Current knowledge and future research opportunities for modeling annual crop mixtures. A review

Growing mixtures of annual arable crop species or genotypes is a promising way to improve crop production without increasing agricultural inputs. To design optimal crop mixtures, choices of species, genotypes, sowing proportion, plant arrangement, and sowing date need to be made but field experiment...

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Published inAgronomy for sustainable development Vol. 39; no. 2; p. 20
Main Authors Gaudio, Noémie, Escobar-Gutiérrez, Abraham J., Casadebaig, Pierre, Evers, Jochem B., Gérard, Frédéric, Louarn, Gaëtan, Colbach, Nathalie, Munz, Sebastian, Launay, Marie, Marrou, Hélène, Barillot, Romain, Hinsinger, Philippe, Bergez, Jacques-Eric, Combes, Didier, Durand, Jean-Louis, Frak, Ela, Pagès, Loïc, Pradal, Christophe, Saint-Jean, Sébastien, Van Der Werf, Wopke, Justes, Eric
Format Journal Article
LanguageEnglish
Published Paris Springer Paris 01.04.2019
Springer Nature B.V
Springer Verlag/EDP Sciences/INRA
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Abstract Growing mixtures of annual arable crop species or genotypes is a promising way to improve crop production without increasing agricultural inputs. To design optimal crop mixtures, choices of species, genotypes, sowing proportion, plant arrangement, and sowing date need to be made but field experiments alone are not sufficient to explore such a large range of factors. Crop modeling allows to study, understand, and ultimately design cropping systems and is an established method for sole crops. Recently, modeling started to be applied to annual crop mixtures as well. Here, we review to what extent crop simulation models and individual-based models are suitable to capture and predict the specificities of annual crop mixtures. We argued that (1) the crop mixture spatio-temporal heterogeneity (influencing the occurrence of ecological processes) determines the choice of the modeling approach (plant or crop centered). (2) Only few crop models (adapted from sole crop models) and individual-based models currently exist to simulate annual crop mixtures. Crop models are mainly used to address issues related to both crop mixtures management and the integration of crop mixtures into larger scales such as the rotation. In contrast, individual-based models are mainly used to identify plant traits involved in crop mixture performance and to quantify the relative contribution of the different ecological processes (niche complementarity, facilitation, competition, plasticity) to crop mixture functioning. This review highlights that modeling of annual crop mixtures is in its infancy and gives to model users some important keys to choose the model based on the questions they want to answer, with awareness of the strengths and weaknesses of each of the modeling approaches.
AbstractList Growing mixtures of annual arable crop species or genotypes is a promising way to improve crop production without increasing agricultural inputs. To design optimal crop mixtures, choices of species, genotypes, sowing proportion, plant arrangement, and sowing date need to be made but field experiments alone are not sufficient to explore such a large range of factors. Crop modeling allows to study, understand, and ultimately design cropping systems and is an established method for sole crops. Recently, modeling started to be applied to annual crop mixtures as well. Here, we review to what extent crop simulation models and individual-based models are suitable to capture and predict the specificities of annual crop mixtures. We argued that (1) the crop mixture spatio-temporal heterogeneity (influencing the occurrence of ecological processes) determines the choice of the modeling approach (plant or crop centered). (2) Only few crop models (adapted from sole crop models) and individual-based models currently exist to simulate annual crop mixtures. Crop models are mainly used to address issues related to both crop mixtures management and the integration of crop mixtures into larger scales such as the rotation. In contrast, individual-based models are mainly used to identify plant traits involved in crop mixture performance and to quantify the relative contribution of the different ecological processes (niche complementarity, facilitation, competition, plasticity) to crop mixture functioning. This review highlights that modeling of annual crop mixtures is in its infancy and gives to model users some important keys to choose the model based on the questions they want to answer, with awareness of the strengths and weaknesses of each of the modeling approaches.
Growing mixtures of annual arable crop species or genotypes is a promising way to improve crop production without increasing agricultural inputs. To design optimal crop mixtures, choices of species, genotypes, sowing proportion, plant arrangement, and sowing date need to be made but field experiments alone are not sufficient to explore such a large range of factors. Crop modeling allows to study, understand, and ultimately design cropping systems and is an established method for sole crops. Recently, modeling started to be applied to annual crop mixtures as well. Here, we review to what extent crop simulation models and individual-based models are suitable to capture and predict the specificities of annual crop mixtures. We argued that (1) the crop mixture spatio-temporal heterogeneity (influencing the occurrence of ecological processes) determines the choice of the model-ing approach (plant or crop centered). (2) Only few crop models (adapted from sole crop models) and individual-based models currently exist to simulate annual crop mixtures. Crop models are mainly used to address issues related to both crop mixtures management and the integration of crop mixtures into larger scales such as the rotation. In contrast, individual-based models are mainly used to identify plant traits involved in crop mixture performance and to quantify the relative contribution of the different ecological processes (niche complementarity, facilitation, competition, plasticity) to crop mixture functioning. This review highlights that modeling of annual crop mixtures is in its infancy and gives to model users some important keys to choose the model based on the questions they want to answer, with awareness of the strengths and weaknesses of each of the modeling approaches.
ArticleNumber 20
Author Pagès, Loïc
Combes, Didier
Bergez, Jacques-Eric
Saint-Jean, Sébastien
Casadebaig, Pierre
Justes, Eric
Colbach, Nathalie
Pradal, Christophe
Louarn, Gaëtan
Hinsinger, Philippe
Gérard, Frédéric
Marrou, Hélène
Van Der Werf, Wopke
Gaudio, Noémie
Munz, Sebastian
Escobar-Gutiérrez, Abraham J.
Evers, Jochem B.
Barillot, Romain
Durand, Jean-Louis
Frak, Ela
Launay, Marie
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ContentType Journal Article
Copyright INRA and Springer-Verlag France SAS, part of Springer Nature 2019
2019© INRA and Springer-Verlag France SAS, part of Springer Nature 2019
Distributed under a Creative Commons Attribution 4.0 International License
Wageningen University & Research
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– notice: 2019© INRA and Springer-Verlag France SAS, part of Springer Nature 2019
– notice: Distributed under a Creative Commons Attribution 4.0 International License
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ID FETCH-LOGICAL-c477t-65eb7953371501f4e8140ce812e53ae082a36bce92eea90598c2cc116e22dc3e3
IEDL.DBID U2A
ISSN 1774-0746
IngestDate Thu Oct 13 09:31:22 EDT 2022
Wed Aug 27 07:31:27 EDT 2025
Thu Jul 10 23:55:33 EDT 2025
Wed Aug 13 10:40:53 EDT 2025
Tue Jul 01 02:36:36 EDT 2025
Thu Apr 24 22:56:44 EDT 2025
Fri Feb 21 02:32:11 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 2
Keywords Intercrops
Individual-based models
Model users
Annual crop mixtures
Crop models
Genotypes mixtures
Functional–structural plant models
intercrops
crop models
annual crop mixtures
individual-based models
genotypes mixtures
functional-structural plant models
model users
Language English
License Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c477t-65eb7953371501f4e8140ce812e53ae082a36bce92eea90598c2cc116e22dc3e3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ORCID 0000-0002-4528-9851
0000-0002-5111-9998
0000-0003-2751-168X
0000-0003-3467-2617
0000-0002-2555-761X
0000-0002-9956-9103
0000-0002-0401-0711
0000-0002-3791-037X
0000-0001-5458-1259
0000-0002-5083-1561
0000-0001-7225-936X
0000-0002-1676-5249
OpenAccessLink https://inria.hal.science/hal-02228974
PQID 2189208348
PQPubID 2044462
PageCount 1
ParticipantIDs wageningen_narcis_oai_library_wur_nl_wurpubs_549347
hal_primary_oai_HAL_hal_02228974v1
proquest_miscellaneous_2237544014
proquest_journals_2189208348
crossref_citationtrail_10_1007_s13593_019_0562_6
crossref_primary_10_1007_s13593_019_0562_6
springer_journals_10_1007_s13593_019_0562_6
ProviderPackageCode CITATION
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PublicationCentury 2000
PublicationDate 2019-04-01
PublicationDateYYYYMMDD 2019-04-01
PublicationDate_xml – month: 04
  year: 2019
  text: 2019-04-01
  day: 01
PublicationDecade 2010
PublicationPlace Paris
PublicationPlace_xml – name: Paris
– name: Heidelberg
PublicationSubtitle Official journal of the Institut National de la Recherche Agronomique (INRA)
PublicationTitle Agronomy for sustainable development
PublicationTitleAbbrev Agron. Sustain. Dev
PublicationYear 2019
Publisher Springer Paris
Springer Nature B.V
Springer Verlag/EDP Sciences/INRA
Publisher_xml – name: Springer Paris
– name: Springer Nature B.V
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Snippet Growing mixtures of annual arable crop species or genotypes is a promising way to improve crop production without increasing agricultural inputs. To design...
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SubjectTerms Agricultural land
Agricultural practices
Agriculture
Agronomy
Annual crop mixtures
annuals
Arable land
Biomedical and Life Sciences
Complementarity
Computer Science
Crop models
Crop production
crop rotation
Cropping systems
Crops
Ecological effects
field experimentation
Field tests
Functional–structural plant models
genotype
Genotypes
Genotypes mixtures
Heterogeneity
Individual-based models
Intercrops
Life Sciences
Model users
Modeling and Simulation
Modelling
Review Article
simulation models
Soil Science & Conservation
Sole cropping
sowing date
Sustainable Development
Title Current knowledge and future research opportunities for modeling annual crop mixtures. A review
URI https://link.springer.com/article/10.1007/s13593-019-0562-6
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Volume 39
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