A spatial map layer of soil type (Australian Soil Classification) for Victoria. The harmonised map consists of 3,300 land units (totaling about 225,000 polygons) derived from around 100 soil and land surveys carried out in Victoria over the past 70 years. The land units have been attributed according to the Australian Soil Classification (Order and Suborder levels of the classification scheme) based on their likely dominant soil type. Particular attention was given to harmonising land units across survey boundaries. A reliability index has been assigned to each land unit based on the quality and relevance of the originating survey, providing a qualitative reliability measure to support interpretation and data use.

Soil site data contained in the Victorian Soil Information System (VSIS), and information on the Victorian Resources Online (VRO) website and original study reports have been combined with landscape knowledge to develop the new maps. Data from approximately 10,000 existing sites recorded, mostly recorded in the VSIS have been used.

The soil type is based on land mapping conducted at different times, at variable scale, and for different purposes. Land units are therefore of variable scale and quality in relation to the soil they are representing. Many units will be comprised of multiple soil types and a range of soil properties, and local variability (e.g. at paddock scale level) can also sometimes be high. The mapping, therefore, is intended to represent the dominant, or most prevalent, broad soil type within the map unit. It is therefore adequate for regional or state-wide overviews but may not often be accurate enough for localised or within-farm assessments. For more detailed soil and land information, users are advised to refer to the original land study for any given map unit (e.g. via Victorian Resources Online website).

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. A spatial dataset of soil and landform classification in Gippsland. The map units are broad packages' of land - divided primarily on the basis of soil type, landform pattern and geology. It contains soil and land information at a scale of 1:100 000 for all land in the region. The dataset has been derived from a combination of …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. A spatial dataset of soil and landform classification in Gippsland. The map units are broadpackages' of land - divided primarily on the basis of soil type, landform pattern and geology. It contains soil and land information at a scale of 1:100 000 for all land in the region. The dataset has been derived from a combination of past studies and has been collated primarily by Ian Sargeant and Mark Imhof from 1994 to 2013. Data from older surveys have also been included in this consolidated dataset. Mapping in east and northern Gippsland regions is restricted to freehold lands. Webpages on Victorian Resources Online provide a description of each of the map units and indicate source studies used to define the map unit. In June 2013 a dominant soil type was assigned to each unit (by David Rees, Mark Imhof and Ian Sargeant) to facilitate the creation of a digital soil map of Victoria. Australian Soil Classification (Order and SubOrder) have been included in the dataset's attribute table. At the map scale of this dataset soil-landform units are not homogeneous. For each defined soil-landform unit, the number and proportion of landforms and soil types will vary. Representative sites and their associated profile properties are recorded on the Victorian Resources Online website (http://vro.depi.vic.gov.au/dpi/vro/wgregn.nsf/pages/wg_soil_detailed). Importantly it should be noted that soil attributes (for example texture, sodicity, pH) are expected to vary between acquired soil sites. As the variability of soil attributes within a map unit is difficult to predict, it is important to note that representative soils should be used as a guide only. Purpose Showing soil types and extent within the Gippsland region. Dataset History Data Set Source: Remote Sensed (Radiometrics, DEM), Expert Interviews, Soil site data, Field work, earlier land studies Collection Method: Field work, API, and derived with other datasets Processing Steps: Survey of existing soil and land unit mapping data from earlier studies. New field work and observations to collection soil, land and land use information. Combining old and new data with radiometrics and DEM in GIS. Additional Metadata: The detail available in the current datasets is good for their mapping scale but is not sufficient to provide landscape analysis at finer scales and should not therefore be used to plan land use strategies at more detailed scales (1:25 000 and larger) unless additional soil and land survey is captured to enhance map line work and subdivide the map units. It should be noted that soil attributes (for example texture, sodicity, pH) are expected to vary between acquired soil sites. As the variability of soil attributes within a map unit is difficult to predict, it is important to note that representative soils should be used as a guide only. http://vro.depi.vic.gov.au/dpi/vro/wgregn.nsf/pages/wg_soil_detailed Dataset Citation Victorian Department of Environment and Primary Industries (2014) Land units of the Gippsland region of Victoria. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/5c7f4d52-8e46-4bda-a5c8-70124aaad67b.

This dataset comprises soil property mapping across the whole State of Victoria at 6 prescribed depths. The set depths are 0 to 5 cm, 5 to 15 cm, 15 to 30 cm, 30 to 60 cm, 60 to 100 cm and 100 to 200 cm. The mapped soil properties are pH (1:5 water), EC (dS/m), % clay and soil organic carbon (SOC %).

The dataset has been created by the Understanding Soil and Farming Systems project (CMI 102922)and is referred to as Version 1.0 of the Victorian Digital Soil Map (VIC DSM 1.0).

Soil point data stored in the Victorian Soil Information System (VSIS) from over 6,000 sites has been standardised to the set depths (using equal area splines or a value weighting derived from the proportional contruibution of each sample to the depth class). This processed data was used to attribute soil land units from a collection of surveys (mapped at 1:100k or better) collated to provide the best map unit coverage across the State. Only data from sites that match the soil type of the dominant soil within the land unit being attributed were used. Sites and land units were assigned an Australian Soil Classification (to the Suborder level) to aid this process.

The raw profile data stored in the VSIS (as of March 2013) used to produce these maps were: pH data were either laboratory based (1:5 soil/water suspension) or field pH (Raupach and Tucker 1959). Clay % was laboratory derived particle size data (PSA all methods), or converted field observations of texture class (McKenzie et al. 2000). Organic Carbon measurements methods was either Walkley and Black or Heanes wet oxidation. Electical Conductivity was 1:5 soil/water extract (dS/m).

The data is available in polygonal format (i.e. the land units) with soil property median value, standard deviation and assignment qualifier attributes. ESRI grids in ascii format at 100 m cell resolution have been generated from the attributed land unit polygon dataset for each soil property at each depth interval.

The assignment qualifiers have been created in order to provide a level of quality evaluation for the soil property assignment to each polygon. Reliability maps generated from these qualifiers have been produced together with each soil property map.

The strength of these products is our ability to leverage on the significant investment in soil site and survey mapping data procurement and the capture of tacit knowledge of former soil surveyors.

A revised version of these digital soil maps is due to be released at the end of 2014.

These polygons represent approximate native soil sub-bases and derived textures based on digitized maps from the Geological Survey of Victoria (c. 1956). Soils are expected to be extensively modified from these types throughout Melbourne due to extensive disturbance, and cut and fill associated with the city's development.

This dataset depicts a national map of available ASS mapping and ASS qualification inferred from surrogate datasets. ASS mapping is classified with a nationally consistent legend that includes risk assessment criteria and correlations between Australian and International Soil Classification Systems.

Existing digital datasets of ASS mapping have been sourced from each coastal state and territory and combined into a single national dataset. Original state classifications have been translated to a common national classification system by the respective creators of the original data and other experts. This component of the Atlas is referred to as the “Coastal” ASS mapping. The remainder of Australia beyond the extent of state ASS mapping has been “backfilled” with a provisional ASS classification inferred from national and state soils, hydrography and landscape coverages. This component is referred to as the “Inland” ASS mapping.

For the state Coastal ASS mapping, the mapping scale of source data ranges from 1:10K aerial photography in SA to 1:250K vegetation mapping in WA and NT, with most East coast mapping being at the 1:100K scale. For the backfilled inferred Inland ASS mapping the base scale is 1:2.5 million (except Tas.) overlaid with 1:250k hydography. As at 06/08, the Tasmanian inland mapping has been re-modelled using superior soil classification map derived from 1:100k landscape unit mapping.

NOTE: This is composite data layer sourced from best available data with polygons depicted at varying scales and classified with varying levels of confidence. Great care must be taken when interpreting this map and particular attention paid to the “map scale” and confidence rating of a given polygon. It is stressed that polygons rated with Confidence = 4 are provisional classifications inferred from surrogate data with no on ground verification. Also some fields contain a “-“, denoting that a qualification was not able to be made, usually because a necessary component of source mapping coverage did not extend to the given polygon. Lineage: Coastal ASS component:

Existing state CASS mapping was received and processed to varying degrees to conform to the NatCASS national ASS classification system. Spatially, all datasets were reprojected from their original projections to geographic GDA94. Classification of state mapping polygons to the NatCASS classification system was as follows. In the case of SA, NSW, Qld and WA it was a matter of directly translating the original state ASS classifications to the NatCASS classifications. These translations were undertaken by the creators of the state data and other experts within the respective states.

Due to the more broad classifications of the original Vic and Tas ASS mapping, polygons for these two states were initially translated to a NatCASS classification group (eg Tidal, Non-Tidal) by the data custodians then subsequently differentiated further through intersecting with other layers. These included the 3 second SRTM DEM and North Coast Mangrove mapping GIS datasets. The former being used to differentiate within the Non-Tidal zones (ie classes Ae-j and Be-j) and the latter used to differentiate the Tidal zones (ie Ab-d, Bb-d).

Mapping of the Tidal-Zone classes was augmented for all states except SA and NSW with 1:100K Coastal Waterways Geomorphic Habitat Mapping (Geoscience Australia). This dataset was used to infer additional areas of subaqueous material in subtidal wetland (class Aa & Ba) and Intertidal Flats (class Ab & Bb).

Inland ASS component:

Provisional Inland ASS classifications are derived from National and (in the case of Tasmania) state soil classification coverages combined with 1:250K series 3 Hydrography and Multiresolution Valley Bottom Floor Index (MrVBF).

We used Digital Soil Mapping (DSM) technologies combined with the real-time collations of soil attribute data from TERN's recently developed Soil Data Federation System, to produce a map of Australian Soil Classification Soil Order classes with quantified estimates of mapping reliability at a 90m resolution.

The map gives an estimate of the spatial distribution of soil types across Australia.

Soil classes are based on The Australian Soil Classification - Second Edition by the National Committee on Soil and Terrain, R Isbell - https://ebooks.publish.csiro.au/content/australian-soil-classification-9781486304646

Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

Period (temporal coverage; approximately): 1950-2021; Spatial resolution: 3 arc seconds (approx 90m); Number of pixels with coverage per layer: 2007M (49200 * 40800); Data license : Creative Commons Attribution 4.0 (CC BY); Format: Cloud Optimised GeoTIFF; Lineage: The map was produced as per methods described at - https://aussoilsdsm.esoil.io/slga-version-2-products/australian-soil-classification-map

Soil classification data was extracted from the SoilDataFederator (SDF) - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html)

A total of 195,383 observations with either an Australian Soil Classification (ASC) or a Principal Profile Form (PPF) classification or a Great Soil Group (GSG) classification were extracted (Figure 1). Of these observations 130,570 of them had an ASC directly assigned by a pedologist. The remaining 64,813 observations either had a PPF or an ASC assigned to them by pedologists. The PPF and GSG classification where then transformed to an ASC using these remap tables.

The 90m raster covariate data was obtained from TERNs publicly available raster covariate stack - https://esoil.io/TERNLandscapes/Public/Products/TERN/Covariates/Mosaics .A parsimonious set of these covariates was used in the modelling.

We used the R "Ranger" Random Forest package to implement a machine learning model as per standard Digital Soil Mapping (DSM) methodologies.

The observed geographic locations in the ASC data set were used to extract cell values from the raster covariate stack using the R "raster" package. This data set was then divided into a 90/10% split of training and external validation sets. The training data was then bootstrapped sampled 50 times to create 50 bootstrap training sets. These training sets were then used to generate 50 Random Forest model realisations.

Using the CSIRO Pearcey High Performance Compute (HPC) cluster the Random Forest models were evaluated against the input covariate raster data stack. This was done for each 90m raster cell across the nation for each of the 50 bootstrapped model realisations. The modal ASC value across the 50 realisations for each cell was determined and assigned as the most probable soil type for that cell in the output raster. The ratio of the second most probable soil to the most probable soil was also calculated to generate a model confusion index, an estimate of the structural uncertainty in the Random Forest model.

The Australian Soil Resource Information System (ASRIS) contains a product that is a compilation of all existing polygon mapping conducted by state and federal soil survey agencies across all of Australia. This product is made up of a diverse range of field mapping products at a range of mapping scales. From this product we extracted all polygons that were mapped at a scale of 1:100,000 or finer, as defined in the Guidelines For Surveying Soil And Land Resources (Blue Book). Polygons mapped at this scale are high quality spatial estimates of the distribution of soil attributes. We then rasterised these polygon ASC values and merged these values into our final estimates of ASC, i.e., where an ASRIS 100,000 scale polygon exists it will replace the modelled ASC value.

All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Code - https://github.com/AusSoilsDSM/SLGA Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html

This dataset is the primary data output from the north-east land resource assessment project undertaken in 2001-02. It contains soil and land information at a scale of 1:100 000 for all freehold land in north-east Victoria. It also includes generic soil erosion risk assessments and agricultural capability.

At the map scale of this dataset soil-landform units are not homogeneous. For each defined soil-landform unit, dominant soil types were identified prior to assessing their capability to support various enterprises. Often a co-dominant and minor soil type have been described as part of this process.

Importantly it should be noted that soil attributes (for example texture, sodicity, pH) are expected to vary between acquired soil sites. As the variability of soil attributes within a map unit is difficult to predict, it is important to note that representative soils should be used as a guide only.

The study report describing the project methodology and dataset attributes is available from the Victorian Resources Online website (http://vro.depi.vic.gov.au/dpi/vro/neregn.nsf/pages/ne_soil_landform_survey)

DOI 10.4226/92/58e71be578ac0

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the chemical soil variable organic carbon density (ocd) which measures carbon mass in proportion to volume of soil (mass divided by volume.)From Agriculture Victoria: Soil carbon provides a source of nutrients through mineralisation, helps to aggregate soil particles (structure) to provide resilience to physical degradation, increases microbial activity, increases water storage and availability to plants, and protects soil from erosion.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for organic carbon density are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Organic carbon density in kg/m³Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for ocd were used to create this layer. You may access organic carbon density values in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

This dataset is the primary data output from the Glenelg Hopkins land resource assessment project undertaken in 1999-2001. It contains soil and land information at a scale of 1:100 000 for all land in the south western corner of Victoria. The study also includes generic soil erosion risk assessments and agricultural capability, although these are mapped in separate datasets.

At the map scale of this dataset soil-landform units are not homogeneous. For each defined soil-landform unit, the number and proportion of landforms and soil types will vary. A dominant soil type has been identified within each unit and soil property attributes provided by 'representative' sites.

Importantly it should be noted that soil attributes (for example texture, sodicity, pH) are expected to vary between acquired soil sites. As the variability of soil attributes within a map unit is difficult to predict, it is important to note that representative soils should be used as a guide only.

The study report describing the project methodology and dataset attributes is available from the Victorian Resources Online website (http://vro.depi.vic.gov.au/dpi/vro/glenregn.nsf/pages/glenelg_soil_map).

DOI 10.4226/92/58e717be5073e

This dataset is the primary data output from the Corangamite land resource assessment project undertaken in 2002-2003. It contains soil and land information at a scale of 1:100 000 for all land in the region. The study also includes land degradation assessments for each unit.

At the map scale of this dataset soil-landform units are not homogeneous. For each defined soil-landform unit, the number and proportion of landforms and soil types will vary. A group or groups of soils have been associated with each unit. representative sites and their associated profile properties are recorded in the study report.

Importantly it should be noted that soil attributes (for example texture, sodicity, pH) are expected to vary between acquired soil sites. As the variability of soil attributes within a map unit is difficult to predict, it is important to note that representative soils should be used as a guide only.

The study report describing the project methodology and dataset attributes, including representative soil profile data, is available from the Victorian Resources Online website (http://vro.depi.vic.gov.au/dpi/vro/coranregn.nsf/pages/soil_landform_map).

DOI 10.4226/92/58e7149507e74

This dataset is the primary data output from the Goulburn Broken Dryland Regional Development Project conducted from 1998 to 2000. It contains soil and land information at a scale of 1:100 000 for all land in the region.

At the map scale of this dataset soil-landform units are not homogeneous. For each defined soil-landform unit, the number and proportion of landforms and soil types will vary. A group or groups of soils have been associated with each unit. Representative sites and their associated profile properties are recorded in the study report.

Importantly it should be noted that soil attributes (for example texture, sodicity, pH) are expected to vary between acquired soil sites. As the variability of soil attributes within a map unit is difficult to predict, it is important to note that representative soils should be used as a guide only.

The Ground Cover Reference Sites Database of nullhas been collected by nullas part of the Ground Cover Monitoring for Australia project. The data is being used to calibrate, validate and improve vegetation fractional cover products derived from remote sensing, in particular the satellite sensors MODIS and Landsat. The data is being used to improve the national fractional vegetation cover product of Guerschman et al. (2009) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). This algorithm enables national, monthly identification of ground cover separating the photosynthetic and non-photosynthetic components by applying a linear unmixing methodology for spectral reflectance evey 8 days as 16-day composites. For confidence in its ground cover estimates, the results were verified in the field at selected sites across Australia to allow more extensive calibration, validation and verification of accuracy of the remote sensing method. The Ground Cover Reference Sites Database represents the results of the field validation of remotely determined cover measurements by observing cover along point intersects with a total of 300 points (or 200 points with crops). It also has additional observations and measures such as landscape features, fire evidence, erosion evidence, biotic disturbance evidence, biomass estimates, basal area measurements, soil features and dominant vegetation species, as well as site photographs. The Ground Cover Reference Sites Database focuses on sites in extensive grazing systems of the rangelands and, to a lesser extent, in the mixed farming or intensive land use zone. Field validation aims at obtaining a wide spatial coverage of sites, with limited site revisits for temporal coverage. Please use the following URL to access the dataset: http://aekos.org.au/collection/gov.au/abares/gcrs_VIC

The Primary Production Landscapes (PPLs) is a high level spatial framework that divides the state into 6 regions and 22 sub regions. Soil and landscape data, land use maps, the climatic record, and regional experience of agronomists and land managers have been used to define the PPLs. The PPLs have been characterised for dominant soil types and associated inherent management issues. PPLs were also described by major agricultural industries and practices that occur within them. Statewide maps for key climatic variables including temperature, rainfall and growing season rainfall were used to provide context for predicted changes in climate across the PPLs. These detailed descriptions are available on the Victorian Resources Online website (http://vro.depi.vic.gov.au/dpi/vro/vrosite.nsf/pages/primary_prod_landscapes).

DOI 10.4226/92/58e72567b7d51

Soil surface condition is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Victoria River Water Resource Assessment (VIWRA) through the digital soil mapping process (DSM). Soil surface condition is described when dry as defined by the National Committee on Soil and Terrain 2009 (NCST) surface condition descriptions. This raster data represents a modelled dataset of soil surface condition and is derived from field measured site data and environmental covariates. Data values are: 1 Self mulching or self mulching and cracking, 2 Loose and/or soft, 3 Firm and/or hardsetting, 4 Surface crust, 5 Cracking. Soil surface condition is a parameter used in land suitability assessments of soil physical factors and affects; water infiltration, seedling establishment and machinery workability. This raster data provides improved soil information used to underpin and identify opportunities and promote detailed investigation for a range of sustainable regional development options and was created within the ‘Land Suitability’ activity of the CSIRO VIWRA. A companion dataset and statistics reflecting reliability of this data are also provided and can be found described in the lineage section of this metadata record. Processing information is supplied in ranger R scripts and attributes were modelled using a Random Forest approach. The DSM process is described in the CSIRO VIWRA published report ‘Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. The Victoria River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Victoria catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: This soil surface condition dataset has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO VIWRA published reports and in particular ' Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create soil surface condition Digital Soil Mapping (DSM) attribute raster dataset. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 8. QA Quality assessment of this DSM attribute data was conducted by three methods. Method 1: Statistical (quantitative) method of the model and input data. Testing the quality of the DSM models was carried out using data withheld from model computations and expressed as OOB and confusion matrix results, giving an estimate of the reliability of the model predictions. These results are supplied. Method 2: Statistical (quantitative) assessment of the spatial attribute output data presented as a raster of the attributes “reliability”. This used the 500 individual trees of the attributes RF models to generate 500 datasets of the attribute to estimate model reliability for each attribute. For categorical attributes the method for estimating reliability is the Confusion Index. This data is supplied. Method 3: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. Across each of the study areas a two week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling using the reliability data of the attribute. The modelled DSM attribute value was assessed against the actual on-ground value. These results are published in the report cited in this metadata record.

This dataset is the primary data output from the Wimmera land resource assessment project undertaken in 2004-06. It contains soil and land information at a scale of 1:100 000 for all freehold land in the Wimmera region of Victoria.

The dataset was developed by the project "A Land Resource Assessment of the Wimmera Region" conducted by Robinson et al. (2006). This project was undertaken by DPI's PIRVic Division for the Wimmera Catchment Management Authority to provide consistent land resource information across the region. It utilised data from existing soil surveys at varying scales and intensity conducted over the previous 60 years, remote sensing information and additional field work to develop updated 1:100 000 scale soil/landform mapping across the region.

The nominal scale of the dataset is appropriate for broadscale assessment of land capability and regional planning.

At the map scale of this dataset soil-landform units are not homogeneous. For each defined soil-landform unit, soil types were identified and an assessment of their risk of degradation (compaction, erosion, sodicity and acidity) was made.

Importantly it should be noted that soil attributes (for example texture, sodicity, pH) are expected to vary between acquired soil sites. As the variability of soil attributes within a map unit is difficult to predict, it is important to note that representative soils should be used as a guide only.

The study report describing the project methodology and dataset attributes is available from the Victorian Resources Online website.

DOI 10.4226/92/58e729e8aea3e

These land suitability raster data (GeoTIFF format) indicates areas of potential suitability for 21 crop groups and their specific irrigation management systems and seasons in the Victoria catchment of the Northern Territory. This data provides improved land evaluation information to identify opportunities and promote detailed investigation for a range of sustainable development options and was created within the ‘Land suitability’ activity of the Victoria River Water Resource Assessment (VIWRA). There are five land suitability classes coded 1-5. 1 – Highly suitable land with negligible limitations 2 – Suitable land with minor limitations 3 – Moderately suitable land with considerable limitations 4 – Currently unsuitable land with severe limitations and 5 – Unsuitable land with extreme limitations. The land suitability evaluation methods used to produce this data are a modification of the Food and Agriculture Organisation (FAO) land evaluation approach. The land suitability analysis is described in full in the CSIRO VIWRA published report ‘Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. Companion datasets showing the reliability of these suitability data (showing areas of the catchment where there is greater or lesser confidence in the accuracy of the suitability data) are also supplied. The naming convention for these data is; ‘crop group’ underscore ‘major crop’ underscore ‘season code’ underscore ‘irrigation type code’ underscore ‘catchment code’ underscore ‘data type’ eg ‘CG7_CottonGrains_D_Fw_V_Suit’ is Cotton and grain crops grown in the dry season with furrow irrigation in the Victoria catchment suitability data. The codes for season are; W – wet season; D – dry season; P – perennial. The codes for irrigation type are; S – overhead spray irrigation; T – trickle irrigation; Fd – flood irrigation; Fw – furrow irrigation; R – rainfed. The codes for data type are; suit – suitability data, CI – reliability data expressed as confusion index. It is important to emphasize that this is a regional-scale assessment: further data collection and detailed soil physical, chemical and nutrient analyses would be required to plan development at a scheme, enterprise or property scale. Several limitations that may have a bearing on land suitability were out of scope and not assessed as part of this activity (refer to the report), these limitations include biophysical and socio-cultural. For example these land suitability raster datasets do not include consideration of the licensing of water, flood risk, contiguous land, risk of irrigation induced secondary salinity, or land tenure and other legislative controls. Some of these may be addressed elsewhere in VIWRA eg flooding was investigated by the Earth observation remote sensing group in the surface water activity. The Victoria River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Victoria catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: These suitability raster datasets have been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO VIWRA published report ' Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create Digital Soil Mapping (DSM) attribute raster datasets. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Land management options were chosen and suitability rules created for DSM attributes. 8. Suitability rules were run to produce limitation subclass datasets using a modification on the FAO methods. 9. Final suitability data created for all land management options. 10. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 11. QA Quality assessment of these land suitability data was conducted by two methods. Method 1: Statistical (quantitative) assessment of the "reliability" of the spatial output data presented as a raster of the Confusion Index. Method 2: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. A two-week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling. The modelled land suitability value was assessed against the actual on-ground value. These results are published in the report referenced above.

This versatile agricultural land data is a collection of raster datasets (GeoTIFF format) used to provide a synopsis of the land suitability data of the 21 crop groups and their specific irrigation management systems and seasons in the Victoria catchment of the Northern Territory. Five datasets are in this collection. The definitive versatile agricultural land dataset (Ag_Versatility_14_Crops_V.tif) was determined by identifying where the largest number of the 14 selected land management options were mapped as being suitable (i.e. suitability classes 1 to 3, refer to report cited with this metadata record). This analysis summarised the suitability of the selected land management options for each pixel, and highlights those pixels that are potentially more versatile for agricultural development because they are likely to suit a larger range of land management options and enterprises eg the score of zero represents the least versatile land, while the score of 14 represents the most versatile. The data values represent the number of land management options suitable for that pixel. The selected land management options were chosen to be relative to general potential agronomic experience and development aspirations of potential stakeholders in the catchment and were derived in consultation with the agricultural viability activity in VIWRA. These selections are presented in Table 3-26 of the published report; ' Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. Similarly, the selection of a different representative set from the land management options would result in a different versatility map outcome. In addition to the selected set of 14 land management options, versatile agricultural land is also presented using the subsets of each of the irrigation types (and rainfed cropping). In this case, the land management options were assigned to rainfed (8), furrow (17), spray (23) or trickle irrigation (10). The data values represent the number of land management options suitable for that pixel. Analytical products like these help to identify land where particular types of irrigation-related infrastructure investment may be best targeted. This data provides improved land evaluation information to identify opportunities and promote detailed investigation for a range of sustainable development options. It is important to emphasize that this is a regional-scale assessment: further data collection and detailed soil physical, chemical and nutrient analyses would be required to plan development at a scheme, enterprise or property scale. Several limitations that may have a bearing on land suitability were out of scope and not assessed as part of this activity (refer to the report), these limitations include biophysical and socio-cultural. For example these versatile agricultural land raster datasets do not include consideration of the licensing of water, flood risk, contiguous land, risk of irrigation induced secondary salinity, or land tenure and other legislative controls. Some of these may be addressed elsewhere in VIWRA eg flooding was investigated by the Earth observation remote sensing group in the surface water activity. The Victoria River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Victoria catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: These versatile agricultural land raster datasets have been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO VIWRA published report ' Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create Digital Soil Mapping (DSM) attribute raster datasets. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Land management options were chosen and suitability rules created for DSM attributes. 8. Suitability rules were run to produce limitation subclass datasets using a modification on the FAO methods. 9. Final suitability data created for all land management options. 10. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 11. QA Quality assessment of these land suitability data was conducted by two methods. Method 1: Statistical (quantitative) assessment of the "reliability" of the spatial output data presented as a raster of the Confusion Index. Method 2: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. A two-week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling. The modelled land suitability value was assessed against the actual on-ground value. These results are published in the report referenced above. 12. Select the 14 land management options for each catchment in consultation with the agricultural viability activity. 13. Calculate the versatile agricultural land datasets

These contiguous suitable area data are based on the land suitability data from the Victoria River Water Resource Assessment in the NT (VIWRA). To address operational farming constraints imposed by parcels of suitable land being too small or oddly shaped according to natural variability of land, or physical limits on suitable farming land parcel sizes, contiguous suitable area data was generated. This contiguous suitable area data is based on crop-specific minimum areas and minimum length/width of contiguous suitable land and is produced as standalone data products for all crop groups. The rules are provided for download. The data was generated to remove the component of landscape complexity that natural distributions of soil and land variability and specific crop requirements produce. This data provides improved land evaluation information to identify opportunities and promote detailed investigation for a range of sustainable development options. The land suitability evaluation methods used to produce the underlying data are a modification of the Food and Agriculture Organisation (FAO) land evaluation approach. The land suitability analysis is described in full in the CSIRO VIWRA published report ‘Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. The naming convention for these data is; ‘crop group’ underscore ‘major crop’ underscore ‘season code’ underscore ‘irrigation type code’ underscore ‘catchment code’ underscore ‘data type’ eg ‘CG7_CottonGrains_D_Fw_V_ContigArea’ is Cotton and grain crops grown in the dry season with furrow irrigation in the Victoria catchment contiguous suitable area data. The codes for season are; W – wet season; D – dry season; P – perennial. The codes for irrigation type are; S – overhead spray irrigation; T – trickle irrigation; Fd – flood irrigation; Fw – furrow irrigation; R – rainfed. It is important to emphasize that this is a regional-scale assessment: further data collection and detailed soil physical, chemical and nutrient analyses would be required to plan development at a scheme, enterprise or property scale. Several limitations that may have a bearing on land suitability were out of scope and not assessed as part of this activity (refer to the report), these limitations include biophysical and socio-cultural. For example these land suitability raster datasets do not include consideration of the licensing of water, flood risk, contiguous land, risk of irrigation induced secondary salinity, or land tenure and other legislative controls. Some of these may be addressed elsewhere in VIWRA eg flooding was investigated by the Earth observation remote sensing group in the surface water activity. The Victoria River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Victoria catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: These contiguous suitable area raster datasets have been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO VIWRA published report ' Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create Digital Soil Mapping (DSM) attribute raster datasets. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Land management options were chosen and suitability rules created for DSM attributes. 8. Suitability rules were run to produce limitation subclass datasets using a modification on the FAO methods. 9. Final suitability data created for all land management options. 10. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 11. QA Quality assessment of these land suitability data was conducted by two methods. Method 1: Statistical (quantitative) assessment of the "reliability" of the spatial output data presented as a raster of the Confusion Index. Method 2: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. A two-week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling. The modelled land suitability value was assessed against the actual on-ground value. These results are published in the report referenced above. 12. A two-step process was developed to simplify the data and was applied across the suitability data of the catchments. First the five suitability classes were aggregated to two: ‘suitable’ for suitability classes 1, 2 and 3, or ‘not suitable’ for class 4 and 5. Second, to further simplify the data, and to reflect the on-ground spatial constraints of farming practices, isolated one or two pixels of ‘not suitable’ contained in larger ‘suitable’ areas were reclassified as ‘suitable’. 13. For each crop group, a minimum area and width were defined based on knowledge of farming practices. Depending on the possible land use, minimum areas were deemed as 2.5 ha, 5 ha, 10 ha or 25 ha and minimum widths of 80 m or 120 m (rules are provided for download). 14. For each crop rule the minimum width was imposed by removing those parts of the suitable area that are narrower (in any direction) than the required minimum width. The remaining groups of connected cells were then tested to see if they meet the required minimum area and removed if they did not.