Abstract
Purpose-built soil monitoring networks have been established in many countries to identify where soil functionality is threatened and to target remediation initiatives. An alternative to purpose-built soil monitoring networks is to use legacy soils information. Such information yields almost instant assessments of soil change but the results should be interpreted with caution since the information was not collected with monitoring in mind. We assess the threat of soil acidification in Victoria using two legacy datasets: (i) the Victorian Soils Information System (VSIS) which is a repository of the results of soil analyses conducted for scientific purposes since the 1950s and (ii) a database of 75 000 routine soil test results requested by farmers between 1973 and 1993. We find that the VSIS measurements are clustered in space and time and are therefore suitable for local rather than broad-scale assessments of soil change. The farmers’ results have better spatial and temporal coverage and space-time models can be used to quantify the spatial and temporal trends in the pH measurements. However, careful validation of these findings is required since we do not completely understand how the measured paddocks were selected and we cannot be certain that sampling or laboratory protocols have not changed with time.
Highlights
Around the world there are threats to soils and the important functions which they perform
Often a purposive design consists of a regular grid since this ensures that the samples are evenly dispersed over the study area and are suitable for producing maps
The main advantage of probabilistic designs is that they can be analyzed by classical statistical methods which require very few assumptions and lead to unbiased estimates of the mean and variance of the status or rate of change of property of interest. If these classical methods are applied to data collected according to a purposive design biased estimates can occur
Summary
Around the world there are threats to soils and the important functions which they perform. There is an important role for broader-scale soil monitoring networks to determine more general trends Such information permits governments to devise targeted policies to maintain soil quality where it is under threat and to test whether such policy measures have been effective. The main advantage of probabilistic designs is that they can be analyzed by classical statistical methods which require very few assumptions and lead to unbiased estimates of the mean and variance of the status or rate of change of property of interest. If these classical methods are applied to data collected according to a purposive design biased estimates can occur. Note that in the case of a regular grid the average of the collected data is an unbiased estimate of the mean of the soil property because locations are not preferentially sampled according to any attribute of the soil
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