Functional digital soil mapping: A case study from Namarroi, Mozambique

Abstract

The value of soil is often neglected in developing countries, partially due to a lack of spatial soil data. Conventional methods of soil survey are too cumbersome and expensive to fulfil the need for soil maps in these countries. This study presents an expert knowledge based digital soil mapping (DSM) approach to provide in-time spatial soil information in developing countries. The objective of this study was to evaluate the potential of DSM soil survey methods to rapidly produce land suitability maps of a large area with acceptable accuracy. An expert knowledge approach was used, with soil surveyors creating conceptual soil distribution patterns, and populating the patterns with covariate values to create soil–landscape rules. A soil class map was created by running an inference with those rules. The map achieved an absolute validation accuracy of 80%, and 59% at a 95% confidence level. Land suitability maps were created based on the soil class map. Furthermore the data indicated that 14 or more soil observations are needed per homogeneous area to achieve acceptable results and that multiple scale covariates were useful to map different parts of the landscape.