Sub-sampling a large physical soil archive for additional analyses to support spatial mapping; a pre-registered experiment in the Southern Nations, Nationalities, and Peoples Region (SNNPR) of Ethiopia

The value of physical archives of soil material from field sampling activities has been widely recognized. If we want to use archive material for new destructive analyses to support a task, such as spatial mapping, then an efficient sub-sampling strategy is needed, both to manage analytical costs and to conserve the archive material. In this paper we present an approach to this problem when the objective is spatial mapping by ordinary kriging. Our objective was to subsample the physical archive from the Ethiopia Soil Information System (EthioSIS) survey of the Southern Nations, Nationalities and Peoples Region (SNNPR) for spatial mapping of two variables, concentrations of particular fractions of selenium and iodine in the soil, which had not been measured there. We used data from cognate parts of surrounding regions of Ethiopia to estimate variograms of these properties, and then computed prediction error variances for maps in SNNPR based on proposed subsets of the archive of different size, selected to optimize a spatial coverage criterion (with some close sample pairs included). On this basis a subsample was selected. This is a preregistered experiment in that we have proposed criteria for evaluating the success of our approach, and are publishing that in advance of receiving analytical data on the subsampled material from the laboratories where they are being processed. A subsequent short report will publish the outcome. The use of preregistered trials is widely recommended and used in areas of science including public health, and we believe that it is a sound strategy to promote reproducible research in soil science.

Automated summary

This paper presents an approach for subsampling physical archives in spatial mapping using ordinary kriging. The method involves estimating variograms and prediction error variances based on data from surrounding regions, and selecting a subsample to optimize spatial coverage. This preregistered experiment aims to promote reproducible research in soil science.