Entering the digital world (Pedometrics 2009)

Development in pedometrics has not only shaped the research agenda in soil science but also attracted the attention of practitioners from other communities such as environmental modelling and land management who require digital information on soils. At the same time, demands from these communities and developments in information technology help to fuel and drive the research agenda of pedometrics. These factors have combined to draw scientists with diverse backgrounds and interests into the field of pedometrics over its short history as a distinctive subdiscipline of soil science

Strengthening Capacity in Environmental Physics, Hydrogeology and Statistics for Conservation Agriculture Research: Statistical Checklist for Planning Experiments

This document presents a checklist to be used when planning experiments. It is produced initially for the use of Working Groups of the CEPHaS project, but is also offered as a wider resource for capacity strengthening in conservation agriculture research. In preparing this checklist we are glad to acknowledge the inspiration of J.R.N. Jeffers’s Statistical Checklist series (e.g. Jeffers, 1978). Jeffers’s checklist is available in open-access form (see the references for a link) and we recommend that it is read in conjunction with this checklist. This checklist is not a substitute for a discussion with a statistical advisor prior to committing to a particular experimental design. Rather it is intended that it should help identify possible issues which a particular experiment might face, and to facilitate discussion with a statistician by ensuring that key issues have been thought about in advance. In the context of the CEPHaS project it is proposed that this checklist be completed and responses recorded in a separate document and sent to the co-leads of Working Group 4 (Murray Lark and Joseph Chimungu) along with any additional commentary as a basis for consultation and advice

Information for agriculture from regional geochemical surveys: the example of soil pH in the Tellus and Tellus Border data

The variation of pH of pasture soils across the Tellus and Tellus Border survey area has been analysed. Geostatistical methods allow us to quantify the uncertainty in mapped soil pH and its implications for the health and management of pasture soils. Soil pH indicates that there is a widespread requirement for liming of pasture soils across the area. We exemplify how the uncertainty in statistical predictions can be communicated to a general audience using a verbal scale

How should a spatial-coverage sample design for a geostatistical soil survey be supplemented to support estimation of spatial covariance parameters?

We use an expression for the error variance of geostatistical predictions, which includes the effect of uncertainty in the spatial covariance parameters, to examine the performance of sample designs in which a proportion of the total number of observations are distributed according to a spatial coverage design, and the remaining observations are added at supplementary close locations. This expression has been used in previous studies on numerical optimization of spatial sampling, the objective of this study was to use it to discover simple rules of thumb for practical geostatistical sampling. Results for a range of sample sizes and contrasting properties of the underlying random variables show that there is an improvement on adding just a few sample points and close pairs, and a rather slower increase in the prediction error variance as the proportion of sample points allocated in this way is increased above 10 to 20% of the total sample size. One may therefore propose a rule of thumb that, for a fixed sample size, 90% of sample sites are distributed according to a spatial coverage design, and 10% are then added at short distances from sites in the larger subset to support estimation of spatial covariance parameters

Distinct spatial dependency of carbon distribution between soil pools in grassland SOIL

Grassland soils play a key role in climate change and food security, and carbon (C) and nitrogen (N) mineralization is central to this. Although there are a number of mathematical models available to estimate C and N mineralization, they do not encompass the variability of the process and there is uncertainty in their predictions. The input parameters of the SOMA model (Soil Organic Matter “A”) have been conceptualized and validated to predict mineralization in arable soils. The objective of this research was to measure the spatial dependence of the input parameters in order to further ob - tain spatial predictions of mineralisation in a grassland system. A nested design was applied using sampling intervals of 30 m, 10 m, 1 m, and 0.12 m as sources of variation. From each sampling point a soil sample was taken (0-23 cm) and physical sequential fractionation was applied to obtain the free light fraction (FLF) and intra-aggregate light fraction (IALF). The C and N contents in the fractions were measured by mass spectrometry, and the results analysed by residual maximum likelihood (REML) to obtain components of variance at each stage, and then accumulated to plot the approach to a variogram. Both fractions showed spatial dependence at the finest scales measured, and the general pattern was different from that in an arable site. The recommended soil sampling interval where C and N mineralization predictions would be spatially distributed according to the correlation of input light fractions parameters of SOMA is 0.5m

Antecedent precipitation as a potential proxy for landslide incidence in South West UK

This paper considers the effects of antecedent precipitation on landslide incidence in the UK. During 2012-2013 an extraordinary amount of precipitation resulted in an increase in the number of landslides reported in the UK, highlighting the importance of hydrogeological triggering. Slope failures (landslides on engineered slopes) in particular caused widespread disruption to transport services and damage to property. SW England and S Wales were most affected. Easy-to-use and accessible indicators of potential landslide activity are required for planning, preparedness and response and therefore analyses have been carried out to determine whether antecedent effective precipitation can be used as a proxy for landslide incidence. It is shown that for all landslides long-term antecedent precipitation provides an important preparatory factor and that relatively small landslides, such as slope failures, occur within a short period of time following subsequent heavy precipitation. Deep-seated, rotational landslides have a longer response time as their pathway to instability follows a much more complex hydrogeological response. Statistical analyses of the BGS landslide database and of weather records has enabled determination of the probability of at least one landslide occurring based on antecedent precipitation signals for SW England and S Wales. This ongoing research is of part of a suite of analyses to provide tools to identify the likelihood of regional landslides occurrence in the UK

Uncertainty in geological interpretations : Effectiveness of expert elicitations

We would like to thank all those who took part in our elicitations, as well as all those who helped in their facilitation. This work was undertaken while C.H. Randle held a joint University of Aberdeen, College of Physical Science Ph.D. Award and British Geological Survey University Funding Initiative (BUFI) Ph.D. Studentship at Aberdeen University, through Natural Environment Research Council (NERC). The contributions by C.H. Randle, R.M. Lark, and A.A. Monaghan are published with the permission of the Executive Director of BGS (NERC). The authors would like to thank Hazel Gibson and an anonymous reviewer for their comments on the manuscript and confirm that all views expressed are the opinions of the authors.Peer reviewedPublisher PD

Can uncertainty in geological cross-section interpretations be quantified and predicted?

This work was undertaken while C.H. Randle held a joint British Geological Survey University Funding Initiative (BUFI) and University of Aberdeen, College of Physical Sciences Ph.D. Studentship at Aberdeen University. The contributions by C.H. Randle, R.M. Lark, and A.A. Monaghan are published with the permission of the Executive Director of the British Geological Survey Natural Environment Research Council. We would also like to thank all those who took part in both experiments as well as the many people who have given input on our results.Peer reviewedPublisher PD

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

When unlikely outcomes occur: the role of communication format in maintaining communicator credibility

The public expects science to reduce or eliminate uncertainty (Kinzig & Starrett, 2003), yet scientific forecasts are probabilistic (at best) and it is simply not possible to make predictions with certainty. Whilst an ‘unlikely’ outcome is not expected to occur, an ‘unlikely’ outcome will still occur one in five times (based on a translation of 20%, e.g. Theil, 2002), according to a frequentist perspective. When an ‘unlikely’ outcome does occur, the prediction may be deemed ‘erroneous’, reflecting a misunderstanding of the nature of uncertainty. Such misunderstandings could have ramifications for the subsequent (perceived) credibility of the communicator who made such a prediction. We examine whether the effect of ‘erroneous’ predictions on perceived credibility differs according to the communication format used. Specifically, we consider verbal, numerical (point and range [wide / narrow]) and mixed format probability expressions. We consistently find that subsequent perceptions are least affected by the ‘erroneous’ prediction when it is expressed numerically, regardless of whether it is a point or range estimate. Our findings suggest numbers should be used in consequential risk communications regarding ‘unlikely’ events, wherever possible