Disaggregation of soil testing data on organic matter by the summary statistics approach to area-to-point kriging

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

The summary statistics (SS) approach to area-to-point (ATP) kriging is applied to map organic matter concentration in the topsoil of agricultural fields from aggregated soil testing data. The differences between the SS approach and earlier published ATP kriging methods are explained and the behavior of the SS predictions is illustrated with a simulation experiment. In conventional ATP kriging, the areal means provide the data and are treated as errorless. In the SS approach, the data for each areal unit comprise the number of observations from the unit and the mean and variance of these observations; the approach deals with these data so that uncertainty in the areal means is accounted for. The SS approach is based on a point support covariance model, which is recovered from the areal data by restricted maximum likelihood. Validation using 339 georeferenced fields showed that the mean squared error (MSE) with the SS approach was slightly smaller than with the reference method that uses the postcode district (PCD) average as predictor. In PCDs with less than 25 sampled fields the reference MSE was reduced by 10%, but with more than 100 sampled fields the MSEs were about equal. Validation of the prediction variances was performed using the standardized squared prediction errors (SSEs). The SS approach slightly over-estimated the prediction error variances, although the mean and median of the SSEs were not outside the expected range of values. The reference method under-estimated the prediction error variance. (C) 2014 Elsevier B.V. All rights reserved.
LanguageEnglish
Pages151-159
JournalGeoderma
Volume226-227
DOIs
Publication statusPublished - 2014

Fingerprint

kriging
soil analysis
statistics
organic matter
prediction
soil
topsoil
uncertainty
methodology
simulation
method

Keywords

  • spatial interpolation
  • geostatistical approach
  • agricultural land
  • phosphorus
  • carbon
  • prediction
  • netherlands
  • variograms
  • inventory
  • model

Cite this

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title = "Disaggregation of soil testing data on organic matter by the summary statistics approach to area-to-point kriging",
abstract = "The summary statistics (SS) approach to area-to-point (ATP) kriging is applied to map organic matter concentration in the topsoil of agricultural fields from aggregated soil testing data. The differences between the SS approach and earlier published ATP kriging methods are explained and the behavior of the SS predictions is illustrated with a simulation experiment. In conventional ATP kriging, the areal means provide the data and are treated as errorless. In the SS approach, the data for each areal unit comprise the number of observations from the unit and the mean and variance of these observations; the approach deals with these data so that uncertainty in the areal means is accounted for. The SS approach is based on a point support covariance model, which is recovered from the areal data by restricted maximum likelihood. Validation using 339 georeferenced fields showed that the mean squared error (MSE) with the SS approach was slightly smaller than with the reference method that uses the postcode district (PCD) average as predictor. In PCDs with less than 25 sampled fields the reference MSE was reduced by 10{\%}, but with more than 100 sampled fields the MSEs were about equal. Validation of the prediction variances was performed using the standardized squared prediction errors (SSEs). The SS approach slightly over-estimated the prediction error variances, although the mean and median of the SSEs were not outside the expected range of values. The reference method under-estimated the prediction error variance. (C) 2014 Elsevier B.V. All rights reserved.",
keywords = "spatial interpolation, geostatistical approach, agricultural land, phosphorus, carbon, prediction, netherlands, variograms, inventory, model",
author = "D.J. Brus and T.G. Orton and D.J.J. Walvoort and J.A. Reijneveld and O. Oenema",
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Disaggregation of soil testing data on organic matter by the summary statistics approach to area-to-point kriging. / Brus, D.J.; Orton, T.G.; Walvoort, D.J.J.; Reijneveld, J.A.; Oenema, O.

In: Geoderma, Vol. 226-227, 2014, p. 151-159.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Disaggregation of soil testing data on organic matter by the summary statistics approach to area-to-point kriging

AU - Brus, D.J.

AU - Orton, T.G.

AU - Walvoort, D.J.J.

AU - Reijneveld, J.A.

AU - Oenema, O.

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PY - 2014

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N2 - The summary statistics (SS) approach to area-to-point (ATP) kriging is applied to map organic matter concentration in the topsoil of agricultural fields from aggregated soil testing data. The differences between the SS approach and earlier published ATP kriging methods are explained and the behavior of the SS predictions is illustrated with a simulation experiment. In conventional ATP kriging, the areal means provide the data and are treated as errorless. In the SS approach, the data for each areal unit comprise the number of observations from the unit and the mean and variance of these observations; the approach deals with these data so that uncertainty in the areal means is accounted for. The SS approach is based on a point support covariance model, which is recovered from the areal data by restricted maximum likelihood. Validation using 339 georeferenced fields showed that the mean squared error (MSE) with the SS approach was slightly smaller than with the reference method that uses the postcode district (PCD) average as predictor. In PCDs with less than 25 sampled fields the reference MSE was reduced by 10%, but with more than 100 sampled fields the MSEs were about equal. Validation of the prediction variances was performed using the standardized squared prediction errors (SSEs). The SS approach slightly over-estimated the prediction error variances, although the mean and median of the SSEs were not outside the expected range of values. The reference method under-estimated the prediction error variance. (C) 2014 Elsevier B.V. All rights reserved.

AB - The summary statistics (SS) approach to area-to-point (ATP) kriging is applied to map organic matter concentration in the topsoil of agricultural fields from aggregated soil testing data. The differences between the SS approach and earlier published ATP kriging methods are explained and the behavior of the SS predictions is illustrated with a simulation experiment. In conventional ATP kriging, the areal means provide the data and are treated as errorless. In the SS approach, the data for each areal unit comprise the number of observations from the unit and the mean and variance of these observations; the approach deals with these data so that uncertainty in the areal means is accounted for. The SS approach is based on a point support covariance model, which is recovered from the areal data by restricted maximum likelihood. Validation using 339 georeferenced fields showed that the mean squared error (MSE) with the SS approach was slightly smaller than with the reference method that uses the postcode district (PCD) average as predictor. In PCDs with less than 25 sampled fields the reference MSE was reduced by 10%, but with more than 100 sampled fields the MSEs were about equal. Validation of the prediction variances was performed using the standardized squared prediction errors (SSEs). The SS approach slightly over-estimated the prediction error variances, although the mean and median of the SSEs were not outside the expected range of values. The reference method under-estimated the prediction error variance. (C) 2014 Elsevier B.V. All rights reserved.

KW - spatial interpolation

KW - geostatistical approach

KW - agricultural land

KW - phosphorus

KW - carbon

KW - prediction

KW - netherlands

KW - variograms

KW - inventory

KW - model

U2 - 10.1016/j.geoderma.2014.02.011

DO - 10.1016/j.geoderma.2014.02.011

M3 - Article

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SP - 151

EP - 159

JO - Geoderma

T2 - Geoderma

JF - Geoderma

SN - 0016-7061

ER -