Research on Prediction Model and Characteristic Parameters on Dry Matter Accumulation in Wheat Based on Normalized Method and Grey System View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Juan Liu , Xiaoli Zhao , Shuping Xiong , Xinming Ma , Yanfeng Wang , Jing Wang

ABSTRACT

To investigate a model to simulate wheat dry matter accumulation, three wheat cultivars with different tillering abilities were grown at three densities each in a field experiment. Five simulation models with high correlation coefficients for relative dry matter accumulation were established by the method of normalized. Among these models, the Richards equation was the best in fitting and forecasting, i.e., y = 1.1435/(1 + e0.2776 − 4.6558x )1/0.1130, r = 0.9927. Correlation coefficient of grey comprehensive relationship degree between actual dry matter accumulation in occurrence time of maximum rate of dry matter accumulation and dry matter weight was highest, so a higher actual dry matter weight in occurrence time of maximum rate of dry matter accumulation played an important role in stabilizing and improving dry matter weight of wheat. More... »

PAGES

142-149

References to SciGraph publications

Book

TITLE

Computer and Computing Technologies in Agriculture VI

ISBN

978-3-642-36136-4
978-3-642-36137-1

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-36137-1_18

DOI

http://dx.doi.org/10.1007/978-3-642-36137-1_18

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015684899


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/2008/1402", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Applied Economics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/14", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Economics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Henan Agricultural University", 
          "id": "https://www.grid.ac/institutes/grid.108266.b", 
          "name": [
            "Key Laboratory of Physiology, Ecology and Genetic Improvement of Food Crops in Henan Province, College of Agronomy, Henan Agriculture University, Zhengzhou, Henan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Juan", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Henan Agricultural University", 
          "id": "https://www.grid.ac/institutes/grid.108266.b", 
          "name": [
            "College of Information and Management Science, Henan Agriculture University, Zhengzhou, Henan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhao", 
        "givenName": "Xiaoli", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Henan Agricultural University", 
          "id": "https://www.grid.ac/institutes/grid.108266.b", 
          "name": [
            "Key Laboratory of Physiology, Ecology and Genetic Improvement of Food Crops in Henan Province, College of Agronomy, Henan Agriculture University, Zhengzhou, Henan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xiong", 
        "givenName": "Shuping", 
        "id": "sg:person.013424471313.99", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013424471313.99"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Henan Agricultural University", 
          "id": "https://www.grid.ac/institutes/grid.108266.b", 
          "name": [
            "Key Laboratory of Physiology, Ecology and Genetic Improvement of Food Crops in Henan Province, College of Agronomy, Henan Agriculture University, Zhengzhou, Henan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ma", 
        "givenName": "Xinming", 
        "id": "sg:person.010313654213.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010313654213.27"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Henan Agricultural University", 
          "id": "https://www.grid.ac/institutes/grid.108266.b", 
          "name": [
            "Key Laboratory of Physiology, Ecology and Genetic Improvement of Food Crops in Henan Province, College of Agronomy, Henan Agriculture University, Zhengzhou, Henan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Yanfeng", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Henan Agricultural University", 
          "id": "https://www.grid.ac/institutes/grid.108266.b", 
          "name": [
            "Key Laboratory of Physiology, Ecology and Genetic Improvement of Food Crops in Henan Province, College of Agronomy, Henan Agriculture University, Zhengzhou, Henan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Jing", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.fcr.2008.06.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003334044"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1010275701", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-011-0750-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010275701", 
          "https://doi.org/10.1007/978-94-011-0750-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-011-0750-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010275701", 
          "https://doi.org/10.1007/978-94-011-0750-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.fcr.2007.03.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016650437"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/s0021859697004668", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053775654"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/s0021859697004668", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053775654"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3724/sp.j.1006.2009.02258", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071318986"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3724/sp.j.1006.2010.02143", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071319191"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2013", 
    "datePublishedReg": "2013-01-01", 
    "description": "To investigate a model to simulate wheat dry matter accumulation, three wheat cultivars with different tillering abilities were grown at three densities each in a field experiment. Five simulation models with high correlation coefficients for relative dry matter accumulation were established by the method of normalized. Among these models, the Richards equation was the best in fitting and forecasting, i.e., y\u2009=\u20091.1435/(1\u2009+\u2009e0.2776\u2009\u2212\u20094.6558x )1/0.1130, r\u2009=\u20090.9927. Correlation coefficient of grey comprehensive relationship degree between actual dry matter accumulation in occurrence time of maximum rate of dry matter accumulation and dry matter weight was highest, so a higher actual dry matter weight in occurrence time of maximum rate of dry matter accumulation played an important role in stabilizing and improving dry matter weight of wheat.", 
    "editor": [
      {
        "familyName": "Li", 
        "givenName": "Daoliang", 
        "type": "Person"
      }, 
      {
        "familyName": "Chen", 
        "givenName": "Yingyi", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-36137-1_18", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-36136-4", 
        "978-3-642-36137-1"
      ], 
      "name": "Computer and Computing Technologies in Agriculture VI", 
      "type": "Book"
    }, 
    "name": "Research on Prediction Model and Characteristic Parameters on Dry Matter Accumulation in Wheat Based on Normalized Method and Grey System", 
    "pagination": "142-149", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-36137-1_18"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "4abb52b4890c7ba582d04a5d44049a58bb24018e133f76763ce6aa438b2c608e"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1015684899"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-36137-1_18", 
      "https://app.dimensions.ai/details/publication/pub.1015684899"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T21:01", 
    "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_8690_00000252.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-642-36137-1_18"
  }
]
 

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.10.1007/978-3-642-36137-1_18'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-36137-1_18'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-36137-1_18'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-36137-1_18'


 

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

123 TRIPLES      23 PREDICATES      34 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-36137-1_18 schema:about anzsrc-for:14
2 anzsrc-for:1402
3 schema:author Nc27b1460239e489685dfde660268d518
4 schema:citation sg:pub.10.1007/978-94-011-0750-1
5 https://app.dimensions.ai/details/publication/pub.1010275701
6 https://doi.org/10.1016/j.fcr.2007.03.009
7 https://doi.org/10.1016/j.fcr.2008.06.011
8 https://doi.org/10.1017/s0021859697004668
9 https://doi.org/10.3724/sp.j.1006.2009.02258
10 https://doi.org/10.3724/sp.j.1006.2010.02143
11 schema:datePublished 2013
12 schema:datePublishedReg 2013-01-01
13 schema:description To investigate a model to simulate wheat dry matter accumulation, three wheat cultivars with different tillering abilities were grown at three densities each in a field experiment. Five simulation models with high correlation coefficients for relative dry matter accumulation were established by the method of normalized. Among these models, the Richards equation was the best in fitting and forecasting, i.e., y = 1.1435/(1 + e0.2776 − 4.6558x )1/0.1130, r = 0.9927. Correlation coefficient of grey comprehensive relationship degree between actual dry matter accumulation in occurrence time of maximum rate of dry matter accumulation and dry matter weight was highest, so a higher actual dry matter weight in occurrence time of maximum rate of dry matter accumulation played an important role in stabilizing and improving dry matter weight of wheat.
14 schema:editor N15f47366b87942968d4f7b05f4a75d44
15 schema:genre chapter
16 schema:inLanguage en
17 schema:isAccessibleForFree false
18 schema:isPartOf Na51643713038478d9931b0333575afa0
19 schema:name Research on Prediction Model and Characteristic Parameters on Dry Matter Accumulation in Wheat Based on Normalized Method and Grey System
20 schema:pagination 142-149
21 schema:productId N13d66e187d434e53a6f76978e512e1d7
22 N6b391f675a7f454a88b6d1d079838b45
23 Nbeb250c87cb24a089e4b727c89d117e4
24 schema:publisher Nb439da3ed051434392a0fa78b0697423
25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015684899
26 https://doi.org/10.1007/978-3-642-36137-1_18
27 schema:sdDatePublished 2019-04-15T21:01
28 schema:sdLicense https://scigraph.springernature.com/explorer/license/
29 schema:sdPublisher N66b58ed910a944deab3e1a89d680e0b9
30 schema:url http://link.springer.com/10.1007/978-3-642-36137-1_18
31 sgo:license sg:explorer/license/
32 sgo:sdDataset chapters
33 rdf:type schema:Chapter
34 N0033c486c82f44329ad205987203e6d7 rdf:first sg:person.013424471313.99
35 rdf:rest Nc81746eb82ca4d9cb7d460e22239c33a
36 N13d66e187d434e53a6f76978e512e1d7 schema:name doi
37 schema:value 10.1007/978-3-642-36137-1_18
38 rdf:type schema:PropertyValue
39 N15f47366b87942968d4f7b05f4a75d44 rdf:first Ne3960af615be4d20b368adc90db8d152
40 rdf:rest N19fcd1f9b89d40e893c5ad48e7db9d18
41 N19fcd1f9b89d40e893c5ad48e7db9d18 rdf:first N3ae7e3dfbae541ffbedb810e6dc2e60b
42 rdf:rest rdf:nil
43 N3ae7e3dfbae541ffbedb810e6dc2e60b schema:familyName Chen
44 schema:givenName Yingyi
45 rdf:type schema:Person
46 N66b58ed910a944deab3e1a89d680e0b9 schema:name Springer Nature - SN SciGraph project
47 rdf:type schema:Organization
48 N683a4a752d1e4f8fb98e5b9bdf2015c8 schema:affiliation https://www.grid.ac/institutes/grid.108266.b
49 schema:familyName Liu
50 schema:givenName Juan
51 rdf:type schema:Person
52 N685e44be05914311807195e0f1d86b59 rdf:first Ne38fd9c89631498bb07662fc7641d46f
53 rdf:rest rdf:nil
54 N6b391f675a7f454a88b6d1d079838b45 schema:name dimensions_id
55 schema:value pub.1015684899
56 rdf:type schema:PropertyValue
57 N7d94a235de2a49aaa6d00c68ae51be68 rdf:first Nc1bc957770894141ac32abca3cfaf6c5
58 rdf:rest N0033c486c82f44329ad205987203e6d7
59 Na51643713038478d9931b0333575afa0 schema:isbn 978-3-642-36136-4
60 978-3-642-36137-1
61 schema:name Computer and Computing Technologies in Agriculture VI
62 rdf:type schema:Book
63 Na593591ea9d84729bece0d11b7c35126 rdf:first Nc56215e3276c4ece97a45b62851dfa8b
64 rdf:rest N685e44be05914311807195e0f1d86b59
65 Nb439da3ed051434392a0fa78b0697423 schema:location Berlin, Heidelberg
66 schema:name Springer Berlin Heidelberg
67 rdf:type schema:Organisation
68 Nbeb250c87cb24a089e4b727c89d117e4 schema:name readcube_id
69 schema:value 4abb52b4890c7ba582d04a5d44049a58bb24018e133f76763ce6aa438b2c608e
70 rdf:type schema:PropertyValue
71 Nc1bc957770894141ac32abca3cfaf6c5 schema:affiliation https://www.grid.ac/institutes/grid.108266.b
72 schema:familyName Zhao
73 schema:givenName Xiaoli
74 rdf:type schema:Person
75 Nc27b1460239e489685dfde660268d518 rdf:first N683a4a752d1e4f8fb98e5b9bdf2015c8
76 rdf:rest N7d94a235de2a49aaa6d00c68ae51be68
77 Nc56215e3276c4ece97a45b62851dfa8b schema:affiliation https://www.grid.ac/institutes/grid.108266.b
78 schema:familyName Wang
79 schema:givenName Yanfeng
80 rdf:type schema:Person
81 Nc81746eb82ca4d9cb7d460e22239c33a rdf:first sg:person.010313654213.27
82 rdf:rest Na593591ea9d84729bece0d11b7c35126
83 Ne38fd9c89631498bb07662fc7641d46f schema:affiliation https://www.grid.ac/institutes/grid.108266.b
84 schema:familyName Wang
85 schema:givenName Jing
86 rdf:type schema:Person
87 Ne3960af615be4d20b368adc90db8d152 schema:familyName Li
88 schema:givenName Daoliang
89 rdf:type schema:Person
90 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
91 schema:name Economics
92 rdf:type schema:DefinedTerm
93 anzsrc-for:1402 schema:inDefinedTermSet anzsrc-for:
94 schema:name Applied Economics
95 rdf:type schema:DefinedTerm
96 sg:person.010313654213.27 schema:affiliation https://www.grid.ac/institutes/grid.108266.b
97 schema:familyName Ma
98 schema:givenName Xinming
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010313654213.27
100 rdf:type schema:Person
101 sg:person.013424471313.99 schema:affiliation https://www.grid.ac/institutes/grid.108266.b
102 schema:familyName Xiong
103 schema:givenName Shuping
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013424471313.99
105 rdf:type schema:Person
106 sg:pub.10.1007/978-94-011-0750-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010275701
107 https://doi.org/10.1007/978-94-011-0750-1
108 rdf:type schema:CreativeWork
109 https://app.dimensions.ai/details/publication/pub.1010275701 schema:CreativeWork
110 https://doi.org/10.1016/j.fcr.2007.03.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016650437
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1016/j.fcr.2008.06.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003334044
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1017/s0021859697004668 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053775654
115 rdf:type schema:CreativeWork
116 https://doi.org/10.3724/sp.j.1006.2009.02258 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071318986
117 rdf:type schema:CreativeWork
118 https://doi.org/10.3724/sp.j.1006.2010.02143 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071319191
119 rdf:type schema:CreativeWork
120 https://www.grid.ac/institutes/grid.108266.b schema:alternateName Henan Agricultural University
121 schema:name College of Information and Management Science, Henan Agriculture University, Zhengzhou, Henan, China
122 Key Laboratory of Physiology, Ecology and Genetic Improvement of Food Crops in Henan Province, College of Agronomy, Henan Agriculture University, Zhengzhou, Henan, China
123 rdf:type schema:Organization
 




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


...