چکیده
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Soil organic carbon (SOC) has been assessed
in three dimension (3D) in several studies, but little is
known about the combined effects of land use and soil
depth on SOC stocks in semi-arid areas. This paper investigates
the 3D distribution of SOC to a depth of 1 m in
a 4600-ha area in southeastern Iran with different land
uses under the irrigated farming (IF), dry farming (DF),
orchards (Or), range plants on the Gachsaran formation
(RaG), and range plants on a quaternary formation (RaQ).
Predictions were made using the artificial neural networks
(ANNs), regression trees (RTs), and spline functions with
auxiliary covariates derived from a digital elevationmodel
(DEM), the Landsat 8 imagery, and land use types.
Correlation analysis showed that the main predictors for
SOC in the topsoil were covariates derived from the
imagery; however, for the lower depths, covariates derived
from both the DEM and imagery were important.
ANNs showed more efficiency than did RTs in predicting
SOC. The results showed that 3D distribution of SOC was
significantly affected by land use types. SOC stocks of
soils under Or and IF were significantly higher than those
under DF, RaG, and RaQ. The SOC below 30 cm
accounted for about 59% of the total soil stock. Results
showed that depth functions combined with digital soil
mapping techniques provide a promising approach to
evaluate 3D SOC distribution under different land uses
in semi-arid regions and could be used to assess changes
in time to determine appropriate management strategies.
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