Good quality spatial information about the soil at the appropriate scale is a necessary requirement for understanding soil functioning, its quality and value. DSM provides this information.

Soils are by nature heterogeneous and consequently obtaining exhaustive spatial information on soil properties is resource and time consuming. There is also an enormous amount of legacy and existing soil information that is semi-quantitative or qualitative rather than strictly quantitative. Digital Soil Mapping is the nexus where existing and new environmental data are combined with quantitative landscape models to supply much needed spatial information on soil properties. Our interest is in the development and use of mathematical and statistical models to generate predictions on soil properties. These mathematical models are then used to predict soil properties at unsampled locations with uncertainty at local, regional, national and global scales. Integrating this information into soil process or functional models results in a digital assessment of land capability.

Xiong, X., Grunwald, S., Corstanje R., Yu, C., Bliznyuk, N., Scale-dependent variability of soil organic carbon coupled to land use and land cover. Soil Tillage Research, 160: 101-109 (2016).

Albanito, F., Beringer, T., Corstanje, R., Poulter, B., Stephenson, A. Zawadzka, J. Smith, P. Carbon Implications of Converting Cropland to Bioenergy Crops or Forest for Climate Mitigation: A Global Assessment. Global Change Biology Bioenergy, 8: 81–95 (2016).

Simo, I., Schulte, R.P.O., Corstanje, R., Hannam, J., Creamer, R.E. Validating digital soil maps using soil taxonomic distance: A case study of Ireland.  Geoderma Regional, 5, 188–197 (2015)

Taalab, K., Corstanje, R., Whelan, M.K., Creamer, R., On the Application of Bayesian Belief Networks in Digital Soil Mapping. Geoderma 259-260: 134-148 (2015)

Taalab, K., Corstanje, R., Whelan, M.K., Creamer, R., On the use of expert knowledge in Soil Mapping. European Journal of Soil Science, 66: 930–941 (2015)

Zawadzka J., Mayr, T., Bellamy, P., Corstanje, R., Comparing physiographic maps with different categorisations, Geomorphology, 231: 94-100 (2015).

Veronesi, F., Corstanje, R., Mayr, T. Landscape scale estimation of soil carbon stock in three-dimensions. Science for the Total Environment. 487: 578-586 (2014).

Taalab, K., Corstanje, R., Creamer, R., Whelan, M. Predicting soil bulkdensity at the landscape scale and its consequences to uncertainty in C stock estimations. Biogeosciences, 10:4691-4704 (2013).

Cavazzi, S., Corstanje R., Mayr T., Hannam J., Fealy R.. Are fine resolution digital elevation models always the best choice in digital soil mapping? Geoderma, 195–196: 111-121 (2013).