Accuracy and uncertainty of spatial patterns of soil organic matter

Abstract

Accurate quantification of the spatial patterns of soil organic matter (SOM) is essential for both SOM management and for the application of SOM models. The objective of this study is to determine whether elevation could be used to increase the accuracy of spatial predictions and the corresponding prediction uncertainty of soil organic matter. The sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were compared with respect to the accuracy of predictions as well as to the uncertainty inherent in the spatial prediction of soil organic matter. The SGS algorithm accounted for only the SOM data. The SGCS accounted for both SOM data and intensive elevation data. The root mean square errors revealed that the more accurate simulations were those accounting for intensive elevation information by the SGCS method for the two areas compared with SGS. As regards modelling local uncertainty, SGCS performed better at modelling prediction uncertainty than SGS. In addition, the results of assessing the standard deviation confirmed that the exhaustive elevation data could be used to reduce the spatial uncertainty of SOM by SGCS compared with the SGS algorithm. © 2007 Taylor & Francis Group, LLC.