Links
diff --git a/pkgdown.yml b/pkgdown.yml
index a987ea0..c1d5077 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -3,7 +3,7 @@ pkgdown: 2.1.1
pkgdown_sha: ~
articles:
read.abares: read.abares.html
-last_built: 2024-12-08T03:44Z
+last_built: 2024-12-08T06:27Z
urls:
reference: https://adamhsparks.github.io/read.abares/reference
article: https://adamhsparks.github.io/read.abares/articles
diff --git a/search.json b/search.json
index 9f5f41d..dc7ff12 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"how-do-i-submit-a-good-bug-report","dir":"","previous_headings":"","what":"How Do I Submit a Good Bug Report?","title":"NA","text":"must never report security related issues, vulnerabilities bugs including sensitive information issue tracker, elsewhere public. Instead sensitive bugs must sent email marcelo.araya@ucr.ac.cr. use GitHub issues track bugs errors. run issue project: Open Issue. (Since can’t sure point whether bug , ask talk bug yet label issue.) Explain behavior expect actual behavior. Please provide much context possible describe reproduction steps someone else can follow recreate issue . usually includes code. good bug reports isolate problem create reduced test case. Provide information collected previous section. marked needs-fix, well possibly tags (critical), issue left implemented someone.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"suggesting-enhancements","dir":"","previous_headings":"","what":"Suggesting Enhancements","title":"NA","text":"section guides submitting enhancement suggestion, including completely new features minor improvements existing functionality. Following guidelines help maintainers community understand suggestion find related suggestions.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"before-submitting-an-enhancement","dir":"","previous_headings":"Suggesting Enhancements","what":"Before Submitting an Enhancement","title":"NA","text":"Make sure using latest version. Read documentation carefully find functionality already covered, maybe individual configuration. Perform search see enhancement already suggested. , add comment existing issue instead opening new one. Find whether idea fits scope alved better serve inspiration.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"NA","text":"guide based contributing.md. Make !nd aims project. ’s make strong case convince project’s developers merits feature. Keep mind want features useful majority users just small subset. ’re just targeting minority users, consider writing add-/plugin library.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"how-do-i-submit-a-good-enhancement-suggestion","dir":"","previous_headings":"Attribution","what":"How Do I Submit a Good Enhancement Suggestion?","title":"NA","text":"Enhancement suggestions tracked GitHub issues. Use clear descriptive title issue identify suggestion. Provide step--step description suggested enhancement many details possible. Describe current behavior explain behavior expected see instead . point can also tell alternatives work . may want include screenshots animated GIFs help demonstrate steps point part suggestion related . can use tool record GIFs macOS Windows, tool tool Linux. Explain enhancement useful users. may also want point projects ’s filed: project team label issue accordingly. team member try reproduce issue provided steps. reproduction steps obvious way reproduce issue, team ask steps mark issue needs-repro. Bugs needs-repro tag addressed reproduced. team able reproduce issue, # Contributing First , thanks taking time contribute! types contributions encouraged valued. See Table Contents different ways help details project handles . Please make sure read relevant section making contribution. make lot easier us maintainers smooth experience involved. community looks forward contributions. like project, just don’t time contribute, ’s fine. easy ways support project show appreciation, also happy : - Star project - Tweet - Refer project project’s readme - Mention project local meetups tell friends/colleagues","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"table-of-contents","dir":"","previous_headings":"","what":"Table of Contents","title":"NA","text":"Code Conduct Question Want Contribute Reporting Bugs Suggesting Enhancements","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"NA","text":"Please note baRulho released Contributor Code Conduct. contributing project agree abide terms. See rOpenSci contributing guide details.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"i-have-a-question","dir":"","previous_headings":"","what":"I Have a Question","title":"NA","text":"want ask question, assume read available Documentation. ask question, best search existing Issues might help . case found suitable issue still need clarification, can write question issue. also advisable search internet answers first. still feel need ask question need clarification, recommend following: Open https://github.com/ropensci/baRulho/issues/. Provide much context can ’re running . Provide project platform versions (nodejs, npm, etc), depending seems relevant. take care issue soon possible.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"i-want-to-contribute","dir":"","previous_headings":"","what":"I Want To Contribute","title":"NA","text":"contributing project, must agree authored 100% content, necessary rights content content contribute may provided project license.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"before-submitting-a-bug-report","dir":"","previous_headings":"I Want To Contribute > Reporting Bugs","what":"Before Submitting a Bug Report","title":"NA","text":"good bug report shouldn’t leave others needing chase information. Therefore, ask investigate carefully, collect information describe issue detail report. Please complete following steps advance help us fix potential bug fast possible. Make sure using latest version. Determine bug really bug error side e.g. using incompatible environment components/versions (Make sure read documentation. looking support, might want check section). see users experienced (potentially already solved) issue , check already bug report existing bug erro. Also make sure search internet (including Stack Overflow) see users outside GitHub community discussed issue. Collect information bug: Stack trace (Traceback) OS, Platform Version (Windows, Linux, macOS, x86, ARM) Version interpreter, compiler, SDK, runtime environment, package manager, depending wha","code":""},{"path":"https://adamhsparks.github.io/read.abares/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 read.abares authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://adamhsparks.github.io/read.abares/articles/read.abares.html","id":"working-with-agfd-data","dir":"Articles","previous_headings":"","what":"Working With AGFD Data","title":"read.abares","text":"can download files pipe directly class object desire Australian Farm Gridded Data (AGFD) data.","code":""},{"path":"https://adamhsparks.github.io/read.abares/articles/read.abares.html","id":"description-of-the-australian-farm-gridded-data","dir":"Articles","previous_headings":"Working With AGFD Data","what":"Description of the Australian Farm Gridded Data","title":"read.abares","text":"Directly DAFF website: Australian Gridded Farm Data set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced read.abares part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using read.abares farmpredict model, turn based read.abares Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices. Australian Gridded Farm Data remain active development, considered experimental. – Australian Department Agriculture, Fisheries Forestry. Load {read.abares} library. Check file format information NetCDF files. Download load local cache read AGFD files list {stars} objects. Download load local cache read AGFD files terra::rast object. Download load local cache read AGFD files list {tidync} objects. Download load local cache read AGFD files {data.table} object.","code":"library(read.abares) print_agfd_nc_file_format() #> ──────────────────────────────────────────────────────────────────────────────────────────────────── #> Each of the layers in simulation output data is represented as a 2D raster in NETCDF files, with #> the following grid format: #> CRS: EPSG:4326 - WGS 84 – Geographic #> Extent: 111.975 -44.525 156.275 -9.975 #> Unit: Degrees #> Width: 886 #> Height: 691 #> Cell size: 0.05 degree x 0.05 degree #> ──────────────────────────────────────────────────────────────────────────────────────────────────── #> For further details, see the ABARES website, #>
## A list of {stars} objects star <- get_agfd(cache = TRUE) |> read_agfd_stars() #> 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. head(star[[1]]) #> stars object with 2 dimensions and 6 attributes #> attribute(s): #> Min. 1st Qu. Median Mean 3rd Qu. #> farmno 15612.000000 233091.50000000 329567.0000000 324737.7187618 418508.5000000 #> R_total_hat_ha 2.954396 7.88312157 21.7520529 169.5139301 174.8553843 #> C_total_hat_ha 1.304440 4.34079101 9.9449849 93.2210542 95.7221857 #> FBP_fci_hat_ha -143.759785 3.60529967 11.5796641 76.2928759 77.6748501 #> FBP_fbp_hat_ha -349.521639 3.36599833 11.5074294 60.0750936 62.8596117 #> A_wheat_hat_ha 0.000000 0.04062786 0.1114289 0.1365683 0.2112845 #> Max. NA's #> farmno 669706.0000000 443899 #> R_total_hat_ha 2415.7556059 443899 #> C_total_hat_ha 1853.5385298 443899 #> FBP_fci_hat_ha 1186.5830232 443899 #> FBP_fbp_hat_ha 1240.6003218 443899 #> A_wheat_hat_ha 0.5047761 565224 #> dimension(s): #> from to refsys values x/y #> lon 1 886 WGS 84 [886] 112,...,156.2 [x] #> lat 1 691 WGS 84 [691] -44.5,...,-10 [y] ## A {terra} `rast` object terr <- get_agfd(cache = TRUE) |> read_agfd_terra() head(terr[[1]]) #> class : SpatRaster #> dimensions : 6, 886, 41 (nrow, ncol, nlyr) #> resolution : 0.05, 0.05 (x, y) #> extent : 111.975, 156.275, -10.275, -9.975 (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84) #> source(s) : memory #> names : farmno, R_tot~at_ha, C_tot~at_ha, FBP_f~at_ha, FBP_f~at_ha, A_whe~at_ha, ... #> min values : NaN, NaN, NaN, NaN, NaN, NaN, ... #> max values : NaN, NaN, NaN, NaN, NaN, NaN, ... ## A list of {tidync} objects tdnc <- get_agfd(cache = TRUE) |> read_agfd_tidync() head(tdnc[[1]]) #> $source #> # A tibble: 1 × 2 #> access source #> #> 1 2024-12-08 10:28:31 /Users/adamsparks/Library/Caches/org.R-project.R/R/read.abares/historical_cli… #> #> $axis #> # A tibble: 84 × 3 #> axis variable dimension #> #> 1 1 lon 0 #> 2 2 lat 1 #> 3 3 farmno 0 #> 4 4 farmno 1 #> 5 5 R_total_hat_ha 0 #> 6 6 R_total_hat_ha 1 #> 7 7 C_total_hat_ha 0 #> 8 8 C_total_hat_ha 1 #> 9 9 FBP_fci_hat_ha 0 #> 10 10 FBP_fci_hat_ha 1 #> # ℹ 74 more rows #> #> $grid #> # A tibble: 3 × 4 #> grid ndims variables nvars #> #> 1 D0,D1 2 41 #> 2 D0 1 1 #> 3 D1 1 1 #> #> $dimension #> # A tibble: 2 × 8 #> id name length unlim coord_dim active start count #> #> 1 0 lon 886 FALSE TRUE TRUE 1 886 #> 2 1 lat 691 FALSE TRUE TRUE 1 691 #> #> $variable #> # A tibble: 43 × 7 #> id name type ndims natts dim_coord active #> #> 1 0 lon NC_DOUBLE 1 2 TRUE FALSE #> 2 1 lat NC_DOUBLE 1 2 TRUE FALSE #> 3 2 farmno NC_DOUBLE 2 1 FALSE TRUE #> 4 3 R_total_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> 5 4 C_total_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> 6 5 FBP_fci_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> 7 6 FBP_fbp_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> 8 7 A_wheat_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> 9 8 H_wheat_dot_hat NC_DOUBLE 2 1 FALSE TRUE #> 10 9 A_barley_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> # ℹ 33 more rows #> #> $attribute #> # A tibble: 49 × 4 #> id name variable value #> #> 1 0 _FillValue lon #> 2 1 units lon #> 3 0 _FillValue lat #> 4 1 units lat #> 5 0 _FillValue farmno #> 6 0 _FillValue R_total_hat_ha #> 7 0 _FillValue C_total_hat_ha #> 8 0 _FillValue FBP_fci_hat_ha #> 9 0 _FillValue FBP_fbp_hat_ha #> 10 0 _FillValue A_wheat_hat_ha #> # ℹ 39 more rows ## A {data.table} object get_agfd(cache = TRUE) |> read_agfd_dt() |> head() #> id farmno R_total_hat_ha C_total_hat_ha FBP_fci_hat_ha FBP_fbp_hat_ha #> #> 1: f2022.c1991.p2022.t2022.nc 15612 7.636519 4.405228 3.231292 1.766127 #> 2: f2022.c1991.p2022.t2022.nc 21495 14.811169 9.165632 5.645538 6.178280 #> 3: f2022.c1991.p2022.t2022.nc 23418 24.874456 14.858595 10.015861 15.504923 #> 4: f2022.c1991.p2022.t2022.nc 24494 15.043653 9.326359 5.717294 7.212161 #> 5: f2022.c1991.p2022.t2022.nc 32429 23.630099 13.681063 9.949036 9.612778 #> 6: f2022.c1991.p2022.t2022.nc 32485 15.009926 9.815501 5.194425 6.582035 #> A_wheat_hat_ha H_wheat_dot_hat A_barley_hat_ha H_barley_dot_hat A_sorghum_hat_ha #> #> 1: NaN NaN NaN NaN NaN #> 2: NaN NaN NaN NaN NaN #> 3: NaN NaN NaN NaN NaN #> 4: NaN NaN NaN NaN NaN #> 5: NaN NaN NaN NaN NaN #> 6: NaN NaN NaN NaN NaN #> H_sorghum_dot_hat A_oilseeds_hat_ha H_oilseeds_dot_hat R_wheat_hat_ha R_sorghum_hat_ha #> #> 1: NaN NaN NaN NaN NaN #> 2: NaN NaN NaN NaN NaN #> 3: NaN NaN NaN NaN NaN #> 4: NaN NaN NaN NaN NaN #> 5: NaN NaN NaN NaN NaN #> 6: NaN NaN NaN NaN NaN #> R_oilseeds_hat_ha R_barley_hat_ha Q_wheat_hat_ha Q_barley_hat_ha Q_sorghum_hat_ha #> #> 1: NaN NaN NaN NaN NaN #> 2: NaN NaN NaN NaN NaN #> 3: NaN NaN NaN NaN NaN #> 4: NaN NaN NaN NaN NaN #> 5: NaN NaN NaN NaN NaN #> 6: NaN NaN NaN NaN NaN #> Q_oilseeds_hat_ha S_wheat_cl_hat_ha S_sheep_cl_hat_ha S_sheep_births_hat_ha #> #> 1: NaN NaN 0.000046854152 0.000048411171 #> 2: NaN NaN 0.000066325878 0.000057753874 #> 3: NaN NaN 0.000007771546 0.000007320093 #> 4: NaN NaN 0.000070963917 0.000062521929 #> 5: NaN NaN 0.000007780997 0.000006834211 #> 6: NaN NaN 0.000059600116 0.000053976389 #> S_sheep_deaths_hat_ha S_beef_cl_hat_ha S_beef_births_hat_ha S_beef_deaths_hat_ha Q_beef_hat_ha #> #> 1: 0.000007187978 0.02034820 0.005212591 0.000989490 0.004790528 #> 2: 0.000009039695 0.02974461 0.007970856 0.001468278 0.009646485 #> 3: 0.000000000000 0.05393181 0.014745383 0.002867331 0.014401773 #> 4: 0.000009726773 0.03057606 0.008602196 0.001446424 0.009577272 #> 5: 0.000000000000 0.04944272 0.011527594 0.002491037 0.014668761 #> 6: 0.000008478467 0.03322463 0.008456550 0.001627910 0.009281578 #> Q_sheep_hat_ha Q_lamb_hat_ha R_beef_hat_ha R_sheep_hat_ha R_lamb_hat_ha C_fodder_hat_ha #> #> 1: 0.00007117650 0 7.392679 0.010222802 0 0.3553107 #> 2: 0.00009448864 0 14.281910 0.014485890 0 0.7040333 #> 3: 0.00001299674 0 24.308574 0.001821158 0 0.9473936 #> 4: 0.00010191595 0 14.518771 0.015352095 0 0.7060111 #> 5: 0.00001283228 0 23.060943 0.001892115 0 1.0269189 #> 6: 0.00008869032 0 14.474964 0.013278806 0 0.7019839 #> C_fert_hat_ha C_fuel_hat_ha C_chem_hat_ha A_total_cropped_ha FBP_pfe_hat_ha farmland_per_cell #> #> 1: 0.0007795925 0.4282799 0.0002169123 0.000001588013 2.142158 62.26270 #> 2: 0.0670951492 0.5663560 0.0212989625 0.000144292922 6.679382 61.71605 #> 3: 0.1475929946 0.9244438 0.0398376851 0.000296036096 16.185389 61.82964 #> 4: 0.0764850563 0.5688555 0.0223214940 0.000151675639 7.711993 72.85995 #> 5: 0.1592835324 0.8337981 0.0416492516 0.000316535762 10.294743 61.82964 #> 6: 0.0997758317 0.5575842 0.0293469147 0.000201161236 7.101658 61.71605 #> lon lat #> #> 1: 142.60 -10.75 #> 2: 136.75 -11.05 #> 3: 132.90 -11.15 #> 4: 136.70 -11.20 #> 5: 133.45 -11.60 #> 6: 136.25 -11.60"},{"path":"https://adamhsparks.github.io/read.abares/articles/read.abares.html","id":"working-with-the-soil-thickness-map","dir":"Articles","previous_headings":"","what":"Working With the Soil Thickness Map","title":"read.abares","text":"can download soil depth map import {stars} terra::rast() object. convenience, {read.abares} re-exports terra::plot(), can just use plot() {terra} objects {read.abares}.","code":"library(read.abares) get_soil_thickness(cache = TRUE) |> read_soil_thickness_stars() #> stars object with 2 dimensions and 1 attribute #> attribute(s), summary of first 100000 cells: #> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's #> thpk_1 NA NA NA NaN NA NA 100000 #> dimension(s): #> from to offset delta refsys x/y #> x 1 4150 112.5 0.01 WGS 84 [x] #> y 1 3401 -9.995 -0.01 WGS 84 [y] x <- get_soil_thickness(cache = TRUE) |> read_soil_thickness_terra() plot(x)"},{"path":"https://adamhsparks.github.io/read.abares/articles/read.abares.html","id":"soil-thickness-metadata","dir":"Articles","previous_headings":"Working With the Soil Thickness Map","what":"Soil Thickness Metadata","title":"read.abares","text":"default, brief bit metadata printed console call soil thickness object R session. , {read.abares} provides function browse soil thickness metadata console. can also access directly use pander::pander() include document like vignette. Dataset ANZLIC ID ANZCW1202000149 Title Soil Thickness Australian areas intensive agriculture Layer 1 (Horizon - top-soil) (derived soil mapping) Custodian CSIRO, Land & Water Jurisdiction Australia Description Abstract Surface predicted Thickness soil layer 1 (Horizon - top-soil) surface intensive agricultural areas Australia. Data modelled area based observations made soil agencies State CSIRO presented .0.01 degree grid cells. Topsoils (horizons) defined surface soil layers organic matter accumulates, may include dominantly organic surface layers (O P horizons). depth topsoil important , higher organic matter contents, topsoils (horizon) generally suitable properties agriculture, including higher permeability higher levels soil nutrients. Estimates soil depths needed calculate amount soil constituent either volume mass terms (bulk density also needed) - example, volume water stored rooting zone potentially available plant use, assess total stores soil carbon Greenhouse inventory assess total stores nutrients. pattern soil depth strongly related topography - shape slope land. Deeper soils typically found river valleys soils accumulate floodplains footslopes ranges (zones deposition), soils hillslopes (zones erosion) tend shallow. Map thickness topsoil derived soil map data interpreted tables soil properties specific soil groups. quality data soil depth existing soil profile datasets questionable thickness soil horizons varies locally topography, values map units general averages. final ASRIS polygon attributed surfaces mosaic data obtained various state federal agencies. surfaces constructed best available soil survey information available time. surfaces also rely number assumptions. One area weighted mean good estimate soil attributes polygon map-unit. Another assumption made look-tables provided McKenzie et al. (2000), state territories accurately depict soil attribute values soil type. accuracy maps dependent scale original polygon data sets level soil survey taken place state. scale various soil maps used deriving map available accessing data-source grid, scale used assessment likely accuracy modelling. Atlas Australian Soils considered least accurate dataset therefore used state based data. state datasets Western Australian sub-systems, South Australian land systems NSW soil landscapes reconnaissance mapping reliable based scale. NSW soil landscapes reconnaissance mapping use one dominant soil type per polygon estimation attributes. South Australia Western Australia use several soil types per polygon map-unit. digital map data provided geographical coordinates based World Geodetic System 1984 (WGS84) datum. raster data set grid resolution 0.001 degrees (approximately equivalent 1.1 km). data set product National Land Water Resources Audit (NLWRA) 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 Update Frequency PLANNED Access Stored Data Format DIGITAL - ESRI Arc/Info integer GRID Available Format Type DIGITAL - ESRI Arc/Info integer GRID Access Constraint Subject terms & condition data access & management agreement National Land & Water Audit ANZLIC parties Data Quality Lineage soil attribute surface created using following datasets 1. digital polygon coverage Soil-Landforms Murray Darling Basis (MDBSIS)(Bui et al. 1998), classified principal profile forms (PPF’s) (Northcote 1979). 2. digital Atlas 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 Natural Resources Environment - NRE). 7. NSW Soil Landscapes reconnaissance soil landscape mapping (NSW Department Land Water Conservation - DLWC). 8. New South Wales Land systems west (NSW Department Land Water Conservation - DLWC). 9. South Australia soil land-systems (Primary Industries Resources South Australia - PIRSA). 10. Northern Territory soils coverage (Northern Territory Department Lands, Planning Environment). 11. mosaic Queensland soils coverages (Queensland Department Natural Resources - QDNR). 12. look-table linking PPF values Atlas Australian Soils interpreted soil attributes (McKenzie et al. 2000). 13. Look_up tables provided WA Agriculture linking WA soil groups interpreted soil attributes. 14. Look_up tables provided PIRSA linking SA soil groups interpreted soil attributes. continuous raster surface representing Thickness soil layer 1 created combining national state level digitised land systems maps soil surveys linked look-tables listing soil type corresponding attribute values. thickness used sparingly Factual Key, estimations thickness look-tables made using empirical correlations particular soil types. estimate soil attribute one soil type given polygon map-unit, soil attribute values related soil type look-table weighted according area occupied soil type within polygon map-unit. final soil attribute values area weighted average polygon map-unit. polygon data converted continuous raster surface using soil attribute values calculated polygon. ASRIS soil attribute surfaces created using polygon attribution relied number data sets various state agencies. polygon data set turned continuous surface grid based calculated soil attribute value polygon. grids merged basis , available, state data replaced Atlas Australian Soils MDBSIS. MDBSIS derived soil attribute values restricted areas MDBSIS deemed accurate Atlas Australian Soils (see Carlile et al (2001a). cases soil type missing look-table layer 2 exist soil type, percent area soils remaining adjusted prior calculating final soil attribute value. method used attribute polygons dependent data supplied individual State agencies. modelled grid resampled 0.0025 degree cells 0.01 degree cells using bilinear interpolation Positional Accuracy predictive surface 0.01 X 0.01 degree grid locational accurate 1m. positional accuracy defining polygons variable positional accuracy locations expected within 100m recorded location. vertical accuracy relevant. positional assessment made considering tools used generate locational information contacting data providers. parameters used production led surface range positional accuracy ranging + - 50 m + - kilometres. contribute loss attribute accuracy surface. Attribute Accuracy Input attribute accuracy areas highly variable. predictive variable much lower attribute accuracy due irregular distribution limited positional accuracy parameters used modelling. several sources error estimating soil depth thickness horizons look-tables. thickness used sparingly Factual Key, estimations thickness look-tables made using empirical correlations particular soil types. quality data soil depth existing soil profile datasets questionable, soil mapping, thickness soil horizons varies locally topography, values map units general averages. definition depth soil regolith imprecise can difficult determine lower limit soil. assumption made area weighted mean soil attribute values based soil type good estimation soil property debatable, supply soil attribute value given location. Rather designed show national regional patterns soil properties. use surfaces farm catchment scale modelling may prove inaccurate. Also use look-tables attribute soil types accurate number observations used estimate attribute value soil type. soil types look-tables may observations, yet average attribute value still taken attribute value soil type. Different states using different taxonomic schemes making national soil database difficult. Another downfall area weighted approach soil types may listed look-tables. soil type dominant one within polygon map-unit, listed within look-table attributed within look-table final soil attribute value polygon biased towards minor soil types exist. may also happen large area occupied soil type B horizon. case final soil attribute value area weighted soils B horizon, ignoring major soil type within polygon map-unit. layer 2 surfaces large areas -data soils listed particular map-unit polygon B horizon. Logical Consistency Surface fully logically consistent one parameter shown, predicted average Soil Thickness within grid cell Completeness Surface nearly complete. areas (%1 missing) insufficient parameters known provide useful prediction thus attributes absent areas. Contact Information Contact Organisation (s) CSIRO, Land & Water Contact Position Project Leader Mail Address ACLEP, GPO 1666 Locality Canberra State ACT Country AUSTRALIA Postcode 2601 Telephone 02 6246 5922 Facsimile 02 6246 5965 Electronic Mail Address neil.mckenzie@cbr.clw.csiro.au Metadata Date Metadata Date 2001-07-01 Additional Metadata Additional Metadata Entity Attributes Entity Name Soil Thickness Layer 1 (derived mapping) Entity description Estimated Soil Thickness (mm) Layer 1 cell cell basis Feature attribute name VALUE Feature attribute definition Predicted average Thickness (mm) soil layer 1 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 soil depths needed calculate amount soil constituent either volume mass terms (bulk density also needed) - example, volume water stored rooting zone potentially available plant use, assess total stores soil carbon Greenhouse inventory assess total stores nutrients. Provide indications probable Thickness soil layer 1 agricultural areas soil thickness testing carried ","code":"library(read.abares) get_soil_thickness(cache = TRUE) #> #> ── Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Horizon - top-soil) ── #> #> ── 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()` on a soil thickness object in your #> R session. library(read.abares) get_soil_thickness(cache = TRUE) |> print_soil_thickness_metadata() #> #> ── Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Horizon - top-soil) ── #> #> ── 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 neil.mckenzie@cbr.clw.csiro.au #> #> Metadata Date Metadata Date 2001-07-01 #> #> Additional Metadata Additional Metadata #> #> Entity and Attributes Entity Name Soil Thickness Layer 1 (derived from mapping) #> #> Entity description Estimated Soil Thickness (mm) of Layer 1 on a cell by cell basis #> #> Feature attribute name VALUE #> #> Feature attribute definition Predicted average Thickness (mm) of soil layer 1 in the 0.01 X 0.01 #> degree quadrat #> #> Data Type Spatial representation type RASTER #> #> Projection Map projection GEOGRAPHIC #> #> Datum WGS84 #> #> Map units DECIMAL DEGREES #> #> Scale Scale/ resolution 1:1 000 000 #> #> Usage Purpose Estimates of soil depths are needed to calculate the amount of any soil constituent #> in either volume or mass terms (bulk density is also needed) - for example, the volume of water #> stored in the rooting zone potentially available for plant use, to assess total stores of soil #> carbon for Greenhouse inventory or to assess total stores of nutrients. #> #> Provide indications of probable Thickness soil layer 1 in agricultural areas where soil thickness #> testing has not been carried out #> #> Use Use Limitation This dataset is bound by the requirements set down by the National Land & Water #> Resources Audit library(read.abares) library(pander) x <- get_soil_thickness(cache = TRUE) y <- x$metadata pander(y)"},{"path":"https://adamhsparks.github.io/read.abares/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Adam H. Sparks. Maintainer, author. Jacob. Contributor. Assisted troubleshooting formatting documentation display '<' '>' properly Curtin University Technology. Copyright holder. Provided support Adam Sparks's time. Grains Research Development Corporation. Funder, copyright holder. GRDC Project CUR2210-005OPX (AAGI-CU)","code":""},{"path":"https://adamhsparks.github.io/read.abares/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Sparks (????). read.abares: Simple downloading importing ABARES Data. R package version 1.0.0, https://adamhsparks.github.io/read.abares/.","code":"@Manual{, title = {{read.abares}: Simple downloading and importing of ABARES Data}, author = {Adam H. Sparks}, note = {R package version 1.0.0}, url = {https://adamhsparks.github.io/read.abares/}, }"},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"readabares-simple-downloading-and-importing-of-abares-data-","dir":"","previous_headings":"","what":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"R package automated downloading ingestion data Australian Bureau Agricultural Resource Economics Sciences. ABARES data serviced. list hand-picked reasonably useful maintainable, .e., frequently updated values included , e.g., Australian crop reports. However, data set feel useful serviced {read.abares}, please feel free open issue details data set better yet, open pull request! Data serviced include: Historical National Estimates, read_historical_national_estimate(); Historical State Estimates, read_historical_state_estimates(); Historical Regional Estimates, read_historical_regional_estimates(); Estimates Size, read_estimates_by_size(); Estimates Performance Category, read_estimates_by_performance_category(); Australian Gridded Farm Data (AGFD) set, get_agfd(); Australian Agricultural Grazing Industries Survey (AAGIS) region mapping files, get_aagis_regions(); Historical Agricultural Forecast Database, read_historical_forecast_database(); Soil Thickness Australian areas intensive agriculture Layer 1 (Horizon - top-soil) (derived soil mapping) map, get_soil_thickness() ; Trade Data, read_abares_trade() ; Trade Region Data, read_abares_trade_regions(). files freely available CSV files, zip archives NetCDF files zip archives geospatial shape files. {read.abares} facilitates downloading, caching importing files R session choice class resulting object(s).","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"installation","dir":"","previous_headings":"Get Started","what":"Installation","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"{read.abares} available CRAN (yet). can install like :","code":"if (!require(\"remotes\")) install.packages(\"remotes\") remotes::install_git(\"https://github.com/adamhsparks/read.abares\")"},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"caching","dir":"","previous_headings":"Features","what":"Caching","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"{read.abares} supports caching files using tools::R_user_dir(package = \"read.abares\", = \"cache\") save files standardised location across platforms don’t worry files went ’re still . requesting files, {read.abares} first check available locally either cached temporary storage. Caching mandatory, can just work downloaded files tempdir(), cleaned R session ends.","code":""},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"multiple-classes-supported","dir":"","previous_headings":"Features","what":"Multiple Classes Supported","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"{read.abares} supports multiple classes objects support workflow. Select spatial classes: {stars}, {terra} {tidync} data.frame objects: {data.table}","code":""},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"a-note-on-testing","dir":"","previous_headings":"","what":"A Note on Testing","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"’ve aimed make testing package complete possible. files downloaded >1GB may take several minutes hour download due size, wish include package . Therefore, tests rely already downloaded locally cached files. wish work development {read.abares} please aware take time establish local cache testing somewhat faster. considering including pared examples tests data released CC 4.0 License, now opted just use locally cached data simplicity.","code":""},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"metadata","dir":"","previous_headings":"","what":"Metadata","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"Please report issues bugs. License: MIT","code":""},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"citations","dir":"","previous_headings":"Metadata","what":"Citations","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"Citing data: Please refer ABARES website, https://www.agriculture.gov.au/abares/products/citations, cite data use . Citing {read.abares}: citing use package, please use,","code":"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 citation(\"read.abares\") #> To cite package 'read.abares' in publications use: #> #> Sparks A (????). _read.abares: Simple downloading and importing of #> ABARES Data_. R package version 1.0.0, #> . #> #> A BibTeX entry for LaTeX users is #> #> @Manual{, #> title = {{read.abares}: Simple downloading and importing of ABARES Data}, #> author = {Adam H. Sparks}, #> note = {R package version 1.0.0}, #> url = {https://adamhsparks.github.io/read.abares/}, #> }"},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"Please note {read.abares} project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/clear_cache.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Files in Users' Cache Directory — clear_cache","title":"Remove Files in Users' Cache Directory — clear_cache","text":"Removes files read.abares cache exist.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/clear_cache.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Files in Users' Cache Directory — clear_cache","text":"","code":"clear_cache()"},{"path":"https://adamhsparks.github.io/read.abares/reference/clear_cache.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove Files in Users' Cache Directory — clear_cache","text":"Nothing, called side-effects, clearing cached files","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/clear_cache.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remove Files in Users' Cache Directory — clear_cache","text":"","code":"# not run because cached files shouldn't exist on CRAN or testing envs if (FALSE) { # \\dontrun{ clear_cache() } # }"},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Australian Gridded Farm Data for Local Use — get_agfd","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"Downloads Australian Gridded Farm Data (AGFD) data unzips compressed files NetCDF importing.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"","code":"get_agfd(fixed_prices = TRUE, cache = TRUE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"Historical climate prices fixed – https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1036161/3, Historical climate prices – https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1036161/2","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"fixed_prices Boolean Download historical climate prices historical climate fixed prices described (Hughes et al. 2022). Defaults TRUE downloads data historical climate fixed prices “isolate effects climate variability financial incomes broadacre farm businesses” (ABARES 2024). Using TRUE download simulations global output input price indexes fixed values recently completed financial year. cache Boolean Cache Australian Gridded Farm Data files download using tools::R_user_dir identify proper directory storing user data cache package. Defaults TRUE, caching files locally. FALSE, function uses tempdir() files deleted upon closing R session.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"read.abares.agfd.nc.files object, list NetCDF files containing Australian Gridded Farm Data","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"ABARES website: “Australian Gridded Farm Data (AGFD) set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced ABARES part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data.Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices.” – ABARES, 2024-11-25 sets data large file size, .e., >1GB, require time download.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"historical-climate-fixed-prices-","dir":"Reference","previous_headings":"","what":"Historical climate (fixed prices)","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"Historical climate (fixed prices) scenario similar described Hughes et al. (2022) intended isolate effects climate variability financial incomes broadacre farm businesses. simulations, global output input price indexes fixed values recently completed financial year. However, scenarios spread domestic global grain (wheat, barley sorghum) prices, along Australian fodder prices allowed vary response climate data (capture domestic increases grain fodder prices drought years, see Hughes et al. 2022). 33-year historical climate sequence (including historical simulated crop pasture data AADI project) simulated grid cell (1990-91 2022-23).","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"historical-climate-and-prices","dir":"Reference","previous_headings":"","what":"Historical climate and prices","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"part AADI project additional scenario developed accounting changes climate conditions output input prices (.e., global commodity market variability). Historical climate prices scenario 33-year reference period allows variation \\ historical climate conditions historical prices. scenario, historical price indexes de-trended, account consistent long- term trends real commodity prices (particularly sheep lamb). resulting simulation results percentile indicators intended reflect combined impacts annual climate commodity price variability.\" – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"data-files","dir":"Reference","previous_headings":"","what":"Data files","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"Simulation output data saved multilayer NetCDF files, named using following convention: f.c.p.t.nc : = Financial year farm business data used simulations. = Financial year climate data used simulations. = Financial year output input prices used simulations. = Financial year farm ‘technology’ (equal farm year simulations) financial years referred closing calendar year (e.g., 2022 = 1 July 2021 30 June 2022). – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"data-layers","dir":"Reference","previous_headings":"","what":"Data layers","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"data layers downloaded NetCDF files described Table 2 seen Australian Bureau Agricultural Resource Economics Sciences (2024). Following copy Table 2 convenience, please refer full document methods metadata.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"Australian gridded farm data, Australian Bureau Agricultural Resource Economics Sciences, Canberra, July 2024, DOI: 10.25814/7n6z-ev41. CC 4.0. N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought perspective Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, DOI: 10.1016/j.crm.2022.100420.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"","code":"if (FALSE) { # interactive() get_agfd() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"Get Soil Thickness Australian Areas Intensive Agriculture Layer 1 Local Use","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"","code":"get_soil_thickness(cache = TRUE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"https://anrdl-integration-web-catalog-saxfirxkxt.s3-ap-southeast-2.amazonaws.com/warehouse/staiar9cl__059/staiar9cl__05911a01eg_geo___.zip","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"cache Boolean Cache soil thickness data files download using tools::R_user_dir() identify proper directory storing user data cache package. Defaults TRUE, caching files locally. FALSE, function uses tempdir() files deleted upon closing R session. custom print method provided print metadata associated data. Examples provided interacting metadata directly.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"read.abares.soil.thickness object, named list file path resulting ESRI Grid file text file metadata","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"https://data.agriculture.gov.au/geonetwork/srv/eng/catalog.search#/metadata/faa9f157-8e17-4b23-b6a7-37eb7920ead6","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"","code":"if (FALSE) { # interactive() x <- get_soil_thickness() # View the metadata with pretty printing x # Extract the metadata as an object in your R session and use it with # {pander}, useful for Markdown files library(pander) y <- x$metadata pander(y) }"},{"path":"https://adamhsparks.github.io/read.abares/reference/inspect_cache.html","id":null,"dir":"Reference","previous_headings":"","what":"List the File Path to Users' Cache Directory — inspect_cache","title":"List the File Path to Users' Cache Directory — inspect_cache","text":"Check files exist read.abares file cache. function always return full file names, .e., directory path prepended. See help file list.files() full.names argument. wish strip file path return directory file names, use basename(). See examples .","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/inspect_cache.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List the File Path to Users' Cache Directory — inspect_cache","text":"","code":"inspect_cache(recursive = FALSE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/inspect_cache.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List the File Path to Users' Cache Directory — inspect_cache","text":"recursive Boolean value indicating whether list files subdirectories cache directory. Defaults FALSE returning top-level directories contained cache directory.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/inspect_cache.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List the File Path to Users' Cache Directory — inspect_cache","text":"list directories files cache","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/inspect_cache.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List the File Path to Users' Cache Directory — inspect_cache","text":"","code":"# not run because cached files shouldn't exist on CRAN or testing envs if (FALSE) { # \\dontrun{ # list directories in cache only inspect_cache() # list directory names, stripping the file path basename(inspect_cache) # list all files in subdirectories of the cache inspect_cache(recursive = TRUE) # list all files in subdirectories, stripping the file path basename(inspect_cache(recursive_true)) } # }"},{"path":"https://adamhsparks.github.io/read.abares/reference/print_agfd_nc_file_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","title":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","text":"Print file format section 3.2 Australian Bureau Agricultural Resource Economics Sciences.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/print_agfd_nc_file_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","text":"","code":"print_agfd_nc_file_format()"},{"path":"https://adamhsparks.github.io/read.abares/reference/print_agfd_nc_file_format.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","text":"https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1036161/0","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/print_agfd_nc_file_format.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","text":"Australian gridded farm data, Australian Bureau Agricultural Resource Economics Sciences, Canberra, July, DOI: ","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/print_agfd_nc_file_format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","text":"","code":"print_agfd_nc_file_format() #> ──────────────────────────────────────────────────────────────────────────────── #> Each of the layers in simulation output data is represented as a 2D raster in #> NETCDF files, with the following grid format: #> #> CRS: EPSG:4326 - WGS 84 - Geographic #> Extent: 111.975 -44.525 156.275 -9.975 #> Unit: Degrees #> Width: 886 #> Height: 691 #> Cell size: 0.05 degree x 0.05 degree #> ──────────────────────────────────────────────────────────────────────────────── #> For further details, see the ABARES website, #> "},{"path":"https://adamhsparks.github.io/read.abares/reference/print_soil_thickness_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","title":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","text":"Displays complete set metadata associated soil thickness data R console. including metadata documents methods outside R, see get_soil_thickness example using pander::pander print metadata.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/print_soil_thickness_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","text":"","code":"print_soil_thickness_metadata(x)"},{"path":"https://adamhsparks.github.io/read.abares/reference/print_soil_thickness_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","text":"x read.abares.soil.thickness.files object","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/print_soil_thickness_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","text":"Nothing, called side effects, prints complete metadata file R console","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/print_soil_thickness_metadata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","text":"","code":"if (FALSE) { # interactive() get_soil_thickness(cache = TRUE) |> print_soil_thickness_metadata() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read.abares-package.html","id":null,"dir":"Reference","previous_headings":"","what":"read.abares: Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources — read.abares-package","title":"read.abares: Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources — read.abares-package","text":"Download import data Australian Bureau Agricultural Resource Economics Sciences (ABARES) https://www.agriculture.gov.au/abares.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read.abares-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"read.abares: Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources — read.abares-package","text":"Maintainer: Adam H. Sparks adamhsparks@gmail.com (ORCID) contributors: Jacob jacob@wujciak.de (ORCID) (Assisted troubleshooting formatting documentation display '<' '>' properly) [contributor] Curtin University Technology (Provided support Adam Sparks's time.) [copyright holder] Grains Research Development Corporation (GRDC Project CUR2210-005OPX (AAGI-CU)) [funder, copyright holder]","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":null,"dir":"Reference","previous_headings":"","what":"Read AAGIS Region Mapping Files — read_aagis_regions","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"Download, cache import Australian Agricultural Grazing Industries Survey (AAGIS regions geospatial shapefile. Upon import, geometries automatically corrected fix invalid geometries present original shapefile.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"","code":"read_aagis_regions(cache = TRUE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"https://www.agriculture.gov.au/sites/default/files/documents/aagis_asgs16v1_g5a.shp_.zip","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"cache Boolean Cache AAGIS regions' geospatial file downloading using tools::R_user_dir(\"read.abares\", \"cache\") identify proper directory storing user data cache package. Defaults TRUE, caching files locally Geopackage. FALSE, function uses tempdir() files deleted upon closing R session.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"sf object AAGIS regions","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"https://www.agriculture.gov.au/abares/research-topics/surveys/farm-definitions-methods#regions","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"","code":"if (FALSE) { # interactive() aagis <- read_aagis_regions() plot(aagis) }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Data From the ABARES Trade Dashboard — read_abares_trade","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"Fetches imports ABARES trade data.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"","code":"read_abares_trade(cache = TRUE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1033841/0","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"cache Boolean Cache ABARES trade data download using tools::R_user_dir() identify proper directory storing user data cache package. Defaults TRUE, caching files locally native R object. FALSE, function uses tempdir() files deleted upon closing R session.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"data.table object ABARES trade data.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"Columns renamed consistency ABARES products serviced package using snake_case format ordered consistently.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"https://www.agriculture.gov.au/abares/research-topics/trade/dashboard","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"","code":"if (FALSE) { # interactive() trade <- read_abares_trade() trade }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":null,"dir":"Reference","previous_headings":"","what":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"Fetches imports ABARES trade regions data.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"","code":"read_abares_trade_regions(cache = TRUE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1033841/2","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"cache Boolean Cache ABARES trade regions data download using tools::R_user_dir() identify proper directory storing user data cache package. Defaults TRUE, caching files locally native R object. FALSE, function uses tempdir() files deleted upon closing R session.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"data.table object ABARES trade data regions.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"Columns renamed consistency ABARES products serviced package using snake_case format ordered consistently.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1033841/0","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"","code":"if (FALSE) { # interactive() trade_regions <- read_abares_trade_regions() trade_regions }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Read AGFD NCDF Files as a data.table — read_agfd_dt","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"Read Australian Gridded Farm Data, (AGFD) data.table object.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"","code":"read_agfd_dt(files)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"files list AGFD NetCDF files import","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"data.table::data.table object Australian Gridded Farm Data","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"ABARES website: “Australian Gridded Farm Data (AGFD) set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced ABARES part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data.Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices.” – ABARES, 2024-11-25 sets data large file size, .e., >1GB, require time download.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"historical-climate-fixed-prices-","dir":"Reference","previous_headings":"","what":"Historical climate (fixed prices)","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"Historical climate (fixed prices) scenario similar described Hughes et al. (2022) intended isolate effects climate variability financial incomes broadacre farm businesses. simulations, global output input price indexes fixed values recently completed financial year. However, scenarios spread domestic global grain (wheat, barley sorghum) prices, along Australian fodder prices allowed vary response climate data (capture domestic increases grain fodder prices drought years, see Hughes et al. 2022). 33-year historical climate sequence (including historical simulated crop pasture data AADI project) simulated grid cell (1990-91 2022-23).","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"historical-climate-and-prices","dir":"Reference","previous_headings":"","what":"Historical climate and prices","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"part AADI project additional scenario developed accounting changes climate conditions output input prices (.e., global commodity market variability). Historical climate prices scenario 33-year reference period allows variation \\ historical climate conditions historical prices. scenario, historical price indexes de-trended, account consistent long- term trends real commodity prices (particularly sheep lamb). resulting simulation results percentile indicators intended reflect combined impacts annual climate commodity price variability.\" – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"data-files","dir":"Reference","previous_headings":"","what":"Data files","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"Simulation output data saved multilayer NetCDF files, named using following convention: f.c.p.t.nc : = Financial year farm business data used simulations. = Financial year climate data used simulations. = Financial year output input prices used simulations. = Financial year farm ‘technology’ (equal farm year simulations) financial years referred closing calendar year (e.g., 2022 = 1 July 2021 30 June 2022). – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"data-layers","dir":"Reference","previous_headings":"","what":"Data layers","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"data layers downloaded NetCDF files described Table 2 seen Australian Bureau Agricultural Resource Economics Sciences (2024). Following copy Table 2 convenience, please refer full document methods metadata.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"Australian gridded farm data, Australian Bureau Agricultural Resource Economics Sciences, Canberra, July 2024, DOI: 10.25814/7n6z-ev41. CC 4.0. N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought perspective Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, DOI: 10.1016/j.crm.2022.100420.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"","code":"if (FALSE) { # interactive() # using piping, which can use the {read.abares} cache after the first DL get_agfd(cache = TRUE) |> read_agfd_dt() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":null,"dir":"Reference","previous_headings":"","what":"Read AGFD NCDF Files With stars — read_agfd_stars","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"Read Australian Gridded Farm Data, (AGFD) list stars objects.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"","code":"read_agfd_stars(files)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"files list AGFD NetCDF files import","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"list object stars objects Australian Gridded Farm Data file names list's objects' names","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"ABARES website: “Australian Gridded Farm Data (AGFD) set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced ABARES part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data.Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices.” – ABARES, 2024-11-25 sets data large file size, .e., >1GB, require time download.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"historical-climate-fixed-prices-","dir":"Reference","previous_headings":"","what":"Historical climate (fixed prices)","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"Historical climate (fixed prices) scenario similar described Hughes et al. (2022) intended isolate effects climate variability financial incomes broadacre farm businesses. simulations, global output input price indexes fixed values recently completed financial year. However, scenarios spread domestic global grain (wheat, barley sorghum) prices, along Australian fodder prices allowed vary response climate data (capture domestic increases grain fodder prices drought years, see Hughes et al. 2022). 33-year historical climate sequence (including historical simulated crop pasture data AADI project) simulated grid cell (1990-91 2022-23).","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"historical-climate-and-prices","dir":"Reference","previous_headings":"","what":"Historical climate and prices","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"part AADI project additional scenario developed accounting changes climate conditions output input prices (.e., global commodity market variability). Historical climate prices scenario 33-year reference period allows variation \\ historical climate conditions historical prices. scenario, historical price indexes de-trended, account consistent long- term trends real commodity prices (particularly sheep lamb). resulting simulation results percentile indicators intended reflect combined impacts annual climate commodity price variability.\" – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"data-files","dir":"Reference","previous_headings":"","what":"Data files","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"Simulation output data saved multilayer NetCDF files, named using following convention: f.c.p.t.nc : = Financial year farm business data used simulations. = Financial year climate data used simulations. = Financial year output input prices used simulations. = Financial year farm ‘technology’ (equal farm year simulations) financial years referred closing calendar year (e.g., 2022 = 1 July 2021 30 June 2022). – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"data-layers","dir":"Reference","previous_headings":"","what":"Data layers","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"data layers downloaded NetCDF files described Table 2 seen Australian Bureau Agricultural Resource Economics Sciences (2024). Following copy Table 2 convenience, please refer full document methods metadata.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"Australian gridded farm data, Australian Bureau Agricultural Resource Economics Sciences, Canberra, July 2024, DOI: 10.25814/7n6z-ev41. CC 4.0. N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought perspective Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, DOI: 10.1016/j.crm.2022.100420.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"","code":"if (FALSE) { # interactive() # using piping, which can use the {read.abares} cache after the first DL agfd <- get_agfd(cache = TRUE) |> read_agfd_stars() head(agfd) plot(agfd[[1]]) }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":null,"dir":"Reference","previous_headings":"","what":"Read AGFD NCDF Files With terra — read_agfd_terra","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"Read Australian Gridded Farm Data, (AGFD) list terra::rast objects.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"","code":"read_agfd_terra(files)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"files list AGFD NetCDF files import","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"list object terra::rast objects Australian Gridded Farm Data file names list's objects' names","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"ABARES website: “Australian Gridded Farm Data (AGFD) set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced ABARES part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data.Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices.” – ABARES, 2024-11-25 sets data large file size, .e., >1GB, require time download.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"historical-climate-fixed-prices-","dir":"Reference","previous_headings":"","what":"Historical climate (fixed prices)","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"Historical climate (fixed prices) scenario similar described Hughes et al. (2022) intended isolate effects climate variability financial incomes broadacre farm businesses. simulations, global output input price indexes fixed values recently completed financial year. However, scenarios spread domestic global grain (wheat, barley sorghum) prices, along Australian fodder prices allowed vary response climate data (capture domestic increases grain fodder prices drought years, see Hughes et al. 2022). 33-year historical climate sequence (including historical simulated crop pasture data AADI project) simulated grid cell (1990-91 2022-23).","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"historical-climate-and-prices","dir":"Reference","previous_headings":"","what":"Historical climate and prices","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"part AADI project additional scenario developed accounting changes climate conditions output input prices (.e., global commodity market variability). Historical climate prices scenario 33-year reference period allows variation \\ historical climate conditions historical prices. scenario, historical price indexes de-trended, account consistent long- term trends real commodity prices (particularly sheep lamb). resulting simulation results percentile indicators intended reflect combined impacts annual climate commodity price variability.\" – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"data-files","dir":"Reference","previous_headings":"","what":"Data files","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"Simulation output data saved multilayer NetCDF files, named using following convention: f.c.p.t.nc : = Financial year farm business data used simulations. = Financial year climate data used simulations. = Financial year output input prices used simulations. = Financial year farm ‘technology’ (equal farm year simulations) financial years referred closing calendar year (e.g., 2022 = 1 July 2021 30 June 2022). – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"data-layers","dir":"Reference","previous_headings":"","what":"Data layers","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"data layers downloaded NetCDF files described Table 2 seen Australian Bureau Agricultural Resource Economics Sciences (2024). Following copy Table 2 convenience, please refer full document methods metadata.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"Australian gridded farm data, Australian Bureau Agricultural Resource Economics Sciences, Canberra, July 2024, DOI: 10.25814/7n6z-ev41. CC 4.0. N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought perspective Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, DOI: 10.1016/j.crm.2022.100420.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"","code":"if (FALSE) { # interactive() # using piping, which can use the {read.abares} cache after the first DL get_agfd(cache = TRUE) |> read_agfd_terra() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":null,"dir":"Reference","previous_headings":"","what":"Read agfd NCDF Files With tidync — read_agfd_tidync","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"Read Australian Gridded Farm Data, (AGFD) list tidync objects","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"","code":"read_agfd_tidync(files)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"files list AGFD NetCDF files import","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"list object tidync tidync::tidync objects Australian Gridded Farm Data file names list's objects' names.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"ABARES website: “Australian Gridded Farm Data (AGFD) set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced ABARES part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data.Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices.” – ABARES, 2024-11-25 sets data large file size, .e., >1GB, require time download.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"historical-climate-fixed-prices-","dir":"Reference","previous_headings":"","what":"Historical climate (fixed prices)","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"Historical climate (fixed prices) scenario similar described Hughes et al. (2022) intended isolate effects climate variability financial incomes broadacre farm businesses. simulations, global output input price indexes fixed values recently completed financial year. However, scenarios spread domestic global grain (wheat, barley sorghum) prices, along Australian fodder prices allowed vary response climate data (capture domestic increases grain fodder prices drought years, see Hughes et al. 2022). 33-year historical climate sequence (including historical simulated crop pasture data AADI project) simulated grid cell (1990-91 2022-23).","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"historical-climate-and-prices","dir":"Reference","previous_headings":"","what":"Historical climate and prices","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"part AADI project additional scenario developed accounting changes climate conditions output input prices (.e., global commodity market variability). Historical climate prices scenario 33-year reference period allows variation \\ historical climate conditions historical prices. scenario, historical price indexes de-trended, account consistent long- term trends real commodity prices (particularly sheep lamb). resulting simulation results percentile indicators intended reflect combined impacts annual climate commodity price variability.\" – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"data-files","dir":"Reference","previous_headings":"","what":"Data files","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"Simulation output data saved multilayer NetCDF files, named using following convention: f.c.p.t.nc : = Financial year farm business data used simulations. = Financial year climate data used simulations. = Financial year output input prices used simulations. = Financial year farm ‘technology’ (equal farm year simulations) financial years referred closing calendar year (e.g., 2022 = 1 July 2021 30 June 2022). – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"data-layers","dir":"Reference","previous_headings":"","what":"Data layers","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"data layers downloaded NetCDF files described Table 2 seen Australian Bureau Agricultural Resource Economics Sciences (2024). Following copy Table 2 convenience, please refer full document methods metadata.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"Australian gridded farm data, Australian Bureau Agricultural Resource Economics Sciences, Canberra, July 2024, DOI: 10.25814/7n6z-ev41. CC 4.0. N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought perspective Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, DOI: 10.1016/j.crm.2022.100420.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"","code":"if (FALSE) { # interactive() # using piping, which can use the {read.abares} cache after the first DL x <- get_agfd(cache = TRUE) |> read_agfd_tidync() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_performance_category.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Estimates by Size From ABARES — read_estimates_by_performance_category","title":"Read Estimates by Size From ABARES — read_estimates_by_performance_category","text":"Read Estimates Size ABARES","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_performance_category.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Estimates by Size From ABARES — read_estimates_by_performance_category","text":"","code":"read_estimates_by_performance_category() read_est_by_perf_cat()"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_performance_category.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read Estimates by Size From ABARES — read_estimates_by_performance_category","text":"https://www.agriculture.gov.au/sites/default/files/documents/fdp-BySize-ByPerformance.csv","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_performance_category.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Estimates by Size From ABARES — read_estimates_by_performance_category","text":"data.table::data.table object","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_performance_category.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read Estimates by Size From ABARES — read_estimates_by_performance_category","text":"https://www.agriculture.gov.au/abares/data/farm-data-portal#data-download","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_performance_category.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Estimates by Size From ABARES — read_estimates_by_performance_category","text":"","code":"if (FALSE) { # interactive() read_estimates_by_performance_category() # or shorter read_est_by_perf_cat() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_size.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Estimates by Size From ABARES — read_estimates_by_size","title":"Read Estimates by Size From ABARES — read_estimates_by_size","text":"Read Estimates Size ABARES","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_size.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Estimates by Size From ABARES — read_estimates_by_size","text":"","code":"read_estimates_by_size() read_est_by_size()"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_size.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read Estimates by Size From ABARES — read_estimates_by_size","text":"https://www.agriculture.gov.au/sites/default/files/documents/fdp-national-historical.csv","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_size.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Estimates by Size From ABARES — read_estimates_by_size","text":"data.table::data.table object Variable field key.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_size.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Read Estimates by Size From ABARES — read_estimates_by_size","text":"Columns renamed reordered consistency.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_size.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read Estimates by Size From ABARES — read_estimates_by_size","text":"https://www.agriculture.gov.au/abares/data/farm-data-portal#data-download","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_estimates_by_size.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Estimates by Size From ABARES — read_estimates_by_size","text":"","code":"if (FALSE) { # interactive() read_estimates_by_size() # or shorter read_est_by_size() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_forecast_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Historical Forecast Database From ABARES — read_historical_forecast_database","title":"Read Historical Forecast Database From ABARES — read_historical_forecast_database","text":"Read Historical Forecast Database ABARES","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_forecast_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Historical Forecast Database From ABARES — read_historical_forecast_database","text":"","code":"read_historical_forecast_database() read_historical_forecast()"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_forecast_database.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read Historical Forecast Database From ABARES — read_historical_forecast_database","text":"https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1031941/0","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_forecast_database.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Historical Forecast Database From ABARES — read_historical_forecast_database","text":"data.table::data.table object","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_forecast_database.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Read Historical Forecast Database From ABARES — read_historical_forecast_database","text":"Columns renamed consistency ABARES products serviced package using snake_case format ordered consistently. \"Month_issued\" column converted character string numeric value representing month year, e.g., \"March\" converted 3.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_forecast_database.html","id":"data-dictionary","dir":"Reference","previous_headings":"","what":"Data Dictionary","title":"Read Historical Forecast Database From ABARES — read_historical_forecast_database","text":"resulting object contain following fields.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_forecast_database.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read Historical Forecast Database From ABARES — read_historical_forecast_database","text":"https://www.agriculture.gov.au/abares/research-topics/agricultural-outlook/historical-forecasts#:~:text=%20the%20historical%20agricultural%20forecast,relevant%20to%20Australian%20agricultural%20markets","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_forecast_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Historical Forecast Database From ABARES — read_historical_forecast_database","text":"","code":"if (FALSE) { # interactive() read_historical_forecast_database() # or shorter read_historical_forecast() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_national_estimates.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Historical National Estimates from ABARES — read_historical_national_estimates","title":"Read Historical National Estimates from ABARES — read_historical_national_estimates","text":"Read Historical National Estimates ABARES","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_national_estimates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Historical National Estimates from ABARES — read_historical_national_estimates","text":"","code":"read_historical_national_estimates() read_hist_nat_est()"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_national_estimates.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read Historical National Estimates from ABARES — read_historical_national_estimates","text":"https://www.agriculture.gov.au/sites/default/files/documents/fdp-national-historical.csv","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_national_estimates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Historical National Estimates from ABARES — read_historical_national_estimates","text":"data.table::data.table object Variable field key.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_national_estimates.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Read Historical National Estimates from ABARES — read_historical_national_estimates","text":"Columns renamed consistency ABARES products serviced package using snake_case format ordered consistently.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_national_estimates.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read Historical National Estimates from ABARES — read_historical_national_estimates","text":"https://www.agriculture.gov.au/abares/data/farm-data-portal#data-download","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_national_estimates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Historical National Estimates from ABARES — read_historical_national_estimates","text":"","code":"if (FALSE) { # interactive() read_historical_national_estimates() # or shorter read_hist_nat_est() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_regional_estimates.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Historical Regional Estimates from ABARES — read_historical_regional_estimates","title":"Read Historical Regional Estimates from ABARES — read_historical_regional_estimates","text":"Read Historical Regional Estimates ABARES","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_regional_estimates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Historical Regional Estimates from ABARES — read_historical_regional_estimates","text":"","code":"read_historical_regional_estimates() read_hist_reg_est()"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_regional_estimates.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read Historical Regional Estimates from ABARES — read_historical_regional_estimates","text":"https://www.agriculture.gov.au/sites/default/files/documents/fdp-regional-historical.csv","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_regional_estimates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Historical Regional Estimates from ABARES — read_historical_regional_estimates","text":"data.table::data.table object Variable field key.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_regional_estimates.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Read Historical Regional Estimates from ABARES — read_historical_regional_estimates","text":"Columns renamed consistency ABARES products serviced package using snake_case format ordered consistently.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_regional_estimates.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read Historical Regional Estimates from ABARES — read_historical_regional_estimates","text":"https://www.agriculture.gov.au/abares/data/farm-data-portal#data-download","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_regional_estimates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Historical Regional Estimates from ABARES — read_historical_regional_estimates","text":"","code":"if (FALSE) { # interactive() read_historical_regional_estimates() # or shorter read_hist_reg_est() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_state_estimates.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Historical State Estimates from ABARES — read_historical_state_estimates","title":"Read Historical State Estimates from ABARES — read_historical_state_estimates","text":"Read Historical State Estimates ABARES","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_state_estimates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Historical State Estimates from ABARES — read_historical_state_estimates","text":"","code":"read_historical_state_estimates() read_hist_sta_est()"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_state_estimates.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read Historical State Estimates from ABARES — read_historical_state_estimates","text":"https://www.agriculture.gov.au/sites/default/files/documents/fdp-state-historical.csv","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_state_estimates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Historical State Estimates from ABARES — read_historical_state_estimates","text":"data.table::data.table object Variable field key.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_state_estimates.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Read Historical State Estimates from ABARES — read_historical_state_estimates","text":"Columns renamed consistency ABARES products serviced package using snake_case format ordered consistently.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_state_estimates.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read Historical State Estimates from ABARES — read_historical_state_estimates","text":"https://www.agriculture.gov.au/abares/data/farm-data-portal#data-download","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_historical_state_estimates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Historical State Estimates from ABARES — read_historical_state_estimates","text":"","code":"if (FALSE) { # interactive() read_historical_state_estimates() # or shorter read_hist_sta_est() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_stars.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Soil Thickness File With stars — read_soil_thickness_stars","title":"Read Soil Thickness File With stars — read_soil_thickness_stars","text":"Read Soil Thickness Australian Areas Intensive Agriculture Layer 1 data stars object.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_stars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Soil Thickness File With stars — read_soil_thickness_stars","text":"","code":"read_soil_thickness_stars(files)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_stars.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read Soil Thickness File With stars — read_soil_thickness_stars","text":"https://anrdl-integration-web-catalog-saxfirxkxt.s3-ap-southeast-2.amazonaws.com/warehouse/staiar9cl__059/staiar9cl__05911a01eg_geo___.zip","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_stars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read Soil Thickness File With stars — read_soil_thickness_stars","text":"files read.abares read.abares.soil.thickness object, list contains ESRI grid file import","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_stars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Soil Thickness File With stars — read_soil_thickness_stars","text":"stars object Soil Thickness Australian Areas Intensive Agriculture Layer 1","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_stars.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read Soil Thickness File With stars — read_soil_thickness_stars","text":"https://data.agriculture.gov.au/geonetwork/srv/eng/catalog.search#/metadata/faa9f157-8e17-4b23-b6a7-37eb7920ead6","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_stars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Soil Thickness File With stars — read_soil_thickness_stars","text":"","code":"if (FALSE) { # interactive() get_soil_thickness(cache = TRUE) |> read_soil_thickness_stars() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_terra.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Soil Thickness File With terra — read_soil_thickness_terra","title":"Read Soil Thickness File With terra — read_soil_thickness_terra","text":"Read Soil Thickness Australian Areas Intensive Agriculture Layer 1 data terra::rast object.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_terra.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Soil Thickness File With terra — read_soil_thickness_terra","text":"","code":"read_soil_thickness_terra(files)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_terra.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read Soil Thickness File With terra — read_soil_thickness_terra","text":"https://anrdl-integration-web-catalog-saxfirxkxt.s3-ap-southeast-2.amazonaws.com/warehouse/staiar9cl__059/staiar9cl__05911a01eg_geo___.zip","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_terra.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read Soil Thickness File With terra — read_soil_thickness_terra","text":"files read.abares read.abares.soil.thickness object, list contains ESRI grid file import","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_terra.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Soil Thickness File With terra — read_soil_thickness_terra","text":"terra::rast object Soil Thickness Australian Areas Intensive Agriculture Layer 1","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_terra.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read Soil Thickness File With terra — read_soil_thickness_terra","text":"https://data.agriculture.gov.au/geonetwork/srv/eng/catalog.search#/metadata/faa9f157-8e17-4b23-b6a7-37eb7920ead6","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_soil_thickness_terra.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Soil Thickness File With terra — read_soil_thickness_terra","text":"","code":"if (FALSE) { # interactive() x <- get_soil_thickness(cache = TRUE) |> read_soil_thickness_terra() # terra::plot() is reexported for convience plot(x) }"},{"path":"https://adamhsparks.github.io/read.abares/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. terra plot","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/news/index.html","id":"major-changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Major changes","title":"read.abares 1.0.0","text":"Rename functions download read files active R session get_ read_ avoid confusion functions fetch data separate read_ functions Adds new function, print_agfd_nc_file_format() provide details AGFD NetCDF files’ contents Uses Geopackages {sf} objects rather .Rds, faster smaller file sizes caching Checks corrects geometries AAGIS Regions shapefile upon import applies cached object applicable","code":""},{"path":"https://adamhsparks.github.io/read.abares/news/index.html","id":"bug-fixes-1-0-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"read.abares 1.0.0","text":"longer checks length Boolean vector checking number files cache proceeding removing Fixes bugs get_agfd() creating directories saving downloaded file Fixes bug get_aagis_regions() creating cached object file Fixes “URL” field DESCRIPTION file, thanks @mpadge","code":""},{"path":"https://adamhsparks.github.io/read.abares/news/index.html","id":"minor-changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Minor changes","title":"read.abares 1.0.0","text":"Improved documentation data sets now @source field points file provided data sets now @references field points references data Code linting thanks {flint} Use {httr2} handle downloads Increase timeout values deal stubborn long-running file downloads Uses {httr2}’s caching functionality simplify -session caching Use {brio} write downloads disk Use {httptest2} help test downloads Gracefully handle errors AGFD zip files corrupted download, provide user informative message remove corrupted download Tests run parallel quicker testing {sf} operations now quiet reading data possible","code":""},{"path":"https://adamhsparks.github.io/read.abares/news/index.html","id":"readabares-010","dir":"Changelog","previous_headings":"","what":"read.abares 0.1.0","title":"read.abares 0.1.0","text":"Submission rOpenSci peer code review","code":""}]
+[{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"how-do-i-submit-a-good-bug-report","dir":"","previous_headings":"","what":"How Do I Submit a Good Bug Report?","title":"NA","text":"must never report security related issues, vulnerabilities bugs including sensitive information issue tracker, elsewhere public. Instead sensitive bugs must sent email marcelo.araya@ucr.ac.cr. use GitHub issues track bugs errors. run issue project: Open Issue. (Since can’t sure point whether bug , ask talk bug yet label issue.) Explain behavior expect actual behavior. Please provide much context possible describe reproduction steps someone else can follow recreate issue . usually includes code. good bug reports isolate problem create reduced test case. Provide information collected previous section. marked needs-fix, well possibly tags (critical), issue left implemented someone.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"suggesting-enhancements","dir":"","previous_headings":"","what":"Suggesting Enhancements","title":"NA","text":"section guides submitting enhancement suggestion, including completely new features minor improvements existing functionality. Following guidelines help maintainers community understand suggestion find related suggestions.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"before-submitting-an-enhancement","dir":"","previous_headings":"Suggesting Enhancements","what":"Before Submitting an Enhancement","title":"NA","text":"Make sure using latest version. Read documentation carefully find functionality already covered, maybe individual configuration. Perform search see enhancement already suggested. , add comment existing issue instead opening new one. Find whether idea fits scope alved better serve inspiration.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"NA","text":"guide based contributing.md. Make !nd aims project. ’s make strong case convince project’s developers merits feature. Keep mind want features useful majority users just small subset. ’re just targeting minority users, consider writing add-/plugin library.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"how-do-i-submit-a-good-enhancement-suggestion","dir":"","previous_headings":"Attribution","what":"How Do I Submit a Good Enhancement Suggestion?","title":"NA","text":"Enhancement suggestions tracked GitHub issues. Use clear descriptive title issue identify suggestion. Provide step--step description suggested enhancement many details possible. Describe current behavior explain behavior expected see instead . point can also tell alternatives work . may want include screenshots animated GIFs help demonstrate steps point part suggestion related . can use tool record GIFs macOS Windows, tool tool Linux. Explain enhancement useful users. may also want point projects ’s filed: project team label issue accordingly. team member try reproduce issue provided steps. reproduction steps obvious way reproduce issue, team ask steps mark issue needs-repro. Bugs needs-repro tag addressed reproduced. team able reproduce issue, # Contributing First , thanks taking time contribute! types contributions encouraged valued. See Table Contents different ways help details project handles . Please make sure read relevant section making contribution. make lot easier us maintainers smooth experience involved. community looks forward contributions. like project, just don’t time contribute, ’s fine. easy ways support project show appreciation, also happy : - Star project - Tweet - Refer project project’s readme - Mention project local meetups tell friends/colleagues","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"table-of-contents","dir":"","previous_headings":"","what":"Table of Contents","title":"NA","text":"Code Conduct Question Want Contribute Reporting Bugs Suggesting Enhancements","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"NA","text":"Please note baRulho released Contributor Code Conduct. contributing project agree abide terms. See rOpenSci contributing guide details.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"i-have-a-question","dir":"","previous_headings":"","what":"I Have a Question","title":"NA","text":"want ask question, assume read available Documentation. ask question, best search existing Issues might help . case found suitable issue still need clarification, can write question issue. also advisable search internet answers first. still feel need ask question need clarification, recommend following: Open https://github.com/ropensci/baRulho/issues/. Provide much context can ’re running . Provide project platform versions (nodejs, npm, etc), depending seems relevant. take care issue soon possible.","code":""},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"i-want-to-contribute","dir":"","previous_headings":"","what":"I Want To Contribute","title":"NA","text":"contributing project, must agree authored 100% content, necessary rights content content contribute may provided project license.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/CONTRIBUTING.html","id":"before-submitting-a-bug-report","dir":"","previous_headings":"I Want To Contribute > Reporting Bugs","what":"Before Submitting a Bug Report","title":"NA","text":"good bug report shouldn’t leave others needing chase information. Therefore, ask investigate carefully, collect information describe issue detail report. Please complete following steps advance help us fix potential bug fast possible. Make sure using latest version. Determine bug really bug error side e.g. using incompatible environment components/versions (Make sure read documentation. looking support, might want check section). see users experienced (potentially already solved) issue , check already bug report existing bug erro. Also make sure search internet (including Stack Overflow) see users outside GitHub community discussed issue. Collect information bug: Stack trace (Traceback) OS, Platform Version (Windows, Linux, macOS, x86, ARM) Version interpreter, compiler, SDK, runtime environment, package manager, depending wha","code":""},{"path":"https://adamhsparks.github.io/read.abares/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 read.abares authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://adamhsparks.github.io/read.abares/articles/read.abares.html","id":"working-with-agfd-data","dir":"Articles","previous_headings":"","what":"Working With AGFD Data","title":"read.abares","text":"can download files pipe directly class object desire Australian Farm Gridded Data (AGFD) data.","code":""},{"path":"https://adamhsparks.github.io/read.abares/articles/read.abares.html","id":"description-of-the-australian-farm-gridded-data","dir":"Articles","previous_headings":"Working With AGFD Data","what":"Description of the Australian Farm Gridded Data","title":"read.abares","text":"Directly DAFF website: Australian Gridded Farm Data set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced read.abares part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using read.abares farmpredict model, turn based read.abares Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices. Australian Gridded Farm Data remain active development, considered experimental. – Australian Department Agriculture, Fisheries Forestry. Load {read.abares} library. Check file format information NetCDF files. Download load local cache read AGFD files list {stars} objects. Download load local cache read AGFD files terra::rast object. Download load local cache read AGFD files list {tidync} objects. Download load local cache read AGFD files {data.table} object.","code":"library(read.abares) print_agfd_nc_file_format() #> ──────────────────────────────────────────────────────────────────────────────────────────────────── #> Each of the layers in simulation output data is represented as a 2D raster in NETCDF files, with #> the following grid format: #> CRS: EPSG:4326 - WGS 84 – Geographic #> Extent: 111.975 -44.525 156.275 -9.975 #> Unit: Degrees #> Width: 886 #> Height: 691 #> Cell size: 0.05 degree x 0.05 degree #> ──────────────────────────────────────────────────────────────────────────────────────────────────── #> For further details, see the ABARES website, #> ## A list of {stars} objects star <- get_agfd(cache = TRUE) |> read_agfd_stars() #> 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. head(star[[1]]) #> stars object with 2 dimensions and 6 attributes #> attribute(s): #> Min. 1st Qu. Median Mean 3rd Qu. #> farmno 15612.000000 233091.50000000 329567.0000000 324737.7187618 418508.5000000 #> R_total_hat_ha 2.954396 7.88312157 21.7520529 169.5139301 174.8553843 #> C_total_hat_ha 1.304440 4.34079101 9.9449849 93.2210542 95.7221857 #> FBP_fci_hat_ha -143.759785 3.60529967 11.5796641 76.2928759 77.6748501 #> FBP_fbp_hat_ha -349.521639 3.36599833 11.5074294 60.0750936 62.8596117 #> A_wheat_hat_ha 0.000000 0.04062786 0.1114289 0.1365683 0.2112845 #> Max. NA's #> farmno 669706.0000000 443899 #> R_total_hat_ha 2415.7556059 443899 #> C_total_hat_ha 1853.5385298 443899 #> FBP_fci_hat_ha 1186.5830232 443899 #> FBP_fbp_hat_ha 1240.6003218 443899 #> A_wheat_hat_ha 0.5047761 565224 #> dimension(s): #> from to refsys values x/y #> lon 1 886 WGS 84 [886] 112,...,156.2 [x] #> lat 1 691 WGS 84 [691] -44.5,...,-10 [y] ## A {terra} `rast` object terr <- get_agfd(cache = TRUE) |> read_agfd_terra() head(terr[[1]]) #> class : SpatRaster #> dimensions : 6, 886, 41 (nrow, ncol, nlyr) #> resolution : 0.05, 0.05 (x, y) #> extent : 111.975, 156.275, -10.275, -9.975 (xmin, xmax, ymin, ymax) #> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84) #> source(s) : memory #> names : farmno, R_tot~at_ha, C_tot~at_ha, FBP_f~at_ha, FBP_f~at_ha, A_whe~at_ha, ... #> min values : NaN, NaN, NaN, NaN, NaN, NaN, ... #> max values : NaN, NaN, NaN, NaN, NaN, NaN, ... ## A list of {tidync} objects tdnc <- get_agfd(cache = TRUE) |> read_agfd_tidync() head(tdnc[[1]]) #> $source #> # A tibble: 1 × 2 #> access source #> #> 1 2024-12-08 10:28:31 /Users/adamsparks/Library/Caches/org.R-project.R/R/read.abares/historical_cli… #> #> $axis #> # A tibble: 84 × 3 #> axis variable dimension #> #> 1 1 lon 0 #> 2 2 lat 1 #> 3 3 farmno 0 #> 4 4 farmno 1 #> 5 5 R_total_hat_ha 0 #> 6 6 R_total_hat_ha 1 #> 7 7 C_total_hat_ha 0 #> 8 8 C_total_hat_ha 1 #> 9 9 FBP_fci_hat_ha 0 #> 10 10 FBP_fci_hat_ha 1 #> # ℹ 74 more rows #> #> $grid #> # A tibble: 3 × 4 #> grid ndims variables nvars #> #> 1 D0,D1 2 41 #> 2 D0 1 1 #> 3 D1 1 1 #> #> $dimension #> # A tibble: 2 × 8 #> id name length unlim coord_dim active start count #> #> 1 0 lon 886 FALSE TRUE TRUE 1 886 #> 2 1 lat 691 FALSE TRUE TRUE 1 691 #> #> $variable #> # A tibble: 43 × 7 #> id name type ndims natts dim_coord active #> #> 1 0 lon NC_DOUBLE 1 2 TRUE FALSE #> 2 1 lat NC_DOUBLE 1 2 TRUE FALSE #> 3 2 farmno NC_DOUBLE 2 1 FALSE TRUE #> 4 3 R_total_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> 5 4 C_total_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> 6 5 FBP_fci_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> 7 6 FBP_fbp_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> 8 7 A_wheat_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> 9 8 H_wheat_dot_hat NC_DOUBLE 2 1 FALSE TRUE #> 10 9 A_barley_hat_ha NC_DOUBLE 2 1 FALSE TRUE #> # ℹ 33 more rows #> #> $attribute #> # A tibble: 49 × 4 #> id name variable value #> #> 1 0 _FillValue lon #> 2 1 units lon #> 3 0 _FillValue lat #> 4 1 units lat #> 5 0 _FillValue farmno #> 6 0 _FillValue R_total_hat_ha #> 7 0 _FillValue C_total_hat_ha #> 8 0 _FillValue FBP_fci_hat_ha #> 9 0 _FillValue FBP_fbp_hat_ha #> 10 0 _FillValue A_wheat_hat_ha #> # ℹ 39 more rows ## A {data.table} object get_agfd(cache = TRUE) |> read_agfd_dt() |> head() #> id farmno R_total_hat_ha C_total_hat_ha FBP_fci_hat_ha FBP_fbp_hat_ha #> #> 1: f2022.c1991.p2022.t2022.nc 15612 7.636519 4.405228 3.231292 1.766127 #> 2: f2022.c1991.p2022.t2022.nc 21495 14.811169 9.165632 5.645538 6.178280 #> 3: f2022.c1991.p2022.t2022.nc 23418 24.874456 14.858595 10.015861 15.504923 #> 4: f2022.c1991.p2022.t2022.nc 24494 15.043653 9.326359 5.717294 7.212161 #> 5: f2022.c1991.p2022.t2022.nc 32429 23.630099 13.681063 9.949036 9.612778 #> 6: f2022.c1991.p2022.t2022.nc 32485 15.009926 9.815501 5.194425 6.582035 #> A_wheat_hat_ha H_wheat_dot_hat A_barley_hat_ha H_barley_dot_hat A_sorghum_hat_ha #> #> 1: NaN NaN NaN NaN NaN #> 2: NaN NaN NaN NaN NaN #> 3: NaN NaN NaN NaN NaN #> 4: NaN NaN NaN NaN NaN #> 5: NaN NaN NaN NaN NaN #> 6: NaN NaN NaN NaN NaN #> H_sorghum_dot_hat A_oilseeds_hat_ha H_oilseeds_dot_hat R_wheat_hat_ha R_sorghum_hat_ha #> #> 1: NaN NaN NaN NaN NaN #> 2: NaN NaN NaN NaN NaN #> 3: NaN NaN NaN NaN NaN #> 4: NaN NaN NaN NaN NaN #> 5: NaN NaN NaN NaN NaN #> 6: NaN NaN NaN NaN NaN #> R_oilseeds_hat_ha R_barley_hat_ha Q_wheat_hat_ha Q_barley_hat_ha Q_sorghum_hat_ha #> #> 1: NaN NaN NaN NaN NaN #> 2: NaN NaN NaN NaN NaN #> 3: NaN NaN NaN NaN NaN #> 4: NaN NaN NaN NaN NaN #> 5: NaN NaN NaN NaN NaN #> 6: NaN NaN NaN NaN NaN #> Q_oilseeds_hat_ha S_wheat_cl_hat_ha S_sheep_cl_hat_ha S_sheep_births_hat_ha #> #> 1: NaN NaN 0.000046854152 0.000048411171 #> 2: NaN NaN 0.000066325878 0.000057753874 #> 3: NaN NaN 0.000007771546 0.000007320093 #> 4: NaN NaN 0.000070963917 0.000062521929 #> 5: NaN NaN 0.000007780997 0.000006834211 #> 6: NaN NaN 0.000059600116 0.000053976389 #> S_sheep_deaths_hat_ha S_beef_cl_hat_ha S_beef_births_hat_ha S_beef_deaths_hat_ha Q_beef_hat_ha #> #> 1: 0.000007187978 0.02034820 0.005212591 0.000989490 0.004790528 #> 2: 0.000009039695 0.02974461 0.007970856 0.001468278 0.009646485 #> 3: 0.000000000000 0.05393181 0.014745383 0.002867331 0.014401773 #> 4: 0.000009726773 0.03057606 0.008602196 0.001446424 0.009577272 #> 5: 0.000000000000 0.04944272 0.011527594 0.002491037 0.014668761 #> 6: 0.000008478467 0.03322463 0.008456550 0.001627910 0.009281578 #> Q_sheep_hat_ha Q_lamb_hat_ha R_beef_hat_ha R_sheep_hat_ha R_lamb_hat_ha C_fodder_hat_ha #> #> 1: 0.00007117650 0 7.392679 0.010222802 0 0.3553107 #> 2: 0.00009448864 0 14.281910 0.014485890 0 0.7040333 #> 3: 0.00001299674 0 24.308574 0.001821158 0 0.9473936 #> 4: 0.00010191595 0 14.518771 0.015352095 0 0.7060111 #> 5: 0.00001283228 0 23.060943 0.001892115 0 1.0269189 #> 6: 0.00008869032 0 14.474964 0.013278806 0 0.7019839 #> C_fert_hat_ha C_fuel_hat_ha C_chem_hat_ha A_total_cropped_ha FBP_pfe_hat_ha farmland_per_cell #> #> 1: 0.0007795925 0.4282799 0.0002169123 0.000001588013 2.142158 62.26270 #> 2: 0.0670951492 0.5663560 0.0212989625 0.000144292922 6.679382 61.71605 #> 3: 0.1475929946 0.9244438 0.0398376851 0.000296036096 16.185389 61.82964 #> 4: 0.0764850563 0.5688555 0.0223214940 0.000151675639 7.711993 72.85995 #> 5: 0.1592835324 0.8337981 0.0416492516 0.000316535762 10.294743 61.82964 #> 6: 0.0997758317 0.5575842 0.0293469147 0.000201161236 7.101658 61.71605 #> lon lat #> #> 1: 142.60 -10.75 #> 2: 136.75 -11.05 #> 3: 132.90 -11.15 #> 4: 136.70 -11.20 #> 5: 133.45 -11.60 #> 6: 136.25 -11.60"},{"path":"https://adamhsparks.github.io/read.abares/articles/read.abares.html","id":"working-with-the-soil-thickness-map","dir":"Articles","previous_headings":"","what":"Working With the Soil Thickness Map","title":"read.abares","text":"can download soil depth map import {stars} terra::rast() object. convenience, {read.abares} re-exports terra::plot(), can just use plot() {terra} objects {read.abares}.","code":"library(read.abares) get_soil_thickness(cache = TRUE) |> read_soil_thickness_stars() #> stars object with 2 dimensions and 1 attribute #> attribute(s), summary of first 100000 cells: #> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's #> thpk_1 NA NA NA NaN NA NA 100000 #> dimension(s): #> from to offset delta refsys x/y #> x 1 4150 112.5 0.01 WGS 84 [x] #> y 1 3401 -9.995 -0.01 WGS 84 [y] x <- get_soil_thickness(cache = TRUE) |> read_soil_thickness_terra() plot(x)"},{"path":"https://adamhsparks.github.io/read.abares/articles/read.abares.html","id":"soil-thickness-metadata","dir":"Articles","previous_headings":"Working With the Soil Thickness Map","what":"Soil Thickness Metadata","title":"read.abares","text":"default, brief bit metadata printed console call soil thickness object R session. , {read.abares} provides function browse soil thickness metadata console. can also access directly use pander::pander() include document like vignette. Dataset ANZLIC ID ANZCW1202000149 Title Soil Thickness Australian areas intensive agriculture Layer 1 (Horizon - top-soil) (derived soil mapping) Custodian CSIRO, Land & Water Jurisdiction Australia Description Abstract Surface predicted Thickness soil layer 1 (Horizon - top-soil) surface intensive agricultural areas Australia. Data modelled area based observations made soil agencies State CSIRO presented .0.01 degree grid cells. Topsoils (horizons) defined surface soil layers organic matter accumulates, may include dominantly organic surface layers (O P horizons). depth topsoil important , higher organic matter contents, topsoils (horizon) generally suitable properties agriculture, including higher permeability higher levels soil nutrients. Estimates soil depths needed calculate amount soil constituent either volume mass terms (bulk density also needed) - example, volume water stored rooting zone potentially available plant use, assess total stores soil carbon Greenhouse inventory assess total stores nutrients. pattern soil depth strongly related topography - shape slope land. Deeper soils typically found river valleys soils accumulate floodplains footslopes ranges (zones deposition), soils hillslopes (zones erosion) tend shallow. Map thickness topsoil derived soil map data interpreted tables soil properties specific soil groups. quality data soil depth existing soil profile datasets questionable thickness soil horizons varies locally topography, values map units general averages. final ASRIS polygon attributed surfaces mosaic data obtained various state federal agencies. surfaces constructed best available soil survey information available time. surfaces also rely number assumptions. One area weighted mean good estimate soil attributes polygon map-unit. Another assumption made look-tables provided McKenzie et al. (2000), state territories accurately depict soil attribute values soil type. accuracy maps dependent scale original polygon data sets level soil survey taken place state. scale various soil maps used deriving map available accessing data-source grid, scale used assessment likely accuracy modelling. Atlas Australian Soils considered least accurate dataset therefore used state based data. state datasets Western Australian sub-systems, South Australian land systems NSW soil landscapes reconnaissance mapping reliable based scale. NSW soil landscapes reconnaissance mapping use one dominant soil type per polygon estimation attributes. South Australia Western Australia use several soil types per polygon map-unit. digital map data provided geographical coordinates based World Geodetic System 1984 (WGS84) datum. raster data set grid resolution 0.001 degrees (approximately equivalent 1.1 km). data set product National Land Water Resources Audit (NLWRA) 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 Update Frequency PLANNED Access Stored Data Format DIGITAL - ESRI Arc/Info integer GRID Available Format Type DIGITAL - ESRI Arc/Info integer GRID Access Constraint Subject terms & condition data access & management agreement National Land & Water Audit ANZLIC parties Data Quality Lineage soil attribute surface created using following datasets 1. digital polygon coverage Soil-Landforms Murray Darling Basis (MDBSIS)(Bui et al. 1998), classified principal profile forms (PPF’s) (Northcote 1979). 2. digital Atlas 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 Natural Resources Environment - NRE). 7. NSW Soil Landscapes reconnaissance soil landscape mapping (NSW Department Land Water Conservation - DLWC). 8. New South Wales Land systems west (NSW Department Land Water Conservation - DLWC). 9. South Australia soil land-systems (Primary Industries Resources South Australia - PIRSA). 10. Northern Territory soils coverage (Northern Territory Department Lands, Planning Environment). 11. mosaic Queensland soils coverages (Queensland Department Natural Resources - QDNR). 12. look-table linking PPF values Atlas Australian Soils interpreted soil attributes (McKenzie et al. 2000). 13. Look_up tables provided WA Agriculture linking WA soil groups interpreted soil attributes. 14. Look_up tables provided PIRSA linking SA soil groups interpreted soil attributes. continuous raster surface representing Thickness soil layer 1 created combining national state level digitised land systems maps soil surveys linked look-tables listing soil type corresponding attribute values. thickness used sparingly Factual Key, estimations thickness look-tables made using empirical correlations particular soil types. estimate soil attribute one soil type given polygon map-unit, soil attribute values related soil type look-table weighted according area occupied soil type within polygon map-unit. final soil attribute values area weighted average polygon map-unit. polygon data converted continuous raster surface using soil attribute values calculated polygon. ASRIS soil attribute surfaces created using polygon attribution relied number data sets various state agencies. polygon data set turned continuous surface grid based calculated soil attribute value polygon. grids merged basis , available, state data replaced Atlas Australian Soils MDBSIS. MDBSIS derived soil attribute values restricted areas MDBSIS deemed accurate Atlas Australian Soils (see Carlile et al (2001a). cases soil type missing look-table layer 2 exist soil type, percent area soils remaining adjusted prior calculating final soil attribute value. method used attribute polygons dependent data supplied individual State agencies. modelled grid resampled 0.0025 degree cells 0.01 degree cells using bilinear interpolation Positional Accuracy predictive surface 0.01 X 0.01 degree grid locational accurate 1m. positional accuracy defining polygons variable positional accuracy locations expected within 100m recorded location. vertical accuracy relevant. positional assessment made considering tools used generate locational information contacting data providers. parameters used production led surface range positional accuracy ranging + - 50 m + - kilometres. contribute loss attribute accuracy surface. Attribute Accuracy Input attribute accuracy areas highly variable. predictive variable much lower attribute accuracy due irregular distribution limited positional accuracy parameters used modelling. several sources error estimating soil depth thickness horizons look-tables. thickness used sparingly Factual Key, estimations thickness look-tables made using empirical correlations particular soil types. quality data soil depth existing soil profile datasets questionable, soil mapping, thickness soil horizons varies locally topography, values map units general averages. definition depth soil regolith imprecise can difficult determine lower limit soil. assumption made area weighted mean soil attribute values based soil type good estimation soil property debatable, supply soil attribute value given location. Rather designed show national regional patterns soil properties. use surfaces farm catchment scale modelling may prove inaccurate. Also use look-tables attribute soil types accurate number observations used estimate attribute value soil type. soil types look-tables may observations, yet average attribute value still taken attribute value soil type. Different states using different taxonomic schemes making national soil database difficult. Another downfall area weighted approach soil types may listed look-tables. soil type dominant one within polygon map-unit, listed within look-table attributed within look-table final soil attribute value polygon biased towards minor soil types exist. may also happen large area occupied soil type B horizon. case final soil attribute value area weighted soils B horizon, ignoring major soil type within polygon map-unit. layer 2 surfaces large areas -data soils listed particular map-unit polygon B horizon. Logical Consistency Surface fully logically consistent one parameter shown, predicted average Soil Thickness within grid cell Completeness Surface nearly complete. areas (%1 missing) insufficient parameters known provide useful prediction thus attributes absent areas. Contact Information Contact Organisation (s) CSIRO, Land & Water Contact Position Project Leader Mail Address ACLEP, GPO 1666 Locality Canberra State ACT Country AUSTRALIA Postcode 2601 Telephone 02 6246 5922 Facsimile 02 6246 5965 Electronic Mail Address neil.mckenzie@cbr.clw.csiro.au Metadata Date Metadata Date 2001-07-01 Additional Metadata Additional Metadata Entity Attributes Entity Name Soil Thickness Layer 1 (derived mapping) Entity description Estimated Soil Thickness (mm) Layer 1 cell cell basis Feature attribute name VALUE Feature attribute definition Predicted average Thickness (mm) soil layer 1 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 soil depths needed calculate amount soil constituent either volume mass terms (bulk density also needed) - example, volume water stored rooting zone potentially available plant use, assess total stores soil carbon Greenhouse inventory assess total stores nutrients. Provide indications probable Thickness soil layer 1 agricultural areas soil thickness testing carried ","code":"library(read.abares) get_soil_thickness(cache = TRUE) #> #> ── Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Horizon - top-soil) ── #> #> ── 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()` on a soil thickness object in your #> R session. library(read.abares) get_soil_thickness(cache = TRUE) |> print_soil_thickness_metadata() #> #> ── Soil Thickness for Australian areas of intensive agriculture of Layer 1 (A Horizon - top-soil) ── #> #> ── 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 neil.mckenzie@cbr.clw.csiro.au #> #> Metadata Date Metadata Date 2001-07-01 #> #> Additional Metadata Additional Metadata #> #> Entity and Attributes Entity Name Soil Thickness Layer 1 (derived from mapping) #> #> Entity description Estimated Soil Thickness (mm) of Layer 1 on a cell by cell basis #> #> Feature attribute name VALUE #> #> Feature attribute definition Predicted average Thickness (mm) of soil layer 1 in the 0.01 X 0.01 #> degree quadrat #> #> Data Type Spatial representation type RASTER #> #> Projection Map projection GEOGRAPHIC #> #> Datum WGS84 #> #> Map units DECIMAL DEGREES #> #> Scale Scale/ resolution 1:1 000 000 #> #> Usage Purpose Estimates of soil depths are needed to calculate the amount of any soil constituent #> in either volume or mass terms (bulk density is also needed) - for example, the volume of water #> stored in the rooting zone potentially available for plant use, to assess total stores of soil #> carbon for Greenhouse inventory or to assess total stores of nutrients. #> #> Provide indications of probable Thickness soil layer 1 in agricultural areas where soil thickness #> testing has not been carried out #> #> Use Use Limitation This dataset is bound by the requirements set down by the National Land & Water #> Resources Audit library(read.abares) library(pander) x <- get_soil_thickness(cache = TRUE) y <- x$metadata pander(y)"},{"path":"https://adamhsparks.github.io/read.abares/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Adam H. Sparks. Maintainer, author. Jacob. Contributor. Assisted troubleshooting formatting documentation display '<' '>' properly Curtin University Technology. Copyright holder. Provided support Adam Sparks's time. Grains Research Development Corporation. Funder, copyright holder. GRDC Project CUR2210-005OPX (AAGI-CU)","code":""},{"path":"https://adamhsparks.github.io/read.abares/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Sparks (????). read.abares: Simple downloading importing ABARES Data. R package version 1.0.0, https://adamhsparks.github.io/read.abares/.","code":"@Manual{, title = {{read.abares}: Simple downloading and importing of ABARES Data}, author = {Adam H. Sparks}, note = {R package version 1.0.0}, url = {https://adamhsparks.github.io/read.abares/}, }"},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"readabares-simple-downloading-and-importing-of-abares-data-","dir":"","previous_headings":"","what":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"R package automated downloading ingestion data Australian Bureau Agricultural Resource Economics Sciences. ABARES data serviced. list hand-picked reasonably useful maintainable, .e., frequently updated values included , e.g., Australian crop reports. However, data set feel useful serviced {read.abares}, please feel free open issue details data set better yet, open pull request! Data serviced include: ABARES Estimates; Australian Gridded Farm Data (AGFD) set; Australian Agricultural Grazing Industries Survey (AAGIS) region mapping files; Historical Agricultural Forecast Database; Soil Thickness Australian areas intensive agriculture Layer 1 (Horizon - top-soil) (derived soil mapping) map ; trade data ; trade region data. files freely available CSV files, zip archives NetCDF files zip archives geospatial shape files. {read.abares} facilitates downloading, caching importing files R session choice class resulting object(s).","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"installation","dir":"","previous_headings":"Get Started","what":"Installation","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"{read.abares} available CRAN (yet). can install like :","code":"if (!require(\"remotes\")) install.packages(\"remotes\") remotes::install_git(\"https://github.com/adamhsparks/read.abares\")"},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"caching","dir":"","previous_headings":"Features","what":"Caching","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"{read.abares} supports caching files using tools::R_user_dir(package = \"read.abares\", = \"cache\") save files standardised location across platforms don’t worry files went ’re still . requesting files, {read.abares} first check available locally either cached temporary storage. Caching mandatory, can just work downloaded files tempdir(), cleaned R session ends.","code":""},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"multiple-classes-supported","dir":"","previous_headings":"Features","what":"Multiple Classes Supported","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"{read.abares} supports multiple classes objects support workflow. Select spatial classes AGFD NetCDF files: {stars}, {terra} {tidync} data.frame (data.table) objects: {data.table}","code":""},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"metadata","dir":"","previous_headings":"","what":"Metadata","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"Please report issues bugs. License: MIT","code":""},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"citations","dir":"","previous_headings":"Metadata","what":"Citations","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"Citing data: Please refer ABARES website, https://www.agriculture.gov.au/abares/products/citations, cite data use . Citing {read.abares}: citing use package, please use,","code":"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 citation(\"read.abares\") #> To cite package 'read.abares' in publications use: #> #> Sparks A (????). _read.abares: Simple downloading and importing of #> ABARES Data_. R package version 1.0.0, #> . #> #> A BibTeX entry for LaTeX users is #> #> @Manual{, #> title = {{read.abares}: Simple downloading and importing of ABARES Data}, #> author = {Adam H. Sparks}, #> note = {R package version 1.0.0}, #> url = {https://adamhsparks.github.io/read.abares/}, #> }"},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"a-note-on-testing","dir":"","previous_headings":"Metadata > Contributing","what":"A Note on Testing","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"’ve aimed make testing package complete possible. files downloaded >1GB may take several minutes hour download due size, wish include package . Therefore, tests rely already downloaded locally cached files. wish work development {read.abares} please aware take time establish local cache testing somewhat faster. considering including pared examples tests data released CC 4.0 License, now opted just use locally cached data simplicity.","code":""},{"path":"https://adamhsparks.github.io/read.abares/index.html","id":"code-of-conduct","dir":"","previous_headings":"Metadata","what":"Code of Conduct","title":"Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources","text":"Please note {read.abares} project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/clear_cache.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Files in Users' Cache Directory — clear_cache","title":"Remove Files in Users' Cache Directory — clear_cache","text":"Removes files read.abares cache exist.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/clear_cache.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Files in Users' Cache Directory — clear_cache","text":"","code":"clear_cache()"},{"path":"https://adamhsparks.github.io/read.abares/reference/clear_cache.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove Files in Users' Cache Directory — clear_cache","text":"Nothing, called side-effects, clearing cached files","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/clear_cache.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remove Files in Users' Cache Directory — clear_cache","text":"","code":"# not run because cached files shouldn't exist on CRAN or testing envs if (FALSE) { # \\dontrun{ clear_cache() } # }"},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Australian Gridded Farm Data for Local Use — get_agfd","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"Downloads Australian Gridded Farm Data (AGFD) data unzips compressed files NetCDF importing.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"","code":"get_agfd(fixed_prices = TRUE, cache = TRUE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"Historical climate prices fixed – https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1036161/3, Historical climate prices – https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1036161/2","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"fixed_prices Boolean Download historical climate prices historical climate fixed prices described (Hughes et al. 2022). Defaults TRUE downloads data historical climate fixed prices “isolate effects climate variability financial incomes broadacre farm businesses” (ABARES 2024). Using TRUE download simulations global output input price indexes fixed values recently completed financial year. cache Boolean Cache Australian Gridded Farm Data files download using tools::R_user_dir identify proper directory storing user data cache package. Defaults TRUE, caching files locally. FALSE, function uses tempdir() files deleted upon closing R session.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"read.abares.agfd.nc.files object, list NetCDF files containing Australian Gridded Farm Data","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"ABARES website: “Australian Gridded Farm Data (AGFD) set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced ABARES part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data.Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices.” – ABARES, 2024-11-25 sets data large file size, .e., >1GB, require time download.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"historical-climate-fixed-prices-","dir":"Reference","previous_headings":"","what":"Historical climate (fixed prices)","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"Historical climate (fixed prices) scenario similar described Hughes et al. (2022) intended isolate effects climate variability financial incomes broadacre farm businesses. simulations, global output input price indexes fixed values recently completed financial year. However, scenarios spread domestic global grain (wheat, barley sorghum) prices, along Australian fodder prices allowed vary response climate data (capture domestic increases grain fodder prices drought years, see Hughes et al. 2022). 33-year historical climate sequence (including historical simulated crop pasture data AADI project) simulated grid cell (1990-91 2022-23).","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"historical-climate-and-prices","dir":"Reference","previous_headings":"","what":"Historical climate and prices","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"part AADI project additional scenario developed accounting changes climate conditions output input prices (.e., global commodity market variability). Historical climate prices scenario 33-year reference period allows variation \\ historical climate conditions historical prices. scenario, historical price indexes de-trended, account consistent long- term trends real commodity prices (particularly sheep lamb). resulting simulation results percentile indicators intended reflect combined impacts annual climate commodity price variability.\" – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"data-files","dir":"Reference","previous_headings":"","what":"Data files","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"Simulation output data saved multilayer NetCDF files, named using following convention: f.c.p.t.nc : = Financial year farm business data used simulations. = Financial year climate data used simulations. = Financial year output input prices used simulations. = Financial year farm ‘technology’ (equal farm year simulations) financial years referred closing calendar year (e.g., 2022 = 1 July 2021 30 June 2022). – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"data-layers","dir":"Reference","previous_headings":"","what":"Data layers","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"data layers downloaded NetCDF files described Table 2 seen Australian Bureau Agricultural Resource Economics Sciences (2024). Following copy Table 2 convenience, please refer full document methods metadata.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"Australian gridded farm data, Australian Bureau Agricultural Resource Economics Sciences, Canberra, July 2024, DOI: 10.25814/7n6z-ev41. CC 4.0. N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought perspective Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, DOI: 10.1016/j.crm.2022.100420.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/get_agfd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Australian Gridded Farm Data for Local Use — get_agfd","text":"","code":"if (FALSE) { # interactive() get_agfd() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"Get Soil Thickness Australian Areas Intensive Agriculture Layer 1 Local Use","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"","code":"get_soil_thickness(cache = TRUE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"https://anrdl-integration-web-catalog-saxfirxkxt.s3-ap-southeast-2.amazonaws.com/warehouse/staiar9cl__059/staiar9cl__05911a01eg_geo___.zip","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"cache Boolean Cache soil thickness data files download using tools::R_user_dir() identify proper directory storing user data cache package. Defaults TRUE, caching files locally. FALSE, function uses tempdir() files deleted upon closing R session. custom print method provided print metadata associated data. Examples provided interacting metadata directly.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"read.abares.soil.thickness object, named list file path resulting ESRI Grid file text file metadata","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"https://data.agriculture.gov.au/geonetwork/srv/eng/catalog.search#/metadata/faa9f157-8e17-4b23-b6a7-37eb7920ead6","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/get_soil_thickness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Soil Thickness for Australian Areas of Intensive Agriculture of Layer 1 for Local Use — get_soil_thickness","text":"","code":"if (FALSE) { # interactive() x <- get_soil_thickness() # View the metadata with pretty printing x # Extract the metadata as an object in your R session and use it with # {pander}, useful for Markdown files library(pander) y <- x$metadata pander(y) }"},{"path":"https://adamhsparks.github.io/read.abares/reference/inspect_cache.html","id":null,"dir":"Reference","previous_headings":"","what":"List the File Path to Users' Cache Directory — inspect_cache","title":"List the File Path to Users' Cache Directory — inspect_cache","text":"Check files exist read.abares file cache. function always return full file names, .e., directory path prepended. See help file list.files() full.names argument. wish strip file path return directory file names, use basename(). See examples .","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/inspect_cache.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List the File Path to Users' Cache Directory — inspect_cache","text":"","code":"inspect_cache(recursive = FALSE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/inspect_cache.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List the File Path to Users' Cache Directory — inspect_cache","text":"recursive Boolean value indicating whether list files subdirectories cache directory. Defaults FALSE returning top-level directories contained cache directory.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/inspect_cache.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List the File Path to Users' Cache Directory — inspect_cache","text":"list directories files cache","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/inspect_cache.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List the File Path to Users' Cache Directory — inspect_cache","text":"","code":"# not run because cached files shouldn't exist on CRAN or testing envs if (FALSE) { # \\dontrun{ # list directories in cache only inspect_cache() # list directory names, stripping the file path basename(inspect_cache) # list all files in subdirectories of the cache inspect_cache(recursive = TRUE) # list all files in subdirectories, stripping the file path basename(inspect_cache(recursive_true)) } # }"},{"path":"https://adamhsparks.github.io/read.abares/reference/print_agfd_nc_file_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","title":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","text":"Print file format section 3.2 Australian Bureau Agricultural Resource Economics Sciences.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/print_agfd_nc_file_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","text":"","code":"print_agfd_nc_file_format()"},{"path":"https://adamhsparks.github.io/read.abares/reference/print_agfd_nc_file_format.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","text":"https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1036161/0","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/print_agfd_nc_file_format.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","text":"Australian gridded farm data, Australian Bureau Agricultural Resource Economics Sciences, Canberra, July, DOI: ","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/print_agfd_nc_file_format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prints File Format Information for the AGFD NetCDF Files — print_agfd_nc_file_format","text":"","code":"print_agfd_nc_file_format() #> ──────────────────────────────────────────────────────────────────────────────── #> Each of the layers in simulation output data is represented as a 2D raster in #> NETCDF files, with the following grid format: #> #> CRS: EPSG:4326 - WGS 84 - Geographic #> Extent: 111.975 -44.525 156.275 -9.975 #> Unit: Degrees #> Width: 886 #> Height: 691 #> Cell size: 0.05 degree x 0.05 degree #> ──────────────────────────────────────────────────────────────────────────────── #> For further details, see the ABARES website, #> "},{"path":"https://adamhsparks.github.io/read.abares/reference/print_soil_thickness_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","title":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","text":"Displays complete set metadata associated soil thickness data R console. including metadata documents methods outside R, see get_soil_thickness example using pander::pander print metadata.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/print_soil_thickness_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","text":"","code":"print_soil_thickness_metadata(x)"},{"path":"https://adamhsparks.github.io/read.abares/reference/print_soil_thickness_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","text":"x read.abares.soil.thickness.files object","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/print_soil_thickness_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","text":"Nothing, called side effects, prints complete metadata file R console","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/print_soil_thickness_metadata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display Complete Metadata Associated with Soil Thickness Data in the R Console — print_soil_thickness_metadata","text":"","code":"if (FALSE) { # interactive() get_soil_thickness(cache = TRUE) |> print_soil_thickness_metadata() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read.abares-package.html","id":null,"dir":"Reference","previous_headings":"","what":"read.abares: Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources — read.abares-package","title":"read.abares: Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources — read.abares-package","text":"Download import data Australian Bureau Agricultural Resource Economics Sciences (ABARES) https://www.agriculture.gov.au/abares.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read.abares-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"read.abares: Provides simple downloading, parsing and importing of Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) data sources — read.abares-package","text":"Maintainer: Adam H. Sparks adamhsparks@gmail.com (ORCID) contributors: Jacob jacob@wujciak.de (ORCID) (Assisted troubleshooting formatting documentation display '<' '>' properly) [contributor] Curtin University Technology (Provided support Adam Sparks's time.) [copyright holder] Grains Research Development Corporation (GRDC Project CUR2210-005OPX (AAGI-CU)) [funder, copyright holder]","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":null,"dir":"Reference","previous_headings":"","what":"Read AAGIS Region Mapping Files — read_aagis_regions","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"Download, cache import Australian Agricultural Grazing Industries Survey (AAGIS regions geospatial shapefile. Upon import, geometries automatically corrected fix invalid geometries present original shapefile.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"","code":"read_aagis_regions(cache = TRUE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"https://www.agriculture.gov.au/sites/default/files/documents/aagis_asgs16v1_g5a.shp_.zip","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"cache Boolean Cache AAGIS regions' geospatial file downloading using tools::R_user_dir(\"read.abares\", \"cache\") identify proper directory storing user data cache package. Defaults TRUE, caching files locally Geopackage. FALSE, function uses tempdir() files deleted upon closing R session.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"sf object AAGIS regions","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"https://www.agriculture.gov.au/abares/research-topics/surveys/farm-definitions-methods#regions","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_aagis_regions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read AAGIS Region Mapping Files — read_aagis_regions","text":"","code":"if (FALSE) { # interactive() aagis <- read_aagis_regions() plot(aagis) }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Data From the ABARES Trade Dashboard — read_abares_trade","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"Fetches imports ABARES trade data.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"","code":"read_abares_trade(cache = TRUE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1033841/0","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"cache Boolean Cache ABARES trade data download using tools::R_user_dir() identify proper directory storing user data cache package. Defaults TRUE, caching files locally native R object. FALSE, function uses tempdir() files deleted upon closing R session.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"data.table object ABARES trade data.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"Columns renamed consistency ABARES products serviced package using snake_case format ordered consistently.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"https://www.agriculture.gov.au/abares/research-topics/trade/dashboard","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Data From the ABARES Trade Dashboard — read_abares_trade","text":"","code":"if (FALSE) { # interactive() trade <- read_abares_trade() trade }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":null,"dir":"Reference","previous_headings":"","what":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"Fetches imports ABARES trade regions data.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"","code":"read_abares_trade_regions(cache = TRUE)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1033841/2","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"cache Boolean Cache ABARES trade regions data download using tools::R_user_dir() identify proper directory storing user data cache package. Defaults TRUE, caching files locally native R object. FALSE, function uses tempdir() files deleted upon closing R session.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"data.table object ABARES trade data regions.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"Columns renamed consistency ABARES products serviced package using snake_case format ordered consistently.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1033841/0","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_abares_trade_regions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read ABARES Trade Data Regions From the ABARES Trade Dashboard — read_abares_trade_regions","text":"","code":"if (FALSE) { # interactive() trade_regions <- read_abares_trade_regions() trade_regions }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":null,"dir":"Reference","previous_headings":"","what":"Read AGFD NCDF Files as a data.table — read_agfd_dt","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"Read Australian Gridded Farm Data, (AGFD) data.table object.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"","code":"read_agfd_dt(files)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"files list AGFD NetCDF files import","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"data.table::data.table object Australian Gridded Farm Data","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"ABARES website: “Australian Gridded Farm Data (AGFD) set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced ABARES part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data.Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices.” – ABARES, 2024-11-25 sets data large file size, .e., >1GB, require time download.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"historical-climate-fixed-prices-","dir":"Reference","previous_headings":"","what":"Historical climate (fixed prices)","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"Historical climate (fixed prices) scenario similar described Hughes et al. (2022) intended isolate effects climate variability financial incomes broadacre farm businesses. simulations, global output input price indexes fixed values recently completed financial year. However, scenarios spread domestic global grain (wheat, barley sorghum) prices, along Australian fodder prices allowed vary response climate data (capture domestic increases grain fodder prices drought years, see Hughes et al. 2022). 33-year historical climate sequence (including historical simulated crop pasture data AADI project) simulated grid cell (1990-91 2022-23).","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"historical-climate-and-prices","dir":"Reference","previous_headings":"","what":"Historical climate and prices","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"part AADI project additional scenario developed accounting changes climate conditions output input prices (.e., global commodity market variability). Historical climate prices scenario 33-year reference period allows variation \\ historical climate conditions historical prices. scenario, historical price indexes de-trended, account consistent long- term trends real commodity prices (particularly sheep lamb). resulting simulation results percentile indicators intended reflect combined impacts annual climate commodity price variability.\" – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"data-files","dir":"Reference","previous_headings":"","what":"Data files","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"Simulation output data saved multilayer NetCDF files, named using following convention: f.c.p.t.nc : = Financial year farm business data used simulations. = Financial year climate data used simulations. = Financial year output input prices used simulations. = Financial year farm ‘technology’ (equal farm year simulations) financial years referred closing calendar year (e.g., 2022 = 1 July 2021 30 June 2022). – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"data-layers","dir":"Reference","previous_headings":"","what":"Data layers","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"data layers downloaded NetCDF files described Table 2 seen Australian Bureau Agricultural Resource Economics Sciences (2024). Following copy Table 2 convenience, please refer full document methods metadata.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"Australian gridded farm data, Australian Bureau Agricultural Resource Economics Sciences, Canberra, July 2024, DOI: 10.25814/7n6z-ev41. CC 4.0. N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought perspective Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, DOI: 10.1016/j.crm.2022.100420.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_dt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read AGFD NCDF Files as a data.table — read_agfd_dt","text":"","code":"if (FALSE) { # interactive() # using piping, which can use the {read.abares} cache after the first DL get_agfd(cache = TRUE) |> read_agfd_dt() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":null,"dir":"Reference","previous_headings":"","what":"Read AGFD NCDF Files With stars — read_agfd_stars","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"Read Australian Gridded Farm Data, (AGFD) list stars objects.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"","code":"read_agfd_stars(files)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"files list AGFD NetCDF files import","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"list object stars objects Australian Gridded Farm Data file names list's objects' names","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"ABARES website: “Australian Gridded Farm Data (AGFD) set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced ABARES part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data.Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices.” – ABARES, 2024-11-25 sets data large file size, .e., >1GB, require time download.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"historical-climate-fixed-prices-","dir":"Reference","previous_headings":"","what":"Historical climate (fixed prices)","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"Historical climate (fixed prices) scenario similar described Hughes et al. (2022) intended isolate effects climate variability financial incomes broadacre farm businesses. simulations, global output input price indexes fixed values recently completed financial year. However, scenarios spread domestic global grain (wheat, barley sorghum) prices, along Australian fodder prices allowed vary response climate data (capture domestic increases grain fodder prices drought years, see Hughes et al. 2022). 33-year historical climate sequence (including historical simulated crop pasture data AADI project) simulated grid cell (1990-91 2022-23).","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"historical-climate-and-prices","dir":"Reference","previous_headings":"","what":"Historical climate and prices","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"part AADI project additional scenario developed accounting changes climate conditions output input prices (.e., global commodity market variability). Historical climate prices scenario 33-year reference period allows variation \\ historical climate conditions historical prices. scenario, historical price indexes de-trended, account consistent long- term trends real commodity prices (particularly sheep lamb). resulting simulation results percentile indicators intended reflect combined impacts annual climate commodity price variability.\" – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"data-files","dir":"Reference","previous_headings":"","what":"Data files","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"Simulation output data saved multilayer NetCDF files, named using following convention: f.c.p.t.nc : = Financial year farm business data used simulations. = Financial year climate data used simulations. = Financial year output input prices used simulations. = Financial year farm ‘technology’ (equal farm year simulations) financial years referred closing calendar year (e.g., 2022 = 1 July 2021 30 June 2022). – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"data-layers","dir":"Reference","previous_headings":"","what":"Data layers","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"data layers downloaded NetCDF files described Table 2 seen Australian Bureau Agricultural Resource Economics Sciences (2024). Following copy Table 2 convenience, please refer full document methods metadata.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"Australian gridded farm data, Australian Bureau Agricultural Resource Economics Sciences, Canberra, July 2024, DOI: 10.25814/7n6z-ev41. CC 4.0. N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought perspective Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, DOI: 10.1016/j.crm.2022.100420.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_stars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read AGFD NCDF Files With stars — read_agfd_stars","text":"","code":"if (FALSE) { # interactive() # using piping, which can use the {read.abares} cache after the first DL agfd <- get_agfd(cache = TRUE) |> read_agfd_stars() head(agfd) plot(agfd[[1]]) }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":null,"dir":"Reference","previous_headings":"","what":"Read AGFD NCDF Files With terra — read_agfd_terra","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"Read Australian Gridded Farm Data, (AGFD) list terra::rast objects.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"","code":"read_agfd_terra(files)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"files list AGFD NetCDF files import","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"list object terra::rast objects Australian Gridded Farm Data file names list's objects' names","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"ABARES website: “Australian Gridded Farm Data (AGFD) set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced ABARES part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data.Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices.” – ABARES, 2024-11-25 sets data large file size, .e., >1GB, require time download.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"historical-climate-fixed-prices-","dir":"Reference","previous_headings":"","what":"Historical climate (fixed prices)","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"Historical climate (fixed prices) scenario similar described Hughes et al. (2022) intended isolate effects climate variability financial incomes broadacre farm businesses. simulations, global output input price indexes fixed values recently completed financial year. However, scenarios spread domestic global grain (wheat, barley sorghum) prices, along Australian fodder prices allowed vary response climate data (capture domestic increases grain fodder prices drought years, see Hughes et al. 2022). 33-year historical climate sequence (including historical simulated crop pasture data AADI project) simulated grid cell (1990-91 2022-23).","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"historical-climate-and-prices","dir":"Reference","previous_headings":"","what":"Historical climate and prices","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"part AADI project additional scenario developed accounting changes climate conditions output input prices (.e., global commodity market variability). Historical climate prices scenario 33-year reference period allows variation \\ historical climate conditions historical prices. scenario, historical price indexes de-trended, account consistent long- term trends real commodity prices (particularly sheep lamb). resulting simulation results percentile indicators intended reflect combined impacts annual climate commodity price variability.\" – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"data-files","dir":"Reference","previous_headings":"","what":"Data files","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"Simulation output data saved multilayer NetCDF files, named using following convention: f.c.p.t.nc : = Financial year farm business data used simulations. = Financial year climate data used simulations. = Financial year output input prices used simulations. = Financial year farm ‘technology’ (equal farm year simulations) financial years referred closing calendar year (e.g., 2022 = 1 July 2021 30 June 2022). – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"data-layers","dir":"Reference","previous_headings":"","what":"Data layers","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"data layers downloaded NetCDF files described Table 2 seen Australian Bureau Agricultural Resource Economics Sciences (2024). Following copy Table 2 convenience, please refer full document methods metadata.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"Australian gridded farm data, Australian Bureau Agricultural Resource Economics Sciences, Canberra, July 2024, DOI: 10.25814/7n6z-ev41. CC 4.0. N. Hughes, W.Y. Soh, C. Boult, K. Lawson, Defining drought perspective Australian farmers, Climate Risk Management, Volume 35, 2022, 100420, ISSN 2212-0963, DOI: 10.1016/j.crm.2022.100420.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_terra.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read AGFD NCDF Files With terra — read_agfd_terra","text":"","code":"if (FALSE) { # interactive() # using piping, which can use the {read.abares} cache after the first DL get_agfd(cache = TRUE) |> read_agfd_terra() }"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":null,"dir":"Reference","previous_headings":"","what":"Read agfd NCDF Files With tidync — read_agfd_tidync","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"Read Australian Gridded Farm Data, (AGFD) list tidync objects","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"","code":"read_agfd_tidync(files)"},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"files list AGFD NetCDF files import","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"list object tidync tidync::tidync objects Australian Gridded Farm Data file names list's objects' names.","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"ABARES website: “Australian Gridded Farm Data (AGFD) set national scale maps containing simulated data historical broadacre farm business outcomes including farm profitability 0.05-degree (approximately 5 km) grid. data produced ABARES part ongoing Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data.Australian Agricultural Drought Indicator (AADI) project (previously known Drought Early Warning System Project) derived using ABARES farmpredict model, turn based ABARES Agricultural Grazing Industries Survey (AAGIS) data. maps provide estimates farm business profit, revenue, costs production location (grid cell) year period 1990-91 2022-23. data include actual observed outcomes rather model predicted outcomes representative ‘typical’ broadacre farm businesses location considering likely farm characteristics prevailing weather conditions commodity prices.” – ABARES, 2024-11-25 sets data large file size, .e., >1GB, require time download.","code":""},{"path":[]},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"historical-climate-fixed-prices-","dir":"Reference","previous_headings":"","what":"Historical climate (fixed prices)","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"Historical climate (fixed prices) scenario similar described Hughes et al. (2022) intended isolate effects climate variability financial incomes broadacre farm businesses. simulations, global output input price indexes fixed values recently completed financial year. However, scenarios spread domestic global grain (wheat, barley sorghum) prices, along Australian fodder prices allowed vary response climate data (capture domestic increases grain fodder prices drought years, see Hughes et al. 2022). 33-year historical climate sequence (including historical simulated crop pasture data AADI project) simulated grid cell (1990-91 2022-23).","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"historical-climate-and-prices","dir":"Reference","previous_headings":"","what":"Historical climate and prices","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"part AADI project additional scenario developed accounting changes climate conditions output input prices (.e., global commodity market variability). Historical climate prices scenario 33-year reference period allows variation \\ historical climate conditions historical prices. scenario, historical price indexes de-trended, account consistent long- term trends real commodity prices (particularly sheep lamb). resulting simulation results percentile indicators intended reflect combined impacts annual climate commodity price variability.\" – Taken Australian Bureau Agricultural Resource Economics Sciences (2024)","code":""},{"path":"https://adamhsparks.github.io/read.abares/reference/read_agfd_tidync.html","id":"data-files","dir":"Reference","previous_headings":"","what":"Data files","title":"Read agfd NCDF Files With tidync — read_agfd_tidync","text":"Simulation output data saved multilayer NetCDF files, named using following convention: f.c.p