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This article in SSSAJ

  1. Vol. 69 No. 2, p. 500-510
    Received: Jan 19, 2004

    * Corresponding author(s): bruno.devos@lin.vlaanderen.be
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Predictive Quality of Pedotransfer Functions for Estimating Bulk Density of Forest Soils

  1. Bruno De Vos *a,
  2. Marc Van Meirvenneb,
  3. Paul Quataerta,
  4. Jozef Deckersc and
  5. Bart Muysc
  1. a Inst. for Forestry and Game Management, Gaverstraat 4, B-9500 Geraardsbergen, Belgium
    b Dep. of Soil Management and Soil Care, Ghent Univ., Coupure 653, B-9000 Gent, Belgium
    c Lab. for Forest, Nature and Landscape Research, Katholieke Univ. Leuven, Vital Decosterstraat 102, B-3000 Leuven, Belgium


Pedotransfer functions (PTFs) based on easily measured soil variables offer an alternative for labor-intensive bulk density (ρb) measurements. The predictive quality of 12 published PTFs was evaluated using an independent dataset of forest soils (1614 samples) from Flanders, Belgium. For all samples, PTF accuracy and precision was calculated, and for topsoil and subsoil samples separately. All functions were found to produce a systematic underestimation of predicted ρb, with mean prediction errors (MPEs) ranging between −0.01 and −0.51 Mg m−3 Most PTFs performed differently when applied to topsoil or subsoil data. Prediction of topsoil ρb showed the highest prediction error. The evaluation demonstrated the poor performance of some published PTFs, and raised concern that the predictive ability of even the better models may not be adequate. Therefore, two candidate PTFs were recalibrated and validated. With recalibration, accuracy improved considerably and showed a near-zero bias, but precision increased only slightly. The best fitted empirical model was based on loss-on-ignition (LOI): ρb = 1.775 − 0.173(LOI)1/2 Its predictive capacity was not significantly better than the Adams physical two-component model ρb = 100/{(LOI/0.312) + [(100 − LOI)/1.661]}. For the prediction of ρb in forest soils, LOI was two times more important than texture variables, and LOI alone accounted for >55% of the total variation. The lowest root mean squared prediction error (RMSPE) was 0.16 Mg m−3 for LOI-based, and 0.21 Mg m−3 for texture-based models. Separate calibration of topsoil and subsoil layers did not enhance the predictive capacity significantly.

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