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Soil Science Society of America Journal Abstract -

Water Content Effect on Soil Salinity Prediction: A Geostatistical Study Using Cokriging


This article in SSSAJ

  1. Vol. 59 No. 4, p. 1146-1156
    Received: May 23, 1994

    * Corresponding author(s): vpeter@ucrac1.ucr.edu
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  1. Peter J. Vaughan ,
  2. Scott M. Lesch,
  3. Dennis L. Corwin and
  4. David G. Cone
  1. USDA-ARS, U.S. Salinity Lab., 4500 Glenwood Dr., Riverside, CA 92501
    Broadview Water District, P.O. Box 95, Firebaugh, CA 93622



A geostatistical analysis of soil salinity in an agricultural area in the San Joaquin Valley included measurements of electrical conductivity of soil paste extract (ECe) and water content of soil samples supplemented by surface measurements of apparent electrical conductivity (EMH). Prediction of soil salinity at unsampled points by cokriging loge(ECe) and EMH is worthwhile because EMH measurements are quicker than soil sampling. This work studies how patterns of loge(ECe) predicted by cokriging with EMH are influenced by variation in gravimetric water content (W). The data are mean EMH = 1.00 ± 0.13 dS m−1 for 2378 locations, mean loge(ECe) = 1.40 ± 0.29 dS m−1, and mean gravimetric W = 0.260 ± 0.003, both averaged for four samples from 0.3-m intervals to 1.2-m depth for 315 locations. The coefficient of determination (R2) for EMH vs. loge(ECe) increased with depth from 0.05 to 0.54 whereas the R2 for EMH vs. W decreased from 0.48 to 0.28. A gray-scale EMH map contained nine out of 56 quarter-section boundaries coinciding with step variations in EMH. The t-statistics for differences in mean W were six of nine significant at 0.001 and nine of nine at 0.05, but mean loge(ECe) had only two of nine at 0.05, implying that W caused EMH steps. Water-affected EMH impaired prediction of ECe at depth by cokriging, because near-surface variations in W masked ECe. Two subareas were defined, one where management factors, such as irrigation, controlled EMH, causing steps, and one where near-surface W varied less, making cokriging predictions more reliable.

Contribution from the USDA-ARS, U.S. Salinity Lab.

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