Regression modeling nitrogen fertilization requirement for maize crop by combining spectral reflectance and agronomic efficiency

This study aimed to develop a novel regression model for prescription of required nitrogen (N) for maize by combining spectral reflectance data from GreenSeeker sensor and agronomic efficiency. For this end, two field trials at two sites were carried out with maize (Zea mays L.) on Oxisols under no-...

Full description

Saved in:
Bibliographic Details
Published inJournal of plant nutrition Vol. 43; no. 14; pp. 2152 - 2163
Main Authors Kapp-Junior, Cláudio, Caires, Eduardo Fávero, Guimarães, Alaine Margarete, Auler, André Carlos
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 26.08.2020
Taylor & Francis Ltd
Subjects
Online AccessGet full text
ISSN0190-4167
1532-4087
1532-4087
DOI10.1080/01904167.2020.1766074

Cover

Loading…
Abstract This study aimed to develop a novel regression model for prescription of required nitrogen (N) for maize by combining spectral reflectance data from GreenSeeker sensor and agronomic efficiency. For this end, two field trials at two sites were carried out with maize (Zea mays L.) on Oxisols under no-till systems in Southern Brazil. For both experiments, a randomized complete block design, with three replications in a split-plot arrangement, was used. The main plots consisted of four urea-N rates at sowing (0, 20, 40, and 60 kg N ha −1 ) and subplots included four urea-N rates in top dressing (0, 80, 160, and 240 kg N ha −1 ). Based on the results, a novel model for prescription of N rate in top dressing for maize was defined. The increases in maize yields with the N rates estimated by the model (16% to 39%) were similar to that obtained with the N rates used for maximum economic yield (15% to 42%). The estimated N rate by the model provided an economic return 36.5% and 7.0% higher than the N rate for maximum yield, and only 6.6% and 2.0% lower than the N rate for maximum economic yield, at sites 1 and 2, respectively. Thus, the economic return obtained using the model was closer to that reached with the N rate for maximum economic yield than that for maximum yield. Therefore, the developed model, combining spectral reflectance and agronomic efficiency, exhibited great potential to improve the maize N fertilization efficiency.
AbstractList This study aimed to develop a novel regression model for prescription of required nitrogen (N) for maize by combining spectral reflectance data from GreenSeeker sensor and agronomic efficiency. For this end, two field trials at two sites were carried out with maize (Zea mays L.) on Oxisols under no-till systems in Southern Brazil. For both experiments, a randomized complete block design, with three replications in a split-plot arrangement, was used. The main plots consisted of four urea-N rates at sowing (0, 20, 40, and 60 kg N ha −1 ) and subplots included four urea-N rates in top dressing (0, 80, 160, and 240 kg N ha −1 ). Based on the results, a novel model for prescription of N rate in top dressing for maize was defined. The increases in maize yields with the N rates estimated by the model (16% to 39%) were similar to that obtained with the N rates used for maximum economic yield (15% to 42%). The estimated N rate by the model provided an economic return 36.5% and 7.0% higher than the N rate for maximum yield, and only 6.6% and 2.0% lower than the N rate for maximum economic yield, at sites 1 and 2, respectively. Thus, the economic return obtained using the model was closer to that reached with the N rate for maximum economic yield than that for maximum yield. Therefore, the developed model, combining spectral reflectance and agronomic efficiency, exhibited great potential to improve the maize N fertilization efficiency.
This study aimed to develop a novel regression model for prescription of required nitrogen (N) for maize by combining spectral reflectance data from GreenSeeker sensor and agronomic efficiency. For this end, two field trials at two sites were carried out with maize (Zea mays L.) on Oxisols under no-till systems in Southern Brazil. For both experiments, a randomized complete block design, with three replications in a split-plot arrangement, was used. The main plots consisted of four urea-N rates at sowing (0, 20, 40, and 60 kg N ha⁻¹) and subplots included four urea-N rates in top dressing (0, 80, 160, and 240 kg N ha⁻¹). Based on the results, a novel model for prescription of N rate in top dressing for maize was defined. The increases in maize yields with the N rates estimated by the model (16% to 39%) were similar to that obtained with the N rates used for maximum economic yield (15% to 42%). The estimated N rate by the model provided an economic return 36.5% and 7.0% higher than the N rate for maximum yield, and only 6.6% and 2.0% lower than the N rate for maximum economic yield, at sites 1 and 2, respectively. Thus, the economic return obtained using the model was closer to that reached with the N rate for maximum economic yield than that for maximum yield. Therefore, the developed model, combining spectral reflectance and agronomic efficiency, exhibited great potential to improve the maize N fertilization efficiency.
This study aimed to develop a novel regression model for prescription of required nitrogen (N) for maize by combining spectral reflectance data from GreenSeeker sensor and agronomic efficiency. For this end, two field trials at two sites were carried out with maize (Zea mays L.) on Oxisols under no-till systems in Southern Brazil. For both experiments, a randomized complete block design, with three replications in a split-plot arrangement, was used. The main plots consisted of four urea-N rates at sowing (0, 20, 40, and 60 kg N ha−1) and subplots included four urea-N rates in top dressing (0, 80, 160, and 240 kg N ha−1). Based on the results, a novel model for prescription of N rate in top dressing for maize was defined. The increases in maize yields with the N rates estimated by the model (16% to 39%) were similar to that obtained with the N rates used for maximum economic yield (15% to 42%). The estimated N rate by the model provided an economic return 36.5% and 7.0% higher than the N rate for maximum yield, and only 6.6% and 2.0% lower than the N rate for maximum economic yield, at sites 1 and 2, respectively. Thus, the economic return obtained using the model was closer to that reached with the N rate for maximum economic yield than that for maximum yield. Therefore, the developed model, combining spectral reflectance and agronomic efficiency, exhibited great potential to improve the maize N fertilization efficiency.
Author Kapp-Junior, Cláudio
Caires, Eduardo Fávero
Guimarães, Alaine Margarete
Auler, André Carlos
Author_xml – sequence: 1
  givenname: Cláudio
  surname: Kapp-Junior
  fullname: Kapp-Junior, Cláudio
  organization: Department of Rural Economy, ABC Foundation Agricultural Research and Development
– sequence: 2
  givenname: Eduardo Fávero
  surname: Caires
  fullname: Caires, Eduardo Fávero
  email: efcaires@uepg.br
– sequence: 3
  givenname: Alaine Margarete
  surname: Guimarães
  fullname: Guimarães, Alaine Margarete
  organization: Department of Computer Science, State University of Ponta Grossa
– sequence: 4
  givenname: André Carlos
  surname: Auler
  fullname: Auler, André Carlos
  organization: Department of Soils and Agricultural Engineering, Federal University of Paraná
BookMark eNqFkU9rHSEUxaWk0Jc0H6EgdNPNJP6Z0ZFuWkLbFAKFkqzFca4Pg6Mv6qO8rPrR6-SlmyzalaK_c7nnnFN0ElMEhN5RckHJSC4JVaSnQl4wwtqTFILI_hXa0IGzriejPEGblelW6A06LeWeEKLIQDfo90_YZijFp4iXNEPwcYujrzltIWIHufrgH01d_zM87H2GBWLFLmW8GP8I2Oa0w9MB27RMPq7ysgNbswlN4EK7mmgBmzhjs80ppsVbDM556yHaw1v02plQ4Pz5PEN3X7_cXl13Nz--fb_6fNNZLmntlAQ1zxN3YMU4jYJNoNgoB8J5P8kZBBgpmlU5CDcwomTPRw4zTIqrkQjBz9CH49xdTg97KFUvvlgIwURI-6LZwHrOlGJDQ9-_QO_TPse2nWY9VaqNI6xRH49UC6CUZlVbX5-Cat590JTotR39tx29tqOf22nq4YV6l_1i8uG_uk9HnY-tgsX8SjnMuppDSNnllrQvmv97xB-SYqo9
CitedBy_id crossref_primary_10_1016_j_compag_2022_107479
crossref_primary_10_3390_rs12172741
Cites_doi 10.1127/0941-2948/2013/0507
10.1080/00103620701549157
10.2134/agronj2005.0005
10.1016/j.fcr.2018.01.007
10.1590/S0103-84782003000500002
10.1590/S0100-06832009000600021
10.1108/02602280510606499
10.2134/agronj2016.01.0041
10.1007/s11119-010-9210-5
10.1080/01904167.2018.1434202
10.1007/s13593-014-0207-8
10.2134/agronj1991.00021962008300010015x
10.1590/01000683rbcs20140686
10.1007/s13593-015-0296-z
10.1590/S0100-69162013000100018
10.2134/agronj2002.8150
10.1590/0034-737X201663010014
10.1007/s00374-013-0863-x
10.2134/agronj2011.0249
10.1080/00103620500303988
10.1007/s11119-010-9196-z
10.2134/agronj2010.0015
10.1016/j.still.2014.10.011
10.1016/j.still.2016.12.007
10.1590/S0100-204X2008000800018
10.1016/j.ecolind.2015.08.023
10.1016/j.fcr.2016.08.013
10.2134/agronj2004.1572
10.3390/s151127832
10.1016/j.compag.2013.10.007
10.1590/S0100-06832013000500018
10.1016/S0065-2113(05)88004-6
ContentType Journal Article
Copyright 2020 Taylor & Francis Group, LLC 2020
2020 Taylor & Francis Group, LLC
Copyright_xml – notice: 2020 Taylor & Francis Group, LLC 2020
– notice: 2020 Taylor & Francis Group, LLC
DBID AAYXX
CITATION
7ST
7T7
8FD
C1K
FR3
P64
SOI
7S9
L.6
DOI 10.1080/01904167.2020.1766074
DatabaseName CrossRef
Environment Abstracts
Industrial and Applied Microbiology Abstracts (Microbiology A)
Technology Research Database
Environmental Sciences and Pollution Management
Engineering Research Database
Biotechnology and BioEngineering Abstracts
Environment Abstracts
AGRICOLA
AGRICOLA - Academic
DatabaseTitle CrossRef
Engineering Research Database
Technology Research Database
Industrial and Applied Microbiology Abstracts (Microbiology A)
Environment Abstracts
Biotechnology and BioEngineering Abstracts
Environmental Sciences and Pollution Management
AGRICOLA
AGRICOLA - Academic
DatabaseTitleList
AGRICOLA
Engineering Research Database
DeliveryMethod fulltext_linktorsrc
Discipline Botany
Economics
EISSN 1532-4087
EndPage 2163
ExternalDocumentID 10_1080_01904167_2020_1766074
1766074
Genre Article
GeographicLocations Brazil
GeographicLocations_xml – name: Brazil
GrantInformation_xml – fundername: CNPq
  grantid: 69/; -0
GroupedDBID .7F
.QJ
0BK
0R~
29L
30N
4.4
53G
5GY
5VS
AAENE
AAHBH
AAJMT
AALDU
AAMIU
AAPUL
AAQRR
ABCCY
ABFIM
ABHAV
ABJNI
ABLIJ
ABPAQ
ABPEM
ABTAI
ABXUL
ABXYU
ACGEJ
ACGFS
ACPRK
ACTIO
ADCVX
ADGTB
ADXPE
AEISY
AENEX
AEOZL
AEPSL
AEYOC
AFKVX
AFRAH
AGDLA
AGMYJ
AHDZW
AIJEM
AJWEG
AKBVH
AKOOK
ALMA_UNASSIGNED_HOLDINGS
ALQZU
AQRUH
AVBZW
AWYRJ
BLEHA
CCCUG
CE4
CS3
DGEBU
DKSSO
DU5
EBS
ECGQY
E~A
E~B
F5P
GTTXZ
H13
HF~
HZ~
H~P
IPNFZ
J.P
KYCEM
L7B
LJTGL
M4Z
NA5
NX0
O9-
P2P
RIG
RNANH
ROSJB
RTWRZ
S-T
SNACF
TBQAZ
TDBHL
TEI
TFL
TFT
TFW
TQWBC
TTHFI
TUROJ
TWF
TWZ
UT5
UU3
ZGOLN
~KM
~S~
AAGDL
AAHIA
AAYXX
ADYSH
AFRVT
AIYEW
AMPGV
CITATION
7ST
7T7
8FD
C1K
FR3
P64
SOI
TASJS
7S9
L.6
ID FETCH-LOGICAL-c371t-97e9ddb3fec68b862be928750334b7de6ea76087756f520974383edeb93980663
ISSN 0190-4167
1532-4087
IngestDate Wed Jul 02 04:35:36 EDT 2025
Wed Aug 13 11:29:57 EDT 2025
Tue Jul 01 02:51:45 EDT 2025
Thu Apr 24 23:12:25 EDT 2025
Wed Dec 25 09:07:57 EST 2024
IsPeerReviewed true
IsScholarly true
Issue 14
Language English
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c371t-97e9ddb3fec68b862be928750334b7de6ea76087756f520974383edeb93980663
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
PQID 2419906602
PQPubID 196160
PageCount 12
ParticipantIDs crossref_citationtrail_10_1080_01904167_2020_1766074
crossref_primary_10_1080_01904167_2020_1766074
proquest_miscellaneous_2524329925
proquest_journals_2419906602
informaworld_taylorfrancis_310_1080_01904167_2020_1766074
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2020-08-26
PublicationDateYYYYMMDD 2020-08-26
PublicationDate_xml – month: 08
  year: 2020
  text: 2020-08-26
  day: 26
PublicationDecade 2020
PublicationPlace Philadelphia
PublicationPlace_xml – name: Philadelphia
PublicationTitle Journal of plant nutrition
PublicationYear 2020
Publisher Taylor & Francis
Taylor & Francis Ltd
Publisher_xml – name: Taylor & Francis
– name: Taylor & Francis Ltd
References CIT0030
CIT0010
R Foundation for Statistical Computing (CIT0027) 2016
CIT0031
CIT0012
CIT0011
CIT0033
CIT0014
CIT0036
CIT0013
CIT0035
CIT0016
CIT0015
CIT0037
CIT0018
CIT0017
CIT0019
CIT0021
CIT0020
CIT0001
CIT0023
CIT0022
Soil Survey Staff (CIT0034) 2013
Alvarez V. V. H. (CIT0003) 1994
CIT0025
CIT0002
CIT0024
CIT0005
CIT0004
CIT0026
CIT0007
CIT0029
CIT0006
CIT0028
CIT0009
CIT0008
References_xml – ident: CIT0002
  doi: 10.1127/0941-2948/2013/0507
– ident: CIT0020
  doi: 10.1080/00103620701549157
– volume-title: Simplified guide to soil taxonomy
  year: 2013
  ident: CIT0034
– ident: CIT0024
  doi: 10.2134/agronj2005.0005
– volume-title: Avaliação da fertilidade do solo: Superfície de respostas – modelos aproximativos para expressar a relação fator-resposta. [Evaluation of soil fertility: Response surface - approximate models to express the factor-response relationship]
  year: 1994
  ident: CIT0003
– ident: CIT0012
  doi: 10.1016/j.fcr.2018.01.007
– ident: CIT0013
  doi: 10.1590/S0103-84782003000500002
– ident: CIT0015
  doi: 10.1590/S0100-06832009000600021
– ident: CIT0021
  doi: 10.1108/02602280510606499
– ident: CIT0016
  doi: 10.2134/agronj2016.01.0041
– ident: CIT0036
  doi: 10.1007/s11119-010-9210-5
– ident: CIT0010
  doi: 10.1080/01904167.2018.1434202
– ident: CIT0011
  doi: 10.1007/s13593-014-0207-8
– ident: CIT0001
  doi: 10.2134/agronj1991.00021962008300010015x
– ident: CIT0031
  doi: 10.1590/01000683rbcs20140686
– ident: CIT0006
  doi: 10.1007/s13593-015-0296-z
– ident: CIT0009
  doi: 10.1590/S0100-69162013000100018
– ident: CIT0028
  doi: 10.2134/agronj2002.8150
– volume-title: R: A language and environment for statistical computing
  year: 2016
  ident: CIT0027
– ident: CIT0007
  doi: 10.1590/0034-737X201663010014
– ident: CIT0018
  doi: 10.1007/s00374-013-0863-x
– ident: CIT0035
  doi: 10.2134/agronj2011.0249
– ident: CIT0037
– ident: CIT0029
  doi: 10.1080/00103620500303988
– ident: CIT0030
  doi: 10.1007/s11119-010-9196-z
– ident: CIT0019
  doi: 10.2134/agronj2010.0015
– ident: CIT0005
  doi: 10.1016/j.still.2014.10.011
– ident: CIT0025
  doi: 10.1016/j.still.2016.12.007
– ident: CIT0026
  doi: 10.1590/S0100-204X2008000800018
– ident: CIT0004
  doi: 10.1016/j.ecolind.2015.08.023
– ident: CIT0017
  doi: 10.1016/j.fcr.2016.08.013
– ident: CIT0023
  doi: 10.2134/agronj2004.1572
– ident: CIT0033
  doi: 10.3390/s151127832
– ident: CIT0022
  doi: 10.1016/j.compag.2013.10.007
– ident: CIT0008
  doi: 10.1590/S0100-06832013000500018
– ident: CIT0014
  doi: 10.1016/S0065-2113(05)88004-6
SSID ssj0009051
Score 2.261349
Snippet This study aimed to develop a novel regression model for prescription of required nitrogen (N) for maize by combining spectral reflectance data from...
SourceID proquest
crossref
informaworld
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 2152
SubjectTerms agronomic efficiency
Agronomy
Brazil
Cereal crops
Corn
costs and returns
Crop yield
Economic models
Economics
Efficiency
Fertilization
INSEY
maize grain yield
NDVI
Nitrogen
Nitrogen fertilization
no-tillage
Oxisols
Plant growth
plant nutrition
Reflectance
regression analysis
Regression models
Spectra
Spectral reflectance
Urea
urea nitrogen
urea-N
Zea mays
Title Regression modeling nitrogen fertilization requirement for maize crop by combining spectral reflectance and agronomic efficiency
URI https://www.tandfonline.com/doi/abs/10.1080/01904167.2020.1766074
https://www.proquest.com/docview/2419906602
https://www.proquest.com/docview/2524329925
Volume 43
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1db9MwFLVKhwQvCAZohYGMxFuUKp9N81jKSjWNIaFWVLxEce1MlUJSZc3D9sS_5W9wrz_SRJ0Y8BJVcexYvSf2sX3vuYS85567jhgP7DjmYzsIs8xmzji1OZDlLAiEm8osEZ8vR_NlcL4KV73er5bXUr1jw_XtnXEl_2NVuAd2xSjZf7Bs0yjcgN9gX7iCheH6Vzb-Kq6UG2uhMtrgsh8-0aqEGlaGHtO5DrO0KoEuv3IvUHoW_kg3t8LC_F1IQKFzTGaKsGTkZSXV_jPc0W8iCtKrSoUwowvIRo4InRPhFrPd5mAuqzA6_82Ynm639nldbNQOwTSXh_RuzZUrmOT1HBFbWjNVBAYprWkK_W6Y_6ca1TFksa9GuEmeIlM2GXv3EK51kCP6bCp_AHRvycvOTocn_exUOL3E5uIg6UjL80nujcaODfxSzd_CjOceLJH1nK4HfKULZYAddIZvJaerqYDnqsH3YJrRfpnwQnzfEDs7RKVNR2Uc6sp6X35JZsuLi2Rxtlo8IEcerGecPjmazD9-_7bXh3ZC10T2Y5sm2Axl4O96TYdGdUR2D0iFZEqLp-SJBgKdKLw-Iz1RHJOHH0rA0s0xeWRC4a-fk597AFMDYGoATDsApi0AU-gGlQCmCGDKbmgDYGoATFsApgBg2gCY7gH8gixnZ4vp3NYpQey1H7k7O45EzDnzM7EejRmsxpmIYc2PZ_EBi7gYiTQaocZlOMrQwStCJV7BBYv9eIzs-iXpF2UhTghlDneZFwY8RIEmEbAwgsWIy6D9FJ5mAxKYfzhZa718TNuSJ66R1dWGSdAwiTbMgAybalslGHNfhbhtvmQnYZ4phCf-PXVPja0TPS5dJ8DJgWJCuTcg75pimDXwKDAtRFnDM6EX-MBEvfDVn5t4TR7vP8VT0t9VtXgDNHzH3moI_waBhNwy
linkProvider Library Specific Holdings
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB5RqNReCn2p2wJ1pV6z3SR-rI8tAi0U9lCBxM2K4wmqSrNVmj3AqT-9M05CoQhx4Bq_nbH9jT3zDcDHkKWl8UEm1oZpIlVVJX4yLZJAYLmSEtMiRok4muvZiTw4VafXfGHYrJJ16Kojioh7NS9uvoweTOI-sf8zAQlD6l1Gn4zWdBA-gjVlteHwDflk_o94d6LSwWWaywxePHdVc-N8usFeemu3jkfQ3jqUQ-c7y5Mf42Xrx-Xlf7yODxvdBjzrEar43InUc1jB-gU8_rKgGi9ewp9veNaZztYiRtGho0_QttAsSBJFxVba571rp2iQzYzj_aOgAYqfxfdLFBwzTPgLQb3yMTqFiN6eDbVJs8GvCCyIgvovirOmc5sWGJku2E30FZzs7R7vzJI-ikNS5iZtE2vQhuDzCks99aRAebSkpvHzqfQmoMbCaKYlVLpimxzD5KkY0NvcThkQvYbVelHjGxB-ElJPGm1QzKmD0itD-DH1VH9Buf0I5PDvXNlTnHOkjXOXDkyo_dw6nlvXz-0IxlfFfnUcH_cVsNcFw7XxcqXqIqG4_J6ym4MUuX67-O0IRhEqoPRsBB-ukmmh8-tNUeNiSXlUJnMCD5l6-4Dm38OT2fHRoTvcn399B085iW_IM70Jq22zxC2CWK3fjmvoL0SAGKY
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BQYgL5SkWChiJa5ZN4tibI9CuymuFEJW4WZl4XFWUbJVmD-2pP50ZJykUhHroNX47Y_sbe-YbgFc-S2uLXidl6eeJLkJIcDavEs9gOWhNaRWjRHxemt09_eF7MVoTHg9mlaJDh54oIu7VsriPfBgt4l6L-zPjCMvaXcafrDF8Dl6HG4bhiQh2Plv-5t2dFenoMS1lRiee_1Vz4Xi6QF76z2YdT6DFJuDY997w5Md03eG0Pv2L1vFKg7sLdwZ8qt70AnUPrlFzH26-XXGFJw_g7Cvt94azjYoxdPjgU7wptCuWQxXERvtwcOxULYmRcbx9VDw-9bM6OCUlEcMUnijuFMbYFCr6erbcJk-GvCGIGCruvqr2295pWlHkuRAn0Yewt9j59m43GWI4JHVu0y4pLZXeYx6oNnNk9QmpZCVNHk81Wk-GKmuElLAwQSxyrFCnkics83IucOgRbDSrhh6DwplPkfVZXwijDmksLKPHFLn-inPjBPT461w9EJxLnI1Dl448qMPcOplbN8ztBKbnxY56ho_LCpR_yoXr4tVK6OOguPySslujELlhszh2DKIYE3B6NoGX58m8zOXtpmpoteY8RaZzhg5Z8eQKzb-AW1-2F-7T--XHp3BbUuR6PDNbsNG1a3rG-KrD53EF_QLRTBdT
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Regression+modeling+nitrogen+fertilization+requirement+for+maize+crop+by+combining+spectral+reflectance+and+agronomic+efficiency&rft.jtitle=Journal+of+plant+nutrition&rft.au=Kapp-Junior%2C+Cl%C3%A1udio&rft.au=Eduardo+F%C3%A1vero+Caires&rft.au=Guimar%C3%A3es%2C+Alaine+Margarete&rft.au=Auler%2C+Andr%C3%A9+Carlos&rft.date=2020-08-26&rft.pub=Taylor+%26+Francis+Ltd&rft.issn=0190-4167&rft.eissn=1532-4087&rft.volume=43&rft.issue=14&rft.spage=2152&rft.epage=2163&rft_id=info:doi/10.1080%2F01904167.2020.1766074&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0190-4167&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0190-4167&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0190-4167&client=summon