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Agronomy Journal Abstract - Reviews & Interpretations

Ad Hoc Modeling in Agronomy: What Have We Learned in the Last 15 Years?


This article in AJ

  1. Vol. 104 No. 3, p. 735-748
    Received: Nov 23, 2011

    * Corresponding author(s): affholder@cirad.fr
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  1. F. Affholder *a,
  2. P. Tittonella,
  3. M. Corbeelsa,
  4. S. Rouxb,
  5. N. Motisic,
  6. P. Tixierd and
  7. J. Werye
  1. a CIRAD, UR 102 SCA, TA B 102/02 Av. Agropolis, Montpellier, France 34398
    b INRA UMR System, Bâtiment 27, 2 place Viala, Montpellier, France 34060
    c CIRAD, UPR 106, TA A-106/02, Av. Agropolis, Montpellier, France 34398
    d CIRAD, UPR SCBPA, Quartier Petit Morne, France 97285
    e Montpellier Supagro UMR System, Bâtiment 27, 2 place Viala, Montpellier, France 34060


The “Use and Abuse of Crop Simulation Models” special issue of Agronomy Journal published in 1996 ended with the myth of the universal crop model. Sinclair and Seligman consequently recommended tailoring models to specific problems. This paper reviews the fate of the idea of such ad hoc approaches to crop simulation modeling during the past 15 yr. Most crop modelers have since adhered to the principles formulated by Sinclair and Seligman, but yet their practice faces two major issues: (i) how to define the structure of the model as depending on the question to be addressed (model conceptualization) and (ii) how to minimize efforts in software development (model computerization). Progress in model conceptualization as reported in the literature concerns (i) inferring a conceptual model from what is known of the problem to address, (ii) deriving summary models from comprehensive ones, and (iii) using multivariate methods to analyze the hierarchy of drivers of variability in the variable to be predicted. Considerable effort has been invested in the development of frameworks to facilitate model computerization, and the commercial modeling software is constantly improving. But there are limits in the flexibility permitted by these tools. Acquiring basic skills in coding a model using a scientific programming language is preferred by scientists wishing to keep the fullest understanding and control on their crop models. Connecting the model to commercial database software may facilitate this strategy. However, the computerization issue may still lead to tensions between modeling teams concerning the legitimacy to develop their own model.

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