read.abares - Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources #667

adamhsparks · 2024-11-02T03:38:00Z

Submitting Author Name: Adam Sparks
Submitting Author Github Handle: @adamhsparks
Repository: https://github.com/adamhsparks/read.abares
Version submitted: 0.1.0
Submission type: Standard
Editor: TBD
Reviewers: TBD

Archive: TBD
Version accepted: TBD
Language: en

Paste the full DESCRIPTION file inside a code block below:

Type: Package
Package: read.abares
Title: Provides simple downloading, parsing and importing of Australian
    Bureau of Agricultural and Resource Economics and Sciences (ABARES)
    data sources
Version: 0.1.0
Authors@R: 
    person("Adam H.", "Sparks", , "[email protected]", role = c("cre", "aut"),
           comment = c(ORCID = "0000-0002-0061-8359"))
Description: Download and import data from the Australian Bureau of
    Agricultural and Resource Economics and Sciences (ABARES)
    <https://www.agriculture.gov.au/abares>.
License: MIT + file LICENSE
URL: https://github.org/adamhsparks/read.abares,
    https://adamhsparks.github.io/read.abares/
BugReports: https://github.com/adamhsparks/read.abares/issues
Imports: 
    cli,
    curl,
    data.table,
    lubridate,
    openxlsx2,
    purrr,
    readtext,
    sf,
    stars,
    stringr,
    terra,
    tidync,
    withr
Suggests: 
    knitr,
    pander,
    rmarkdown,
    roxyglobals,
    spelling,
    testthat (>= 3.0.0)
VignetteBuilder: 
    knitr
Config/roxyglobals/filename: globals.R
Config/roxyglobals/unique: FALSE
Config/testthat/edition: 3
Encoding: UTF-8
Language: en-US
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c("collate", "namespace", "rd",
    "roxyglobals::global_roclet"))
RoxygenNote: 7.3.2

Scope

Please indicate which category or categories from our package fit policies this package falls under: (Please check an appropriate box below. If you are unsure, we suggest you make a pre-submission inquiry.):
- data retrieval
- data extraction
- data munging
- data deposition
- data validation and testing
- workflow automation
- version control
- citation management and bibliometrics
- scientific software wrappers
- field and lab reproducibility tools
- database software bindings
- geospatial data
- text analysis
Explain how and why the package falls under these categories (briefly, 1-2 sentences):

Provides workflow assistance for fetching freely available data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), the science and economics research division of the Department of Agriculture, Fisheries and Forestry. Data include trade information, soil depth, agricultural survey data on production and model outputs of agricultural production useful for agricultural researchers.

Who is the target audience and what are scientific applications of this package?
- Ag economists
- Biometricians working in agriculture
- Anyone else who is interested in Australian agricultural and economic data
Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

None that I'm aware of.

(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

NA

If you made a pre-submission inquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.
Explain reasons for any pkgcheck items which your package is unable to pass.

Technical checks

Confirm each of the following by checking the box.

I have read the rOpenSci packaging guide.
I have read the author guide and I expect to maintain this package for at least 2 years or to find a replacement.

This package:

does not violate the Terms of Service of any service it interacts with.
has a CRAN and OSI accepted license.
contains a README with instructions for installing the development version.
includes documentation with examples for all functions, created with roxygen2.
contains a vignette with examples of its essential functions and uses.
has a test suite.
has continuous integration, including reporting of test coverage.

Publication options

Do you intend for this package to go on CRAN?
Do you intend for this package to go on Bioconductor?
Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:

MEE Options

The package is novel and will be of interest to the broad readership of the journal.
The manuscript describing the package is no longer than 3000 words.
You intend to archive the code for the package in a long-term repository which meets the requirements of the journal (see MEE's Policy on Publishing Code)
(Scope: Do consider MEE's Aims and Scope for your manuscript. We make no guarantee that your manuscript will be within MEE scope.)
(Although not required, we strongly recommend having a full manuscript prepared when you submit here.)
(Please do not submit your package separately to Methods in Ecology and Evolution)

Code of conduct

I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

The text was updated successfully, but these errors were encountered:

ropensci-review-bot · 2024-11-02T03:38:02Z

Thanks for submitting to rOpenSci, our editors and @ropensci-review-bot will reply soon. Type @ropensci-review-bot help for help.

ropensci-review-bot · 2024-11-02T03:38:05Z

🚀

Editor check started

👋

ropensci-review-bot · 2024-11-04T13:07:33Z

Checks for read.abares (v0.1.0)

git hash: cd3d61e5

✔️ Package name is available
✔️ has a 'codemeta.json' file.
✔️ has a 'contributing' file.
✔️ uses 'roxygen2'.
✔️ 'DESCRIPTION' has a URL field.
✔️ 'DESCRIPTION' has a BugReports field.
✔️ Package has at least one HTML vignette
✔️ All functions have examples.
✔️ Package has continuous integration checks.
✖️ Package coverage failed
✖️ R CMD check found 1 error.
✔️ R CMD check found no warnings.
👀 Function names are duplicated in other packages

Important: All failing checks above must be addressed prior to proceeding

(Checks marked with 👀 may be optionally addressed.)

Package License: MIT + file LICENSE

1. Package Dependencies

Details of Package Dependency Usage (click to open)

The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.

type	package	ncalls
internal	base	77
internal	read.abares	10
internal	methods	3
internal	stats	3
internal	tools	2
internal	utils	2
imports	data.table	21
imports	purrr	6
imports	httr2	5
imports	terra	4
imports	openxlsx2	3
imports	sf	3
imports	stars	3
imports	tidync	3
imports	stringr	2
imports	readtext	1
imports	cli	NA
imports	lubridate	NA
imports	withr	NA
suggests	knitr	NA
suggests	pander	NA
suggests	rmarkdown	NA
suggests	roxyglobals	NA
suggests	spelling	NA
suggests	testthat	NA
linking_to	NA	NA

Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table.

base

file.path (32), tempdir (13), c (12), dirname (7), url (4), lapply (2), list (2), list.files (2), append (1), message (1), nchar (1)

data.table

fread (9), fifelse (7), as.data.table (3), rbindlist (1), setcolorder (1)

read.abares

clear_cache (1), get_aagis_regions (1), get_abares_trade (1), get_abares_trade_regions (1), get_agfd (1), get_historical_forecast_database (1), get_historical_national_estimates (1), get_soil_thickness (1), inspect_cache (1), print.read.abares.agfd.nc.files (1)

purrr

map (4), modify_depth (1), quietly (1)

httr2

req_options (2), request (2), req_retry (1)

terra

rast (4)

methods

new (3)

openxlsx2

read_xlsx (3)

sf

read_sf (2), st_read (1)

stars

read_ncdf (2), read_stars (1)

stats

var (2), dt (1)

tidync

tidync (2), hyper_tibble (1)

stringr

str_locate (1), str_sub (1)

tools

R_user_dir (2)

utils

unzip (2)

readtext

readtext (1)

NOTE: Some imported packages appear to have no associated function calls; please ensure with author that these 'Imports' are listed appropriately.

2. Statistical Properties

This package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing.

Details of statistical properties (click to open)

The package has:

code in R (100% in 23 files) and
1 authors
1 vignette
no internal data file
13 imported packages
26 exported functions (median 9 lines of code)
49 non-exported functions in R (median 15 lines of code)

Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages
The following terminology is used:

loc = "Lines of Code"
fn = "function"
exp/not_exp = exported / not exported

All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the checks_to_markdown() function

The final measure (fn_call_network_size) is the total number of calls between functions (in R), or more abstract relationships between code objects in other languages. Values are flagged as "noteworthy" when they lie in the upper or lower 5th percentile.

measure	value	percentile	noteworthy
files_R	23	83.4
files_vignettes	2	81.7
files_tests	22	95.6
loc_R	607	51.6
loc_vignettes	265	56.7
loc_tests	853	81.1
num_vignettes	1	58.8
n_fns_r	75	67.2
n_fns_r_exported	26	73.8
n_fns_r_not_exported	49	65.1
n_fns_per_file_r	2	34.9
num_params_per_fn	1	1.8	TRUE
loc_per_fn_r	11	33.0
loc_per_fn_r_exp	9	19.8
loc_per_fn_r_not_exp	15	49.9
rel_whitespace_R	21	56.7
rel_whitespace_vignettes	50	70.4
rel_whitespace_tests	10	66.3
doclines_per_fn_exp	25	23.7
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	36	57.9

2a. Network visualisation

Click to see the interactive network visualisation of calls between objects in package

3. `goodpractice` and other checks

Details of goodpractice checks (click to open)

3a. Continuous Integration Badges

GitHub Workflow Results

id	name	conclusion	sha	run_number	date
11649832899	pages build and deployment	success	980cc6	19	2024-11-03
11649818126	pkgdown.yaml	success	cd3d61	19	2024-11-03
11649818121	R-CMD-check.yaml	failure	cd3d61	19	2024-11-03
11649818125	test-coverage.yaml	failure	cd3d61	19	2024-11-03

3b. `goodpractice` results

`R CMD check` with rcmdcheck

R CMD check generated the following error:

checking tests ...
Running ‘spelling.R’
Comparing ‘spelling.Rout’ to ‘spelling.Rout.save’ ... OK
Running ‘testthat.R’
ERROR
Running the tests in ‘tests/testthat.R’ failed.
Last 13 lines of output:
Ran 1/1 deferred expressions
There do not appear to be any files cached for {read.abares}.
There do not appear to be any files cached for {read.abares}.

-- Locally Available {read.abares} Cached Files --------------------------------

test.R

-- Locally Available {read.abares} Cached Files --------------------------------

test.R
Ran 1/1 deferred expressions
Will return stars object with 612226 cells.
No projection information found in nc file.
Coordinate variable units found to be degrees,
assuming WGS84 Lat/Lon.
Killed

R CMD check generated the following test_fail:

This file is part of the standard setup for testthat.

It is recommended that you do not modify it.

Where should you do additional test configuration?

Learn more about the roles of various files in:

* https://r-pkgs.org/testing-design.html#sec-tests-files-overview

* https://testthat.r-lib.org/articles/special-files.html

library(testthat)
library(read.abares)

Attaching package: 'read.abares'

The following object is masked from 'package:graphics':

plot

The following object is masked from 'package:base':

plot

test_check("read.abares")
Ran 1/1 deferred expressions
Ran 2/2 deferred expressions
Ran 1/1 deferred expressions
Ran 1/1 deferred expressions

-- Locally Available ABARES AGFD NetCDF Files ----------------------------------

f2022.c1991.p2022.t2022.nc
f2022.c1992.p2022.t2022.nc
f2022.c1993.p2022.t2022.nc
f2022.c1994.p2022.t2022.nc
f2022.c1995.p2022.t2022.nc
f2022.c1996.p2022.t2022.nc
f2022.c1997.p2022.t2022.nc
f2022.c1998.p2022.t2022.nc
f2022.c1999.p2022.t2022.nc
f2022.c2000.p2022.t2022.nc
f2022.c2001.p2022.t2022.nc
f2022.c2002.p2022.t2022.nc
f2022.c2003.p2022.t2022.nc
f2022.c2004.p2022.t2022.nc
f2022.c2005.p2022.t2022.nc
f2022.c2006.p2022.t2022.nc
f2022.c2007.p2022.t2022.nc
f2022.c2008.p2022.t2022.nc
f2022.c2009.p2022.t2022.nc
f2022.c2010.p2022.t2022.nc
f2022.c2011.p2022.t2022.nc
f2022.c2012.p2022.t2022.nc
f2022.c2013.p2022.t2022.nc
f2022.c2014.p2022.t2022.nc
f2022.c2015.p2022.t2022.nc
f2022.c2016.p2022.t2022.nc
f2022.c2017.p2022.t2022.nc
f2022.c2018.p2022.t2022.nc
f2022.c2019.p2022.t2022.nc
f2022.c2020.p2022.t2022.nc
f2022.c2021.p2022.t2022.nc
f2022.c2022.p2022.t2022.nc
f2022.c2023.p2022.t2022.nc

-- Locally Available ABARES AGFD NetCDF Files ----------------------------------

f2022.c1991.p2022.t2022.nc
f2022.c1992.p2022.t2022.nc
f2022.c1993.p2022.t2022.nc
f2022.c1994.p2022.t2022.nc
f2022.c1995.p2022.t2022.nc
f2022.c1996.p2022.t2022.nc
f2022.c1997.p2022.t2022.nc
f2022.c1998.p2022.t2022.nc
f2022.c1999.p2022.t2022.nc
f2022.c2000.p2022.t2022.nc
f2022.c2001.p2022.t2022.nc
f2022.c2002.p2022.t2022.nc
f2022.c2003.p2022.t2022.nc
f2022.c2004.p2022.t2022.nc
f2022.c2005.p2022.t2022.nc
f2022.c2006.p2022.t2022.nc
f2022.c2007.p2022.t2022.nc
f2022.c2008.p2022.t2022.nc
f2022.c2009.p2022.t2022.nc
f2022.c2010.p2022.t2022.nc
f2022.c2011.p2022.t2022.nc
f2022.c2012.p2022.t2022.nc
f2022.c2013.p2022.t2022.nc
f2022.c2014.p2022.t2022.nc
f2022.c2015.p2022.t2022.nc
f2022.c2016.p2022.t2022.nc
f2022.c2017.p2022.t2022.nc
f2022.c2018.p2022.t2022.nc
f2022.c2019.p2022.t2022.nc
f2022.c2020.p2022.t2022.nc
f2022.c2021.p2022.t2022.nc
f2022.c2022.p2022.t2022.nc
f2022.c2023.p2022.t2022.nc

-- Locally Available ABARES AGFD NetCDF Files ----------------------------------

f2022.c1991.p2022.t2022.nc
f2022.c1992.p2022.t2022.nc
f2022.c1993.p2022.t2022.nc
f2022.c1994.p2022.t2022.nc
f2022.c1995.p2022.t2022.nc
f2022.c1996.p2022.t2022.nc
f2022.c1997.p2022.t2022.nc
f2022.c1998.p2022.t2022.nc
f2022.c1999.p2022.t2022.nc
f2022.c2000.p2022.t2022.nc
f2022.c2001.p2022.t2022.nc
f2022.c2002.p2022.t2022.nc
f2022.c2003.p2022.t2022.nc
f2022.c2004.p2022.t2022.nc
f2022.c2005.p2022.t2022.nc
f2022.c2006.p2022.t2022.nc
f2022.c2007.p2022.t2022.nc
f2022.c2008.p2022.t2022.nc
f2022.c2009.p2022.t2022.nc
f2022.c2010.p2022.t2022.nc
f2022.c2011.p2022.t2022.nc
f2022.c2012.p2022.t2022.nc
f2022.c2013.p2022.t2022.nc
f2022.c2014.p2022.t2022.nc
f2022.c2015.p2022.t2022.nc
f2022.c2016.p2022.t2022.nc
f2022.c2017.p2022.t2022.nc
f2022.c2018.p2022.t2022.nc
f2022.c2019.p2022.t2022.nc
f2022.c2020.p2022.t2022.nc
f2022.c2021.p2022.t2022.nc
f2022.c2022.p2022.t2022.nc
f2022.c2023.p2022.t2022.nc

-- Locally Available ABARES AGFD NetCDF Files ----------------------------------

f2022.c1991.p2022.t2022.nc
f2022.c1992.p2022.t2022.nc
f2022.c1993.p2022.t2022.nc
f2022.c1994.p2022.t2022.nc
f2022.c1995.p2022.t2022.nc
f2022.c1996.p2022.t2022.nc
f2022.c1997.p2022.t2022.nc
f2022.c1998.p2022.t2022.nc
f2022.c1999.p2022.t2022.nc
f2022.c2000.p2022.t2022.nc
f2022.c2001.p2022.t2022.nc
f2022.c2002.p2022.t2022.nc
f2022.c2003.p2022.t2022.nc
f2022.c2004.p2022.t2022.nc
f2022.c2005.p2022.t2022.nc
f2022.c2006.p2022.t2022.nc
f2022.c2007.p2022.t2022.nc
f2022.c2008.p2022.t2022.nc
f2022.c2009.p2022.t2022.nc
f2022.c2010.p2022.t2022.nc
f2022.c2011.p2022.t2022.nc
f2022.c2012.p2022.t2022.nc
f2022.c2013.p2022.t2022.nc
f2022.c2014.p2022.t2022.nc
f2022.c2015.p2022.t2022.nc
f2022.c2016.p2022.t2022.nc
f2022.c2017.p2022.t2022.nc
f2022.c2018.p2022.t2022.nc
f2022.c2019.p2022.t2022.nc
f2022.c2020.p2022.t2022.nc
f2022.c2021.p2022.t2022.nc
f2022.c2022.p2022.t2022.nc
f2022.c2023.p2022.t2022.nc
Ran 1/1 deferred expressions

-- 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 print_soil_thickness_metadata() in your R
session.

-- 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 print_soil_thickness_metadata() in your R
session.

-- Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Ho

-- Dataset ANZLIC ID ANZCW1202000149 --

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 [email protected]

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

-- Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Ho

-- Dataset ANZLIC ID ANZCW1202000149 --

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.