HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Janik, L. J. Articles by Rawson, A. Search for Related Content PubMed Articles by Janik, L. J. Articles by Rawson, A. GeoRef GeoRef Citation Agricola Articles by Janik, L. J. Articles by Rawson, A. Related Collections Soil Methods/Instrumentation Soil Analysis Soil Physics

SOIL PHYSICS

Rapid Prediction of Soil Water Retention using Mid Infrared Spectroscopy

^a CSIRO Land & Water PMB 2, Glen Osmond, South Australia 5064
^b CSIRO Sustainable Ecosystems Waite Rd. Urrbrae South Australia 5062
^c New South Wales Dep. of Natural Resources c/o Faculty of Science and Agriculture Charles Sturt Univ., Leeds Pde, Orange PO Box 883 NSW 2800

^* Corresponding author (sean.forrester{at}csiro.au).

Soil-water properties vary widely with soil composition and texture, but measurements are often time consuming and expensive to determine using traditional laboratory methods. Mid-infrared (MIR) spectroscopy is sensitive to soil composition, allowing multivariate calibrations to be derived between volumetric soil water retention and MIR spectra. Mid-infrared partial least squares (PLS) models can be derived from the spectra of soils and reference data, and can be used to predict the water retention solely from the MIR spectra of unknown samples. Regressions between laboratory-determined volumetric water retentions, _v, at matric suctions from 1 to 1500 kPa and values predicted by MIR PLS analysis are presented for a broad variety of surface soils from southern Australia. Cross-validation produced coefficient of determination values ranging from 0.67 to 0.87 and standard error of cross-validation in the range 4.1 to 3.2. Prediction robustness was tested using an independent set of samples for values of _v at field capacity (10-kPa suction) and permanent wilting point (1500-kPa suction). The prediction standard error for the test set was higher than for cross-validation. This was attributed to a mismatch between spectra for the test set and those of the calibration samples, resulting in a reduced ability of the calibration samples to model the test set spectra. The MIR PLS prediction method performed at least as well as some pedotransfer functions and was shown to be a rapid and inexpensive method for the prediction of volumetric soil moisture content for a range of soil types at a range of matric suctions.

Abbreviations: MIR, mid-infrared • NSW, New South Wales • PC, principal component • PCA, principal components analysis • PLS, partial least squares • RPD, residual predictive deviations • SECV, standard error of cross-validation • SEP, standard error of prediction

This article has been cited by other articles:

				G. Tranter, B. Minasny, A. B. McBratney, R. A. V. Rossel, and B. W. Murphy Comparing Spectral Soil Inference Systems and Mid-Infrared Spectroscopic Predictions of Soil Moisture Retention Soil Sci. Soc. Am. J., August 20, 2008; 72(5): 1394 - 1400. [Abstract] [Full Text] [PDF]