Variograms of Ancillary Data to Aid Sampling for Soil Surveys View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2003-09

AUTHORS

Ruth Kerry, Margaret A. Oliver

ABSTRACT

To provide reliable estimates for mapping soil properties for precision agriculture requires intensive sampling and costly laboratory analyses. If the spatial structure of ancillary data, such as yield, digital information from aerial photographs, and soil electrical conductivity (EC) measurements, relates to that of soil properties they could be used to guide the sampling intensity for soil surveys. Variograms of permanent soil properties at two study sites on different parent materials were compared with each other and with those for ancillary data. The ranges of spatial dependence identified by the variograms of both sets of properties are of similar orders of magnitude for each study site. Maps of the ancillary data appear to show similar patterns of variation and these seem to relate to those of the permanent properties of the soil. Correlation analysis has confirmed these relations. Maps of kriged estimates from sub-sampled data and the original variograms showed that the main patterns of variation were preserved when a sampling interval of less than half the average variogram range of ancillary data was used. Digital data from aerial photographs for different years and EC appear to show a more consistent relation with the soil properties than does yield. Aerial photographs, in particular those of bare soil, seem to be the most useful ancillary data and they are often cheaper to obtain than yield and EC data. More... »

PAGES

261-278

Journal

TITLE

Precision Agriculture

ISSUE

3

VOLUME

4

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1024952406744

DOI

http://dx.doi.org/10.1023/a:1024952406744

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051371616


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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": "University of Reading", 
          "id": "https://www.grid.ac/institutes/grid.9435.b", 
          "name": [
            "Department of Soil Science, The University of Reading, RG6 6DW, Reading, England"
          ], 
          "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, The University of Reading, RG6 6DW, Reading, England"
          ], 
          "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": "sg:pub.10.1023/a:1009917617316", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007889463", 
          "https://doi.org/10.1023/a:1009917617316"
        ], 
        "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": "https://doi.org/10.2134/1995.site-specificmanagement.c35", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088349179"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2134/1997.stateofsitespecific.c1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088349423"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2134/1999.precisionagproc4.c12b", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088349500"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2003-09", 
    "datePublishedReg": "2003-09-01", 
    "description": "To provide reliable estimates for mapping soil properties for precision agriculture requires intensive sampling and costly laboratory analyses. If the spatial structure of ancillary data, such as yield, digital information from aerial photographs, and soil electrical conductivity (EC) measurements, relates to that of soil properties they could be used to guide the sampling intensity for soil surveys. Variograms of permanent soil properties at two study sites on different parent materials were compared with each other and with those for ancillary data. The ranges of spatial dependence identified by the variograms of both sets of properties are of similar orders of magnitude for each study site. Maps of the ancillary data appear to show similar patterns of variation and these seem to relate to those of the permanent properties of the soil. Correlation analysis has confirmed these relations. Maps of kriged estimates from sub-sampled data and the original variograms showed that the main patterns of variation were preserved when a sampling interval of less than half the average variogram range of ancillary data was used. Digital data from aerial photographs for different years and EC appear to show a more consistent relation with the soil properties than does yield. Aerial photographs, in particular those of bare soil, seem to be the most useful ancillary data and they are often cheaper to obtain than yield and EC data.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1023/a:1024952406744", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1135929", 
        "issn": [
          "1385-2256", 
          "1573-1618"
        ], 
        "name": "Precision Agriculture", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "4"
      }
    ], 
    "name": "Variograms of Ancillary Data to Aid Sampling for Soil Surveys", 
    "pagination": "261-278", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "f4b68d969d97fc92da4fbc619abbe5e7302decc44e98e01182a8ca752e08bd79"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1023/a:1024952406744"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1051371616"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1023/a:1024952406744", 
      "https://app.dimensions.ai/details/publication/pub.1051371616"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T16:41", 
    "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_8669_00000508.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1023%2FA%3A1024952406744"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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.10.1023/a:1024952406744'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1023/a:1024952406744'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1023/a:1024952406744'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1023/a:1024952406744'


 

This table displays all metadata directly associated to this object as RDF triples.

85 TRIPLES      21 PREDICATES      32 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1023/a:1024952406744 schema:about anzsrc-for:05
2 anzsrc-for:0503
3 schema:author N64c8fd264687460da755291ebaf624c8
4 schema:citation sg:pub.10.1007/bf02066732
5 sg:pub.10.1023/a:1009917617316
6 https://doi.org/10.2134/1995.site-specificmanagement.c35
7 https://doi.org/10.2134/1997.stateofsitespecific.c1
8 https://doi.org/10.2134/1999.precisionagproc4.c12b
9 schema:datePublished 2003-09
10 schema:datePublishedReg 2003-09-01
11 schema:description To provide reliable estimates for mapping soil properties for precision agriculture requires intensive sampling and costly laboratory analyses. If the spatial structure of ancillary data, such as yield, digital information from aerial photographs, and soil electrical conductivity (EC) measurements, relates to that of soil properties they could be used to guide the sampling intensity for soil surveys. Variograms of permanent soil properties at two study sites on different parent materials were compared with each other and with those for ancillary data. The ranges of spatial dependence identified by the variograms of both sets of properties are of similar orders of magnitude for each study site. Maps of the ancillary data appear to show similar patterns of variation and these seem to relate to those of the permanent properties of the soil. Correlation analysis has confirmed these relations. Maps of kriged estimates from sub-sampled data and the original variograms showed that the main patterns of variation were preserved when a sampling interval of less than half the average variogram range of ancillary data was used. Digital data from aerial photographs for different years and EC appear to show a more consistent relation with the soil properties than does yield. Aerial photographs, in particular those of bare soil, seem to be the most useful ancillary data and they are often cheaper to obtain than yield and EC data.
12 schema:genre research_article
13 schema:inLanguage en
14 schema:isAccessibleForFree false
15 schema:isPartOf N7d83665f99284c29a9b985f137be644c
16 N83d06a932db4470ca77df5b879416411
17 sg:journal.1135929
18 schema:name Variograms of Ancillary Data to Aid Sampling for Soil Surveys
19 schema:pagination 261-278
20 schema:productId N0136e21647f941a7bba60421d6a52c1b
21 N0ae40b4b6bad4094918718295ddd71c6
22 N57ec983974a54851a2658a05ae878c3b
23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051371616
24 https://doi.org/10.1023/a:1024952406744
25 schema:sdDatePublished 2019-04-10T16:41
26 schema:sdLicense https://scigraph.springernature.com/explorer/license/
27 schema:sdPublisher N4e08093c210a4cb28390a26840ee170a
28 schema:url http://link.springer.com/10.1023%2FA%3A1024952406744
29 sgo:license sg:explorer/license/
30 sgo:sdDataset articles
31 rdf:type schema:ScholarlyArticle
32 N0136e21647f941a7bba60421d6a52c1b schema:name dimensions_id
33 schema:value pub.1051371616
34 rdf:type schema:PropertyValue
35 N0ae40b4b6bad4094918718295ddd71c6 schema:name doi
36 schema:value 10.1023/a:1024952406744
37 rdf:type schema:PropertyValue
38 N4e08093c210a4cb28390a26840ee170a schema:name Springer Nature - SN SciGraph project
39 rdf:type schema:Organization
40 N57ec983974a54851a2658a05ae878c3b schema:name readcube_id
41 schema:value f4b68d969d97fc92da4fbc619abbe5e7302decc44e98e01182a8ca752e08bd79
42 rdf:type schema:PropertyValue
43 N64c8fd264687460da755291ebaf624c8 rdf:first sg:person.014421606421.19
44 rdf:rest Na39aba2ae243457eb9e5e978d3c5ac4e
45 N7d83665f99284c29a9b985f137be644c schema:volumeNumber 4
46 rdf:type schema:PublicationVolume
47 N83d06a932db4470ca77df5b879416411 schema:issueNumber 3
48 rdf:type schema:PublicationIssue
49 Na39aba2ae243457eb9e5e978d3c5ac4e rdf:first sg:person.010076772773.20
50 rdf:rest rdf:nil
51 anzsrc-for:05 schema:inDefinedTermSet anzsrc-for:
52 schema:name Environmental Sciences
53 rdf:type schema:DefinedTerm
54 anzsrc-for:0503 schema:inDefinedTermSet anzsrc-for:
55 schema:name Soil Sciences
56 rdf:type schema:DefinedTerm
57 sg:journal.1135929 schema:issn 1385-2256
58 1573-1618
59 schema:name Precision Agriculture
60 rdf:type schema:Periodical
61 sg:person.010076772773.20 schema:affiliation https://www.grid.ac/institutes/grid.9435.b
62 schema:familyName Oliver
63 schema:givenName Margaret A.
64 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010076772773.20
65 rdf:type schema:Person
66 sg:person.014421606421.19 schema:affiliation https://www.grid.ac/institutes/grid.9435.b
67 schema:familyName Kerry
68 schema:givenName Ruth
69 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014421606421.19
70 rdf:type schema:Person
71 sg:pub.10.1007/bf02066732 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046624960
72 https://doi.org/10.1007/bf02066732
73 rdf:type schema:CreativeWork
74 sg:pub.10.1023/a:1009917617316 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007889463
75 https://doi.org/10.1023/a:1009917617316
76 rdf:type schema:CreativeWork
77 https://doi.org/10.2134/1995.site-specificmanagement.c35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088349179
78 rdf:type schema:CreativeWork
79 https://doi.org/10.2134/1997.stateofsitespecific.c1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088349423
80 rdf:type schema:CreativeWork
81 https://doi.org/10.2134/1999.precisionagproc4.c12b schema:sameAs https://app.dimensions.ai/details/publication/pub.1088349500
82 rdf:type schema:CreativeWork
83 https://www.grid.ac/institutes/grid.9435.b schema:alternateName University of Reading
84 schema:name Department of Soil Science, The University of Reading, RG6 6DW, Reading, England
85 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...