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.
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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.