Downloads The Australian Gridded Farm Data (AGFD) data and unzips the compressed files to NetCDF for importing.
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
Arguments
- fixed_prices
Download historical climate and prices or historical climate and fixed prices as described in (Hughes et al. 2022). Defaults to
TRUEand downloads the data with historical climate and fixed prices “to isolate the effects of climate variability on financial incomes for broadacre farm businesses” (ABARES 2024). UsingTRUEwill download simulations where global output and input price indexes are fixed at values from the most recently completed financial year.- cache
Cache the Australian Gridded Farm 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. IfFALSE, this function usestempdir()and the files are deleted upon closing of the active R session.
Value
A read.abares.agfd.nc.files object, a list of NetCDF files
containing the Australian Gridded Farm Data.
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's
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
Both sets of data are large in file size, i.e., >1GB, and will require time to download.
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_aagis_regions(),
read_agfd_dt(),
read_agfd_stars(),
read_agfd_terra(),
read_agfd_tidync()