A collection of high-resolution soil attribute grid surfaces for land areas in Tasmania. There are 17 soil attribute products available that delineate specific soil properties at standardized soil depths (0-5cm, 0-15cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm) and grid resolutions (30m and 80m grid resolutions). Soil attributes available include Available Water Capacity (AWC, %), Bulk Density (BD, Mg/m3), Soil Texture (Clay; Sand; Silt, %), Coarse Fragments (CF, %), Soil Depth (cm), Soil Drainage, Electrical Conductivity (EC/ECse, dS/m), Field Capacity (FC), Soil Organic Carbon (SOC, %), Soil Permeability, pH, Exchangeable Calcium (ExCa, ppm), Exchangeable Magnesium (ExMg, ppm) and Depth to Sodic layer (Sodic Depth, cm). A document that describes each dataset and associated nomenclature can be accessed here: https://nrmdatalibrary.dpipwe.tas.gov.au/FactSheets/WfW/ListMapUserNotes/Inventory_DSM_Tas.pdf Note that these products were developed using datasets held by the Tasmanian Department of Primary Industries Parks Water & Environment (DPIPWE) Soils Database, hosted on the Tasmanian Natural Values Atlas (https://www.naturalvaluesatlas.tas.gov.au/). The mapping was made by using spatial modelling and digital soil mapping (DSM) techniques with the outputs available via a Web Map Service (WMS): https://spatial.dpipwe.tas.gov.au/naturalassets/Soil/wms Or viewed in the following Web Map application: https://arcg.is/4PaT8

The soil attribute maps are generated using spatial modelling and digital soil mapping (DSM) techniques based on soil inventory site data. These originate from the DPIPWE soils database, hosted on the Tasmanian Natural Values Atlas (https://www.naturalvaluesatlas.tas.gov.au/) and involves a compilation of various historical soil surveys undertaken by DPIPWE, CSIRO, Forestry Tasmania and the University of Tasmania that contain morphological and laboratory data for all the soil sites. To interpolate the soil site data, a raster stack of all covariate datasets (includes Digital Elevation Model and derivatives, Gamma Radiometrics, legacy soil maps, surface geology, and satellite imagery) was generated and the target variable (for each soil property at the standard soil depths) were individually intersected with the covariate values to provide a training dataset for modelling. Modelling was undertaken in 'R' software, using Regression tree interpolation (RT), specifically the Cubist R package. The RT approach is a popular modelling approach for many disciplines and has been widely used with DSM. The algorithm works by first applying a data mining-approach to partition the training dataset into a set of structured 'classifier' data. A tree structure is developed by repeatedly partitioning the data into linear models until no significant measure of difference in the calibration data is determined. A series of covariate-based rules (conditions) is then developed, and the linear model corresponding to the covariate conditions are applied to produce the final modelled surface. For this modelling exercise, the number of rules was set within the model controls to let the Cubist algorithm decide upon the optimum number of rules to generate. To map modelled soil data predictions across the state, the predictions were spatially interpolated using the covariate stack along with the model parameters to guide the predictions at the desired spatial resolution. The result produces a continuous raster grid surface saved as geoTIFF files. All outputs produced from this modelling are available for use in a GIS via a Web Map Service (WMS): A document that describes each dataset and associated nomenclature can be accessed here: https://nrmdatalibrary.dpipwe.tas.gov.au/FactSheets/WfW/ListMapUserNotes/Inventory_DSM_Tas.pdf Further reading: Kidd, D., Malone, B., Mcbratney, A., Minasny, B., Odgers, N., Webb, M., Searle, R., 2014a. A New Digital Soil Resource for Tasmania, Australia, 20th WORLD CONGRESS OF SOIL SCIENCE, pp. 612-613; Kidd, D., Webb, M., Malone, B., Minasny, B., McBratney, A., 2015a. 80-metre Resolution 3D Soil Attribute Maps for Tasmania, Australia. Soil Research 53(8), 932-955; Kidd, D., Webb, M., Malone, B., Minasny, B., McBratney, A., 2015b. Digital soil assessment of agricultural suitability, versatility and capital in Tasmania, Australia. Geoderma Regional 6, 7-21; Kidd, D.B., Malone, B.P., McBratney, A.B., Minasny, B., Webb, M.A., 2014b. Digital mapping of a soil drainage index for irrigated enterprise suitability in Tasmania, Australia. Soil Research 52, 107-119; Kidd, D.B., Webb, M.A., Grose, C.J., Moreton, R.M., Malone, B.P., McBratney, A.B., Misnasny, B., Viscarra-Rossel, R.A., Cotching, W.E., Sparrow, L.A., Smith, R., 2012. Digital soil assessment: Guiding irrigation expansion in Tasmania, Australia. In: Minasny, B., A. B. McBratney, et al. (2012). Digital Soil Assessments and Beyond: Proceedings of the 5th Global Workshop on Digital Soil Mapping 2012, Sydney, Australia, Taylor & Francis; Kidd, D.B., Webb, M.A., McBratney, A.B., Minasny, B., Malone, B.P., Grose, C.J., Moreton, R.M., 2014c. Operational Digital Soil Assessment for Enterprise Suitability in Tasmania, Australia., in: Arrouays, D., McKenzie, N.J., Hempel, J., Richer-de-Forges, A.C., McBratney, A. (Eds.), GlobalSoilMap: Basis of the global spatial soil information system. CRC Press, Orleans, France, pp. 113-120 McBratney, A.B., Mendonca Santos, M.L., Minasny, B., 2003, 'On digital soil mapping'. Geoderma 117, 3-52.

Position Accuracy: 30m and 80m resolution (spatial resolution of individual datasets - refer to inventory document above). Attribute Accuracy: Variable depending on the soil attribute modelled. Uncertainty assessment of the grid surfaces was conducted and involved running a K-fold cross validation procedure (the result of which can be requested from the custodian). In general, however, the assessment for most surfaces resulted in acceptable prediction accuracies with the Root Mean Square Error (RMSE), R2 and concordance coefficient within acceptable ranges. Attribute accuracy is expected to be optimal for the 30m resolution grid surface products (where additional efforts were made to improve these products required for enterprise suitability mapping, as part of the Water for Profit project) and in the following areas: Meander irrigation scheme area of 45,000 ha, Flinders Island area of 10,000 ha and 27,000 ha in the Midlands where a greater soil reconnaissance effort was conducted. Furthermore, during the Water for Profit project in 2015-2017, an additional 400 soil sites were collected in the Midlands area representing over 400,000 ha of improved mapping detail for the area. Logical Consistency: Each raster cell data is attributed with soil attribute data (as detailed above). There are no duplicates. Completeness: The spatial layers cover the entire state of Tasmania, excluding Macquarie Island. Additional: Also refer to the Soil and Landscape Grid of Australia: https://www.clw.csiro.au/aclep/soilandlandscapegrid/ProductDetails-SoilAttributes.html