First cropping system model based on expert-knowledge parameterization

  • Rémy BallotEmail author
  • Chantal Loyce
  • Marie-Hélène Jeuffroy
  • Aïcha Ronceux
  • Julie Gombert
  • Claire Lesur-Dumoulin
  • Laurence Guichard
Research Article


Models are promising tools to support the design of cropping systems toward sustainable agriculture. Process-based deterministic models are predominantly used, whereas most of them involve a limited range of crop techniques, and are unsuited to organic agriculture. Moreover, their parameterization and local adaptation require a large amount of experimental data. We thus designed a model simulating the yields of successive crops, taking into account the effects of most crop techniques embedded in a cropping system, and suited for both conventional and organic farming. This model was designed assuming that its parameterization, mostly based on expert-knowledge elicitation, could enlarge the range of environmental conditions and crop techniques considered. The PerSyst model involves three types of parameters based on expert knowledge: (i) reference yields reached in the most common cropping system conditions, (ii) yield change due to crop sequence variation, and (iii) yield change due to variation in crop management. These parameters are stochastic to report yield variability across climatic years. The model was parameterized through an original expert elicitation method—combining individual interviews and collective validation—on three case studies, including one in organic farming. Model accuracy was assessed for two long-term experiments. Parameters such as yield change due to crop sequence and to crop management were close among case studies, highlighting possibilities to compensate for a local lack of knowledge. Moreover, simulated yields in both experiments showed great consistency with observed yields, with average relative root-mean-square error of prediction of 15% for winter wheat and faba bean for example. For the first time, thanks to expert-knowledge parametrization, we built a cropping system model, considering all techniques, which could be easily tailored to a diversity of conditions, both in conventional and organic farming. Lastly, advantages and limits of the PerSyst model to assess innovative cropping systems were discussed.


Arable crops Yield Crop management plan Crop sequence Organic farming RMSEP 



We would like to thank J.F. Garnier et M. Bouviala (ARVALIS-Institut du végétal) for implementing the assessment of the predictive capacity of the PerSyst model in Île-de-France, as well as all the experts who were surveyed to parameterize PerSyst in Bourgogne, Eure-et-Loir, and Île-de-France.

Funding information

This study was funded by the Futurol project, POPSY (2008 STRA 012), DIM Astrea, the Île-de-France Region, and the Agence de l’Eau Seine-Normandie.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Adam M, Wery J, Leffelaar PA, Ewert F, Corbeels M, Van Keulen H (2013) A systematic approach for re-assembly of crop models: an example to simulate pea growth from wheat growth. Ecol Model 250:258–268. CrossRefGoogle Scholar
  2. Agreste (2017) Données en ligne. Accessed 1 March 2017
  3. Bachinger J, Zander P (2007) ROTOR, a tool for generating and evaluating crop rotations for organic farming systems. Eur J Agron 26:130–143. CrossRefGoogle Scholar
  4. Bennett AJ, Bending GD, Chandler D, Hilton S, Mills P (2012) Meeting the demand for crop production: the challenge of yield decline in crops grown in short rotations. Biol Rev 87:52–71. CrossRefPubMedGoogle Scholar
  5. Bockstaller C, Girardin P (2008) Mode de calcul des indicateurs agri-environnementaux de la méthode INDIGO®Google Scholar
  6. Bohanec M, Rajkovic V (1990) DEX: an expert system shell for decision support. Sistemica 1:145–157Google Scholar
  7. Brisson N, Ruget F, Gate P, Lorgeou J, Nicollaud B, Tayot X, Plenet D, Jeuffroy MH, Bouthier A, Ripoche D, Mary B, Justes E (2002) STICS : un modèle générique pour simuler les cultures et leurs bilans hydrique et azoté. II. Evaluation par comparaison à la réalité expérimentale. Agronomie 22:69–92CrossRefGoogle Scholar
  8. Cernay C, Pelzer E, Makowski D (2016) A global experimental dataset for assessing grain legume production. Sci Data 3:UNSP 160084 . doi: CrossRefPubMedPubMedCentralGoogle Scholar
  9. Colbach N, Duby C, Cavelier A, Meynard JM (1997a) Influence of cropping systems on foot and root diseases of winter wheat: fitting of a statistical model. Eur J Agron 6:61–77. CrossRefGoogle Scholar
  10. Colbach N, Lucas P, Cavelier N, Cavelier A (1997b) Influence of cropping system on sharp eyespot in winter wheat. Crop Prot 16:415–422. CrossRefGoogle Scholar
  11. Colnenne-David C, Doré T (2015) Designing innovative productive cropping systems with quantified and ambitious environmental goals. Renew Agric Food Syst 30:487–502. CrossRefGoogle Scholar
  12. Cornelissen AMG, van den Berg J, Koops WJ, Kaymak U (2003) Elicitation of expert knowledge for fuzzy evaluation of agricultural production systems. Agric Ecosyst Environ 95:1–18. CrossRefGoogle Scholar
  13. David C, Jeuffroy M-H (2009) A sequential approach for improving AZODYN crop model under conventional and low-input conditions. Eur J Agron 31:177–182. CrossRefGoogle Scholar
  14. De Wispelare AR, Herren LT, Clemen RT (1995) The use of the probability elicitation in the high-level nuclear waste regulation program. Int J Forecast 11:5–24CrossRefGoogle Scholar
  15. Dogliotti S, Rossing WAH, van Ittersum MK (2003) ROTAT, a tool for systematically generating crop rotations. Eur J Agron 19:239–250. CrossRefGoogle Scholar
  16. Furian T, Ballot R, Guichard L, Huat J (2015) Possible ex-ante assessment of rice-vegetable systems performances when facing data scarcity: use of the PERSYST model in West Africa. In: FSD5 proceedings. Montpellier, pp 507–508Google Scholar
  17. Gardarin A, Duerr C, Colbach N (2012) Modeling the dynamics and emergence of a multispecies weed seed bank with species traits. Ecol Model 240:123–138. CrossRefGoogle Scholar
  18. Girard N, Hubert B (1999) Modelling expert knowledge with knowledge-based systems to design decision aids—the example of a knowledge-based model on grazing management. Agric Syst 59:123–144. CrossRefGoogle Scholar
  19. Jacquet F, Butault J-P, Guichard L (2011) An economic analysis of the possibility of reducing pesticides in French field crops. Ecol Econ 70:1638–1648. CrossRefGoogle Scholar
  20. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18:235–265. CrossRefGoogle Scholar
  21. Laurent A, Loyce C, Makowski D, Pelzer E (2015) Using site-specific data to estimate energy crop yield. Environ Model Softw 74:104–113. CrossRefGoogle Scholar
  22. Loyce C, Rellier JP, Meynard JM (2002) Management planning for winter wheat with multiple objectives (1): the BETHA system. Agric Syst 72:9–31. CrossRefGoogle Scholar
  23. Makowski D, Wallach D, Meynard JM (1999) Models of yield, grain protein, and residual mineral nitrogen responses to applied nitrogen for winter wheat. Agron J 91:377–385CrossRefGoogle Scholar
  24. Makowski D, Wallach D, Meynard JM (2001) Statistical methods for predicting responses to applied nitrogen and calculating optimal nitrogen rates. Agron J 93:531–539CrossRefGoogle Scholar
  25. Meynard JM, Justes E, Machet JM, Recous S (1997) Fertilisation azotée des cultures annuelles de plein champ. In: Annales colloques INRA 83. INRA, Reims (France), pp 183–199Google Scholar
  26. Millenium Ecosystem Assesment (2005) Ecosystems and human well-being: general synthesis. Island Press, Washington D.C.Google Scholar
  27. O’Hagan A, Buck CE, Daneshkhah A, Eiser JE, Garthwaite PH, Jenkinson DJ, Oakley JE, Rakow T (2006) Uncertain judgements: eliciting expert probabilities. John Wiley & Sons, IncCrossRefGoogle Scholar
  28. Pahl H, Krüpl C, Funk T (2000) Grain legumes for European growers—LINK production survey analysis. Grain Legum:22–24Google Scholar
  29. Philibert A, Loyce C, Makowski D (2012) Quantifying uncertainties in N2O emission due to N fertilizer application in cultivated areas. PLoS One 7:e50950. CrossRefPubMedPubMedCentralGoogle Scholar
  30. Plaza-Bonilla D, Nolot J-M, Raffaillac D, Justes E (2015) Cover crops mitigate nitrate leaching in cropping systems including grain legumes: field evidence and model simulations. Agric Ecosyst Environ 212:1–12. CrossRefGoogle Scholar
  31. Rossing W a. H, Meynard JM, vanIttersum MK (1997) Model-based explorations to support development of sustainable farming systems: case studies from France and the Netherlands. Eur J Agron 7:271–283 . doi: CrossRefGoogle Scholar
  32. Sadok W, Angevin F, Bergez J-E, Bockstaller C, Colomb B, Guichard L, Reau R, Dore T (2008) Ex ante assessment of the sustainability of alternative cropping systems: implications for using multi-criteria decision-aid methods—a review. Agron Sustain Dev 28:163–174. CrossRefGoogle Scholar
  33. Schneider A, Huyghe C (2015) Les légumineuses pour des systèmes agricoles et alimentaires durables, QuaeGoogle Scholar
  34. Sebillotte M (1990) Le système de culture, un concept opératoire pour les agronomes. In: Les Systèmes De Culture, INRA. Paris, pp 165–196Google Scholar
  35. Stockle CO, Donatelli M, Nelson R (2003) CropSyst, a cropping systems simulation model. Eur J Agron 18:289–307. CrossRefGoogle Scholar
  36. Van Ittersum MK, Rabbinge R (1997) Concepts in production ecology for analysis and quantification of agricultural input-output combinations. Field Crops Res 52:197–208. CrossRefGoogle Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  1. 1.UMR AgronomieINRA, AgroParisTech, Université Paris-SaclayThiverval-GrignonFrance
  2. 2.Agro-Transfert Ressources et TerritoiresEstrées MonsFrance
  3. 3.FNAMSBrain sur l’AuthionFrance
  4. 4.DEAR, INRA, Univ MontpellierAlényaFrance

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