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Volume 7, issue 7
Biogeosciences, 7, 2081–2089, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Modeling soil system: complexity under your feet

Biogeosciences, 7, 2081–2089, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  02 Jul 2010

02 Jul 2010

A linear mixed model, with non-stationary mean and covariance, for soil potassium based on gamma radiometry

K. A. Haskard1, B. G. Rawlins2, and R. M. Lark1 K. A. Haskard et al.
  • 1Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK
  • 2British Geological Survey, Keyworth, Nottingham, NG12 5GG, UK

Abstract. In this paper we present a linear mixed model for the potassium content of soil across a large region of eastern England in which the mean is modelled as a linear function of the passive gamma-ray emissions of the earth surface in the energy interval commonly associated with potassium decay. Non-stationary models are proposed for the random effect, which is the variation not captured by this regression. Specifically, we assume that the local spectrum of the standardized random effect can be obtained by tempering a common (stationary) spectrum, that is to say raising its values to a power, the tempering parameter, which is itself modelled as a linear function of the radiometric data. This allows the "smoothness" of the random effect to vary locally. In addition the local spatially correlated variance and "nugget" variance (apparently uncorrelated given the resolution of the sampling) can also be modelled as a function of the radiometric data. Using the radiometric signal as a covariate gave some improvement in the precision of predictions of soil potassium at validation sites. In addition, there was evidence that non-stationary models for the random effect fitted the data better than stationary models, and this difference was statistically significant. Non-stationary models also appeared to describe the error variance of predictions at the validation sites better. Further work is needed on selection among alternative non-stationary models, since simple procedures used here, based on comparing log-likelihood ratios of nested models and the Akaike information criterion for non-nested models, did not identify the model which gave the best account of the prediction error variances at validation sites.

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