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This article in SSSAJ

  1. Vol. 68 No. 3, p. 736-743
    Received: Apr 9, 2003

    * Corresponding author(s): geshel@ucdavis.edu
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Critical Evaluation of the Use of Laser Diffraction for Particle-Size Distribution Analysis

  1. G. Eshel *a,
  2. G. J. Levyb,
  3. U. Mingelgrinb and
  4. M. J. Singera
  1. a Dep. of Land, Air, and Water Resources, University of California, Davis, CA 95616
    b Institute of Soil, Water, and Environmental Sciences, Agricultural Research Organization (ARO), The Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel


Determination of soil particle-size distribution (PSD) by sieving, hydrometer, and pipette methods as well as by laser diffraction (LD) suffers from inherent flaws, mainly due to the difficulty in defining the size of irregularly shaped particles. Therefore these methods yield only estimates of PSD. The objective of this study was to determine whether a functional relationship exists between the PSDs obtained by the combined sieve-pipette method and those obtained by LD. Samples from 42 California soils were analyzed. For the LD measurements a Beckman–Coulter LS-230 apparatus with a 750-nm laser beam that measures particles in the range of 0.04 to 2000 μm was used, employing the Mie theory for the PSD calculations. Values of 1.5 and 0.2 for the real part and the imaginary term of the refractive index (RI), respectively, gave satisfactory results for the optical model calculations. Volume percentage of the clay-size fraction obtained by LD was generally lower than mass percentage of the clay fraction derived by the pipette method. The opposite trend was noted for the silt-size fraction. Coefficients of determination for the regression equations for the clay, silt, and sand fractions determined by the two methods were 0.702, 0.689, and 0.821, respectively. Good agreement between measured and calculated LD values for one size class was accompanied by poor agreement between measured and calculated values for the other. The LD method provides a continuous PSD curve, which enables a detailed data analysis and a flexible application of different particle-size dependent classification systems.

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