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.

半干旱气候的土壤有机碳流失，进而影响其农业潜力。地统计学通常用作内插工具，以彻底描述SOC空间分布。为了专注于土壤有机碳（SOC）的消耗，由于集约化的农业实践，森林的衰落和侵蚀的发生，选择了蒂法什流域（阿尔及利亚东北部），这是一个具有重要经济意义的农业地区。

本研究旨在使用地统计学和地理信息系统（GIS）软件预测蒂法奇流域SOC的空间变化，比较两种地统计学方法-普通克里格（OK）和回归克里格（RK）的性能，并评估其作用辅助变量在提高预测准确性和突出土地覆盖在SOC存储中的作用方面。

在整个研究区域收集的42个表层土壤样本（0-10厘米）中确定了蒂法克土壤中的SOC含量，并将结果用于估算非采样地点的SOC密度。

通过计算均方误差（ME），均方根误差（RMSE）和均方根标准误差（RMSSE）来评估这两种方法的预测效率。

插值结果表明，研究区的SOC分布与地形，粘土指数和一般景观特征相关。相对于土地利用，该区域的SOC含量向北增加，范围从0.53至6.9 kg·m ^-2。正如预期的那样，地图显示出植被茂密地区的SOC储量状况良好；相反，估计发生土地退化因素的地方SOC含量很低。

交叉验证结果表明，引入环境变量后，OK上的RK插值精度优于OK，RMSE值为0.02对0.81。这表明与OK相比，RK在Tiffech区域上预测SOC含量的效率更高，这证实了在SOC估计中引入一些与目标变量相关的辅助数据可以显着提高内插精度。