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In this study, we propose to establish a framework for the study of the spatial variability of the soils found in the floodplain of the Sarine River and for the visualisation of soil distribution patterns in two- and three-dimensions (2-D, 3-D). This environment is characterised by a large lateral and vertical spatial variability of soils that corresponds to the temporal and spatial variations of...
Quantitative techniques for spatial prediction in soil survey are developing apace. They generally derive from geostatistics and modern statistics. The recent developments in geostatistics are reviewed particularly with respect to non-linear methods and the use of all types of ancillary information. Additionally analysis based on non-stationarity of a variable and the use of ancillary information...
Although the science of soil was established about 150 years ago with the modern soil science taking off after the Second World War, the new Millennium has brought other challenges and new opportunities. Rapidly increasing population in countries that can least afford it have made them food-insecure. With inadequate inputs in agriculture, developing countries are degrading their lands rapidly and...
Nowadays, French soil scientists tend to gather new and existing soil data into a common database. The use of this database potentially allows for resolving environmental issues, largely through soil mapping. The purpose of this study is to present a methodology for mapping soil types illustrated by typical observations in the soil database, in this case from the La Rochelle area on the French Mid-Atlantic...
We review various recent approaches to making digital soil maps based on geographic information systems (GIS) data layers, note some commonalities and propose a generic framework for the future. We discuss the various methods that have been, or could be, used for fitting quantitative relationships between soil properties or classes and their 'environment'. These include generalised linear models,...
Previous workers have proposed the use of multivariate geostatistics for the problem of estimating temporal change in soil properties for soil monitoring, but this has yet to be evaluated. We present a case study of this approach from the Humber–Trent region in North East England. We extracted data from two sources on cobalt, nickel and vanadium concentrations in the topsoil on two dates. Auto-variograms...
In this paper we use regression kriging to improve predictions of a hard-to-measure soil variable based on an established process model. In our case study the target variable is the rate of nitrous oxide (N 2 O) emission from soil. We used three different process models that each have the same soil properties as input variables, but make different assumptions about the kinetics of denitrification...
Soil scientists often have many covariates that they can use to predict soil properties by regression. They are ill-advised to use all available covariates uncritically, but methods for selection (whether informal or formal) that depend on data for both the predictors and the predictand are subject to selection bias. In this paper we propose an approach that uses automated methods for selecting variables,...
Legacy soil data form an important resource for digital soil mapping and are essential for calibration of models for predicting soil properties from environmental variables. Such data arise from traditional soil survey. Methods of soil survey are generally empirical and based on the mental development of the surveyor, correlating soil with underlying geology, landforms, vegetation and air-photo interpretation...
Over the last 10 years Digital Soil Mapping (DSM) has emerged as a credible alternative to traditional soil mapping. However, DSM should not be seen as an end in itself, but rather as a technique for providing data and information for a new framework for soil assessment which we call Digital Soil Assessment (DSA). Although still somewhat fluid, a procedural framework for DSM and DSA with its links...
The history of digital soil mapping and modeling (DSMM) is marked by adoption of new mapping tools and techniques, data management systems, innovative delivery of soil data, and methods to analyze, integrate, and visualize soil and environmental datasets. DSMM studies are diverse with specialized, mathematical prototype models tested on limited geographic regions and/or datasets and simpler, operational...
Diffuse reflectance spectroscopy (DRS) is increasingly being used to predict numerous soil physical, chemical and biochemical properties. However, soil properties and processes vary at different scales and, as a result, relationships between soil properties often depend on scale. In this paper we report on how the relationship between one such property, cation exchange capacity (CEC), and the DRS...
Self-citation is common practice in most sciences but it differs between disciplines, countries and journals. Here we report on self-citation in soil science. We investigated citations in the major soil science journals and conducted an analysis on a country basis and for the subdiscipline of Pedometrics. It was found that the median rate of individual self-citation was 12%, and ranged from 5 to 60%...
This paper demonstrates for the first time the use of Markov Chain Monte Carlo (MCMC) simulation for parameter inference in model-based soil geostatistics. We implemented the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm to jointly summarize the posterior distribution of variogram parameters and the coefficients of a linear spatial model, and derive estimates of predictive...
The unique selling point of pedometricians still is close to their cradle: ability to map. Irrespective of the many scientific achievements one can ask if offering mapping ability, allbeit in various contexts, is all a pedometrician can do to bring forward soil science's broader agenda. This paper identifies, within some of the hotter issues on the soil science agenda, activities that need the input...
Multiple-point statistics (MPS) is a collection of geostatistical simulation algorithms that uses a multiple-point training image (TI) as structural model instead of a two-point variogram. MPS allows to simulate more complex random fields, like phenomena characterized by spatial connectivity. A very recent development is multivariate MPS in which an ensemble of variables can be simulated simultaneously...
Deficiency or excess of certain trace elements in the soil causes problems for agriculture, including disorders of grazing ruminants. Geostatistics has been used to map the probability that trace element concentrations in soil exceed or fall below particular thresholds. However, deficiency or toxicity problems may depend on interactions between elements in the soil. Here we show how cokriging from...
Soil classification has progressed with the introduction of computers in the mid 20th century to the point where algorithms can be used to organise soil information into clusters that correspond with soil classes. Algorithms such as fuzzy-k means perform well, but can be biased by extreme data. Fuzzy-k means with extragrades was devised to accommodate this problem but estimating the amount of extragrades...
The variation of soil properties down a profile is usually considered continuous. The aim of this study was to develop and test a methodology to model the continuous vertical and lateral distributions of SOC stocks in Scottish soils making explicit the modelling and spatial uncertainty of the results. A comparison with regression kriging and other depth function methods is provided to show that better...
A method is presented for assessing soil natural capital based on the principles of land evaluation. Policymakers are adopting concepts of flows of ecosystem services, and the natural capital stocks that support them, to provide more integrated analyses of the trade-offs between environmental, economic, social and cultural outcomes from land use. Soil is frequently overlooked in these analyses. Techniques...
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