Data on environmental variables are subject to measurement error (ME), and it is important that this ME
should be considered in any statistical analysis. Environmental datasets commonly consist of positive random
variables that have skewed distributions. Measurements are then usually reported with a theoretical
detection limit (DL); measurements less than this DL are deemed not to be statistically different from zero,
and these data are then treated by setting them to an arbitrary value of half of the DL. The skew of the data is
dealt with by taking logarithms, and the geostatistical analysis performed for the transformed variable. The
DL approach, however, is somewhat ad hoc, and in this paper we investigate an alternative approach to
incorporate such measurements in a geostatistical analysis, namely Bayesian hierarchical modelling. This
approach incorporates ‘soft’ data (i.e., imprecise information), and we use soft data to represent the
information that each measurement provides. We can use this approach to combine a lognormal model to
describe the spatial variability with a Gaussian model for the measurement error. We apply the methods to a
dataset on the selenium (Se) concentration in the topsoil throughout the East Anglia region of the UK. We
compare the maps of predictions produced by the approaches, and compare the methods based on their
ability to predict the Se concentration and the associated uncertainty.We also consider how the geostatistical
predictions might be used to aid the effective management of Se-deficient soils, and compare the methods
based on the costs that might be incurred from the selected management strategies. We found that the
Bayesian approach based on soft data resulted in smoother maps, reduced the errors of the predictions, and
provided a better representation of the associated uncertainty. The cost resulting from Se-deficient soils was
generally lower when we used the soft data approach, and we conclude that this provides a more effective
and interpretable model for the data in this case study, and possibly for other environmental datasets with
measurements close to a DL
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