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

[1]  Peter Finke,et al.  Comparison of class and continuous pedotransfer functions to generate soil hydraulic characteristics , 1995 .

[2]  R. C. Palmer,et al.  A COMPARISON OF FIELD ESTIMATES AND LABORATORY ANALYSES OF THE SILT AND CLAY CONTENTS OF SOME WEST MIDLAND SOILS , 1976 .

[3]  Jack F. Paris,et al.  A Physicoempirical Model to Predict the Soil Moisture Characteristic from Particle-Size Distribution and Bulk Density Data 1 , 1981 .

[4]  M. Schaap,et al.  Neural network analysis for hierarchical prediction of soil hydraulic properties , 1998 .

[5]  A. Thomasson,et al.  Water Retention, Porosity and Density of Field Soils , 1977 .

[6]  D. L. Brakensiek,et al.  Estimation of Soil Water Properties , 1982 .

[7]  Gaylon S. Campbell,et al.  A SIMPLE METHOD FOR DETERMINING UNSATURATED CONDUCTIVITY FROM MOISTURE RETENTION DATA , 1974 .

[8]  J.H.M. Wösten,et al.  Testing an Artificial Neural Network for Predicting Soil Hydraulic Conductivity , 1996 .

[9]  William N. Venables,et al.  Modern Applied Statistics with S-Plus. , 1996 .

[10]  Z. Paydar,et al.  Water retention in Australian soils. I. Description and prediction using parametric functions , 1996 .

[11]  G. Gee,et al.  Particle-size Analysis , 2018, SSSA Book Series.

[12]  David A. Ratkowsky,et al.  Handbook of nonlinear regression models , 1990 .

[13]  Johan Bouma,et al.  Using Soil Survey Data for Quantitative Land Evaluation , 1989 .

[14]  L. P. van Reeuwijk,et al.  Pedotransfer functions for the estimation of moisture retention characteristics of Ferralsols and related soils , 1997 .

[15]  A. Klute.  Methods of soil analysis. Part 1. Physical and mineralogical methods. , 1988 .

[16]  J. Wösten,et al.  Development and use of a database of hydraulic properties of European soils , 1999 .

[17]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[18]  J. B. Williams,et al.  THE INFLUENCE OF TEXTURE ON THE MOISTURE CHARACTERISTICS OF SOILS , 1966 .

[19]  Van Genuchten,et al.  A closed-form equation for predicting the hydraulic conductivity of unsaturated soils , 1980 .

[20]  H. L. Shantz,et al.  The Wilting Coefficient and Its Indirect Determination , 1912, Botanical Gazette.

[21]  Gaylon S. Campbell,et al.  Soil physics with BASIC , 1985 .

[22]  R. Brewer,et al.  A Handbook of Australian Soils , 1968 .

[23]  Karl Auerswald,et al.  Regionalization of soil water retention curves in a highly variable soilscape, I. Developing a new pedotransfer function , 1997 .

[24]  W. E. Larson,et al.  Estimating soil water retention characteristics from particle size distribution, organic matter percent, and bulk density , 1979 .

[25]  M. Tapkenhinrichs,et al.  Evaluation of Pedo-Transfer Functions , 1993 .

[26]  Peter J. Gregory,et al.  The relation between soil water retention and particle size distribution parameters for some predominantly sandy Western Australian soils , 1996 .

[27]  Larry Boersma,et al.  A Unifying Quantitative Analysis of Soil Texture1 , 1984 .

[28]  Harry Vereecken,et al.  ESTIMATING THE SOIL MOISTURE RETENTION CHARACTERISTIC FROM TEXTURE, BULK DENSITY, AND CARBON CONTENT , 1989 .

[29]  Attila Nemes,et al.  Evaluation of different procedures to interpolate particle-size distributions to achieve compatibility within soil databases , 1999 .

[30]  A. McBratney,et al.  A continuum approach to soil classification by modified fuzzy k‐means with extragrades , 1992 .

[31]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[32]  E. Bruce Jones,et al.  Watershed Management in the Eighties , 1985 .

[33]  Robert H. Shaw,et al.  ESTIMATION OF THE 15‐ATMOSPHERE MOISTURE PERCENTAGE FROM HYDROMETER DATA , 1958 .

[34]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[35]  A. McBratney,et al.  Optimal interpolation and isarithmic mapping of soil properties: V. Co-regionalization and multiple sampling strategy , 1983 .

[36]  Y. Pachepsky,et al.  Artificial Neural Networks to Estimate Soil Water Retention from Easily Measurable Data , 1996 .

[37]  M. Schaap,et al.  Modeling water retention curves of sandy soils using neural networks , 1996 .