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Soil Science Society of America Journal Abstract - DIVISION S-8—NUTRIENT MANAGEMENT & SOIL & PLANT ANALYSIS

Predictive Mechanistic Model of Soil Phosphorus Dynamics with Readily Available Inputs


This article in SSSAJ

  1. Vol. 68 No. 2, p. 644-653
    Received: Sept 19, 2002

    * Corresponding author(s): ammonst@utk.edu
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  1. T. V. Karpinetsa,
  2. D. J. Greenwoodb and
  3. J. T. Ammons *c
  1. a Dep. of Agrochemistry, All-Russian Institute of Agriculture and Protection of Soil from Erosion, Karl Marks Str., 70-b, Kursk, Russia, 305040
    b Horticulture Research International, Wellesbourne, Warwick, CV35 9EF UK
    c Dep. of Biosystems Engineering and Environmental Science, Univ. of Tennessee, 2505 E.J. Chapman Drive, Knoxville, TN 37996-4531


Fertilizer and manure P applications together with cropping practices can have a lasting effect on soil fertility and can result in pollution of waterways. This study was aimed at providing a general model for the long-term changes in soil P extracted with conventional tests. The model maintains three active pools of soil P: extractable soil P (X), absorbed P that is not extractable but interchanges reversibly with X (Y), and mineral P that provides solubility-product type buffering of X (Pbuffer). There is also an input for the net P addition. Equations derived from the model define most of the published patterns of response of X found in long-term field experiments. They underpin a dynamic version of the model that permits annual inputs and calculates the time-course of the various P pools. A method was devised for calibrating the dynamic version from measurements that are usually made in long-term experiments. Models calibrated in this way gave good fits to the data from six soils from four countries. Values of the coefficients indicated that solubility product buffering had a decisive influence on the P economy of some but not all soils. The model gave predictions of X that were in good agreement with measurements in three long-term experiments that were entirely independent of those used for calibrating the model. Other possible methods of calibration are discussed. The model concisely represents key factors affecting the dynamics of X over the long term and has both interpretative and predictive value.

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