Comparison of different approaches to the development of pedotransfer functions for water-retention curves

Abstract

Pedotransfer functions (PTFs) for estimating water-retention from particle-size and bulk density are presented for Australian soil. The water-retention data sets contain 733 samples for prediction and 109 samples for validation. We present both parametric and point estimation PTFs using different approaches: multiple linear regression (MLR), extended nonlinear regression (ENR) and artificial neural network (ANN). ENR was found to be the most adequate for parametric PTFs. Multiple linear regression cannot be used to predict van Genuchten parameters as no linear relationship was found between soil properties and the curve shape parameters. Using the prediction set, ANN performance was similar to the ENR performance for the prediction dataset, but ENR performed better on the validation set. Since ANN is still considered as a black-box approach, the ENR approach which has a more physical basis is preferred. Point estimation PTFs were estimated for water contents at −10, −33 and −1500 kPa. Multiple linear regression performed better for point estimation. An exponential increase trend was found between particles <2 μm and water contents held at −10, −33 and −1500 kPa. The point estimation ANN did not improve prediction compared to MLR. Increasing the number of functions and parameters in developing PTFs does not necessary improve the prediction. The effect of parameter uncertainty, differences in texture determination and spatial variability on the error in prediction is also discussed.