Ontology type: schema:ScholarlyArticle
2008-04
AUTHORSRuth Kerry, Margaret A. Oliver
ABSTRACTMaps of kriged soil properties for precision agriculture are often based on a variogram estimated from too few data because the costs of sampling and analysis are often prohibitive. If the variogram has been computed by the usual method of moments, it is likely to be unstable when there are fewer than 100 data. The scale of variation in soil properties should be investigated prior to sampling by computing a variogram from ancillary data, such as an aerial photograph of the bare soil. If the sampling interval suggested by this is large in relation to the size of the field there will be too few data to estimate a reliable variogram for kriging. Standardized variograms from aerial photographs can be used with standardized soil data that are sparse, provided the data are spatially structured and the nugget:sill ratio is similar to that of a reliable variogram of the property. The problem remains of how to set this ratio in the absence of an accurate variogram. Several methods of estimating the nugget:sill ratio for selected soil properties are proposed and evaluated. Standardized variograms with nugget:sill ratios set by these methods are more similar to those computed from intensive soil data than are variograms computed from sparse soil data. The results of cross-validation and mapping show that the standardized variograms provide more accurate estimates, and preserve the main patterns of variation better than those computed from sparse data. More... »
PAGES33-56
http://scigraph.springernature.com/pub.10.1007/s11119-008-9058-0
DOIhttp://dx.doi.org/10.1007/s11119-008-9058-0
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1028504113
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0503",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Soil Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/05",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Environmental Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Brigham Young University",
"id": "https://www.grid.ac/institutes/grid.253294.b",
"name": [
"Department of Geography, Brigham Young University, 84602, Provo, UT, USA"
],
"type": "Organization"
},
"familyName": "Kerry",
"givenName": "Ruth",
"id": "sg:person.014421606421.19",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014421606421.19"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Reading",
"id": "https://www.grid.ac/institutes/grid.9435.b",
"name": [
"Department of Soil Science, University of Reading, RG6 6DW, Reading, UK"
],
"type": "Organization"
},
"familyName": "Oliver",
"givenName": "Margaret A.",
"id": "sg:person.010076772773.20",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010076772773.20"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1016/s0098-3004(97)85438-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004709041"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00897500",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018332894",
"https://doi.org/10.1007/bf00897500"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00897500",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018332894",
"https://doi.org/10.1007/bf00897500"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1009995404447",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021648605",
"https://doi.org/10.1023/a:1009995404447"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s1537-5110(02)00283-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022056922"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s1537-5110(02)00283-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022056922"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1365-2389.1987.tb02146.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022690023"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1071/ea97158",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024789811"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jag.2004.07.005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027860254"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.geoderma.2007.04.019",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031896873"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1365-2389.1992.tb00128.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036582751"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1046/j.1365-2389.2000.00345.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043574937"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf02066732",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046624960",
"https://doi.org/10.1007/bf02066732"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf02066732",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046624960",
"https://doi.org/10.1007/bf02066732"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1024952406744",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051371616",
"https://doi.org/10.1023/a:1024952406744"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/9780470517277",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1098661636"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/9780470517277",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1098661636"
],
"type": "CreativeWork"
}
],
"datePublished": "2008-04",
"datePublishedReg": "2008-04-01",
"description": "Maps of kriged soil properties for precision agriculture are often based on a variogram estimated from too few data because the costs of sampling and analysis are often prohibitive. If the variogram has been computed by the usual method of moments, it is likely to be unstable when there are fewer than 100 data. The scale of variation in soil properties should be investigated prior to sampling by computing a variogram from ancillary data, such as an aerial photograph of the bare soil. If the sampling interval suggested by this is large in relation to the size of the field there will be too few data to estimate a reliable variogram for kriging. Standardized variograms from aerial photographs can be used with standardized soil data that are sparse, provided the data are spatially structured and the nugget:sill ratio is similar to that of a reliable variogram of the property. The problem remains of how to set this ratio in the absence of an accurate variogram. Several methods of estimating the nugget:sill ratio for selected soil properties are proposed and evaluated. Standardized variograms with nugget:sill ratios set by these methods are more similar to those computed from intensive soil data than are variograms computed from sparse soil data. The results of cross-validation and mapping show that the standardized variograms provide more accurate estimates, and preserve the main patterns of variation better than those computed from sparse data.",
"genre": "research_article",
"id": "sg:pub.10.1007/s11119-008-9058-0",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1135929",
"issn": [
"1385-2256",
"1573-1618"
],
"name": "Precision Agriculture",
"type": "Periodical"
},
{
"issueNumber": "1-2",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "9"
}
],
"name": "Determining nugget:sill ratios of standardized variograms from aerial photographs to krige sparse soil data",
"pagination": "33-56",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"87c92ad4bff5b8a3bb9512a62eddafd829af20e0fa18b03e24c100bcaa4421cd"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s11119-008-9058-0"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1028504113"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s11119-008-9058-0",
"https://app.dimensions.ai/details/publication/pub.1028504113"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T15:54",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8664_00000522.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007%2Fs11119-008-9058-0"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11119-008-9058-0'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11119-008-9058-0'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11119-008-9058-0'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11119-008-9058-0'
This table displays all metadata directly associated to this object as RDF triples.
114 TRIPLES
21 PREDICATES
40 URIs
19 LITERALS
7 BLANK NODES