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Book: Methods of Introducing System Models into Agricultural Research
Published by: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America



  1.  p. 395-426
    Advances in Agricultural Systems Modeling 2.
    Methods of Introducing System Models into Agricultural Research

    Laj R. Ahuja and Liwang Ma (ed.)

    ISBN: 978-0-89118-196-5


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The STICS Crop Model and Associated Software for Analysis, Parameterization, and Evaluation

  1. S. Buis,
  2. D. Wallach,
  3. S. Guillaume,
  4. H. Varella,
  5. P. Lecharpentier,
  6. M. Launay,
  7. M. Guérif,
  8. J.-E. Bergez and
  9. E. Justes
  1. S. Buis (samuel.buis@avignon.inra.fr), P. Lecharpentier, and M. Guérif, INRA, UMR 1114 EMMAH, Domaine Saint Paul- Site Agroparc, 84914 Avignon Cedex 9, France; D. Wallach, S. Guillaume, J.-E. Bergez, and E. Justes, INRA, UMR 1248 AGIR, BP 52627, 31326 Castanet Tolosan Cedex, France; S. Guillaume, Arvalis-Institut du végétal, 6 chemin de la côte vieille, 31450 Baziege, France; H. Varella, Météo-France, CNRM-GMAP, 42 av Gaspard Coriolis, 31057 Toulouse, France; M. Launay, INRA, Unité Agroclim, Domaine Saint Paul- Site Agroparc, 84914 Avignon Cedex 9, France.


The STICS model simulates the behavior of soil–crop systems for a large range of crops. It is widely used for analyzing, evaluating, and designing cropping systems. Because of the complexity of such models, it is important to adapt methods and tools for analyzing, improving and evaluating them. This chapter presents software providing these services for the STICS model. Its main features are: (i) handling of multiple simulations, (ii) uncertainty and sensitivity analysis, (iii) parameter estimation, and (iv) comparison with observed data. Parameter estimation of crop models presents particular problems. The choice of an appropriate statistical model for model error is one of them. The software proposes options for choosing among a collection of possible assumptions concerning model error including zero or non-zero expectations, independent or correlated errors, known or unknown variances…These options are available both for the frequentist and Bayesian parameter estimation methods implemented. The software also includes special features specifically adapted to crop models, such as automatic procedure for parameter estimation in several stages and simultaneous estimation of varietal and specific parameters. Numerous indicators are provided to help the modeler analyze results. This software thus has two major objectives: (i) to help modelers using and improving the model and (ii) to allow one to compare various methods for parameter estimation on real problems. In this chapter we detail the design, features, and ways of using the software. In closing, we present examples of sensitivity analysis and parameter estimation applications with the STICS crop model.

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