R.M. Lark

The assessment of point and diffuse metal pollution of soils from an urban geochemical survey of Sheffield, England

A model of soil variability as a continuous background process with superimposed point contamination was applied to 569 measurements of metal concentrations (Cr, Ni and Pb) in the topsoils of Sheffield, England. Robust estimators of the variogram were shown to be required to describe spatial variation of the metal concentrations at most sampled locations. This is diagnostic of the presence of a contaminant process. Values of the standardized kriging error from the cross-validation of each datum were used to identify spatial outliers for each metal. The ordinary kriged estimates of Cr, Ni and Pb were mapped after removing the outliers to estimate the background variation. Each of the 35 spatial outliers that occured in gardens have concentrations exceeding their Soil Guideline Value for residential land use with plant uptake, highlighting a potentially significant exposure pathway. The frequent observation of coal and furnace waste at these sites suggests that their dispersal, following domestic use and industrial processes, respectively, represents a significant point contaminant process. There was no evidence for spatial clustering of the point process. However, the spatial outliers of Cr and Ni showed a significant association with disturbed sites identified from historical land use maps, in part due to their prevalence in areas of historical steel manufacture. The magnitude of diffuse pollution for each metal in the urban soil was estimated by removing the spatial outliers and comparing robust measures of location with those from a survey of soils developed over the same parent materials in adjacent rural and peri-urban environments. The Winsorized mean Pb concentrations in urban topsoil (203 mg kg−1) were twice the value in the rural environment (101 mg kg−1), highlighting a very substantial diffuse Pb load to urban soils. The equivalent estimated diffuse components in urban soils for Cr and Ni were, respectively, 25% and 14% higher than the rural soils.

Generic Issues on Broad-Scale Soil Monitoring Schemes： A Review

Numerous scientific challenges arise when designing a soil monitoring network (SMN), especially when assessing large areas and several properties that are driven by numerous controlling factors of various origins and scales. Different broad approaches to the establishment of SMNs are distinguished. It is essential to establish an adequate sampling protocol that can be applied rigorously at each sampling location and time. We make recommendations regarding the within-site sampling of soil. Different statistical methods should be associated with the different types of sampling design. We review new statistical methods that account for different sources of uncertainty. Except for those parameters for which a consensus exists, the question of testing method harmonisation remains a very difficult issue. The establishment of benchmark sites devoted to harmonisation and inter-calibration is advocated as a technical solution. However, to our present knowledge, no study has addressed crucial scientific issues such as how many calibration sites are necessary and how to locate them.

Spectral and wavelet analysis of gilgai patterns from air photography.

Gilgais form repeating patterns that seem to be regular to some degree. We have analysed the patterns of gilgais as they appear on aerial photographs of the Bland Plain of New South Wales to discover to what degree they exhibit regularity and to estimate the spatial frequencies of the repeating patterns. We digitised rectangular sections of the photographs to produce grids of pixels at 0.063-mm intervals, equivalent to 1.3 m on the ground, with the optical density of each pixel recorded as a level of grey in the range 0 (black) to 255 (white). From the data we computed autocorrelograms and power spectra in both 1 and 2 dimensions and wavelet coefficients and wavelet packet coefficients and their variances. Spectra of many of the individual rows of the grids contained peaks corresponding to wavelengths of ≈32 m (at Caragabal) and ≈52 m (at Back Creek). The 2-dimensional spectra have rings of relatively large power corresponding to these wavelengths in addition to their central peaks. The 1-dimensional wavelet variances have pronounced peaks at the 16–32 pixel scale, corresponding to 20–40 m on the ground. The 2-dimensional wavelet analyses revealed peaks in the variances in the same range. Back Creek has in addition a low-frequency feature caused by the much darker than average gilgais in one corner of the digitised rectangle, and this is equally evident in the 1-dimensional analyses of rows that cross this corner, where the largest contribution to wavelet packet variation is at wavelength 84–167 m. Where this feature is absent, the best wavelet packet basis indicates that variation at frequencies at or below the repeating pattern is consistent with an underlying stationary random variable, while higher-frequency components show more complex (non-stationary) behaviour. We conclude that the gilgai patterns we have examined have a regularity with wavelengths in the range 30–50 m.

Three-Dimensional Mapping of Soil Chemical Characteristics at Micrometric Scale by Combining 2D SEM-EDX Data and 3D X-Ray CT Images

There is currently a significant need to improve our understanding of the factors that control a number of critical soil processes by integrating physical, chemical and biological measurements on soils at microscopic scales to help produce 3D maps of the related properties. Because of technological limitations, most chemical and biological measurements can be carried out only on exposed soil surfaces or 2-dimensional cuts through soil samples. Methods need to be developed to produce 3D maps of soil properties based on spatial sequences of 2D maps. In this general context, the objective of the research described here was to develop a method to generate 3D maps of soil chemical properties at the microscale by combining 2D SEM-EDX data with 3D X-ray computed tomography images. A statistical approach using the regression tree method and ordinary kriging applied to the residuals was developed and applied to predict the 3D spatial distribution of carbon, silicon, iron, and oxygen at the microscale. The spatial correlation between the X-ray grayscale intensities and the chemical maps made it possible to use a regression-tree model as an initial step to predict the 3D chemical composition. For chemical elements, e.g., iron, that are sparsely distributed in a soil sample, the regression-tree model provides a good prediction, explaining as much as 90% of the variability in some of the data. However, for chemical elements that are more homogenously distributed, such as carbon, silicon, or oxygen, the additional kriging of the regression tree residuals improved significantly the prediction with an increase in the R2 value from 0.221 to 0.324 for carbon, 0.312 to 0.423 for silicon, and 0.218 to 0.374 for oxygen, respectively. The present research develops for the first time an integrated experimental and theoretical framework, which combines geostatistical methods with imaging techniques to unveil the 3-D chemical structure of soil at very fine scales. The methodology presented in this study can be easily adapted and applied to other types of data such as bacterial or fungal population densities for the 3D characterization of microbial distribution.

Enhancing the value of field experimentation through whole-of-block designs

Precision agriculture (PA) offers opportunities for the development of new approaches to on-farm experimentation to assist farmers with site-specific management decisions. Traditional agricultural experiments are usually implemented in fields with the least possible soil heterogeneity under the assumption that responses to inputs and inherent variation of the soil are additive components of yield variation. However, because the soil in typical fields is not homogeneous, PA has much to offer. Farmers faced with variable conditions need to optimize their management to the variation over space and time on their farm, a problem that is not solved by conventional approaches to experimentation. New designs for on-farm experiments were developed in the 1990s for cereal production in which the whole field was used for the experiment rather than small plots. We explore the extension of this type of experiment to a vineyard in the Clare Valley of South Australia aiming to evaluate options to increase grape yield and vine vigour. Manually sampled indices of vine performance measured on georeferenced ‘target’ grapevines were analysed geostatistically. The major advantage of such an approach is that the spatial variation in response to experimental treatments can be examined. Linear models of coregionalization, pseudo cross-variograms and standardized ordinary cokriging are used to map treatment responses over the experimental area and also the differences between them. The results indicate that both treatment responses and the significance of differences between them are spatially variable. Thus, we conclude that whole-of-block on-farm trials are useful in vineyards.

The Analysis of Spatial Experiments

Anyone with an interest in precision agriculture has already formed a hypothesis that the field is a sub-optimum management unit for cropping. The role of experimentation is to test this hypothesis. Geostatistics can play an important role in analysing experiments for site-specific crop management: put simply, spatial autocorrelation must be accounted for if one is to draw valid inferences. We provide here some background to the basic concepts of agronomic experimentation. We then consider two broad classes of experimental design for precision agriculture (management-class experiments and local-response experiments), and show, with the aid of case studies, how each may be analysed geostatistically. Ultimately though, if farmers are compelled to use relatively simple designs and less formal analyses, then researchers must follow and adapt their geostatistical analyses accordingly.

Designing a sampling scheme to reveal correlations between weeds and soil properties at multiple spatial scales

Summary Weeds tend to aggregate in patches within fields, and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at various scales, the strength of the relations between soil properties and weed density would also be expected to be scale‐dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We developed a general method that uses novel within‐field nested sampling and residual maximum‐likelihood (reml) estimation to explore scale‐dependent relations between weeds and soil properties. We validated the method using a case study of Alopecurus myosuroides in winter wheat. Using reml, we partitioned the variance and covariance into scale‐specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales, we optimised the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment.

On Soil Carbon Monitoring Networks

The design of a Soil Monitoring Network (SMN) poses numerous scientific challenges, especially for the assessment of national or continental areas. The task is particularly challenging because soil carbon content and stocks are driven by controlling factors of disparate origins and scales. Various approaches to the establishment of SMNs are reviewed here. Frameworks for soil monitoring exist in numerous countries, especially in Europe. Although some countries work using standard monitoring methodologies and coverage, there is considerable variation in approaches to the monitoring of soil carbon even within a country. In addition to achieving harmonization, there are generic issues which must be addressed when SMNs are established and operated: the SMN should be effective for different soils, and it must enable the detection of change in soil carbon at relevant spatial and temporal scales with adequate precision and statistical power. We present examples which address these issues and summarize previous reviews on this topic. It is essential to establish an adequate sampling protocol which can be applied at each sampling location and time. The design must address the questions that the user of data has and provide information with accuracy and precision at the spatial and temporal scales that match the users’ needs. Furthermore, the design must match the methods of analysis so that statistical assumptions can be justified. At the global scale, the question of harmonizing sampling and analytical methods is difficult. Here, we propose the establishment of benchmark sites devoted to harmonization and inter-calibration. We present a case study from France which addresses scientific issues such as how many calibration sites are necessary and how they should be selected.

Nested sampling and spatial analysis for reconnaissance investigations of soil: an example from agricultural land near mine tailings in Zambia

Summary A reconnaissance survey was undertaken on soil near mine tailings to investigate variation in the content of copper, chromium and uranium. A nested sampling design was used. The data showed significant relations between the content of copper and uranium in the soil and its organic matter content, and a significant spatial trend in uranium content with distance from the tailings. Soil pH was not significantly related to any of the metals. The variance components associated with different scales of the sample design had large confidence intervals, but it was possible to show that the random variation was spatially dependent for all spatial models, whether for variation around a constant mean, or with a mean given by a linear effect of organic matter or distance to the tailings. For copper, we showed that a fractal or multifractal random model, with equal variance components for scales in a logarithmic progression, could be rejected for the model of variation around the fixed mean. The inclusion of organic matter as an explanatory factor meant that the fractal model could no longer be rejected, suggesting that the effect of organic matter results in spatial variation that is not scale invariant. It was shown, taking uranium as a case study, that further spatially nested sampling to estimate scale-dependent variance components, or to test a non-fractal model with adequate power, would require in the order of 200–250 samples in total. Highlights Sampling was undertaken to investigate spatial variation of metal content in soil near mine tailings. Chromium and uranium were related to soil organic matter content; uranium showed a spatial trend. Spatial variation was scale dependent, variation of copper was not scale-invariant. Characterizing random spatial variation requires substantial sample effort.

Controlling the marginal false discovery rate in inferences from a soil dataset with α-investment

Large datasets on soil provide a temptation to search for relations between variables and then to model and make inferences about them with statistical methods more properly used to test preplanned hypotheses on data from designed experiments or sample surveys. The control of family-wise error rate (FWER) is one way to improve the robustness of inferences from tests of multiple hypotheses. In its simplest form, hypothesis testing with FWER control lacks statistical power. The α-investment approach to controlling the marginal false discovery rate is one method proposed to improve statistical power. In this paper I outline the α-investment approach and then demonstrate it in the analysis of a dataset on the rate of CO2 emission from incubated intact cores of soil from a transect over Cretaceous rocks in eastern England. Hypotheses are advanced after considering the literature and examining relations among the available soil variables that might be proposed as explanatory factors for the variation of CO2 emissions. They are then tested in sequence with α-investment, such that the rejection of null hypotheses increases the power to reject later ones, while controlling the overall marginal false discovery rate at a specified value. This paper illustrates the use of α-investment to test a multiple set of hypotheses on a soil dataset; statistical power is improved by ordering the sequence of hypotheses on the basis of process knowledge. The approach could be useful in other areas of soil science where covariates must be selected for predictive statistical models, notably in the development of pedotransfer functions and in digital soil mapping. Highlights α-investment controls marginal false discovery rate in statistical inference. Hypotheses were advanced about soil factors that affect CO2 emission from soil. These hypotheses were tested in sequence with control of marginal false discovery rate. Soil properties, land use and parent material were significant predictors.