Prediction, mapping, and implication for better soil organic carbon management in Ethiopia

A precise soil organic carbon (SOC) content estimate is crucial soil quality parameter for agricultural produce and ecological safety. Moreover, geospatial modeling of SOC is critical when there are limited laboratory equipment and chemical reagents for soil analysis. This study used geostatistics—ordinary kriging (OK) and inverse distance weighting (IDW)—to map SOC in Libokemkem area, Northwest Ethiopia, for improved SOC management. About 107 soil samples were obtained from the plow layer at a 20‐cm depth and SOC was determined. Statistical Package for Social Sciences version 24.0 was used to generate descriptive statistics, and geostatistical analysis was also performed on the data using ArcGIS platform. The coefficient of determination (R2) and root mean square error (RMSE) derived from the validation of the predicted maps were used to assess the models. The results revealed homogeneity (coefficient of variation