Abstract:
Soil Organic carbon (SOC) is vital to the soil’s ecosystem functioning as well as improving soil fertility. Slight variation in C in the soil has significant potential to be either a source of CO2 in the atmosphere or a sink to be stored in the form of soil organic matter. Location-specific information on soil organic carbon (SOC) helps to identify potential sources and sinks of carbon. However, modeling SOC spatiotemporal changes was challenging due to lack of data to represent the high spatial heterogeneity in soil properties. The less expensive techniques, digital soils mapping (DSM) combined with space-for-time substitution (SFTS), were applied to predict the present and future SOC stock under different LULC and climate scenarios represented by the four Representative Concentration Pathways (RCPs): RCP2.6, RCP4.5, RCP6, and RCP8.5). The objectives of this study were to produce a 30 m-resolution digital maps of present SOC stock, to explore the factors that influence the spatial distribution of SOC stock, to project the SOC stock for projected climate (2050), predict the change in SOC stock by 2050 and estimate the effect of change in SOC due to climate and LULC on water retention at field capacity (FC) and permanent wilting point (PWP) for selected districts (Chiro Zuria, Mieso, Gemechis, and Kuni) in West Hararghe Zone of Ethiopia. The relationship between predictors (environmental covariates and field survey data), and predictand (measured SOC stock for 148 soil samples for 0-30 cm soil depth) was developed using a random forest model. The accuracy of the prediction was tested using the 10-fold cross-validation method. The model explained 36% to 44% of the variance (R 2 ) with a root mean square error (RMSE) of 9.51 to 8.96, respectively in different predictors. Among the predictors, land management practices such as crop rotation, mixing legumes and perennials with cereals, and soil and water conservation etc were the most influential factor of SOC stock spatial distribution followed by Normalized Difference Vegetation Index (NDVI), temperature, and elevation. The mean baseline predicted SOC stock was 70.16 t/ha. The expected mean change (gain) in SOC stock under future climate (2050) were 1.56, 0.59, 1 and 1.89 t/ha (or 5.73, 2.17, 3.67 and 6.94 t/ha CO2 sequetration) under RCPs 2.6, 4.5, 6 and 8.5, respectively. An overall net gain of SOC stock over the present C stock was expected in the study area by 2050 under all climate change scenarios, if the current LULC remain unchanged. xvii The changes in the SOC stock depended on land use land cover (LULC), soil type, and agro ecological zones. The change in SOC concentration in the future climate and LULC change siginificantly affected water retention at field capacity (FC) at a rate of 0.97 for a unit change in SOC which could change the available water content (AWC). Since field collected land management practices were the most important factor on SOC amount, supporting the existing practices are recommended to improve SOC. By 2050, cropland is supposed to lose its SOC stock under all RCPs; therefore, appropriate decisions are crucial to compensate for the loss of C. The findings showed that the random forest approach and easily available environmental covariates combined with space-for-time substitution (SFTS) approach can be applied to monitor the change in SOC stock as well as the effect on water retention in similar landscapes. However, since the projected climate data particularly rainfall is uncertain, further research is recommended to estimate the expected change in SOC stock as well as AWC with the support of field experiments stratified by agro-ecological zones.