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Statistical sampling approaches for soil monitoring
Author(s) -
Brus D. J.
Publication year - 2014
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/ejss.12176
Subject(s) - sampling (signal processing) , sampling design , statistics , statistical inference , weighting , computer science , selection (genetic algorithm) , inference , mathematics , machine learning , artificial intelligence , medicine , filter (signal processing) , population , demography , sociology , computer vision , radiology
Summary This paper describes three statistical sampling approaches for regional soil monitoring, a design‐based, a model‐based and a hybrid approach. In the model‐based approach a space‐time model is exploited to predict global statistical parameters of interest such as the space‐time mean. In the hybrid approach this model is a time‐series model of the spatial means. In the design‐based approach no model is used: estimates are model‐free. Full design‐based inference requires that both sampling locations and times are selected by probability sampling, whereas the hybrid approach requires probability sampling of locations only. In a case study on soil eutrophication and acidification, a rotational panel design was implemented with probability sampling of locations and non‐probability sampling of times. The hybrid and model‐based predictions of the space‐time means and trend of the mean for pH and ammonium at three depths in the soil profile were very similar. For pH the standard errors of the space‐time means were about equal, but for ammonium the full model‐based predictor was more precise than the hybrid predictor. For soil monitoring I advocate the selection of sampling locations by probability sampling so that the statistical inference approach is flexible. Selecting locations by a self‐weighting probability sampling design ensures that the model‐based predictor is not affected by selection bias.