Site-specific Approaches to Cotton Insect Control. Sampling and Remote Sensing Analysis Techniques View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2005-10

AUTHORS

J. L. Willers, J. N. Jenkins, W. L. Ladner, P. D. Gerard, D. L. Boykin, K. B. Hood, P. L. McKibben, S. A. Samson, M. M. Bethel

ABSTRACT

When insect population density varies within the same cotton field, estimation of abundance is difficult. Multiple population densities of the same species occur because cotton fields (due to edaphic and environmental effects) are apportioned into various habitats that are colonized at different rates. These various habitats differ temporally in their spatial distributions, exhibiting varying patterns of interspersion, shape and size. Therefore, when sampling multiple population densities without considering the influence of habitat structure, the estimated population mean represents a summary of diverse population distributions having different means and variances. This single estimate of mean abundance can lead to pest management decisions that are incorrect because it may over- or under-estimate pest density in different areas of the field. Delineation of habitat classes is essential in order to make local control decisions. Within large commercial cotton fields, it is too laborious for observers on the ground to map habitat boundaries, but remote sensing can efficiently create geo-referenced, stratified maps of cotton field habitats. By employing these maps, a simple random sampling design and larger sample unit sizes, it is possible to estimate pest abundance in each habitat without large numbers of samples. Estimates of pest abundance by habitat, when supplemented with ecological precepts and consultant/producer experience, provide the basis for spatial approaches to pest control. Using small sample sizes, the integrated sampling methodology maps the spatial abundance of a cotton insect pest across several large cotton fields. More... »

PAGES

431-452

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11119-005-3680-x

DOI

http://dx.doi.org/10.1007/s11119-005-3680-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015443442


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/2008/07", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Agricultural and Veterinary Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0703", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Crop and Pasture Production", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Genetics and Precision Agriculture Research Unit, USDA ARS, Mississippi State, MS, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Genetics and Precision Agriculture Research Unit, USDA ARS, Mississippi State, MS, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Willers", 
        "givenName": "J. L.", 
        "id": "sg:person.015431005375.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015431005375.40"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Genetics and Precision Agriculture Research Unit, USDA ARS, Mississippi State, MS, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Genetics and Precision Agriculture Research Unit, USDA ARS, Mississippi State, MS, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jenkins", 
        "givenName": "J. N.", 
        "id": "sg:person.0665207223.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0665207223.69"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Genetics and Precision Agriculture Research Unit, USDA ARS, Mississippi State, MS, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Genetics and Precision Agriculture Research Unit, USDA ARS, Mississippi State, MS, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ladner", 
        "givenName": "W. L.", 
        "id": "sg:person.012261730201.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012261730201.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Experimental Statistics Unit, Mississippi State, MS, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Experimental Statistics Unit, Mississippi State, MS, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gerard", 
        "givenName": "P. D.", 
        "id": "sg:person.0644130544.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0644130544.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Statistics Unit, USDA ARS, Stoneville, MS, USA", 
          "id": "http://www.grid.ac/institutes/grid.508985.9", 
          "name": [
            "Statistics Unit, USDA ARS, Stoneville, MS, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Boykin", 
        "givenName": "D. L.", 
        "id": "sg:person.0641277333.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0641277333.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Perthshire Farms, Gunnison, MS, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Perthshire Farms, Gunnison, MS, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hood", 
        "givenName": "K. B.", 
        "id": "sg:person.016642573201.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016642573201.03"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "McKibben Ag Services, LLC, Mathiston, MS, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "McKibben Ag Services, LLC, Mathiston, MS, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "McKibben", 
        "givenName": "P. L.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Extension GIS and GeoResources Institute, Mississippi State, MS, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Extension GIS and GeoResources Institute, Mississippi State, MS, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Samson", 
        "givenName": "S. A.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Stennis Space Center, ITD Spectral Visions, MS, USA", 
          "id": "http://www.grid.ac/institutes/grid.419657.8", 
          "name": [
            "Stennis Space Center, ITD Spectral Visions, MS, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bethel", 
        "givenName": "M. M.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-3-662-03978-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052962971", 
          "https://doi.org/10.1007/978-3-662-03978-6"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2005-10", 
    "datePublishedReg": "2005-10-01", 
    "description": "When insect population density varies within the same cotton field, estimation of abundance is difficult. Multiple population densities of the same species occur because cotton fields (due to edaphic and environmental effects) are apportioned into various habitats that are colonized at different rates. These various habitats differ temporally in their spatial distributions, exhibiting varying patterns of interspersion, shape and size. Therefore, when sampling multiple population densities without considering the influence of habitat structure, the estimated population mean represents a summary of diverse population distributions having different means and variances. This single estimate of mean abundance can lead to pest management decisions that are incorrect because it may over- or under-estimate pest density in different areas of the field. Delineation of habitat classes is essential in order to make local control decisions. Within large commercial cotton fields, it is too laborious for observers on the ground to map habitat boundaries, but remote sensing can efficiently create geo-referenced, stratified maps of cotton field habitats. By employing these maps, a simple random sampling design and larger sample unit sizes, it is possible to estimate pest abundance in each habitat without large numbers of samples. Estimates of pest abundance by habitat, when supplemented with ecological precepts and consultant/producer experience, provide the basis for spatial approaches to pest control. Using small sample sizes, the integrated sampling methodology maps the spatial abundance of a cotton insect pest across several large cotton fields.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s11119-005-3680-x", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1135929", 
        "issn": [
          "1385-2256", 
          "1573-1618"
        ], 
        "name": "Precision Agriculture", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "6"
      }
    ], 
    "keywords": [
      "pest abundance", 
      "cotton fields", 
      "patterns of interspersion", 
      "cotton insect control", 
      "cotton insect pests", 
      "insect population density", 
      "commercial cotton fields", 
      "habitat structure", 
      "habitat boundaries", 
      "population density", 
      "pest management decisions", 
      "insect pests", 
      "field habitats", 
      "sample unit size", 
      "habitats", 
      "insect control", 
      "same species", 
      "habitat classes", 
      "pest density", 
      "diverse population distribution", 
      "mean abundance", 
      "multiple populations", 
      "large cotton fields", 
      "abundance", 
      "spatial abundance", 
      "estimation of abundance", 
      "site-specific approach", 
      "random sampling design", 
      "pests", 
      "species", 
      "interspersion", 
      "sampling design", 
      "stratified maps", 
      "population", 
      "different rates", 
      "remote sensing", 
      "simple random sampling design", 
      "management decisions", 
      "large number", 
      "spatial distribution", 
      "spatial approach", 
      "population distribution", 
      "unit size", 
      "producer\u2019s experience", 
      "maps", 
      "size", 
      "different means", 
      "patterns", 
      "distribution", 
      "local control decisions", 
      "control", 
      "different areas", 
      "summary", 
      "basis", 
      "density", 
      "delineation", 
      "structure", 
      "single estimate", 
      "number", 
      "small sample size", 
      "estimates", 
      "area", 
      "class", 
      "geo", 
      "sample size", 
      "sensing", 
      "approach", 
      "variance", 
      "ground", 
      "field", 
      "decisions", 
      "control decisions", 
      "rate", 
      "shape", 
      "samples", 
      "analysis techniques", 
      "influence", 
      "order", 
      "means", 
      "boundaries", 
      "estimation", 
      "observer", 
      "technique", 
      "design", 
      "precepts", 
      "experience"
    ], 
    "name": "Site-specific Approaches to Cotton Insect Control. Sampling and Remote Sensing Analysis Techniques", 
    "pagination": "431-452", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1015443442"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11119-005-3680-x"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11119-005-3680-x", 
      "https://app.dimensions.ai/details/publication/pub.1015443442"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-20T07:23", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_397.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s11119-005-3680-x"
  }
]
 

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.10.1007/s11119-005-3680-x'

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/s11119-005-3680-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11119-005-3680-x'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11119-005-3680-x'


 

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

215 TRIPLES      22 PREDICATES      113 URIs      104 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11119-005-3680-x schema:about anzsrc-for:07
2 anzsrc-for:0703
3 schema:author Na5ad6ba01d7748fd943d226179966ae9
4 schema:citation sg:pub.10.1007/978-3-662-03978-6
5 schema:datePublished 2005-10
6 schema:datePublishedReg 2005-10-01
7 schema:description When insect population density varies within the same cotton field, estimation of abundance is difficult. Multiple population densities of the same species occur because cotton fields (due to edaphic and environmental effects) are apportioned into various habitats that are colonized at different rates. These various habitats differ temporally in their spatial distributions, exhibiting varying patterns of interspersion, shape and size. Therefore, when sampling multiple population densities without considering the influence of habitat structure, the estimated population mean represents a summary of diverse population distributions having different means and variances. This single estimate of mean abundance can lead to pest management decisions that are incorrect because it may over- or under-estimate pest density in different areas of the field. Delineation of habitat classes is essential in order to make local control decisions. Within large commercial cotton fields, it is too laborious for observers on the ground to map habitat boundaries, but remote sensing can efficiently create geo-referenced, stratified maps of cotton field habitats. By employing these maps, a simple random sampling design and larger sample unit sizes, it is possible to estimate pest abundance in each habitat without large numbers of samples. Estimates of pest abundance by habitat, when supplemented with ecological precepts and consultant/producer experience, provide the basis for spatial approaches to pest control. Using small sample sizes, the integrated sampling methodology maps the spatial abundance of a cotton insect pest across several large cotton fields.
8 schema:genre article
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N773e0bf2c9654e3abf4ff1c73dae2a50
12 N8c40cb1bccba4fb6a4ed162eab25f8d4
13 sg:journal.1135929
14 schema:keywords abundance
15 analysis techniques
16 approach
17 area
18 basis
19 boundaries
20 class
21 commercial cotton fields
22 control
23 control decisions
24 cotton fields
25 cotton insect control
26 cotton insect pests
27 decisions
28 delineation
29 density
30 design
31 different areas
32 different means
33 different rates
34 distribution
35 diverse population distribution
36 estimates
37 estimation
38 estimation of abundance
39 experience
40 field
41 field habitats
42 geo
43 ground
44 habitat boundaries
45 habitat classes
46 habitat structure
47 habitats
48 influence
49 insect control
50 insect pests
51 insect population density
52 interspersion
53 large cotton fields
54 large number
55 local control decisions
56 management decisions
57 maps
58 mean abundance
59 means
60 multiple populations
61 number
62 observer
63 order
64 patterns
65 patterns of interspersion
66 pest abundance
67 pest density
68 pest management decisions
69 pests
70 population
71 population density
72 population distribution
73 precepts
74 producer’s experience
75 random sampling design
76 rate
77 remote sensing
78 same species
79 sample size
80 sample unit size
81 samples
82 sampling design
83 sensing
84 shape
85 simple random sampling design
86 single estimate
87 site-specific approach
88 size
89 small sample size
90 spatial abundance
91 spatial approach
92 spatial distribution
93 species
94 stratified maps
95 structure
96 summary
97 technique
98 unit size
99 variance
100 schema:name Site-specific Approaches to Cotton Insect Control. Sampling and Remote Sensing Analysis Techniques
101 schema:pagination 431-452
102 schema:productId N79f5921cc0a942e596f9f53956f82a5b
103 N8e49e75e2bf94265a0bb77775005e230
104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015443442
105 https://doi.org/10.1007/s11119-005-3680-x
106 schema:sdDatePublished 2022-05-20T07:23
107 schema:sdLicense https://scigraph.springernature.com/explorer/license/
108 schema:sdPublisher N440db4db15c040ba9264310cd6c54303
109 schema:url https://doi.org/10.1007/s11119-005-3680-x
110 sgo:license sg:explorer/license/
111 sgo:sdDataset articles
112 rdf:type schema:ScholarlyArticle
113 N064e4669a0d443828718ca369b591b79 rdf:first sg:person.0665207223.69
114 rdf:rest Nd91e8f81ca974afbb2e1346b7d611c62
115 N440db4db15c040ba9264310cd6c54303 schema:name Springer Nature - SN SciGraph project
116 rdf:type schema:Organization
117 N51b5bab5a96e46c9bd45bc1f06f68f1e rdf:first sg:person.0641277333.33
118 rdf:rest Nf66dcb563987494ebd1515f62c5d9c07
119 N6623ca87f2144beead385fb143d4483d rdf:first Na7bcdef2345b493d8c0098947adf1162
120 rdf:rest Nc0afe164ed0a4c229b2d4734e2f999ea
121 N773e0bf2c9654e3abf4ff1c73dae2a50 schema:issueNumber 5
122 rdf:type schema:PublicationIssue
123 N79f5921cc0a942e596f9f53956f82a5b schema:name doi
124 schema:value 10.1007/s11119-005-3680-x
125 rdf:type schema:PropertyValue
126 N8155b34e95f34b1cb63bb7fcbb6d1fe2 schema:affiliation grid-institutes:grid.419657.8
127 schema:familyName Bethel
128 schema:givenName M. M.
129 rdf:type schema:Person
130 N8c40cb1bccba4fb6a4ed162eab25f8d4 schema:volumeNumber 6
131 rdf:type schema:PublicationVolume
132 N8e49e75e2bf94265a0bb77775005e230 schema:name dimensions_id
133 schema:value pub.1015443442
134 rdf:type schema:PropertyValue
135 Na5ad6ba01d7748fd943d226179966ae9 rdf:first sg:person.015431005375.40
136 rdf:rest N064e4669a0d443828718ca369b591b79
137 Na7bcdef2345b493d8c0098947adf1162 schema:affiliation grid-institutes:None
138 schema:familyName McKibben
139 schema:givenName P. L.
140 rdf:type schema:Person
141 Nb1aca1c9644942af96ac402b3ccea2a4 schema:affiliation grid-institutes:None
142 schema:familyName Samson
143 schema:givenName S. A.
144 rdf:type schema:Person
145 Nc0afe164ed0a4c229b2d4734e2f999ea rdf:first Nb1aca1c9644942af96ac402b3ccea2a4
146 rdf:rest Nd0588ddd718d4627b4785abb7153d395
147 Nc81b42b8116241749cb402a91f412cef rdf:first sg:person.0644130544.49
148 rdf:rest N51b5bab5a96e46c9bd45bc1f06f68f1e
149 Nd0588ddd718d4627b4785abb7153d395 rdf:first N8155b34e95f34b1cb63bb7fcbb6d1fe2
150 rdf:rest rdf:nil
151 Nd91e8f81ca974afbb2e1346b7d611c62 rdf:first sg:person.012261730201.76
152 rdf:rest Nc81b42b8116241749cb402a91f412cef
153 Nf66dcb563987494ebd1515f62c5d9c07 rdf:first sg:person.016642573201.03
154 rdf:rest N6623ca87f2144beead385fb143d4483d
155 anzsrc-for:07 schema:inDefinedTermSet anzsrc-for:
156 schema:name Agricultural and Veterinary Sciences
157 rdf:type schema:DefinedTerm
158 anzsrc-for:0703 schema:inDefinedTermSet anzsrc-for:
159 schema:name Crop and Pasture Production
160 rdf:type schema:DefinedTerm
161 sg:journal.1135929 schema:issn 1385-2256
162 1573-1618
163 schema:name Precision Agriculture
164 schema:publisher Springer Nature
165 rdf:type schema:Periodical
166 sg:person.012261730201.76 schema:affiliation grid-institutes:None
167 schema:familyName Ladner
168 schema:givenName W. L.
169 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012261730201.76
170 rdf:type schema:Person
171 sg:person.015431005375.40 schema:affiliation grid-institutes:None
172 schema:familyName Willers
173 schema:givenName J. L.
174 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015431005375.40
175 rdf:type schema:Person
176 sg:person.016642573201.03 schema:affiliation grid-institutes:None
177 schema:familyName Hood
178 schema:givenName K. B.
179 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016642573201.03
180 rdf:type schema:Person
181 sg:person.0641277333.33 schema:affiliation grid-institutes:grid.508985.9
182 schema:familyName Boykin
183 schema:givenName D. L.
184 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0641277333.33
185 rdf:type schema:Person
186 sg:person.0644130544.49 schema:affiliation grid-institutes:None
187 schema:familyName Gerard
188 schema:givenName P. D.
189 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0644130544.49
190 rdf:type schema:Person
191 sg:person.0665207223.69 schema:affiliation grid-institutes:None
192 schema:familyName Jenkins
193 schema:givenName J. N.
194 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0665207223.69
195 rdf:type schema:Person
196 sg:pub.10.1007/978-3-662-03978-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052962971
197 https://doi.org/10.1007/978-3-662-03978-6
198 rdf:type schema:CreativeWork
199 grid-institutes:None schema:alternateName Experimental Statistics Unit, Mississippi State, MS, USA
200 Extension GIS and GeoResources Institute, Mississippi State, MS, USA
201 Genetics and Precision Agriculture Research Unit, USDA ARS, Mississippi State, MS, USA
202 McKibben Ag Services, LLC, Mathiston, MS, USA
203 Perthshire Farms, Gunnison, MS, USA
204 schema:name Experimental Statistics Unit, Mississippi State, MS, USA
205 Extension GIS and GeoResources Institute, Mississippi State, MS, USA
206 Genetics and Precision Agriculture Research Unit, USDA ARS, Mississippi State, MS, USA
207 McKibben Ag Services, LLC, Mathiston, MS, USA
208 Perthshire Farms, Gunnison, MS, USA
209 rdf:type schema:Organization
210 grid-institutes:grid.419657.8 schema:alternateName Stennis Space Center, ITD Spectral Visions, MS, USA
211 schema:name Stennis Space Center, ITD Spectral Visions, MS, USA
212 rdf:type schema:Organization
213 grid-institutes:grid.508985.9 schema:alternateName Statistics Unit, USDA ARS, Stoneville, MS, USA
214 schema:name Statistics Unit, USDA ARS, Stoneville, MS, USA
215 rdf:type schema:Organization
 




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


...