Extrapolation of Digital Soil Mapping Approaches for Soil Organic Carbon Stock Predictions in an Afromontane Environment
نویسندگان
چکیده
Soil scientists can aid in an essential part of ecological conservation and rehabilitation by quantifying soil properties, such as organic carbon (SOC), is stock (SOCs) SOC crucial for providing ecosystem services, and, through effective C-sequestration, the effects climate change be mitigated. In remote mountainous areas with complex terrain, northern Maloti-Drakensberg South Africa Lesotho, direct quantification stocks or even obtaining sufficient data to construct predictive Digital Mapping (DSM) models a tedious expensive task. Extrapolation DSM model algorithms from relatively accessible area could overcome these challenges. The aim this study was determine if calibrated one headwater catchment (Tugela) extrapolated without re-training other catchments region acceptable accuracy. selected were four different catchments, which included three near Motete River (M1, M2, M3) Lesotho Vemvane adjacent Tugela. Predictions compared measured sampling sites (n = 98) various catchments. Results showed that based on mean results Universal Kriging (R2 0.66, NRMSE 0.200, ρc 0.72), least absolute shrinkage selection operator LASSO 0.67, 0.191, 0.73) Regression cubist 0.61, 0.184, 0.65) had most satisfactory outcome, whereas soil-land inference (SoLIM) struggled predict accurately. Models performed worst all, showing close proximity does not necessarily equal good similarity. concluded extrapolated. However, calibration should representation greater area, otherwise might over- under-predict SOCs. Successfully extrapolating will allow make predictions efforts vulnerable areas.
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ژورنال
عنوان ژورنال: Land
سال: 2023
ISSN: ['2073-445X']
DOI: https://doi.org/10.3390/land12030520