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Inadequacy of spatial soil information is one of the limiting factors to making evidence-based decisions to improve
food security and land management in the developing countries. Various digital soilmapping (DSM) techniques
have been applied inmany parts of theworld to improve availability and usability of soil data, but less has
been done in Africa, particularly in Tanzania and at the scale necessary tomake farmmanagement decisions. The
Kilombero Valley has been identified for intensified rice production. However the valley lacks detailed and up-todate
soil information for decision-making. The overall objective of this study was to develop a predictive soilmap
of a portion of Kilombero Valley using DSM techniques. Two widely used decision tree algorithms and three
sources of Digital ElevationModels (DEMs) were evaluated for their predictive ability. Firstly, a numerical classification
was performed on the collected soil profile data to arrive at soil taxa. Secondly, the derived taxawere spatially
predicted and mapped following SCORPAN framework using Random Forest (RF) and J48 machine learning
algorithms. Datasets to train the model were derived from legacy soil map, RapidEye satellite image and three
DEMs: 1 arc SRTM, 30 m ASTER, and 12 m WorldDEM. Separate predictive models were built using each DEM
source. Mapping showed that RF was less sensitive to the training set sampling intensity. Results also showed
that predictions of soil taxa using 1 arc SRTM and 12mWordDEMwere identical.We suggest the use of RF algorithmand
the freely available SRTMDEMcombination formapping the soils for thewhole Kilombero Valley. This
combination can be tested and applied in other areas which have relatively flat terrain like the Kilombero Valley |
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