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ASRIS – New soil datasets for Australia

RM Johnston1, SJ Barry1, E Bleys1, E Bui2, P Carlile2, B Henderson2, W McDonald3, C Moran2 and D Simon2

1Bureau of Rural Sciences
PO Box E11 Kingston ACT 2604
(02) 62725721 (tel) 02 62725827 (fax)

CSIRO Land and Water
GPO Box 1666, Canberra ACT 2601
National Land and Water Resources Audit
GPO Box 2182, Canberra ACT 2601


ASRIS compiles the best available information into a national database of soil profiles, soil and land resources maps and other relevant datasets. These datasets were used to produce modelled spatial estimates of key soil properties and their uncertainties at a grid cell resolution of approximately 1.1 km2 for Australia’s agricultural zone. New techniques were developed to allow spatial extrapolation from soil profile data. The soil property surfaces are suitable for modelling and assessments at regional to national scales. They include: soil texture (%clay, %silt, %sand, texture class); pH; organic carbon; nitrogen (total); phosphorus (total and plant available); available water holding capacity; bulk density; erodibility; permeability (saturated hydraulic conductivity); and thickness. They are available from the Australian Natural Resources Atlas at


The National Land and Water Resources Audit (the Audit) commissioned the Australian Soil Resources Information System (ASRIS) to upgrade the existing national soils dataset, as part of its brief to carry out assessments of soil, land, water and vegetation – as an important input to future natural resource management decisions. Knowledge about soils and their behaviour is critical to more effectively managing resources at a landscape and ecosystem scale to meet the twin goals of sustainable production and environmental protection.

Prior to the activities of the Audit commencing, the best Australia-wide coverage of soils information was the Atlas of Australian Soils at a scale of 1:2,000,000 (Northcote et al 1960-68; BRS 2000), completed by CSIRO in 1968, and interpretations of soil properties based on this (McKenzie et al 2000). This information was inadequate to answer the key natural resource management questions at a scale relevant to regional planning and development. ASRIS was designed to build a nationally consistent database from the extensive soil point and soil survey map data that has been collected by the State and Territory agencies, and to use these data as the basis for modelling key soil properties for Australia’s agricultural zone.

Collaborating Agencies

ASRIS was developed as a joint project between Commonwealth and State agencies responsible for soil and land management, using the collaborative framework established by the Australian Collaborative Land Evaluation Program (ACLEP). The agencies and organisations that participated in the development of ASRIS included:

  • Bureau of Rural Science (BRS)
  • CSIRO Land and Water
  • NSW Department of Land and Water Conservation (DLWC)
  • Victorian Department of Natural Resources and Environment (NRE)
  • Primary Industries and Resources South Australia (PIRSA)
  • Northern Territory Department of Lands, Planning and Environment
  • Tasmanian Department of Primary Industries, Water & Environment (DPIWE)
  • Agriculture WA
  • Queensland Department of Natural Resources (QDNR)

What is ASRIS?

ASRIS uses the best available information to produce for Australia’s agricultural zone:

1. a national database of existing data relating to soil and land resources, including

• soil profiles

• soil and land resources maps

• other relevant datasets (digital elevation model and derived landscape descriptors, climate surfaces, lithology, Landsat MSS imagery)

2. modelled spatial estimates of key soil properties and their uncertainties at a grid cell resolution of approximately 1.1 km2 (0.01o).

For the ASRIS project, the agricultural zone was defined as catchments designated by the State agencies as containing significant agricultural activity. Catchment boundaries were those defined by the Australian Water Resources Management Committee (AUSLIG 1997). The entire catchment was included (rather than just the area of agricultural activity) because the modelling process involved use of terrain descriptors that relate to position within the catchment.

ASRIS datasets will be available through the Audit Atlas and Data Library ( Modelled surfaces of soil properties are freely available for download. The underlying datasets are available subject to the licence conditions of the custodial agencies – see the website for details.

Soil profile database

Over 160,000 soil profiles were collated into an Oracle database from data held by State and Territory agencies and CSIRO in a range of formats. All data were transformed to the SITES transfer standard format (ACLEP 1997). The minimum requirement for each profile description was a site location (map coordinates) and at least one observation about the site. Table 1 shows the number of profiles from each State. Figure 1 shows the distribution of soil profiles spatially.

Table 1: number of soil profiles from each State and Territory held in the ASRIS database









































Because soil profile data has been collected over a long period, by different agencies and for different purposes, the type and quality of the data varies enormously. There is considerable variation in the degree of description, and the attributes described for each profile. Most sites had morphological description of the profile. Many had data on soil texture and pH. Other chemical data was limited. Soil physical data was very uncommon. Table 2 shows the number of horizons (not profiles) in the database containing data for selected attributes.

Table 2: number of horizons with laboratory data for selected soil properties in the ASRIS database































Organic C








Clay %


Coarse Sand %


Fine Sand






Bulk density




K sat


K unsat


Figure 1: location of soil profiles in the ASRIS database

There are inconsistencies in the way soil horizons are described and named, despite the existence of an agreed standard for horizon description (McDonald et al 1990). In some cases no horizon designator is given, in others non-standard horizon designators (eg E) are used. Similarly, soil taxonomic descriptions are often inconsistent or non-existent. Both these problems limits the degree to which data from different profiles can be compared.

Methods used for laboratory determinations of specific soil properties vary widely and in many cases results cannot be compared directly. For example, Table 3 gives a breakdown of the methods used for pH determination. Note that 20% of the data did not have a method recorded. Only points determined with methods 4A1 (pH of 1:5 soil/water suspension) and 4B1 (pH of 1:5 soil/0.01 M CaCl2 extract) could be compared directly, since conversions exist from pHwater to pHCaCl2 (Peverill et al 1999). A new conversion was developed for ASRIS.

Table 3: comparison of laboratory methods used for determination of pH for samples in the ASRIS database

No. of horizons with laboratory measurement

Method code

Method description



pH of 1:5 soil/water suspension



pH of soil - pH of 1:1 soil/water suspension



pH of soil - pH of 1:2.5 soil/water suspension



pH of 1:5 soil/0.01M calcium chloride extract - direct



pH of 1:5 soil/0.01M calcium chloride extract - following Method 4A1



pH of soil - pH of 1:2.5 Soil/0.1M CaCl2 suspension



pH of 1:5 soil/1M potassium chloride extract - direct



pH of 1:1 soil/1M potassium chloride suspension



pH of hydrogen peroxide extract



Total potential acidity



pH of soil - Not recorded


These issues combined mean that the amount of data available for modelling is much less than expected. For example, of the 151,810 horizons with pH measurements, around a third can be expected to be from A horizons, giving about 50,000 possible points. Only determinations using methods 4A1 and 4B1 can be compared – this is just over 70% of the data, reducing the number to 35,000. After removal of points that lie outside the modelling area, have no or inconsistent horizon designation or some other inconsistency, the total number of points available for modelling pH of the topsoil was 24, 319.

Soil map database

Soil and land resources maps at scales between 1:25,000 and 1:1 million have been compiled from an assortment of different types of maps related to soils including:

  • maps of broad land types, where soil type is included as one of a number of descriptors (for example, land systems, which also include consideration of topography, geomorphology, vegetation etc)
  • maps which are based primarily on soil type or soil-forming processes (such as soil landscape maps).

The preferred soil taxonomic scheme for Australia is the Australian Soil Classification (ASC) (Isbell 1996) and most agencies are working towards incorporating it into their descriptions. However, it has only been in widespread use for the last 10 years. Maps held by State and Territory agencies use different taxonomic schemes including Australian Soil Classification, Northcote Factual Key (Northcote 1979), Great Soil Groups (Stace et al 1968) and standard soil descriptions used within state agencies (eg Western Australian Soil Groups – Schokneckt 2001). It was thus not possible to compile a national map with a standard soil description.

In the eastern states, the most commonly used taxonomy in the available maps was the Northcote Factual Key. In addition, interpretative tables of soil properties were available for Northcote principal profile forms (PPFs) (McKenzie et al 2000), but not for other taxonomic groups. For this reason, a compilation was made of the best available mapping with attribution to PPFs. These include:

  • soil landscape mapping at 1:100,000 and 1:250,000 from NSW (for details, see )
  • mapping for the Comprehensive Regional Assessment of Forests from NSW, at a nominal scale of 1:100,000 (NSW DLWC, 1999a, b)
  • land systems of western NSW (Walker 1991)
  • soils and landforms of SW Victoria (Maher and Martin 1987)
  • land resources of NT (compilation of land systems surveys at various scales)
  • a compilation of soil and land surveys for Queensland at various scales; (for details of Queensland surveys, see
  • Murray-Darling Basin Soil Information System (MDBSIS – Bui et al1998)
  • Digital Atlas of Australian Soils (BRS 2000).

In WA, a comprehensive state mapping program uses standard soil descriptions, based on WA Soil Groups. Tables of soil properties for each soil group are available (Schocknecht 2001). Soil-landscape maps at system (1:250,000) or sub-system (1:100,000) level were compiled for the agricultural zone, and land systems mapping (1:250,000) for the rangelands (see

Similarly, in SA, landscape surveys supplemented by soil information are available at scales of 1:100,000 or finer for the agricultural zone ( Soil descriptions are given in terms of SA Soil Groups, and tables of soil properties are available for each soil group (Hall et al, in prep).

Detailed descriptions of the datasets compiled can be found at .

These maps were used as the basis for polygon-based modelling of soil properties. The scale of these maps in different regions is shown in Figure 2. They do not always represent the best available mapping – particularly in Victoria, much more detailed recent mapping is available in many areas, but without attribution of PPFs. For this reason, the compiled soil maps used in ASRIS will not be released publicly. The datasets are available from the relevant State and Territory agencies.

The difficulties encountered in compiling this national dataset of soil maps highlighted the need for a standard format for soil map data, analogous to the transfer standard for soil profile descriptions. A draft standard is currently under consideration by the Working Group for Land Resource Assessment, under the auspices of ACLEP.

Figure 2: scale of soil mapping used in ASRIS for polygon-based models

Modelling of soil properties

An important objective of ASRIS was to produce nationally consistent spatial estimates of key soil properties, suitable for use in regional to national scale assessments. The collated ASRIS datasets were used as inputs to models to estimate soil properties, based on :

1. point-based models, used when there was sufficient soil profile data to build reliable models; or

2. polygon-based models, used when soil profile data were limited; or

3. combined point and polygon-based models.

Table 4 sets out the soil properties modelled and the method of estimation for each. Modelled estimates were produced for topsoil (the first or thickest A horizon) and subsoil (usually the first B horizon).

Table 4: soil properties estimated and modelling method used


Topsoil (layer 1)

First Subsoil (layer 2)

Point models



Organic carbon

Total phosphorus


Extractable phosphorus (NSW and Vic)


Total nitrogen (derived from C/N ratio)




Clay % (includes polygon model surface)

Polygon models


Clay %

Silt %

Sand %

Layer (horizon) thickness

Solum thickness



Bulk density

Available water

Saturated hydraulic conductivity

Point-polygon models


– pedotransfer, point & polygon model

Point-based models

Point-based modelling methods were developed by CSIRO to predict each soil attribute individually, on the basis of correlations between soil properties (from soil profile data) and other environmental variables (Henderson et al, 2001; The environmental prediction variables and soil profile data are used to compile a matrix of environmental variables against the soil property being predicted for each point, from which regression and classification models can be built. Since the environmental variables are available over the whole area, it is possible to use these models to extrapolate to areas where no measurements have been made, on the basis of their environmental similarity to points with measurements. All models were validated by separating the data into a training set and a test set. Model performance was also assessed by cross-validation on the full data set. For each digital map, an associated map of uncertainty is presented with the map.

The environmental variables used in the modelling included:

  • climate (19 surfaces)
  • terrain attributes (eg elevation, slope, distance to ridges/rivers etc, derived from the DEM)
  • lithology (1:250,000 to 1:1,000,000)
  • Landsat MSS imagery
  • National land use map
  • Australian Soil Classification, digitised from the Atlas of Australian Soils

Modelled surfaces are presented at a grid resolution of 1.1 km2 (0.01o). Most of the input datasets were at 1:250,000 scale or finer, with the exception of the climate surfaces which were based on 5 km2 grids.

Two major sources of uncertainty associated with point-based models are:

  • the predictive power of the models (that is, the degree to which relationships could be established between each soil property and environmental predictors)
  • the degree of extrapolation, which is governed by the local point density and how well that environment is represented in the database.

Generally, the more data available, the more reliable the model and the less uncertainty in the predictions. For most soil properties, there are areas with very few soil points with measured data to support predictions. This is particularly true of the following regions: Northern Territory, Carpentaria, Far North Queensland, western South Australia and northern Western Australia.

Polygon-based models

For some soil properties (particularly soil water attributes) insufficient soil profile data were available to produce reliable models based on the point data. In these cases, maps of soil type linked to look-up tables of soil properties were used, following the method described by McKenzie et al (2000). Dominant soil types in each map unit were identified, the interpreted values for the relevant soil type were weighted by an estimate of the area occupied by each soil type, and the weighted value was ascribed to the map unit. Methods for deriving polygon-based surfaces are described in detail elsewhere (

Datasets used in map-based models are set out in Table 5. Polygon datasets used in the modelling are described above, and the scale of best available data is shown in Figure 2.

Uncertainties associated with polygon-based models are related to:

  • scale and accuracy of the base maps. Figure 2 indicates the scale of the polygon data used in the modelling. Maps at the same nominal scale are not always of comparable quality. For example, maps produced at 1:100,000 scale for the Comprehensive Regional Assessment of forests in NSW are at reconnaissance level only, with no associated laboratory data and limited profile descriptions. Maps produced as part of the 1:100,000 Soil Landscape series are detailed studies which include extensive laboratory data
  • how well the maps represent soil variability – for example, whether one dominant soil or a range of soil types is recorded for each map unit
  • variation in the accuracy of tabulated estimates of the different soil properties
  • whether estimates of soil properties existed for specific soil types.

Table 5: datasets used in polygon-based models


Map data

Soil property data

Western Australia

WA soil landscape mapping (system and subsystem, scale 1:100,000 - 1:250,000)

Tables of soil properties for WA Soil Groups, provided by Agriculture WA

South Australia

SA soil landscape mapping (scale 1:25,000 – 1:100,000)

Tables of soil properties for SA Soil Groups, provided by PIRSA

Other states and territories

Best available mapping with Northcote Principal Profile Form assigned to map units (scale 1:50,000 – 1:2 million). Where no other data were available, the Atlas of Australian Soils was used.

Interpretative tables of soil properties for Northcote PPFs, compiled by McKenzie et al (2000)

Combined point- and polygon-based models

In some cases, point- and polygon-based models were combined. In building the point-based models, it was possible to include a digital map derived from a polygon model as one of the environmental layers. For example, the estimates of % clay derived from a polygon-based model were included as one data layer in the point model of %clay. This has the advantage of retaining some spatial structure from the soil map, while allowing estimates of % clay to vary as a function of other environmental predictors. In this case, the errors in the final map are dominated by the errors in the point-based modelling process, since the polygon model surface is only one input to the point model.

Digital maps derived from different methods can be combined to give estimates of another property. For example, erodibility was estimated using a pedotransfer function based on soil texture and permeability from polygon-based models, and organic carbon from point-based models.


Modelled estimates of soil properties produced in ASRIS are suitable for use at national to regional scales. They have already been used as inputs to comprehensive national assessments of soil and land degradation issues carried out by the Audit, reported in NLWRA (2001).

  • The Audit assessment of waterborne soil erosion used ASRIS soil erodibility (using several primary input attributes) and total phosphorus data to model sediment and nutrient delivery from hillslopes to streams and ultimately estuaries.
  • A spatially explicit acidification risk model was developd based on ASRIS attributes including the distribution of acid soils (pH), capacity for soils to buffer against acidifying practices (organic carbon % and percent clay) and plant yield functions (soil pH and other accessory characteristics).
  • Soil organic carbon and available phosphorus attributes were incorporated into the BIOS model that generated estimates of the soil and litter pools of nutrients and water to model the cycles of carbon, nutrients and water in the landscape.


A new set of modelled surfaces of soil properties is available at higher resolution than the previously available datasets based on the Atlas of Australian Soils and the interpretative tables of McKenzie et al (2000). Where possible, the new surfaces have been derived using soil profile data. Where insufficient soil profile data exists to build reliable models, map-based estimates have been derived using the methods of McKenzie et al (2000) but with best available data.

The compilation of existing soil datasets, while impressive, has highlighted some of the deficiencies in Australia’s soil data. In particular, the absence of a standard for soil mapping prevents comparison of map data nationwide. Adoption of the Australian Soil Classification will go some way to improving this situation, but there is an urgent need for an agreed soil polygon standard comparable to the SITES standard for soil profile data.

Attempts to derive spatial models using the soil profile database have been hampered by paucity of data, and by the fact that existing data, collected for specific survey purposes, has inherent sampling biases. Future survey programs could provide an opportunity to devise a sampling scheme that will support modelling.

It is envisaged that ASRIS will be maintained as an ongoing resource, through a partnership between CSIRO Land and Water and BRS, with the possibility of establishing a distributed data network with the State and Territory agencies. Long-term arrangements for the maintenance and application of ASRIS are being negotiated through the Working Group on Land Resource Assessment, CSIRO Land and Water and BRS.


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