Modeling and mapping spatial distribution of baseline soil organic carbon stock, a case of West Hararghe, Oromia Regional State, Eastern Ethiopia

Abstract

ABSTRACT Location-specific information on soil organic carbon (SOC) helps to identify potential sources and sinks of carbon. The objective of this study was to produce a 30m-resolution digital map of SOC stock for selected districts in West Hararghe Zone of Ethiopia. 148 soil samples(0-30cm) were collected for SOC analysis and BD estimation. Eighteen environmental covariates were acquired from satellite sources, digital elevation model (DEM), and maps. A random forest model was fitted to the data. The accuracy of the prediction was tested using the 10-fold cross-validation method. The model explained 36% of the variance (R2) with a root mean square error of 9.51. The most relevant predictors were normalized difference vegetation index (NDVI), temperature, and elevation. The predicted total SOC stock value was 240.5TgC and ranged between 17.91 and 70.16 t/ha, with a mean of 38.82 t/ha. The soils under agricultural land had a higher amount of carbon than forestland. The mean SOC stock was relatively higher in fine-textured soils and lower in soils, which are dominantly found in drier areas. This study could help provide information on a less expensive method of SOC stock estimation. In addition, the study produced an updatable baseline SOC stock map.

Keywords

• The soil
• random forest
• SOC stock prediction
• environmental covariates
• digital soil mapping