Hierarchical Pedotransfer Functions to Predict Bulk Density of Highly Weathered Soils in Central Africa

Abstract

Bulk density (BD) is a key soil property for sustainable land management and C stock assessment. However, data on BD are often missing in various soil survey reports in Central Africa because its measurement is laborious, time consuming at large scales, and may pose expense and facilities challenges, particularly in resource-poor countries. Therefore, there was a need for developing pedotransfer functions (PTFs) to predict BD for soils of Central Africa as an alternative solution. To do so, two approaches, namely multiple linear regression (MLR) and a pattern-recognition approach (k-nearest neighbor [k-NN]) were tested to predict BD for 196 soils of the Lower Congo. Based on their needs and familiarity with one or both of the two approaches, potential users interested in predicting the BD of strongly weathered soils of Lower Congo in particular or of Central Africa in general can immediately use the proposed equations or later apply the k-NN algorithm. Using an independent data set of low-activity clay soils from different tropical countries, nine individual PTFs and three ensemble PTFs were tested for their predictive capability. The results showed that despite the use of various modeling approaches—MLR, k-NN, or ensembles—BD remains a soil property that is difficult to predict with a satisfactory level of reliability because it can have substantial short-term variability in response to natural as well as human-induced causes. We achieved a minimum root mean square difference value of 0.179 Mg m−3.