Digital soil assessments: Beyond DSM
Introduction
Over the past decade many research papers have dealt with Digital Soil Mapping (DSM), which has been defined as the creation and population of spatial soil information by the use of field and laboratory observational methods coupled with spatial and non-spatial soil inference systems (McBratney et al., 2003, Lagacherie and McBratney, 2007). These techniques have been largely accepted and used by the soil science community (Lagacherie et al., 2007). DSM can be used for two downstream applications which will be explained and defined in this paper: namely, Digital Soil Assessment (DSA) and Digital Soil Risk Assessment (DSRA). DSA is the quantitative modelling of difficult-to-measure soil attributes, necessary for assessing threats to soil (erosion, decline of organic matter, compaction, salinisation, landslides, sealing, floods, decline of biodiversity) and soil functions (biomass production, environmental interactions, physical support, production of raw material, cultural heritage, carbon pool, source of biodiversity) (European Commission, 2006b), using DSM outputs.
DSRA is the quantitative evaluation of soil-related scenarios for providing policy guidance using the outputs from DSA plus socio-economic data and more general information on the environment.
DSM is therefore a precursor to DSA, and DSA is in turn a precursor to DSRA (Fig. 1). This puts some constraints on DSM with respect to required outputs and their level of precision.
The paper considers the scope of DSM in relation to the concepts and the requirements of DSA and DSRA, and is illustrated by means of examples.
Section snippets
The scope of digital soil mapping
The outputs of DSM are soil properties or soil classes derived by a spatial inference system. The procedure is based on a number of predictive approaches involving environmental covariates, prior soil information in point and map form, (McBratney et al., 2003) and field and laboratory observational methods coupled with spatial and non-spatial soil inference systems (Lagacherie et al., 2007). It allows for the prediction of soil properties or classes using soil information and environmental
The scope of DSA
DSA comprises two main processes (Fig. 1): (1) a soil attribute space inference system and (2) an evaluation of the soil functions and the threats to soils. A description of each these two processes follow together with the suggested advice appropriate to policy makers.
The scope of DSRA
Digital Soil Risk Assessment (DSRA) integrates environmental, political, social, economical parameters and the DSA outputs (Fig. 1, Fig. 2). The purpose is then to build, model and test soil-related environmental issues, but also to measure the accuracy of the predicted risk so that the modeller can provide a degree of uncertainty to decision-makers.
The following section deals with how to build scenarios, how to evaluate the accuracy of the risk assessment, and then how decision-makers can use
Background to the problem
The Earth's climate is a complex system in which many factors interact to produce regional climates and local weather systems. Climate can favour life and life can change climate. Human activities have apparently altered the Earth's atmosphere and changed the balance of our natural climate (UNFCCC, 2004). Greenhouse gases (GHGs) such as carbon dioxide, methane and nitrous oxide exist naturally in the atmosphere, trapping heat and warming the air in much the same way glass warms the inside of a
Conclusions
This paper has attempted to devise, illustrate and discuss a schema for digital soil mapping, digital soil assessment and digital risk assessment. The schema shows a logical flow of data from the soil sampling to the end-user with appropriate feedbacks. The main points are:
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The combination of DSM with DSA is very powerful since DSA provides the basic requirements of DSM and DSM provides the necessary inputs to DSA.
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The digital soil mapping community needs to be much more aware of end-user
Acknowledgments
This review has been based partly on the report of the (European) Digital Soil Mapping Working Group: L. Boruvka, J. Daroussin, E, Dobos, P. Finke, A. Freudenschuss, M.H. Greve, T. Hengl, V. Hennings, G.H.M. Heuvelink, B. Houskova, D. King, P. Lagacherie, H. Lilja, E. Micheli, L. Pasztor, H. I., Reuter, J. Sobocka, F. Terribile, G. Tóth, B. Vrščaj (members of the European Soil Bureau Network and of the Joint Research Centre). We would like to gratefully thank them.
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