Mapping soil nutrients via different covariates combinations: theory and an example from Morocco

Abstract

Background: Mapping of soil nutrients using different covariates was carried out in northern Morocco. This study was undertaken in response to the region's urgent requirement for an updated soil map. It aimed to test various covariates combinations for predicting the variability in soil properties using ordinary kriging and kriging with external drift. Methods: A total of 1819 soil samples were collected at a depth of 0–40 cm using the 1-km grid sampling method. Samples were screened for their pH, soil organic matter (SOM), potassium (K2O), and phosphorus (P2O5) using standard laboratory protocols. Terrain attributes (T) computed using a 30-m resolution digital elevation model, bioclimatic data (C), and vegetation indices (V) were used as covariates in the study. Each targeted soil property was modeled using covariates separately and then combined (e.g., pH ~ T, pH ~ C, pH ~ V, and pH ~ T + C + V). k = tenfold cross-validation was applied to examine the performance of each employed model. The statistical parameter RMSE was used to determine the accuracy of different models. Results: The pH of the area is slightly above the neutral level with a corresponding 7.82% of SOM, 290.34 ppm of K2O, and 100.86 ppm of P2O5. This was used for all the selected targeted soil properties. As a result, the studied soil properties showed a linear relationship with the selected covariates. pH, SOM, and K2O presented a moderate spatial autocorrelation, while P2O5 revealed a strong autocorrelation. The cross-validation result revealed that soil pH (RMSE = 0.281) and SOM (RMSE = 9.505%) were best predicted by climatic variables. P2O5 (RMSE = 106.511 ppm) produced the best maps with climate, while K2O (RMSE = 209.764 ppm) yielded the best map with terrain attributes. Conclusions: The findings suggest that a combination of too many environmental covariates might not provide the actual variability of a targeted soil property. This demonstrates that specific covariates with close relationships with certain soil properties might perform better than the compilation of different environmental covariates, introducing errors due to randomness. In brief, the approach of the present study is new and can be inspiring to decision-makers in the region and other world areas as well.