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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.

Dietary zinc (Zn) deficiency is widespread globally, and in particular among people in sub-Saharan Africa (SSA). In Malawi, dietary sources of Zn are dominated by maize and spatially dependent variation in grain Zn concentration, which will affect dietary Zn intake, has been reported at distances of up to ~ 100 km. The aim of this study was to identify potential soil properties and environmental covariates which might explain this longer-range spatial variation in maize grain Zn concentration. Data for maize grain Zn concentrations, soil properties, and environmental covariates were obtained from a spatially representative survey in Malawi (n = 1600 locations). Labile and non-labile soil Zn forms were determined using isotopic dilution methods, alongside conventional agronomic soil analyses. Soil properties and environmental covariates as potential predictors of the concentration of Zn in maize grain were tested using a priori expert rankings and false discovery rate (FDR) controls within the linear mixed model (LMM) framework that informed the original survey design. Mean and median grain Zn concentrations were 21.8 and 21.5 mg kg−1, respectively (standard deviation 4.5; range 10.0–48.1). A LMM for grain Zn concentration was constructed for which the independent variables: soil pH(water), isotopically exchangeable Zn (ZnE), and diethylenetriaminepentaacetic acid (DTPA) extractable Zn (ZnDTPA) had predictive value (p