Combining two national‐scale datasets to map soil properties, the case of available magnesium in England and Wales

Given the costs of soil survey it is necessary to make the best use of available datasets, but data that differ with respect to some aspect of the sampling or analytical protocol cannot be combined simply. In this paper we consider a case where two datasets were available on the concentration of plant‐available magnesium in the topsoil. The datasets were the Representative Soil Sampling Scheme (RSSS) and the National Soil Inventory (NSI) of England and Wales. The variable was measured over the same depth interval and with the same laboratory method, but the sample supports were different and so the datasets differ in their variance. We used a multivariate geostatistical model, the linear model of coregionalization (LMCR), to model the joint spatial distribution of the two datasets. The model allowed us to elucidate the effects of the sample support on the two datasets, and to show that there was a strong correlation between the underlying variables. The LMCR allowed us to make spatial predictions of the variable on the RSSS support by cokriging the RSSS data with the NSI data. We used cross‐validation to test the validity of the LMCR and showed how incorporating the NSI data restricted the range of prediction error variances relative to univariate ordinary kriging predictions from the RSSS data alone. The standardized squared prediction errors were computed and the coverage of prediction intervals (i.e. the proportion of sites at which the prediction interval included the observed value of the variable). Both these statistics suggested that the prediction error variances were consistent for the cokriging predictions but not for the ordinary kriging predictions from the simple combination of the RSSS and NSI data, which might be proposed on the basis of their very similar mean values. The LMCR is therefore proposed as a general tool for the combined analysis of different datasets on soil properties

Methods for estimating types of soil organic carbon and their application to surveys of UK urban areas

The occurrence of substantial quantities of black carbon (BC) in urban soil due to local dispersal following incomplete combustion of fossil fuel complicates the determination of labile soil organic carbon (SOC). Estimates of SOC content were made from loss on ignition (LOI) analyses undertaken on samples (0–15 cm depth) from comprehensive soil geochemical surveys of three UK urban areas. We randomly selected 10 samples from each decile of the LOI distribution for each of the surveys of Coventry (n = 808), Stoke-on-Trent (n = 737) and Glasgow (n = 1382) to investigate the proportions of labile SOC and BC. We determined their total organic carbon (TOC) and BC contents, and by difference the labile SOC content, and investigated the linear relationship of the latter with SOC estimates based on LOI analyses. There was no evidence for a difference in the slope of the regression for the three urban areas. We then used a linear regression of labile SOC based on LOI analyses (r2 = 0.81) to predict labile SOC for all survey samples from the three urban areas. We attribute the significantly higher median BC concentrations in Glasgow (1.77%, compared with 0.46 and 0.59% in Coventry and Stoke-on-Trent) to greater dispersal of coal ash across the former. An analysis of the 30 samples showed that LOI at 450 C accounts for a consistent proportion of BC in each sample (r2 = 0.97). Differences between TOC (combustion at 1050 C after removal of inorganic carbon) and an LOI estimate of SOC may be a cost-effective method for estimation of BC. Previous approaches to estimation of urban SOC contents based on half the mean SOC content of the equivalent associations under pasture, underestimate the empirical mean value