Pedotransfer functions for predicting bulk density of coastal soils in East China

Abstract

Soil bulk density (BD) is an important physical property and an essential factor for weight-to-volume conversion. However, BD is often missing from soil databases because its direct measurement is labor-intensive, time-consuming, and sometimes impractical, particularly on a large scale. Therefore, pedotransfer functions (PTFs) have been developed over several decades to predict BD. Here, six previously revised PTFs (including five basic functions and stepwise multiple linear regression (SMLR)) and two new PTFs, partial least squares regression (PLSR) and support vector machine regression (SVMR), were used to develop BD-predicting PTFs for coastal soils in East China. Predictor variables included soil organic carbon (SOC) and particle size distribution (PSD). To compare the robustness and reliability of the PTFs used, the calibration and prediction processes were performed 1 000 times using the calibration and validation sets divided by a random sampling algorithm. The results showed that SOC was the most important predictor, and the revised PTFs performed reasonably although only SOC was included. The PSD data were useful for a better prediction of BD, and sand and clay fractions were the second and third most important properties for predicting BD. Compared to the other PTFs, the PLSR was shown to be slightly better for the study area (the average adjusted coefficient of determination for prediction was 0.581). These results suggest that PLSR with SOC and PSD data can be used to fill in the missing BD data in coastal soil databases and provide important information to estimate coastal carbon storage, which will further improve our understanding of sea-land interactions under the conditions of ongoing global warming.