Abstract
The predictive nature of digital soil mapping makes it a labour- and cost-effective way of facilitating soil surveys. A digital elevation model was used to generate terrain attributes that can be used to infer the distribution of soil associations relative to the topography. Two study areas – Gladstone and Potsane – in the Free State Province of South Africa were considered. Slope, aspect, contour and plan curvature, topographic wetness index and topographic morphological unit were used to develop a model for predicting soil associations. Discriminant analysis was employed to develop the model. The model was trained on data obtained from Gladstone and validated on data from Gladstone and Potsane. Predicting soil form was unsatisfactory. Prediction done on soil associations, with soils grouped as deep, shallow and valley-bottom soils (criteria closely related to the suitability for in-field rainwater harvesting), achieved acceptable improvement in prediction accuracy. For Gladstone, when analysis was done using equal prior probability, accuracy percentages of 56.9%, 51.5% and 58.3% were found for calibration, cross-validation and areas suited to in-field rainwater harvesting, respectively. With prior probability set in accordance to sample frequency, the accuracy percentages were improved to 83.1%, 80.0% and 94.6%, respectively. In Potsane, the prediction accuracy percentage was low (38.23%) with equal prior probability but markedly improved (67.65%) when prior probability was similar to sample frequency. These results support the validity of the statement that the predictive nature of digital soil mapping makes it a labour- and cost-effective way of facilitating soil surveys.
Highlights
Predictive soil mapping (PSM), as part of digital soil mapping, is an important contribution to soil surveying
In the first instance of the discriminant analysis run, the chance of encountering any of the soils was set to be equal by assigning them the same prior probability
There is considerable potential in modelling digital terrain attributes in order to predict the distribution of soil associations, for example those suitable for in-field rainwater harvesting (IRWH), in a land type
Summary
Predictive soil mapping (PSM), as part of digital soil mapping, is an important contribution to soil surveying. PSM is associated with digital soil mapping which is geared towards producing digital maps of soil types and soil properties. This procedure relies heavily on computer support and different software applications to process the observations (data) that are used as a basis of inference. PSM uses variables that can be quantitatively measured, thereby allowing predictions to be done consistently and objectively. Another advantage of PSM is its role in facilitating soil mapping in contrast to the polygon delineations of traditional soil surveys. PSM thereby provides a means of controlling the resolution of the map produced
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
