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This article in SSSAJ

  1. Vol. 64 No. 3, p. 1009-1017
     
    Received: Apr 23, 1999


    * Corresponding author(s): johnt@acss.usyd.edu.au
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doi:10.2136/sssaj2000.6431009x

Calibrating an Electromagnetic Induction Instrument to Measure Salinity in Soil under Irrigated Cotton

  1. J. Triantafilis *a,
  2. G. M. Laslettb and
  3. A. B. McBratneya
  1. a Australian Cotton Cooperative Research Centre, Dep. of Agricultural Chemistry and Soil Science, The University of Sydney, Sydney NSW 2006, Australia
    b CSIRO Mathematical and Information Sciences, Private Bag 10, Clayton MDC, Clayton, Victoria 3169, Australia

Abstract

Various calibration approaches have been proposed for determining profiles of apparent soil electrical conductivity (ECa) or soil electrical conductivity of a saturated soil paste extract (ECe) using an EM38 instrument. One of the most promising of these, the established-coefficients approach, was selected for calibration of the EM38 to some irrigated cotton (Gossypium hirsutum L.)-growing soil located in the Edgeroi district of the lower Namoi valley, northern NSW, Australia. However, the fitted salinity profiles were locally erratic, although global trends could be recognized. This method was compared with simple linear regression applied to the data from each depth increment separately. This was also unsatisfactory, and in fact, was not obviously different from the established-coefficients method on the data. An alternative and statistically more rigorous approach was used in which a logistic curve is fitted to each calibration hole (the logistic profile model). This mixed random and fixed effects nonlinear model predicts ECe from measurements generated by an electromagnetic (EM) instrument (EM38) held in the horizontal mode of operation EM0,H The logistic profile model fitted the data well, with no obvious patterns in the residuals, and depended on far fewer parameters than the two alternative methods. In addition, unlike the established-coefficients approach, the logistic model provided meaningful prediction errors.

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