About Us | Help Videos | Contact Us | Subscriptions
 

Abstract

 

This article in SSSAJ

  1. Vol. 56 No. 1, p. 187-192
     

 View
 Download
 Alerts
 Permissions
Request Permissions
 Share

doi:10.2136/sssaj1992.03615995005600010029x

Measuring Field Variability of Disturbed Soils for Simulation Purposes

  1. P. A. Finke,
  2. J. Bouma and
  3. A. Stein
  1. Dep. of Soil Science and Geology, Agricultural University, P.O. Box 37, 6700 AA Wageningen, the Netherlands.

Abstract

Abstract

Spatial variation of soil profiles disturbed by leveling was inventoried on a field scale to obtain representative data for simulation purposes. Depth of occurrence, thickness, and morphology of functional layers, which are different pedogenetic horizons with comparable soil physical properties, were considered to be regionalized variables. The layers served as carriers of physical information, such as water-retention and hydraulic-conductivity characteristics and organic-matter content. An impression of the variability within each layer was obtained by six fold sampling. Spatial variability, expressed by variations in thickness of functional layers, was inventoried in a two-step soil survey. First, semivariograms were constructed using data obtained following a nested sampling scheme supplemented by a nugget estimation procedure. Variograms were used to evaluate cost/quality ratios at varying potential grid sampling densities, using the root of the prediction error variance (RPEV) to compare quality of interpolations. Based on these evaluations and a sequential sampling test, a grid mesh of 12 m was chosen. Second, a grid soil survey and an independent quality test were done, in which root mean square errors (RMSE) on test points were compared with RPEV. The RPEV to RMSE ratios varied between 0.7 and 1.1 for the sampled grid mesh, and had comparable values for other grid meshes. Estimations on test points by an hypothesized spatial mean, based on 26 measurements by a sequential sampling method, produced RMSE values not significantly different from RMSE values from kriging interpolations. However, sequential sampling required 26 observations whereas kriging required 153, a saving of 83%.

  Please view the pdf by using the Full Text (PDF) link under 'View' to the left.

Copyright © . Soil Science Society of America