural resources providing input data for complex mathematical models that address environmental processes and their spatial distribution. Ten interpolation methods and one empirical-based classification grounded in empirical knowledge, with a total of 929 soil samples, were used to create the most accurate spatial prediction maps for clay, sand, humus, and soil depth in Montenegro. These analyses serve as a preparatory phase and prioritize the practical application of the obtained results for the implementation and improvement of the MEDALUS model. This model, used to assess sensitivity to land degradation, effectively integrates into broader current and future research. The study emphasizes the importance of incorporating auxiliary variables, such as topography, climate, and vegetation data, enhancing explanatory power and accuracy in delineating the environmental characteristics, ensuring better adaptability to the studied area. The results were validated by the coefficient of determination (R2) and root mean square error (RMSE). For the clay, EBKRP (empirical Bayesian kriging regression prediction) achieved R2 = 0.35 and RMSE = 6.95%, for the sand, it achieved R2 = 0.34 and RMSE = 17.38%, for the humus, it achieved R2 = 0.50 and RMSE = 3.80%, and for the soil depth, it achieved R2 = 0.76 and RMSE = 5.36 cm. These results indicate that EBKRP is the optimal method for accurately mapping soil characteristics in future research in Montenegro.