Download Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1

Usage

get_soil_thickness(cache = TRUE)

Arguments

cache

Boolean Cache the soil thickness data files after download using tools::R_user_dir() to identify the proper directory for storing user data in a cache for this package. Defaults to TRUE, caching the files locally. If FALSE, this function uses tempdir() and the files are deleted upon closing of the R session.

A custom print method is provided that will print the metadata associated with these data. Examples are provided for interacting with the metadata directly.

Value

An read.abares.soil.thickness object, which is a named list with the file path of the resulting ESRI Grid file and text file of metadata

References

https://data.agriculture.gov.au/geonetwork/srv/eng/catalog.search#/metadata/faa9f157-8e17-4b23-b6a7-37eb7920ead6

Examples

x <- get_soil_thickness()
#> Error in list_files(file, ignore_missing, TRUE, verbosity): File '' does not exist.

# View the metadata with pretty printing
x
#> 
#> ── Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Ho
#> 
#> ── Dataset ANZLIC ID ANZCW1202000149 ──
#> 
#> Feature attribute definition Predicted average Thickness (mm) of soil layer 1
#> in the 0.01 X 0.01 degree quadrat.
#> 
#> Custodian: CSIRO Land & Water
#> 
#> Jurisdiction Australia
#> 
#> Short Description The digital map data is provided in geographical coordinates
#> based on the World Geodetic System 1984 (WGS84) datum. This raster data set has
#> a grid resolution of 0.001 degrees (approximately equivalent to 1.1 km).
#> 
#> The data set is a product of the National Land and Water Resources Audit
#> (NLWRA) as a base dataset.
#> 
#> Data Type: Spatial representation type RASTER
#> 
#> Projection Map: projection GEOGRAPHIC
#> 
#> Datum: WGS84
#> 
#> Map Units: DECIMAL DEGREES
#> 
#> Scale: Scale/ resolution 1:1 000 000
#> 
#> Usage Purpose Estimates of soil depths are needed to calculate the amount of
#> any soil constituent in either volume or mass terms (bulk density is also
#> needed) - for example, the volume of water stored in the rooting zone
#> potentially available for plant use, to assess total stores of soil carbon for
#> Greenhouse inventory or to assess total stores of nutrients.
#> 
#> Provide indications of probable Thickness soil layer 1 in agricultural areas
#> where soil thickness testing has not been carried out.
#> 
#> Use Limitation: This dataset is bound by the requirements set down by the
#> National Land & Water Resources Audit
#> To see the full metadata, call `display_soil_thickness_metadata()` in your R
#> session.
#> 

# Extract the metadata as an object in your R session and use it with
# {pander}, useful for Markdown files

library(pander)
y <- x$metadata
pander(y)
#> Dataset
#> ANZLIC ID
#> ANZCW1202000149
#> 
#> Title
#> Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Horizon - top-soil) (derived from soil mapping)
#> 
#> Custodian
#> CSIRO, Land & Water
#> 
#> Jurisdiction
#> Australia
#> 
#> Description
#> Abstract
#> Surface of predicted Thickness of soil layer 1 (A Horizon - top-soil) surface for the intensive agricultural areas of Australia.  Data modelled from area based observations made by soil agencies both State and CSIRO and presented as .0.01 degree grid cells.
#> 
#> Topsoils (A horizons) are defined as the surface soil layers in which organic matter accumulates, and may include dominantly organic surface layers (O and P horizons).
#> 
#> The depth of topsoil is important because, with their higher organic matter contents, topsoils (A horizon) generally have more suitable properties for agriculture, including higher permeability and higher levels of soil nutrients.
#> 
#> Estimates of soil depths are needed to calculate the amount of any soil constituent in either volume or mass terms (bulk density is also needed) - for example, the volume of water stored in the rooting zone potentially available for plant use, to assess total stores of soil carbon for Greenhouse inventory or to assess total stores of nutrients.
#> 
#> The pattern of soil depth is strongly related to topography - the shape and slope of the land.  Deeper soils are typically found in the river valleys where soils accumulate on floodplains and at the footslopes of ranges (zones of deposition), while soils on hillslopes (zones of erosion) tend to be shallow. 
#> Map of thickness of topsoil was derived from soil map data and interpreted tables of soil properties for specific soil groups.
#> 
#> The quality of data on soil depth in existing soil profile datasets is questionable and as the thickness of soil horizons varies locally with topography, values for map units are general averages.
#> 
#> The final ASRIS polygon attributed surfaces are a mosaic of all of the data obtained from various state and federal agencies. The surfaces have been constructed with the best available soil survey information available at the time. The surfaces also rely on a number of assumptions. One being that an area weighted mean is a good estimate of the soil attributes for that polygon or map-unit. Another assumption made is that the look-up tables provided by McKenzie et al. (2000), state and territories accurately depict the soil attribute values for each soil type.
#> 
#> The accuracy of the maps is most dependent on the scale of the original polygon data sets and the level of soil survey that has taken place in each state.  The scale of the various soil maps used in deriving this map is available by accessing the data-source grid, the scale is used as an assessment of the likely accuracy of the modelling.  The Atlas of Australian Soils is considered to be the least accurate dataset and has therefore only been used where there is no state based data.  Of the state datasets Western Australian sub-systems, South Australian land systems and NSW soil landscapes and reconnaissance mapping would be the most reliable based on scale. NSW soil landscapes and reconnaissance mapping use only one dominant soil type per polygon in the estimation of attributes.  South Australia and Western Australia use several soil types per polygon or map-unit.
#> 
#> The digital map data is provided in geographical coordinates based on the World Geodetic System 1984 (WGS84) datum. This raster data set has a grid resolution of 0.001 degrees  (approximately equivalent to 1.1 km).
#> 
#> The data set is a product of the National Land and Water Resources Audit (NLWRA) as a base dataset. 
#> 
#> 
#> Search Word(s)
#> AGRICULTURE  
#> SOIL Physics Models 
#> 
#> Geographic Extent Name(s)
#> GEN Category
#> 
#> GEN Custodial Jurisdiction
#> 
#> GEN Name
#> 
#> Geographic Bounding Box
#> North Bounding Latitude
#> -10.707149
#> South Bounding Latitude
#> -43.516831
#> East Bounding Longitude
#> 113.19673
#> West Bounding Longitude
#> 153.990779
#> 
#> Geographic Extent Polygon(s)
#> 115.0 -33.5,115.7 -33.3,115.7 -31.7,113.2 -26.2,113.5 -25.4,114.1 -26.4,114.3 -26.0,113.4 -24.3,114.1 -21.8,122.3 -18.2,122.2 -17.2,126.7 -13.6,129.1 -14.9,130.6 -12.3,132.6 -12.1,132.5 -11.6,131.9 -11.3,132.0 -11.1,137.0 -12.2,135.4 -14.7,140.0 -17.7,140.8 -17.4,141.7 -15.1,141.4 -13.7,142.2 -10.9,142.7 -10.7,143.9 -14.5,144.6 -14.1,145.3 -14.9,146.3 -18.8,148.9 -20.5,150.9 -22.6,153.2 -25.9,153.7 -28.8,153.0 -31.3,150.8 -34.8,150.0 -37.5,147.8 -37.9,146.3 -39.0,144.7 -38.4,143.5 -38.8,141.3 -38.4,139.7 -37.3,139.7 -36.9,139.9 -36.7,138.9 -35.5,138.1 -35.7,138.6 -34.7,138.1 -34.2,137.8 -35.1,136.9 -35.3,137.0 -34.9,137.5 -34.9,137.4 -34.0,137.9 -33.5,137.8 -32.6,137.3 -33.6,135.9 -34.7,136.1 -34.8,136.0 -35.0,135.1 -34.6,135.2 -34.5,135.4 -34.5,134.7 -33.3,134.0 -32.9,133.7 -32.1,133.3 -32.2,132.2 -32.0,131.3 -31.5,127.3 -32.3,126.0 -32.3,123.6 -33.9,123.2 -34.0,122.1 -34.0,121.9 -33.8,119.9 -34.0,119.6 -34.4,118.0 -35.1,116.0 -34.8,115.0 -34.3,115.0 -33.5
#> 
#> 147.8 -42.9,147.9 -42.6,148.2 -42.1,148.3 -42.3,148.3 -41.3,148.3 -41.0,148.0 -40.7,147.4 -41.0,146.7 -41.1,146.6 -41.2,146.5 -41.1,146.4 -41.2,145.3 -40.8,145.3 -40.7,145.2 -40.8,145.2 -40.8,145.2 -40.8,145.0 -40.8,144.7 -40.7,144.7 -41.2,145.2 -42.2,145.4 -42.2,145.5 -42.4,145.5 -42.5,145.2 -42.3,145.5 -43.0,146.0 -43.3,146.0 -43.6,146.9 -43.6,146.9 -43.5,147.1 -43.3,147.0 -43.1,147.2 -43.3,147.3 -42.8,147.4 -42.9,147.6 -42.8,147.5 -42.8,147.8 -42.9,147.9 -43.0,147.7 -43.0,147.8 -43.2,147.9 -43.2,147.9 -43.2,148.0 -43.2,148.0 -43.1,148.0 -42.9,147.8 -42.9
#> 
#> 136.7 -13.8,136.7 -13.7,136.6 -13.7,136.6 -13.8,136.4 -13.8,136.4 -14.1,136.3 -14.2,136.9 -14.3,137.0 -14.2,136.9 -14.2,136.7 -14.1,136.9 -13.8,136.7 -13.8,136.7 -13.8
#> 
#> 139.5 -16.6,139.7 -16.5,139.4 -16.5,139.2 -16.7,139.3 -16.7,139.5 -16.6
#> 
#> 153.0 -25.2,153.0 -25.7,153.1 -25.8,153.4 -25.0,153.2 -24.7,153.2 -25.0,153.0 -25.2
#> 
#> 137.5 -36.1,137.7 -35.9,138.1 -35.9,137.9 -35.7,137.6 -35.7,137.6 -35.6,136.6 -35.8,136.7 -36.1,137.2 -36.0,137.5 -36.1
#> 
#> 143.9 -39.7,144.0 -39.6,144.1 -39.8,143.9 -40.2,143.9 -40.0,143.9 -39.7
#> 
#> 148.0 -39.7,147.7 -39.9,147.9 -39.9,148.0 -40.1,148.1 -40.3,148.3 -40.2,148.3 -40.0,148.0 -39.7
#> 
#> 148.1 -40.4,148.0 -40.4,148.4 -40.3,148.4 -40.5,148.1 -40.4
#> 
#> 130.4 -11.3,130.4 -11.2,130.6 -11.3,130.7 -11.4,130.9 -11.3,131.0 -11.4,131.1 -11.3,131.2 -11.4,131.3 -11.2,131.5 -11.4,131.5 -11.5,131.0 -11.9,130.8 -11.8,130.6 -11.7,130.0 -11.8,130.1 -11.7,130.3 -11.7,130.1 -11.5,130.4 -11.3
#> 
#> 
#> Data Currency
#> Beginning date
#> 1999-09-01
#> 
#> Ending date
#> 2001-03-31
#> 
#> Dataset Status
#> Progress
#> COMPLETE
#> 
#> Maintenance and Update Frequency
#> NOT PLANNED
#> 
#> Access
#> Stored Data Format
#> DIGITAL - ESRI Arc/Info integer GRID
#> 
#> Available Format Type
#> DIGITAL - ESRI Arc/Info integer GRID
#> 
#> Access Constraint
#> Subject to the terms & condition of the data access & management agreement between the National Land & Water Audit and ANZLIC parties
#> 
#> Data Quality
#> Lineage
#> The soil attribute surface was created using the following datasets
#> 1. The digital polygon coverage of the Soil-Landforms of the Murray Darling Basis (MDBSIS)(Bui et al. 1998), classified as principal profile forms (PPF's) (Northcote 1979).
#> 2. The digital Atlas of Australian Soils (Northcote et al.1960-1968)(Leahy, 1993).
#> 3. Western Australia land systems coverage (Agriculture WA).
#> 4. Western Australia sub-systems coverage (Agriculture WA).
#> 5. Ord river catchment soils coverage (Agriculture WA).
#> 6. Victoria soils coverage (Victorian Department of Natural Resources and Environment - NRE).
#> 7. NSW Soil Landscapes and reconnaissance soil landscape mapping (NSW Department of Land and Water Conservation
#> - DLWC).
#> 8. New South Wales Land systems west (NSW Department of Land and Water Conservation - DLWC).
#> 9. South Australia soil land-systems (Primary Industries and Resources South Australia - PIRSA).
#> 10. Northern Territory soils coverage (Northern Territory Department of Lands, Planning and Environment).
#> 11. A mosaic of Queensland soils coverages (Queensland Department of Natural Resources - QDNR).
#> 12. A look-up table linking PPF values from the Atlas of Australian Soils with interpreted soil attributes (McKenzie et al. 2000).
#> 13. Look_up tables provided by WA Agriculture linking WA soil groups with interpreted soil attributes.
#> 14. Look_up tables provided by PIRSA linking SA soil groups with interpreted soil attributes.
#> 
#> The continuous raster surface representing Thickness of soil layer 1 was created by combining national and state level digitised land systems maps and soil surveys linked to look-up tables listing soil type and corresponding attribute values.
#> 
#> Because thickness is used sparingly in the Factual Key, estimations of thickness in the look-up tables were made using empirical correlations for particular soil types.
#> 
#> To estimate a soil attribute where more than one soil type was given for a polygon or map-unit, the soil attribute values related to each soil type in the look-up table were weighted according to the area occupied by that soil type within the polygon or map-unit. The final soil attribute values are an area weighted average for a polygon or map-unit.  The polygon data was then converted to a continuous raster surface using the soil attribute values calculated for each polygon.
#> 
#> The ASRIS soil attribute surfaces created using polygon attribution relied on a number of data sets from various state agencies.  Each polygon data set was turned into a continuous surface grid based on the calculated soil attribute value for that polygon.  The grids where then merged on the basis that, where available, state data replaced the Atlas of Australian Soils and MDBSIS.  MDBSIS derived soil attribute values were restricted to areas where MDBSIS was deemed to be more accurate that the Atlas of Australian Soils (see Carlile et al (2001a).
#> 
#> In cases where a soil type was missing from the look-up table or layer 2 did not exist for that soil type, the percent area of the soils remaining were adjusted prior to calculating the final soil attribute value. The method used to attribute polygons was dependent on the data supplied by individual State agencies.
#> 
#> The modelled grid was resampled from 0.0025 degree cells to 0.01 degree cells using bilinear interpolation
#> 
#> Positional Accuracy
#> The predictive surface is a 0.01 X 0.01 degree grid and has a locational accurate of about 1m.
#> 
#> The positional accuracy of the defining polygons have variable positional accuracy most locations are expected to be within 100m of the recorded location.  The vertical accuracy is not relevant. The positional assessment has been made by considering the tools used to generate the locational information and contacting the data providers.
#> 
#>  The other parameters used in the production of the led surface have a range of positional accuracy ranging from + - 50 m to + - kilometres.  This contribute to the loss of attribute accuracy in the surface.
#> 
#> Attribute Accuracy
#> Input attribute accuracy for the areas is highly variable.  The predictive has a variable and much lower attribute accuracy due to the irregular distribution and the limited positional accuracy of the parameters used for modelling.
#> 
#> There are several sources of error in estimating soil depth and thickness of horizons for the look-up tables.  Because thickness is used sparingly in the Factual Key, estimations of thickness in the look-up tables were made using empirical correlations for particular soil types.  The quality of data on soil depth in existing soil profile datasets is questionable, in soil mapping, thickness of soil horizons varies locally with topography, so values for map units are general averages.  The definition of the depth of soil or regolith is imprecise and it can be difficult to determine the lower limit of soil.
#> 
#> The assumption made that an area weighted mean of soil attribute values based on soil type is a good estimation of a soil property is debatable, in that it does not supply the soil attribute value at any given location. Rather it is designed to show national and regional patterns in soil properties. The use of the surfaces at farm or catchment scale modelling may prove inaccurate. Also the use of look-up tables to attribute soil types is only as accurate as the number of observations used to estimate a attribute value for a soil type. Some soil types in the look-up tables may have few observations, yet the average attribute value is still taken as the attribute value for that soil type. Different states are using different taxonomic schemes making a national soil database difficult.
#> Another downfall of the area weighted approach is that some soil types may not be listed in look-up tables. If a soil type is a dominant one within a polygon or map-unit, but is not listed within the look-up table or is not attributed within the look-up table then the final soil attribute value for that polygon will be biased towards the minor soil types that do exist. This may also happen when a large area is occupied by a soil type which has no B horizon. In this case the final soil attribute value will be area weighted on the soils with a B horizon, ignoring a major soil type within that polygon or map-unit. The layer 2 surfaces have large areas of no-data because all soils listed for a particular map-unit or polygon had no B horizon.
#> 
#> Logical Consistency
#> Surface is fully logically consistent as only one parameter is shown, as predicted average Soil Thickness within each grid cell 
#> 
#> Completeness
#> Surface is nearly complete.  There are some areas (about %1 missing) for which insufficient parameters were known to provide a useful prediction and thus attributes are absent in these areas.
#> 
#> 
#> Contact Information
#> Contact Organisation (s)
#> CSIRO, Land & Water
#> 
#> Contact Position
#> Project Leader
#> 
#> Mail Address
#> ACLEP, GPO 1666
#> 
#> Locality
#> Canberra
#> 
#> State
#> ACT
#> 
#> Country
#> AUSTRALIA
#> 
#> Postcode
#> 2601
#> 
#> Telephone
#> 02 6246 5922
#> 
#> Facsimile
#> 02 6246 5965
#> 
#> Electronic Mail Address
#> neil.mckenzie@cbr.clw.csiro.au
#> 
#> Metadata Date
#> Metadata Date
#> 2001-07-01
#> 
#> Additional Metadata
#> Additional Metadata
#> 
#> Entity and Attributes
#> Entity Name
#> Soil Thickness Layer 1 (derived from mapping) 
#> 
#> Entity description
#> Estimated Soil Thickness (mm) of Layer 1 on a cell by cell basis
#> 
#> Feature attribute name
#> VALUE
#> 
#> Feature attribute definition
#> Predicted average Thickness (mm) of soil layer 1 in the 0.01 X 0.01 degree quadrat
#> 
#> Data Type
#> Spatial representation type
#> RASTER
#> 
#> Projection
#> Map projection
#> GEOGRAPHIC
#> 
#> Datum
#> WGS84
#> 
#> Map units
#> DECIMAL DEGREES
#> 
#> Scale
#> Scale/ resolution
#> 1:1 000 000
#> 
#> Usage
#> Purpose
#> Estimates of soil depths are needed to calculate the amount of any soil constituent in either volume or mass terms (bulk density is also needed) - for example, the volume of water stored in the rooting zone potentially available for plant use, to assess total stores of soil carbon for Greenhouse inventory or to assess total stores of nutrients.
#> 
#> Provide indications of probable Thickness  soil layer 1 in agricultural areas where soil thickness testing has not been carried out
#> 
#> Use
#> Use Limitation
#> This dataset is bound by the requirements set down by the National Land & Water Resources Audit