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This article in SSSAJ

  1. Vol. 72 No. 5, p. 1394-1400
     
    Received: May 24, 2007
    Published: Sept, 2008


    * Corresponding author(s): g.tranter@usyd.edu.au
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doi:10.2136/sssaj2007.0188

Comparing Spectral Soil Inference Systems and Mid-Infrared Spectroscopic Predictions of Soil Moisture Retention

  1. G. Tranter *a,
  2. B. Minasnya,
  3. A. B. McBratneya,
  4. R. A. Viscarra Rosselab and
  5. B. W. Murphyc
  1. a Faculty of Agriculture, Food and Natural Resour., Univ. of Sydney, NSW, Australia
    b Currently at, CSIRO Land and Water, Bruce E. Butler Laboratory, Canberra, ACT, Australia
    c New South Wales Dep. of Environ, and Climate Change, Cowra, NSW, Australia

Abstract

Mid-infrared spectroscopy has been proposed as a cheap yet accurate alternative to a number of laboratory methods for measuring soil properties. While accurate predictions of a number of basic soil constituents have been reported, properties associated with soil structure have received far less attention. In this study, we looked at the efficacy of mid-infrared reflectance spectroscopy in predicting moisture retention and whether better predictions can be achieved using pedotransfer functions using spectroscopic predictions of basic soil constituents as inputs. Three methods were used to predict volumetric moisture retention: (i) mid-infrared (MIR) spectra and partial least squares regression, (ii) a neural network pedotransfer function (PTF) using laboratory particle-size distribution and bulk density data, and (iii) a pedotransfer function with MIR-predicted particle-size distribution and bulk density as inputs. We used Lin's concordance correlation coefficient as a goodness-of-fit measure. Predictions of volumetric moisture retention on intact structured soils were generally poor, particularly at the wet end. Improved predictions were observed at dry-end matric potentials, where moisture retention was more correlated with particle-size distribution than soil structure. The neural network PTF was found to have better goodness of fit for all matric potentials; however, predictions at larger matric potentials (wet end) were still poor due to poor bulk density predictions. In light of this work, we propose that while MIR spectroscopy may be a valuable predictor of fundamental soil constituents such as particle-size fractions and organic C, predictions of soil properties dependant on soil structure, such as volumetric moisture retention, may prove difficult. Mid-infrared spectroscopy in combination with PTFs should provide improvements to moisture retention predictions through improved representation of the influential processes, namely soil structure and adsorptive forces.

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