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dc.contributor.authorRossel, RA Viscarra
dc.contributor.authorChen, C
dc.contributor.authorGrundy, MJ
dc.contributor.authorSearle, R
dc.contributor.authorClifford, D
dc.contributor.authorCampbell, PH
dc.date.accessioned2019-09-10T05:30:16Z
dc.date.available2019-09-10T05:30:16Z
dc.date.issued2015
dc.identifier.issn1838-675X
dc.identifier.doi10.1071/SR14366
dc.identifier.urihttp://hdl.handle.net/10072/387200
dc.description.abstractInformation on the geographic variation in soil has traditionally been presented in polygon (choropleth) maps at coarse scales. Now scientists, planners, managers and politicians want quantitative information on the variation and functioning of soil at finer resolutions; they want it to plan better land use for agriculture, water supply and the mitigation of climate change land degradation and desertification. The GlobalSoilMap project aims to produce a grid of soil attributes at a fine spatial resolution (approximately 100m), and at six depths, for the purpose. This paper describes the three-dimensional spatial modelling used to produce the Australian soil grid, which consists of Australia-wide soil attribute maps. The modelling combines historical soil data plus estimates derived from visible and infrared soil spectra. Together they provide a good coverage of data across Australia. The soil attributes so far include sand, silt and clay contents, bulk density, available water capacity, organic carbon, pH, effective cation exchange capacity, total phosphorus and total nitrogen. The data on these attributes were harmonised to six depth layers, namely 0-0.05m, 0.05-0.15m, 0.15-0.30m, 0.30-0.60m, 0.60-1.00m and 1.00-2.00m, and the resulting values were incorporated simultaneously in the models. The modelling itself combined the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. At each layer, values of the soil attributes were predicted at the nodes of a 3 arcsecond (approximately 90m) grid and mapped together with their uncertainties. The assessment statistics for each attribute mapped show that the models explained between 30% and 70% of their total variation. The outcomes are illustrated with maps of sand, silt and clay contents and their uncertainties. The Australian three-dimensional soil maps fill a significant gap in the availability of quantitative soil information in Australia.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherCSIRO Publishing
dc.relation.ispartofpagefrom845
dc.relation.ispartofpageto864
dc.relation.ispartofissue8
dc.relation.ispartofjournalSoil Research
dc.relation.ispartofvolume53
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsSoil Science
dc.subject.keywordsAgriculture
dc.subject.keywordsGlobalSoilMap
dc.titleThe Australian three-dimensional soil grid: Australia's contribution to the GlobalSoilMap project
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationRossel, RAV; Chen, C; Grundy, MJ; Searle, R; Clifford, D; Campbell, PH, The Australian three-dimensional soil grid: Australia's contribution to the GlobalSoilMap project, Soil Research, 2015, 53 (8), pp. 845-864
dc.date.updated2019-09-10T05:29:11Z
gro.hasfulltextNo Full Text
gro.griffith.authorChen, Chengrong


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