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

  • Noémie GaudioEmail author
  • Abraham J. Escobar-Gutiérrez
  • Pierre Casadebaig
  • Jochem B. Evers
  • Frédéric Gérard
  • Gaëtan Louarn
  • Nathalie Colbach
  • Sebastian Munz
  • Marie Launay
  • Hélène Marrou
  • Romain Barillot
  • Philippe Hinsinger
  • Jacques-Eric Bergez
  • Didier Combes
  • Jean-Louis Durand
  • Ela Frak
  • Loïc Pagès
  • Christophe Pradal
  • Sébastien Saint-Jean
  • Wopke Van Der Werf
  • Eric Justes
Review Article


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.


Annual crop mixtures Intercrops Genotypes mixtures Crop models Individual-based models Functional–structural plant models Model users 



The authors acknowledge support from European Union through the project H2020 ReMIX (Redesigning European cropping systems based on species mixtures, and from INRA Environment and Agronomy Division through the project IDEA (Intra- and interspecific diversity mixture in agriculture). We also thank Michael and Michelle Corson for their helpful comments and English revision and the two anonymous reviewers for their valuable comments.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2019

Authors and Affiliations

  • Noémie Gaudio
    • 1
    Email author
  • Abraham J. Escobar-Gutiérrez
    • 2
  • Pierre Casadebaig
    • 1
  • Jochem B. Evers
    • 3
  • Frédéric Gérard
    • 4
  • Gaëtan Louarn
    • 2
  • Nathalie Colbach
    • 5
  • Sebastian Munz
    • 6
  • Marie Launay
    • 7
  • Hélène Marrou
    • 8
  • Romain Barillot
    • 2
  • Philippe Hinsinger
    • 4
  • Jacques-Eric Bergez
    • 1
  • Didier Combes
    • 2
  • Jean-Louis Durand
    • 2
  • Ela Frak
    • 2
  • Loïc Pagès
    • 9
  • Christophe Pradal
    • 10
    • 11
  • Sébastien Saint-Jean
    • 12
  • Wopke Van Der Werf
    • 3
  • Eric Justes
    • 8
  1. 1.AGIR, University of Toulouse, INRACastanet-TolosanFrance
  2. 2.INRA, URP3F, Equipe Ecophysiologie des Plantes FourragèresLusignanFrance
  3. 3.Centre for Crop Systems AnalysisWageningen University and ResearchWageningenthe Netherlands
  4. 4.Eco&Sols, University Montpellier, CIRAD, INRA, IRD, SupAgroMontpellierFrance
  5. 5.Agroécologie, AgroSup Dijon, INRA, University Bourgogne Franche-ComtéDijonFrance
  6. 6.Department of Agronomy, Institute of Crop ScienceUniversity of HohenheimStuttgartGermany
  7. 7.INRA, US1116 AgroClimAvignon Cedex 9France
  8. 8.CIRAD, UMR SYSTEM, University Montpellier, CIHEAM-IAMM, CIRAD, INRA, Montpellier SupagroMontpellierFrance
  9. 9.INRA, Centre PACA, UR PSH 1115Avignon Cedex 9France
  10. 10.AGAP, University Montpellier, CIRAD, INRA, SupAgroMontpellierFrance
  11. 11.CIRAD, AGAP, and INRIA Zenith, Univ MontpellierMontpellierFrance
  12. 12.UMR ECOSYS INRA, AgroParisTech, Université Paris-SaclayThiverval-GrignonFrance

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