| Type: | Package |
| Title: | Read Data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) and Australian Bureau of Statistics (ABS) |
| Version: | 2.0.0 |
| Description: | Download and import agricultural data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) https://www.agriculture.gov.au/abares and Australian Bureau of Statistics (ABS) https://www.abs.gov.au. Data types serviced include spreadsheets, comma separated value (CSV) files, geospatial data including shape files and geotiffs covering topics including broadacre crops, livestock, soil data, commodities and more. Unifies field names and formats for data interoperability making analysis easier by standardising names between data formats. Also simplifies importing geospatial data as well as correcting issues in the geospatial data upon import. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/ropensci/read.abares, https://docs.ropensci.org/read.abares/ |
| BugReports: | https://github.com/ropensci/read.abares/issues |
| Depends: | R (≥ 4.1.0) |
| Imports: | brio, cli, data.table, fs, htm2txt, httr2, lubridate, purrr, readxl, rlang, sf, stars, stringr, terra, tidync, utils, whoami, withr |
| Suggests: | bit64, knitr, openxlsx, pander, rmarkdown, roxyglobals, skimr, spelling, testthat (≥ 3.0.0), zip |
| VignetteBuilder: | knitr |
| Config/Needs/build: | moodymudskipper/devtag |
| Config/roxyglobals/filename: | globals.R |
| Config/roxyglobals/unique: | FALSE |
| Config/testthat/edition: | 3 |
| Config/testthat/parallel: | true |
| Encoding: | UTF-8 |
| Language: | en-US |
| RoxygenNote: | 7.3.3 |
| NeedsCompilation: | no |
| Packaged: | 2026-01-17 04:47:29 UTC; adamsparks |
| Author: | Adam H. Sparks 
[cre, aut],
Curtin University  [fnd, cph]
(http://www.curtin.edu.au/),
Grains Research and Development Corporation
[fnd, cph] (Project: GRDC
Project CUR2210-005OPX (AAGI-CU)),
Jacob Wujciak-Jens
[ctb]
(Assisted with troubleshooting formatting in documentation to
display '<' and '>' properly),
Nicholas Potter
[rev] (rOpenSci Review
https://github.com/ropensci/software-review/issues/667),
María Paula Caldas
[rev]
(rOpenSci Review
https://github.com/ropensci/software-review/issues/667) |
| Maintainer: | Adam H. Sparks <adamhsparks@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-01-22 09:30:02 UTC |
read.abares: Read Data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) and Australian Bureau of Statistics (ABS)
Description
Download and import agricultural data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) https://www.agriculture.gov.au/abares and Australian Bureau of Statistics (ABS) https://www.abs.gov.au. Data types serviced include spreadsheets, comma separated value (CSV) files, geospatial data including shape files and geotiffs covering topics including broadacre crops, livestock, soil data, commodities and more. Unifies field names and formats for data interoperability making analysis easier by standardising names between data formats. Also simplifies importing geospatial data as well as correcting issues in the geospatial data upon import.
Author(s)
Maintainer: Adam H. Sparks adamhsparks@gmail.com (ORCID)
Other contributors:
Curtin University (ROR) (http://www.curtin.edu.au/) [funder, copyright holder]
Grains Research and Development Corporation (ROR) (GRDC Project CUR2210-005OPX (AAGI-CU)) [funder, copyright holder]
Jacob Wujciak-Jens jacob@wujciak.de (ORCID) (Assisted with troubleshooting formatting in documentation to display '<' and '>' properly) [contributor]
Nicholas Potter econpotter@gmail.com (ORCID) (rOpenSci Review https://github.com/ropensci/software-review/issues/667) [reviewer]
María Paula Caldas mpaulacaldas@gmail.com (ORCID) (rOpenSci Review https://github.com/ropensci/software-review/issues/667) [reviewer]
See Also
Useful links:
Report bugs at https://github.com/ropensci/read.abares/issues
Prints the text file Metadata for ABARES' Topsoil Thickness for "Australian Areas of Intensive Agriculture of Layer 1"
Description
Prints the complete set of metadata associated with the soil thickness
data in your R console. For including the metadata in documents or other
methods outside of R, see .get_topsoil_thickness() for an example using
pander::pander() to print the metadata.
Usage
print_topsoil_thickness_metadata(x = NULL)
Arguments
x |
An optional file path to a zip file containing the topsoil thickness
data from ABARES. If left as |
Value
Nothing, called for its side effects, it prints the complete metadata file to the R console.
Note
The original metadata use a title of "Soil Thickness", in the context of this package, we refer to it as "Topsoil Thickness" to be consistent with the actual values in the data.
Source
References
<https://data.agriculture.gov.au/geonetwork/srv/eng/catalog.search#/metadata/faa9f157-8e17-4b23-b6a7-37eb7920ead6.
See Also
Other topsoil thickness:
read_topsoil_thickness_stars(),
read_topsoil_thickness_terra()
Examples
print_print_topsoil_thickness_metadata()
Package Options for read.abares
Description
This page documents the global options used by the read.abares package.
Details
The following options can be set via [options()] to control package behavior:
read.abares.user_agentCharacter string to set a custom user agent for web requests. Default is
read.abares R package {version no.} https://github.com/ropensci/read.abares.
read.abares.timeoutInteger providing the timeout in seconds for download operations. Default is
2000.read.abares.timeout_connectInteger providing the connection timeout in seconds. Default is
20.read.abares.max_triesInteger providing the number of times to retry download before giving up. Default is
3.read.abares.verbositySet the desired level of verbosity.
"quiet" - no messages at all but errors will be reported,
"minimal" - warnings and errors only reported,
"verbose" - full messages including downloading, importing files, etc. reported.
These options can be set globally using:
options(read.abares.user_agent = "myCustomUserAgent") or read.abares_options(read.abares.user_agent = "myCustomUserAgent") [options()]: R:options()
See Also
Other read.abares-options:
read.abares_options()
Get or Set read.abares Options
Description
A convenience function to get or set options used by read.abares.
Usage
read.abares_options(...)
Arguments
... |
Named options to set, or no arguments to retrieve current values. |
Value
A list of current option values.
See Also
Other read.abares-options:
read.abares-options
Examples
# See currently set options for {read.abares}
read.abares_options()
# Set to "verbosity" to "quiet" suppress messages
read.abares_options(read.abares.verbosity = "quiet")
read.abares_options()
Read ABARES' "Australian Agricultural and Grazing Industries Survey" (AAGIS) Region Mapping Files
Description
Download import the "Australian Agricultural and Grazing Industries Survey" (AAGIS) regions geospatial shapefile.
Usage
read_aagis_regions(x = NULL)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Value
An sf object of the AAGIS regions.
Note
Upon import a few operations are carried out,
the geometries are automatically corrected to fix invalid geometries that are present in the original shapefile,
column names are set to start with a upper-case letter,
the original column named, "name", is set to "AAGIS_region" to align with column names that the
data.table::data.table()provided byread_historical_regional_estimates()to allow for easier merging of data for mapping, and,a new column, "State" is added to be used for mapping state estimates with data for mapping state historical estimate values found in the
data.table::data.table()fromread_historical_state_estimates().
Source
https://www.agriculture.gov.au/sites/default/files/documents/aagis_asgs16v1_g5a.shp_.zip.
References
https://www.agriculture.gov.au/abares/research-topics/surveys/farm-definitions-methods#regions.
Examples
aagis <- read_aagis_regions()
plot(aagis)
Read Data from the ABARES Trade Dashboard
Description
Fetches and imports ABARES trade data. As the data x is large, ~1.4GB uncompressed CSV x.
Usage
read_abares_trade(x = NULL)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Value
A data.table object of the ABARES trade data.
Note
Columns are renamed for consistency with other ABARES products serviced in this package using a snake_case format and ordered consistently.
Source
https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1033841/0
References
https://www.agriculture.gov.au/abares/research-topics/trade/dashboard
See Also
Other Trade:
read_abares_trade_regions()
Examples
trade <- read_abares_trade()
trade
Read "Trade Data Regions" from the ABARES Trade Dashboard
Description
Fetches and imports ABARES "Trade Data Regions".
Usage
read_abares_trade_regions(x = NULL)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Value
A data.table object of the ABARES trade data regions.
Note
Columns are renamed for consistency with other ABARES products serviced in this package using a snake_case format and ordered consistently.
Source
https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1033841/2
References
https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1033841/0
See Also
Other Trade:
read_abares_trade()
Examples
trade_regions <- read_abares_trade_regions()
trade_regions
Get ABS' Broadacre Crops Production and Value by Australia, State and Territory by Year
Description
Automates downloading and importing of ABS broadacre crop production data. Please view the comments embedded in the spreadsheets themselves (that really should be columns of comments on the data) for important information.
Usage
read_abs_broadacre_data(data_set = "winter", year = "latest", x = NULL)
Arguments
data_set |
A character vector providing the desired cropping data, one of:
|
year |
A string value providing the year of interest to download.
Formatted as |
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Details
Technically these data are from the Australian Bureau of Statistics (ABS, not ABARES, but the data is agricultural and so it's serviced in this package.
Value
A data.table::data.table() object of the requested data.
References
https://www.abs.gov.au/statistics/industry/agriculture/australian-agriculture-broadacre-crops.
See Also
Other ABS:
read_abs_horticulture_data(),
read_abs_livestock_data()
Examples
broadacre_data <- read_abs_broadacre_data()
broadacre_data
Get ABS' Horticulture Crops Production and Value by Australia, State and Territory by Year
Description
Automates downloading and importing of ABS horticulture crop production data. Please view the comments embedded in the spreadsheets themselves (that really should be columns of comments on the data) for important information.
Usage
read_abs_horticulture_data(year = "latest", x = NULL)
Arguments
year |
A string value providing the year of interest to download.
Formatted as |
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Details
Technically these data are from the Australian Bureau of Statistics (ABS, not ABARES, but the data is agricultural and so it's serviced in this package.
Value
A data.table::data.table() object of the requested data.
References
https://www.abs.gov.au/statistics/industry/agriculture/australian-agriculture-horticulture.
See Also
Other ABS:
read_abs_broadacre_data(),
read_abs_livestock_data()
Examples
horticulture_data <- read_abs_horticulture_data()
horticulture_data
Read ABS' Livestock Production and Value by Australia, State and Territory by Year
Description
Automates downloading and importing of ABS livestock production data. Please view the comments embedded in the spreadsheets themselves (that really should be columns of comments on the data) for important information.
Usage
read_abs_livestock_data(data_set = "livestock_and_products", x = NULL)
Arguments
data_set |
A string value providing the desired livestock data, one of:
|
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Details
Technically these data are from the Australian Bureau of Statistics (ABS, not ABARES, but the data is agricultural and so it's serviced in this package.
Value
A data.table::data.table() object of the requested data.
References
https://www.abs.gov.au/statistics/industry/agriculture/australian-agriculture-livestock.
See Also
Other ABS:
read_abs_broadacre_data(),
read_abs_horticulture_data()
Examples
livestock_data <- read_abs_livestock_data()
livestock_data
Read ABARES' "Australian Gridded Farm Data" (AGFD) NCDF Files as a data.table Object
Description
Read "Australian Gridded Farm Data", (AGFD), as a
data.table::data.table() object.
Usage
read_agfd_dt(yyyy = 1991:2023, fixed_prices = TRUE, x = NULL)
Arguments
yyyy |
Returns only data for the specified year or years for climate
data (fixed prices) or the years for historical climate and prices depending
upon the setting of |
fixed_prices |
Download historical climate and prices or historical
climate and fixed prices as described in (Hughes et al. 2022). Defaults
to |
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Details
From the ABARES website:
"The Australian Gridded Farm Data (AGFD) are a set of national
scale maps containing simulated data on historical broadacre farm business
outcomes including farm profitability on an 0.05-degree (approximately 5 km)
grid.
These data have been produced by ABARES as part of the ongoing
Australian Agricultural Drought Indicator (AADI) project
(previously known as the Drought Early Warning System Project) and were
derived using ABARES
farmpredict
model, which in turn is based on ABARES Agricultural and Grazing Industries
Survey (AAGIS) data.
Australian Agricultural Drought Indicator
(AADI) project (previously known as the Drought Early Warning
System Project) and were derived using ABARES
farmpredict
model, which in turn is based on ABARES Agricultural and Grazing Industries Survey
(AAGIS) data.
These maps provide estimates of farm business profit, revenue, costs and
production by location (grid cell) and year for the period 1990-91 to
2022-23. The data do not include actual observed outcomes but rather model
predicted outcomes for representative or 'typical' broadacre farm
businesses at each location considering likely farm characteristics and
prevailing weather conditions and commodity prices."
– ABARES, 2024-11-25
If you have not already downloaded the files, both sets of data are large in file size, i.e., >1GB, and will require time to download.
Value
A data.table::data.table() object of the "Australian Gridded Farm
Data".
Model scenarios
Historical Climate (fixed prices)
The Historical Climate (fixed prices) scenario is similar to that described in Hughes et al. (2022) and is intended to isolate the effects of climate variability on financial incomes for broadacre farm businesses. In these simulations, global output and input price indexes are fixed at values from the most recently completed financial year. However, in these scenarios the spread between domestic and global grain (wheat, barley and sorghum) prices, along with Australian fodder prices are allowed to vary in response to climate data (to capture domestic increases in grain and fodder prices in drought years, see Hughes et al. 2022). A 33-year historical climate sequence (including historical simulated crop and pasture data from the AADI project) is simulated for each grid cell (1990-91 to 2022-23).
Historical Climate and Prices
As part of the AADI project an additional scenario was developed accounting for changes in both climate conditions and output and input prices (i.e., global commodity market variability). In this historical climate and prices scenario the 33-year reference period allows for variation in both historical climate conditions and historical prices. For this scenario, historical price indexes were de-trended, to account for consistent long-term trends in some real commodity prices (particularly sheep and lamb). The resulting simulation results and percentile indicators are intended to reflect the combined impacts of annual climate and commodity price variability."
– Taken from Australian Bureau of Agricultural and Resource Economics and Sciences (2024)
Data files
Simulation output data are saved as multilayer NetCDF files, which are named using following convention:
f<farm year>.c<climate year>.p<price year>.t<technology year>.nc
where:
-
<farm year> = Financial year of farm business data is used in simulations.
-
<climate year> = Financial year of climate data is used in simulations.
-
<price year> = Financial year of output and input prices used in simulations.
-
<technology year> = Financial year of farm 'technology' (equal to farm year in all simulations) Here financial years are referred to by the closing calendar year (e.g., 2022 = 1 July 2021 to 30 June 2022).
– Taken from Australian Bureau of Agricultural and Resource Economics and Sciences (2024)
Data layers
The data layers from the downloaded NetCDF files are described in Table 2 as seen in Australian Bureau of Agricultural and Resource Economics and Sciences (2024).
Following is a copy of Table 2 for your convenience, please refer to the full document for all methods and metadata.
| Layer | Unit | Description |
| farmno | - | Row index and column index of the grid cell in the form of YYYXXX |
| A_barley_hat_ha | - | Proportion of total farm area planted to barley |
| A_oilseeds_hat_ha | - | Proportion of total farm area planted to canola |
| A_sorghum_hat_ha | - | Proportion of total farm area planted to sorghum |
| A_total_cropped_ha | - | Proportion of total farm area planted to crops |
| A_wheat_hat_ha | - | Proportion of total farm area planted to wheat |
| C_chem_hat_ha | $/ha | Expenditure on crop and pasture chemicals per hectare |
| C_fert_hat_ha | $/ha | Expenditure on fertiliser per hectare |
| C_fodder_hat_ha | $/ha | Expenditure on fodder per hectare |
| C_fuel_hat_ha | $/ha | Expenditure on fuel, oil and grease per hectare |
| C_total_hat_ha | $/ha | Total cash costs per hectare |
| FBP_fci_hat_ha | $/ha | Farm cash income per hectare |
| FBP_fbp_hat_ha | $/ha | Farm business profit per hectare, cash income adjusted for family labour, depreciation, and changes in stocks |
| FBP_pfe_hat_ha | $/ha | Profit at full equity per hectare |
| H_barley_dot_hat | t/ha | Barley yield (production per hectare planted) |
| H_oilseeds_dot_hat | t/ha | Oilseeds yield (production per hectare planted) |
| H_sorghum_dot_hat | t/ha | Sorghum yield (production per hectare planted) |
| H_wheat_dot_hat | t/ha | Wheat yield (production per hectare planted) |
| Q_barley_hat_ha | t/ha | Barley sold per hectare (total farm area) |
| Q_beef_hat_ha | Number/ha | Beef number sold per hectare |
| Q_lamb_hat_ha | Number/ha | Prime lamb number sold per hectare |
| Q_oilseeds_hat_ha | t/ha | Canola sold per hectare (total farm area) |
| Q_sheep_hat_ha | Number/ha | Sheep number sold per hectare |
| Q_sorghum_hat_ha | t/ha | Sorghum sold per hectare (total farm area) |
| Q_wheat_hat_ha | t/ha | Wheat sold per hectare (total farm area) |
| R_barley_hat_ha | $/ha | Barley gross receipts per hectare |
| R_beef_hat_ha | $/ha | Beef cattle receipts per hectare |
| R_lamb_hat_ha | $/ha | Prime lamb net receipts per hectare |
| R_oilseeds_hat_ha | $/ha | Receipts for oilseeds this FY for oilseeds sold this FY or in previous FYs per hectare |
| R_sheep_hat_ha | $/ha | Sheep gross receipts per hectare |
| R_sorghum_hat_ha | $/ha | Sorghum gross receipts per hectare |
| R_total_hat_ha | $/ha | Total farm receipts per hectare |
| R_wheat_hat_ha | $/ha | Wheat gross receipts per hectare |
| S_beef_births_hat_ha | Number/ha | Beef cattle births per hectare |
| S_beef_cl_hat_ha | Number/ha | Beef cattle on hand per hectare on 30 June |
| S_beef_deaths_hat_ha | Number/ha | Beef cattle deaths per hectare |
| S_sheep_births_hat_ha | Number/ha | Sheep births per hectare |
| S_sheep_cl_hat_ha | Number/ha | Sheep on hand per hectare on 30 June |
| S_sheep_deaths_hat_ha | Number/ha | Sheep deaths per hectare |
| S_wheat_cl_hat_ha | t/ha | Wheat on hand per hectare on 30 June |
| farmland_per_cell | ha | Indicative area of farmland in the grid cell |
Source
Historical climate prices fixed – https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1036161/3,
Historical climate and prices – https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1036161/2
References
Australian gridded farm data, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, July 2024, doi:10.25814/7n6z-ev41. CC BY 4.0.
N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought from the perspective of Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, doi:10.1016/j.crm.2022.100420.
See Also
Other AGFD:
read_agfd_stars(),
read_agfd_terra(),
read_agfd_tidync()
Examples
# download and import AGFD files
agfd_dt <- read_agfd_dt()
agfd_dt
Read ABARES' "Australian Gridded Farm Data" (AGFD) NCDF files with stars
Description
Read "Australian Gridded Farm Data", (AGFD), as a list of stars objects.
Usage
read_agfd_stars(yyyy = 1991:2003, fixed_prices = TRUE, x = NULL)
Arguments
yyyy |
Returns only data for the specified year or years for climate
data (fixed prices) or the years for historical climate and prices depending
upon the setting of |
fixed_prices |
Download historical climate and prices or historical
climate and fixed prices as described in (Hughes et al. 2022). Defaults
to |
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Details
From the ABARES website:
"The Australian Gridded Farm Data (AGFD) are a set of national
scale maps containing simulated data on historical broadacre farm business
outcomes including farm profitability on an 0.05-degree (approximately 5 km)
grid.
These data have been produced by ABARES as part of the ongoing
Australian Agricultural Drought Indicator (AADI) project
(previously known as the Drought Early Warning System Project) and were
derived using ABARES
farmpredict
model, which in turn is based on ABARES Agricultural and Grazing Industries
Survey (AAGIS) data.
Australian Agricultural Drought Indicator
(AADI) project (previously known as the Drought Early Warning
System Project) and were derived using ABARES
farmpredict
model, which in turn is based on ABARES Agricultural and Grazing Industries Survey
(AAGIS) data.
These maps provide estimates of farm business profit, revenue, costs and
production by location (grid cell) and year for the period 1990-91 to
2022-23. The data do not include actual observed outcomes but rather model
predicted outcomes for representative or 'typical' broadacre farm
businesses at each location considering likely farm characteristics and
prevailing weather conditions and commodity prices."
– ABARES, 2024-11-25
If you have not already downloaded the files, both sets of data are large in file size, i.e., >1GB, and will require time to download.
Value
A list object of stars objects of the "Australian
Gridded Farm Data" with the NetCDF objects' names as "year_yyyy".
Model scenarios
Historical Climate (fixed prices)
The Historical Climate (fixed prices) scenario is similar to that described in Hughes et al. (2022) and is intended to isolate the effects of climate variability on financial incomes for broadacre farm businesses. In these simulations, global output and input price indexes are fixed at values from the most recently completed financial year. However, in these scenarios the spread between domestic and global grain (wheat, barley and sorghum) prices, along with Australian fodder prices are allowed to vary in response to climate data (to capture domestic increases in grain and fodder prices in drought years, see Hughes et al. 2022). A 33-year historical climate sequence (including historical simulated crop and pasture data from the AADI project) is simulated for each grid cell (1990-91 to 2022-23).
Historical Climate and Prices
As part of the AADI project an additional scenario was developed accounting for changes in both climate conditions and output and input prices (i.e., global commodity market variability). In this historical climate and prices scenario the 33-year reference period allows for variation in both historical climate conditions and historical prices. For this scenario, historical price indexes were de-trended, to account for consistent long-term trends in some real commodity prices (particularly sheep and lamb). The resulting simulation results and percentile indicators are intended to reflect the combined impacts of annual climate and commodity price variability."
– Taken from Australian Bureau of Agricultural and Resource Economics and Sciences (2024)
Data files
Simulation output data are saved as multilayer NetCDF files, which are named using following convention:
f<farm year>.c<climate year>.p<price year>.t<technology year>.nc
where:
-
<farm year> = Financial year of farm business data is used in simulations.
-
<climate year> = Financial year of climate data is used in simulations.
-
<price year> = Financial year of output and input prices used in simulations.
-
<technology year> = Financial year of farm 'technology' (equal to farm year in all simulations) Here financial years are referred to by the closing calendar year (e.g., 2022 = 1 July 2021 to 30 June 2022).
– Taken from Australian Bureau of Agricultural and Resource Economics and Sciences (2024)
Data layers
The data layers from the downloaded NetCDF files are described in Table 2 as seen in Australian Bureau of Agricultural and Resource Economics and Sciences (2024).
Following is a copy of Table 2 for your convenience, please refer to the full document for all methods and metadata.
| Layer | Unit | Description |
| farmno | - | Row index and column index of the grid cell in the form of YYYXXX |
| A_barley_hat_ha | - | Proportion of total farm area planted to barley |
| A_oilseeds_hat_ha | - | Proportion of total farm area planted to canola |
| A_sorghum_hat_ha | - | Proportion of total farm area planted to sorghum |
| A_total_cropped_ha | - | Proportion of total farm area planted to crops |
| A_wheat_hat_ha | - | Proportion of total farm area planted to wheat |
| C_chem_hat_ha | $/ha | Expenditure on crop and pasture chemicals per hectare |
| C_fert_hat_ha | $/ha | Expenditure on fertiliser per hectare |
| C_fodder_hat_ha | $/ha | Expenditure on fodder per hectare |
| C_fuel_hat_ha | $/ha | Expenditure on fuel, oil and grease per hectare |
| C_total_hat_ha | $/ha | Total cash costs per hectare |
| FBP_fci_hat_ha | $/ha | Farm cash income per hectare |
| FBP_fbp_hat_ha | $/ha | Farm business profit per hectare, cash income adjusted for family labour, depreciation, and changes in stocks |
| FBP_pfe_hat_ha | $/ha | Profit at full equity per hectare |
| H_barley_dot_hat | t/ha | Barley yield (production per hectare planted) |
| H_oilseeds_dot_hat | t/ha | Oilseeds yield (production per hectare planted) |
| H_sorghum_dot_hat | t/ha | Sorghum yield (production per hectare planted) |
| H_wheat_dot_hat | t/ha | Wheat yield (production per hectare planted) |
| Q_barley_hat_ha | t/ha | Barley sold per hectare (total farm area) |
| Q_beef_hat_ha | Number/ha | Beef number sold per hectare |
| Q_lamb_hat_ha | Number/ha | Prime lamb number sold per hectare |
| Q_oilseeds_hat_ha | t/ha | Canola sold per hectare (total farm area) |
| Q_sheep_hat_ha | Number/ha | Sheep number sold per hectare |
| Q_sorghum_hat_ha | t/ha | Sorghum sold per hectare (total farm area) |
| Q_wheat_hat_ha | t/ha | Wheat sold per hectare (total farm area) |
| R_barley_hat_ha | $/ha | Barley gross receipts per hectare |
| R_beef_hat_ha | $/ha | Beef cattle receipts per hectare |
| R_lamb_hat_ha | $/ha | Prime lamb net receipts per hectare |
| R_oilseeds_hat_ha | $/ha | Receipts for oilseeds this FY for oilseeds sold this FY or in previous FYs per hectare |
| R_sheep_hat_ha | $/ha | Sheep gross receipts per hectare |
| R_sorghum_hat_ha | $/ha | Sorghum gross receipts per hectare |
| R_total_hat_ha | $/ha | Total farm receipts per hectare |
| R_wheat_hat_ha | $/ha | Wheat gross receipts per hectare |
| S_beef_births_hat_ha | Number/ha | Beef cattle births per hectare |
| S_beef_cl_hat_ha | Number/ha | Beef cattle on hand per hectare on 30 June |
| S_beef_deaths_hat_ha | Number/ha | Beef cattle deaths per hectare |
| S_sheep_births_hat_ha | Number/ha | Sheep births per hectare |
| S_sheep_cl_hat_ha | Number/ha | Sheep on hand per hectare on 30 June |
| S_sheep_deaths_hat_ha | Number/ha | Sheep deaths per hectare |
| S_wheat_cl_hat_ha | t/ha | Wheat on hand per hectare on 30 June |
| farmland_per_cell | ha | Indicative area of farmland in the grid cell |
References
Australian gridded farm data, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, July 2024, doi:10.25814/7n6z-ev41. CC BY 4.0.
N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought from the perspective of Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, doi:10.1016/j.crm.2022.100420.
See Also
Other AGFD:
read_agfd_dt(),
read_agfd_terra(),
read_agfd_tidync()
Examples
agfd_stars <- read_agfd_stars()
head(agfd_stars)
plot(agfd_stars[[1]])
Read ABARES' "Australian Gridded Farm Data" (AGFD) NCDF Files with terra
Description
Read "Australian Gridded Farm Data", (AGFD), as a terra::rast()
object.
Usage
read_agfd_terra(yyyy = 1991:2023, fixed_prices = TRUE, x = NULL)
Arguments
yyyy |
Returns only data for the specified year or years for climate
data (fixed prices) or the years for historical climate and prices depending
upon the setting of |
fixed_prices |
Download historical climate and prices or historical
climate and fixed prices as described in (Hughes et al. 2022). Defaults
to |
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Details
From the ABARES website:
"The Australian Gridded Farm Data (AGFD) are a set of national
scale maps containing simulated data on historical broadacre farm business
outcomes including farm profitability on an 0.05-degree (approximately 5 km)
grid.
These data have been produced by ABARES as part of the ongoing
Australian Agricultural Drought Indicator (AADI) project
(previously known as the Drought Early Warning System Project) and were
derived using ABARES
farmpredict
model, which in turn is based on ABARES Agricultural and Grazing Industries
Survey (AAGIS) data.
Australian Agricultural Drought Indicator
(AADI) project (previously known as the Drought Early Warning
System Project) and were derived using ABARES
farmpredict
model, which in turn is based on ABARES Agricultural and Grazing Industries Survey
(AAGIS) data.
These maps provide estimates of farm business profit, revenue, costs and
production by location (grid cell) and year for the period 1990-91 to
2022-23. The data do not include actual observed outcomes but rather model
predicted outcomes for representative or 'typical' broadacre farm
businesses at each location considering likely farm characteristics and
prevailing weather conditions and commodity prices."
– ABARES, 2024-11-25
If you have not already downloaded the files, both sets of data are large in file size, i.e., >1GB, and will require time to download.
Value
A list of terra SpatRaster objects of the "Australian
Gridded Farm Data" with the NetCDF objects' names as "year_yyyy".
Model scenarios
Historical Climate (fixed prices)
The Historical Climate (fixed prices) scenario is similar to that described in Hughes et al. (2022) and is intended to isolate the effects of climate variability on financial incomes for broadacre farm businesses. In these simulations, global output and input price indexes are fixed at values from the most recently completed financial year. However, in these scenarios the spread between domestic and global grain (wheat, barley and sorghum) prices, along with Australian fodder prices are allowed to vary in response to climate data (to capture domestic increases in grain and fodder prices in drought years, see Hughes et al. 2022). A 33-year historical climate sequence (including historical simulated crop and pasture data from the AADI project) is simulated for each grid cell (1990-91 to 2022-23).
Historical Climate and Prices
As part of the AADI project an additional scenario was developed accounting for changes in both climate conditions and output and input prices (i.e., global commodity market variability). In this historical climate and prices scenario the 33-year reference period allows for variation in both historical climate conditions and historical prices. For this scenario, historical price indexes were de-trended, to account for consistent long-term trends in some real commodity prices (particularly sheep and lamb). The resulting simulation results and percentile indicators are intended to reflect the combined impacts of annual climate and commodity price variability."
– Taken from Australian Bureau of Agricultural and Resource Economics and Sciences (2024)
Data files
Simulation output data are saved as multilayer NetCDF files, which are named using following convention:
f<farm year>.c<climate year>.p<price year>.t<technology year>.nc
where:
-
<farm year> = Financial year of farm business data is used in simulations.
-
<climate year> = Financial year of climate data is used in simulations.
-
<price year> = Financial year of output and input prices used in simulations.
-
<technology year> = Financial year of farm 'technology' (equal to farm year in all simulations) Here financial years are referred to by the closing calendar year (e.g., 2022 = 1 July 2021 to 30 June 2022).
– Taken from Australian Bureau of Agricultural and Resource Economics and Sciences (2024)
Data layers
The data layers from the downloaded NetCDF files are described in Table 2 as seen in Australian Bureau of Agricultural and Resource Economics and Sciences (2024).
Following is a copy of Table 2 for your convenience, please refer to the full document for all methods and metadata.
| Layer | Unit | Description |
| farmno | - | Row index and column index of the grid cell in the form of YYYXXX |
| A_barley_hat_ha | - | Proportion of total farm area planted to barley |
| A_oilseeds_hat_ha | - | Proportion of total farm area planted to canola |
| A_sorghum_hat_ha | - | Proportion of total farm area planted to sorghum |
| A_total_cropped_ha | - | Proportion of total farm area planted to crops |
| A_wheat_hat_ha | - | Proportion of total farm area planted to wheat |
| C_chem_hat_ha | $/ha | Expenditure on crop and pasture chemicals per hectare |
| C_fert_hat_ha | $/ha | Expenditure on fertiliser per hectare |
| C_fodder_hat_ha | $/ha | Expenditure on fodder per hectare |
| C_fuel_hat_ha | $/ha | Expenditure on fuel, oil and grease per hectare |
| C_total_hat_ha | $/ha | Total cash costs per hectare |
| FBP_fci_hat_ha | $/ha | Farm cash income per hectare |
| FBP_fbp_hat_ha | $/ha | Farm business profit per hectare, cash income adjusted for family labour, depreciation, and changes in stocks |
| FBP_pfe_hat_ha | $/ha | Profit at full equity per hectare |
| H_barley_dot_hat | t/ha | Barley yield (production per hectare planted) |
| H_oilseeds_dot_hat | t/ha | Oilseeds yield (production per hectare planted) |
| H_sorghum_dot_hat | t/ha | Sorghum yield (production per hectare planted) |
| H_wheat_dot_hat | t/ha | Wheat yield (production per hectare planted) |
| Q_barley_hat_ha | t/ha | Barley sold per hectare (total farm area) |
| Q_beef_hat_ha | Number/ha | Beef number sold per hectare |
| Q_lamb_hat_ha | Number/ha | Prime lamb number sold per hectare |
| Q_oilseeds_hat_ha | t/ha | Canola sold per hectare (total farm area) |
| Q_sheep_hat_ha | Number/ha | Sheep number sold per hectare |
| Q_sorghum_hat_ha | t/ha | Sorghum sold per hectare (total farm area) |
| Q_wheat_hat_ha | t/ha | Wheat sold per hectare (total farm area) |
| R_barley_hat_ha | $/ha | Barley gross receipts per hectare |
| R_beef_hat_ha | $/ha | Beef cattle receipts per hectare |
| R_lamb_hat_ha | $/ha | Prime lamb net receipts per hectare |
| R_oilseeds_hat_ha | $/ha | Receipts for oilseeds this FY for oilseeds sold this FY or in previous FYs per hectare |
| R_sheep_hat_ha | $/ha | Sheep gross receipts per hectare |
| R_sorghum_hat_ha | $/ha | Sorghum gross receipts per hectare |
| R_total_hat_ha | $/ha | Total farm receipts per hectare |
| R_wheat_hat_ha | $/ha | Wheat gross receipts per hectare |
| S_beef_births_hat_ha | Number/ha | Beef cattle births per hectare |
| S_beef_cl_hat_ha | Number/ha | Beef cattle on hand per hectare on 30 June |
| S_beef_deaths_hat_ha | Number/ha | Beef cattle deaths per hectare |
| S_sheep_births_hat_ha | Number/ha | Sheep births per hectare |
| S_sheep_cl_hat_ha | Number/ha | Sheep on hand per hectare on 30 June |
| S_sheep_deaths_hat_ha | Number/ha | Sheep deaths per hectare |
| S_wheat_cl_hat_ha | t/ha | Wheat on hand per hectare on 30 June |
| farmland_per_cell | ha | Indicative area of farmland in the grid cell |
References
Australian gridded farm data, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, July 2024, doi:10.25814/7n6z-ev41. CC BY 4.0.
N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought from the perspective of Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, doi:10.1016/j.crm.2022.100420.
See Also
Other AGFD:
read_agfd_dt(),
read_agfd_stars(),
read_agfd_tidync()
Examples
agfd_terra <- read_agfd_terra()
head(agfd_terra)
# `plot()` is rexported from the `terra` package
plot(agfd_terra[[1]][[1]])
Read ABARES' "Australian Gridded Farm Data" (AGFD) NCDF Files with tidync
Description
Read "Australian Gridded Farm Data", (AGFD), as a list of
tidync::tidync() objects.
Usage
read_agfd_tidync(yyyy = 1991:2023, fixed_prices = TRUE, x = NULL)
Arguments
yyyy |
Returns only data for the specified year or years for climate
data (fixed prices) or the years for historical climate and prices depending
upon the setting of |
fixed_prices |
Download historical climate and prices or historical
climate and fixed prices as described in (Hughes et al. 2022). Defaults
to |
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Details
From the ABARES website:
"The Australian Gridded Farm Data (AGFD) are a set of national
scale maps containing simulated data on historical broadacre farm business
outcomes including farm profitability on an 0.05-degree (approximately 5 km)
grid.
These data have been produced by ABARES as part of the ongoing
Australian Agricultural Drought Indicator (AADI) project
(previously known as the Drought Early Warning System Project) and were
derived using ABARES
farmpredict
model, which in turn is based on ABARES Agricultural and Grazing Industries
Survey (AAGIS) data.
Australian Agricultural Drought Indicator
(AADI) project (previously known as the Drought Early Warning
System Project) and were derived using ABARES
farmpredict
model, which in turn is based on ABARES Agricultural and Grazing Industries Survey
(AAGIS) data.
These maps provide estimates of farm business profit, revenue, costs and
production by location (grid cell) and year for the period 1990-91 to
2022-23. The data do not include actual observed outcomes but rather model
predicted outcomes for representative or 'typical' broadacre farm
businesses at each location considering likely farm characteristics and
prevailing weather conditions and commodity prices."
– ABARES, 2024-11-25
If you have not already downloaded the files, both sets of data are large in file size, i.e., >1GB, and will require time to download.
Value
A list of tidync objects of the "Australian Gridded Farm Data" with the NetCDF objects' names as "year_yyyy".
Model scenarios
Historical Climate (fixed prices)
The Historical Climate (fixed prices) scenario is similar to that described in Hughes et al. (2022) and is intended to isolate the effects of climate variability on financial incomes for broadacre farm businesses. In these simulations, global output and input price indexes are fixed at values from the most recently completed financial year. However, in these scenarios the spread between domestic and global grain (wheat, barley and sorghum) prices, along with Australian fodder prices are allowed to vary in response to climate data (to capture domestic increases in grain and fodder prices in drought years, see Hughes et al. 2022). A 33-year historical climate sequence (including historical simulated crop and pasture data from the AADI project) is simulated for each grid cell (1990-91 to 2022-23).
Historical Climate and Prices
As part of the AADI project an additional scenario was developed accounting for changes in both climate conditions and output and input prices (i.e., global commodity market variability). In this historical climate and prices scenario the 33-year reference period allows for variation in both historical climate conditions and historical prices. For this scenario, historical price indexes were de-trended, to account for consistent long-term trends in some real commodity prices (particularly sheep and lamb). The resulting simulation results and percentile indicators are intended to reflect the combined impacts of annual climate and commodity price variability."
– Taken from Australian Bureau of Agricultural and Resource Economics and Sciences (2024)
Data files
Simulation output data are saved as multilayer NetCDF files, which are named using following convention:
f<farm year>.c<climate year>.p<price year>.t<technology year>.nc
where:
-
<farm year> = Financial year of farm business data is used in simulations.
-
<climate year> = Financial year of climate data is used in simulations.
-
<price year> = Financial year of output and input prices used in simulations.
-
<technology year> = Financial year of farm 'technology' (equal to farm year in all simulations) Here financial years are referred to by the closing calendar year (e.g., 2022 = 1 July 2021 to 30 June 2022).
– Taken from Australian Bureau of Agricultural and Resource Economics and Sciences (2024)
Data layers
The data layers from the downloaded NetCDF files are described in Table 2 as seen in Australian Bureau of Agricultural and Resource Economics and Sciences (2024).
Following is a copy of Table 2 for your convenience, please refer to the full document for all methods and metadata.
| Layer | Unit | Description |
| farmno | - | Row index and column index of the grid cell in the form of YYYXXX |
| A_barley_hat_ha | - | Proportion of total farm area planted to barley |
| A_oilseeds_hat_ha | - | Proportion of total farm area planted to canola |
| A_sorghum_hat_ha | - | Proportion of total farm area planted to sorghum |
| A_total_cropped_ha | - | Proportion of total farm area planted to crops |
| A_wheat_hat_ha | - | Proportion of total farm area planted to wheat |
| C_chem_hat_ha | $/ha | Expenditure on crop and pasture chemicals per hectare |
| C_fert_hat_ha | $/ha | Expenditure on fertiliser per hectare |
| C_fodder_hat_ha | $/ha | Expenditure on fodder per hectare |
| C_fuel_hat_ha | $/ha | Expenditure on fuel, oil and grease per hectare |
| C_total_hat_ha | $/ha | Total cash costs per hectare |
| FBP_fci_hat_ha | $/ha | Farm cash income per hectare |
| FBP_fbp_hat_ha | $/ha | Farm business profit per hectare, cash income adjusted for family labour, depreciation, and changes in stocks |
| FBP_pfe_hat_ha | $/ha | Profit at full equity per hectare |
| H_barley_dot_hat | t/ha | Barley yield (production per hectare planted) |
| H_oilseeds_dot_hat | t/ha | Oilseeds yield (production per hectare planted) |
| H_sorghum_dot_hat | t/ha | Sorghum yield (production per hectare planted) |
| H_wheat_dot_hat | t/ha | Wheat yield (production per hectare planted) |
| Q_barley_hat_ha | t/ha | Barley sold per hectare (total farm area) |
| Q_beef_hat_ha | Number/ha | Beef number sold per hectare |
| Q_lamb_hat_ha | Number/ha | Prime lamb number sold per hectare |
| Q_oilseeds_hat_ha | t/ha | Canola sold per hectare (total farm area) |
| Q_sheep_hat_ha | Number/ha | Sheep number sold per hectare |
| Q_sorghum_hat_ha | t/ha | Sorghum sold per hectare (total farm area) |
| Q_wheat_hat_ha | t/ha | Wheat sold per hectare (total farm area) |
| R_barley_hat_ha | $/ha | Barley gross receipts per hectare |
| R_beef_hat_ha | $/ha | Beef cattle receipts per hectare |
| R_lamb_hat_ha | $/ha | Prime lamb net receipts per hectare |
| R_oilseeds_hat_ha | $/ha | Receipts for oilseeds this FY for oilseeds sold this FY or in previous FYs per hectare |
| R_sheep_hat_ha | $/ha | Sheep gross receipts per hectare |
| R_sorghum_hat_ha | $/ha | Sorghum gross receipts per hectare |
| R_total_hat_ha | $/ha | Total farm receipts per hectare |
| R_wheat_hat_ha | $/ha | Wheat gross receipts per hectare |
| S_beef_births_hat_ha | Number/ha | Beef cattle births per hectare |
| S_beef_cl_hat_ha | Number/ha | Beef cattle on hand per hectare on 30 June |
| S_beef_deaths_hat_ha | Number/ha | Beef cattle deaths per hectare |
| S_sheep_births_hat_ha | Number/ha | Sheep births per hectare |
| S_sheep_cl_hat_ha | Number/ha | Sheep on hand per hectare on 30 June |
| S_sheep_deaths_hat_ha | Number/ha | Sheep deaths per hectare |
| S_wheat_cl_hat_ha | t/ha | Wheat on hand per hectare on 30 June |
| farmland_per_cell | ha | Indicative area of farmland in the grid cell |
References
Australian gridded farm data, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, July 2024, doi:10.25814/7n6z-ev41. CC BY 4.0.
N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought from the perspective of Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, doi:10.1016/j.crm.2022.100420.
See Also
Other AGFD:
read_agfd_dt(),
read_agfd_stars(),
read_agfd_terra()
Examples
agfd_tnc <- read_agfd_tidync()
head(agfd_tnc)
Read ABARES' Catchment Scale "Land Use of Australia" Commodities Shapefile
Description
Download (if desired) catchment level land use commodity data shapefile and import it into your active R session after correcting invalid geometries.
Usage
read_clum_commodities(x = NULL)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Value
An sf::sf() object.
Source
References
ABARES 2024, Catchment Scale Land Use of Australia – Update December 2023 version 2, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, June, CC BY 4.0, DOI: doi:10.25814/2w2p-ph98.
Examples
clum_commodities <- read_clum_commodities()
clum_commodities
Read ABARES' Catchment Scale "Land Use of Australia" Data Using Stars
Description
Download and import catchment scale "Land Use of Australia" GeoTIFFs as a stars object.
Usage
read_clum_stars(data_set = "clum_50m_2023_v2", x = NULL, ...)
Arguments
data_set |
A string value indicating the data desired for download. One of:
. |
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
... |
Additional arguments passed to |
Details
From the ABARES documentation "The Catchment Scale Land Use of Australia – Update December 2023 version 2 dataset is the national compilation of catchment scale land use data available for Australia (CLUM), as at December 2023. It replaces the Catchment Scale Land Use of Australia – Update December 2020. It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. The CLUM data shows a single dominant land use for a given area, based on the primary management objective of the land manager (as identified by state and territory agencies). Land use is classified according to the Australian Land Use and Management Classification version 8. It has been compiled from vector land use datasets collected as part of state and territory mapping programs and other authoritative sources, through the Australian Collaborative Land Use and Management Program. Catchment scale land use data was produced by combining land tenure and other types of land use information including, fine-scale satellite data, ancillary datasets, and information collected in the field. The date of mapping (2008 to 2023) and scale of mapping (1:5,000 to 1:250,000) vary, reflecting the source data, capture date and scale. Date and scale of mapping are provided in supporting datasets." – ABARES, 2024-06-27
Value
a stars object that may be one or many layers depending upon the requested data set.
Note
The raster will load with the default category for each data set, but you can
specify a different category to use by passing the RAT argument through
the .... To see which categories are available, please refer
to the metadata for these data. The PDF can be accessed in your
default PDF viewer by using view_nlum_metadata_pdf().
Source
References
ABARES 2024, Catchment Scale Land Use of Australia – Update December 2023 version 2, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, June, CC BY 4.0, DOI: doi:10.25814/2w2p-ph98.
See Also
Other clum:
read_clum_terra()
Examples
clum_stars <- read_clum_stars(data_set = "clum_50m_2023_v2")
clum_stars
plot(clum_stars)
Read ABARES' Catchment Scale "Land Use of Australia" GeoTIFFs Using terra
Description
Download and import catchment scale "Land Use of Australia" GeoTIFFs using
terra as a categorical terra::rast() object.
Usage
read_clum_terra(data_set = "clum_50m_2023_v2", x = NULL, ...)
Arguments
data_set |
A string value indicating the data desired for download. One of:
. |
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
... |
Additional arguments passed to |
Details
From the ABARES documentation "The Catchment Scale Land Use of Australia – Update December 2023 version 2 dataset is the national compilation of catchment scale land use data available for Australia (CLUM), as at December 2023. It replaces the Catchment Scale Land Use of Australia – Update December 2020. It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. The CLUM data shows a single dominant land use for a given area, based on the primary management objective of the land manager (as identified by state and territory agencies). Land use is classified according to the Australian Land Use and Management Classification version 8. It has been compiled from vector land use datasets collected as part of state and territory mapping programs and other authoritative sources, through the Australian Collaborative Land Use and Management Program. Catchment scale land use data was produced by combining land tenure and other types of land use information including, fine-scale satellite data, ancillary datasets, and information collected in the field. The date of mapping (2008 to 2023) and scale of mapping (1:5,000 to 1:250,000) vary, reflecting the source data, capture date and scale. Date and scale of mapping are provided in supporting datasets." – ABARES, 2024-06-27
Value
A terra SpatRaster object that may be one or many layers
depending upon the requested data set.
Active categories
The catchment scale land use data set is a categorical raster with many
categories. The raster will load with the default category for each data
set, but you can specify a different category to use through
terra::activeCat() after loading. To see which categories are available,
please refer to the metadata for these data. The PDF can be accessed in
your default web browser by using view_clum_metadata_pdf().
Map colours
Where ABARES has provided a style guide, it will be applied by default to the raster object. Not all GeoTiff files have a colour guide available.
Source
References
ABARES 2024, Catchment Scale Land Use of Australia – Update December 2023 version 2, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, June, CC BY 4.0, DOI: doi:10.25814/2w2p-ph98
See Also
Other clum:
read_clum_stars()
Examples
clum_terra <- read_clum_terra(data_set = "clum_50m_2023_v2")
clum_terra
plot(clum_terra)
Read ABARES' "Estimates by Performance Category" Data
Description
Fetches and imports ABARES estimates by performance category data.
Usage
read_estimates_by_performance_category(x = NULL)
read_est_by_perf_cat(x = NULL)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Value
A data.table::data.table() object.
Note
Columns are renamed for consistency with other ABARES products serviced in this package using a snake_case format and ordered consistently.
Source
()https://www.agriculture.gov.au/sites/default/files/documents/fdp-BySize-ByPerformance.csv.
References
https://www.agriculture.gov.au/abares/data/farm-data-portal#data-download.
See Also
Other Estimates:
read_estimates_by_size(),
read_historical_national_estimates(),
read_historical_regional_estimates(),
read_historical_state_estimates()
Examples
read_estimates_by_performance_category()
# or shorter
read_est_by_perf_cat()
Read ABARES' "Estimates by Size"
Description
Fetches and imports ABARES estimates by size data.
Usage
read_estimates_by_size(x = NULL)
read_est_by_size(x = NULL)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Value
A data.table::data.table() object with the Variable field as the
key.
Note
Columns are renamed for consistency with other ABARES products serviced in this package using a snake_case format and ordered consistently.
Source
https://www.agriculture.gov.au/sites/default/files/documents/fdp-national-historical.csv.
References
https://www.agriculture.gov.au/abares/data/farm-data-portal#data-download.
See Also
Other Estimates:
read_estimates_by_performance_category(),
read_historical_national_estimates(),
read_historical_regional_estimates(),
read_historical_state_estimates()
Examples
read_estimates_by_size()
# or shorter
read_est_by_size()
Read ABARES' "Historical Forecast Database"
Description
Fetches and imports ABARES "Historical Forecast Database" performance data.
Usage
read_historical_forecast_database(x = NULL)
read_historical_forecast(x = NULL)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived locally. This function does not provide any
checking whether this function is the proper function for the provided file.
Defaults to |
Value
A data.table::data.table() object.
Data Dictionary
The resulting object will contain the following fields.
| Field | Description |
| Commodity | Broad description of commodity (includes the Australian dollar) |
| Estimate_type | Broad grouping of estimate by theme e.g., animal numbers, area, production, price, export and volume measures. |
| Estimate_description | Detailed description of each series. |
| Unit | Measurement unit of series. e.g., kt, $m, $/t. |
| Region | Relevant region for each series. "World" denotes relevant international market. |
| Year_Issued | Year that forecast was originally issued. |
| Month_issued | Month that forecast was originally issued. |
| Year_Issued_FY | Australian financial year (July-June) that forecast was originally issued. |
| Forecast_Year_FY | Australian financial year (July-June) for which the forecast was issued. Where forecast year is earlier than Year Issued (FY), value is a backcast. |
| Forecast_Value | Forecast as originally issued. |
| Actual_Value | Actual outcome observed. Note that historical time series can be revised. Latest available data at time of update, including any revisions, are included in database. |
Note
Columns are renamed for consistency with other ABARES products serviced in this package using a snake_case format and ordered consistently.
The "Month_issued" column is converted from a character string to a numeric
value representing the month of year, e.g., "March" is converted to 3.
Source
https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1031941/0.
References
Examples
read_historical_forecast_database()
# or shorter
read_historical_forecast()
Read ABARES' "Historical National Estimates"
Description
Fetches and imports ABARES "Historical National Estimates" data.
Usage
read_historical_national_estimates(x = NULL)
read_hist_nat_est(x = NULL)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Value
A data.table::data.table() object with the Variable field as the
key.
Note
Columns are renamed for consistency with other ABARES products serviced in this package using a snake_case format and ordered consistently.
Source
https://www.agriculture.gov.au/sites/default/files/documents/fdp-national-historical.csv.
References
https://www.agriculture.gov.au/abares/data/farm-data-portal#data-download.
See Also
Other Estimates:
read_estimates_by_performance_category(),
read_estimates_by_size(),
read_historical_regional_estimates(),
read_historical_state_estimates()
Examples
read_historical_national_estimates()
# or shorter
read_hist_nat_est()
Read ABARES' "Historical Regional Estimates"
Description
Fetches and imports ABARES "Historical Regional Estimates" data.
Usage
read_historical_regional_estimates(x = NULL)
read_hist_reg_est(x = NULL)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Value
A data.table::data.table() object with the Variable field as the
key.
Note
Columns are renamed for consistency with other ABARES products serviced in this package using a snake_case format and ordered consistently.
Source
https://www.agriculture.gov.au/sites/default/files/documents/fdp-regional-historical.csv.
References
https://www.agriculture.gov.au/abares/data/farm-data-portal#data-download.
See Also
Other Estimates:
read_estimates_by_performance_category(),
read_estimates_by_size(),
read_historical_national_estimates(),
read_historical_state_estimates()
Examples
read_historical_regional_estimates()
# or shorter
read_hist_reg_est()
Read ABARES' "Historical State Estimates"
Description
Fetches and imports ABARES "Historical State Estimates" data.
Usage
read_historical_state_estimates(x = NULL)
read_hist_st_est(x = NULL)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
Value
A data.table::data.table() object with the Variable field as the
key.
Note
Columns are renamed for consistency with other ABARES products serviced in this package using a snake_case format and ordered consistently.
Source
https://www.agriculture.gov.au/sites/default/files/documents/fdp-state-historical.csv.
References
https://www.agriculture.gov.au/abares/data/farm-data-portal#data-download.
See Also
Other Estimates:
read_estimates_by_performance_category(),
read_estimates_by_size(),
read_historical_national_estimates(),
read_historical_regional_estimates()
Examples
read_historical_state_estimates()
# or shorter
read_hist_st_est()
Read ABARES' National Scale "Land Use of Australia" Data Using stars
Description
Download and import national scale "Land Use of Australia v7" GeoTIFFs as a stars object.
Usage
read_nlum_stars(data_set = NULL, x = NULL, ...)
Arguments
data_set |
A string value indicating the GeoTIFF desired for download. One of:
.
This argument is ignored if |
x |
A character string of a file path to a local zip file that has
been downloaded outside of R that contains the NLUM data.
This argument is ignored if |
... |
Additional arguments passed to |
Details
From the ABARES website: "The Land use of Australia 2010–11 to 2020–21 data package consists of seamless continental rasters that present land use at national scale for 2010–11, 2015–16 and 2020–21 and the associated change between each target period. Non-agricultural land uses are mapped using 7 thematic layers, derived from existing datasets provided by state and territory jurisdictions and external agencies. These 7 layers are: protected areas, topographic features, land tenure, forest type, catchment scale land use, urban boundaries, and stock routes. The agricultural land uses are based on the Australian Bureau of Statistics’ 2010–11, 2015–16 and 2020–21 agricultural census data; with spatial distributions modelled using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery and training data, assisted by spatial constraint layers for cultivation, horticulture, and irrigation. Land use is specified according to the Australian Land Use and Management (ALUM) Classification version 8. The same method is applied to all target periods using representative national datasets for each period, where available. All rasters are in GeoTIFF format with geographic coordinates in Geocentric Datum of Australian 1994 (GDA94) and a 0.002197 degree (~250 metre) cell size. The Land use of Australia 2010–11 to 2020–21 data package is a product of the Australian Collaborative Land Use and Management Program. This data package replaces the Land use of Australia 2010–11 to 2015–16 data package, with updates to these time periods." – ABARES, 2024-11-28
Value
a stars object that may be one or many layers depending upon the requested data set.
proj
Which should you choose? If you need accurate area calculations (e.g., hectares of land use), use Albers. If you just need global compatibility or want to overlay with other lat/long datasets, use Geographic.
Note
The raster will load with the default category for each data set, but you can
specify a different category to use by passing the RAT argument through
the .... To see which categories are available, please refer
to the metadata for these data. The PDF can be accessed in your
default PDF viewer by using view_nlum_metadata_pdf().
Source
- Y201011
- Y201516
- Y202021
- C201021
- T201011
- T201516
- T202021
- P201011
- P201516
- P202021
.
References
ABARES 2024, Land use of Australia 2010–11 to 2020–21, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, November, CC BY 4.0. doi:10.25814/w175-xh85
See Also
Other nlum:
read_nlum_terra(),
view_clum_metadata_pdf(),
view_nlum_metadata_pdf()
Examples
nlum_stars <- read_nlum_stars("Y202021")
nlum_stars
plot(nlum_stars)
Read ABARES' National Scale "Land Use of Australia" Data Using terra
Description
Download and import national scale "Land Use of Australia v7" GeoTIFFs as
categorical terra::rast() objects.
Usage
read_nlum_terra(data_set = NULL, x = NULL, ...)
Arguments
data_set |
A string value indicating the GeoTIFF desired for download. One of:
.
This argument is ignored if |
x |
A character string of a file path to a local zip file that has
been downloaded outside of R that contains the NLUM data.
This argument is ignored if |
... |
Other arguments passed to |
Details
From the ABARES website: "The Land use of Australia 2010–11 to 2020–21 data package consists of seamless continental rasters that present land use at national scale for 2010–11, 2015–16 and 2020–21 and the associated change between each target period. Non-agricultural land uses are mapped using 7 thematic layers, derived from existing datasets provided by state and territory jurisdictions and external agencies. These 7 layers are: protected areas, topographic features, land tenure, forest type, catchment scale land use, urban boundaries, and stock routes. The agricultural land uses are based on the Australian Bureau of Statistics’ 2010–11, 2015–16 and 2020–21 agricultural census data; with spatial distributions modelled using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery and training data, assisted by spatial constraint layers for cultivation, horticulture, and irrigation. Land use is specified according to the Australian Land Use and Management (ALUM) Classification version 8. The same method is applied to all target periods using representative national datasets for each period, where available. All rasters are in GeoTIFF format with geographic coordinates in Geocentric Datum of Australian 1994 (GDA94) and a 0.002197 degree (~250 metre) cell size. The Land use of Australia 2010–11 to 2020–21 data package is a product of the Australian Collaborative Land Use and Management Program. This data package replaces the Land use of Australia 2010–11 to 2015–16 data package, with updates to these time periods." – ABARES, 2024-11-28
Value
A terra SpatRaster object that may be one or many layers
depending upon the requested data set.
Note
The raster will load with the default category for each data set, but you can
specify a different category to use through terra::activeCat(). To see
which categories are available, please refer to the metadata for these data.
The PDF can be accessed in your default web browser by using
view_nlum_metadata_pdf().
Source
- Y201011
- Y201516
- Y202021
- C201021
- T201011
- T201516
- T202021
- P201011
- P201516
- P202021
.
References
ABARES 2024, Land use of Australia 2010–11 to 2020–21, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, November, CC BY 4.0. doi:10.25814/w175-xh85.
See Also
Other nlum:
read_nlum_stars(),
view_clum_metadata_pdf(),
view_nlum_metadata_pdf()
Examples
nlum_terra <- read_nlum_terra(data_set = "Y202021")
nlum_terra
plot(nlum_terra)
Read ABARES' "Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1" with stars
Description
Read "Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1" data as a stars object.
Usage
read_topsoil_thickness_stars(x = NULL, ...)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
... |
Additional arguments passed to |
Value
A stars object of the "Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1".
Note
This function converts a terra::rast() object internally rather than
reading directly from a file.
Source
References
See Also
Other topsoil thickness:
print_topsoil_thickness_metadata(),
read_topsoil_thickness_terra()
Examples
st_stars <- read_topsoil_thickness_stars()
plot(st_stars)
Read ABARES' "Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1" with terra
Description
Read "Soil Thickness for Australian Areas of Intensive Agriculture of Layer
1" as a terra::rast() object.
Usage
read_topsoil_thickness_terra(x = NULL, ...)
Arguments
x |
A file path providing the file with the data to be imported. The
file is assumed to be unarchived (i.e., still zipped). This function does
not provide any checking whether this function is the proper function for
the provided file. Defaults to |
... |
Additional arguments passed to |
Value
A terra::rast() object of the "Soil Thickness for Australian Areas
of Intensive Agriculture of Layer 1".
Source
References
See Also
Other topsoil thickness:
print_topsoil_thickness_metadata(),
read_topsoil_thickness_stars()
Examples
st_terra <- read_topsoil_thickness_terra()
# terra::plot() is reexported for convenience
plot(st_terra)
Objects exported from other packages
Description
These objects are imported from other packages. Follow the links below to see their documentation.
- terra
Displays the PDF Metadata for ABARES' "Catchment Land Use" (CLUM) Raster Files in a Native Viewer
Description
Each "Catchment Land Use" (CLUM) raster file comes with a PDF of metadata. This function will open and display that file using the native PDF viewer for any system with a graphical user interface and PDF viewer configured. If the file does not exist locally, it will be fetched and displayed.
Usage
view_clum_metadata_pdf(commodities = FALSE)
Arguments
commodities |
A |
Value
An invisible NULL. Called for its side-effects, opens the system's
native PDF viewer to display the requested metadata PDF
document.
Source
- CLUM Metadata
https://www.agriculture.gov.au/sites/default/files/documents/CLUM_DescriptiveMetadata_December2023_v2.pdf
- CLUM Commodities Metadata
https://www.agriculture.gov.au/sites/default/files/documents/CLUMC_DescriptiveMetadata_December2023.pdf
See Also
Other nlum:
read_nlum_stars(),
read_nlum_terra(),
view_nlum_metadata_pdf()
Examples
view_clum_metadata_pdf()
Displays PDF Metadata for ABARES' "National Land Use" (NLUM) Raster Files in a Native Viewer
Description
Each National Land Use (NLUM) raster file comes with a PDF of metadata. This function will open and display that file using the native PDF viewer for any system with a graphical user interface and PDF viewer configured. If the file does not exist locally, it will be fetched and displayed.
Usage
view_nlum_metadata_pdf()
Value
An invisible NULL. Called for its side-effects, opens the system's
native PDF viewer to display the requested metadata PDF
document.
See Also
Other nlum:
read_nlum_stars(),
read_nlum_terra(),
view_clum_metadata_pdf()
Examples
view_nlum_metadata_pdf()