Predictive mapping of soil organic carbon in Northeast Algeria

Soils of semi-arid climates undergo organic carbon loss, which in turn affects their agricultural potential. Geostatistics is often used as an interpolation tool to thoroughly describe SOC spatial distribution. To focus on soil organic carbon (SOC) depletion, the Tiffech watershed (Northeast of Algeria), an economically important agricultural area, was chosen due to intensive agricultural practices, decline of forests and occurrence of erosion. The present study aimed to predict the spatial variation of SOC in Tiffech watershed using geostatistics and a Geographical Information System (GIS) software, comparing the performance of two geostatistical methods-Ordinary Kriging (OK) and Regression Kriging (RK)-also assessing the role of auxiliary variables in improving the prediction accuracy and highlighting the role of land cover in SOC storage. The SOC content in Tiffech soils was determined on 42 soil samples from the surface layer (0-10 cm) collected all over the study area and the results were used to estimate SOC density in non-sampled locations. The prediction efficiency of the two methods was evaluated by calculating the Mean Error (ME), the Root Mean Square Error (RMSE) and the Root Mean Square Standardized Error (RMSSE). The interpolation results showed that SOC distribution in the study area was correlated to the topography, the clay index, and general landscape features. SOC content increased northwards in the area, ranging from 0.53 to 6.9 kg.m(-2) in relation to land use. As expected, maps figured a good conservation status of SOC stocks in areas with dense vegetation; conversely poor SOC contents were estimated where land degradation factors take place. Cross-validation results showed an outperformance in the interpolation accuracy of RK on OK after the introduction of environmental variables, with an RMSE value of 0.02 versus 0.81. This suggests a higher efficiency of RK in predicting SOC content across the Tiffech area in comparison with OK, confirming that introducing some auxiliary data correlated to the target variable in SOC estimation, considerably improved the interpolation accuracy.