Abstract
Automatic Differentiation (AD) is often applied to codes that solve partial differential equations, e.g. in geophysical sciences or Computational Fluid Dynamics. In agronomy, the differentiation of crop models has never been performed since these models are more empirical than fully mecanistic, derived from equations. This study shows the feasibility of constructing the adjoint model of a crop model referent in the agronomic community (STICS) with the TAPENADE tool, and the use of this accurate adjoint to perform some sensitivity analysis. This paper reports on the experience from AD users of the environmental domain, in which AD usage is not very widespread.
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Notes
- 1.
- 2.
ADAM experiment (Data Assimilation through Agro-Modelling). Project and database at http://kalideos.cnes.fr/spip.php?article68
- 3.
All the parameters of STICS are described in http://www.avignon.inra.fr/agroclim_stics_eng/notices_d_utilisation
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Acknowledgements
This study was conducted thanks to a grant provided by CNES within the ADAM project (http://kalideos.cnes.fr/spip.php?article68), during the Ph.D. of the first author at INRA Avignon and the University of Grenoble.
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Lauvernet, C., Hascoët, L., Le Dimet, FX., Baret, F. (2012). Using Automatic Differentiation to Study the Sensitivity of a Crop Model. In: Forth, S., Hovland, P., Phipps, E., Utke, J., Walther, A. (eds) Recent Advances in Algorithmic Differentiation. Lecture Notes in Computational Science and Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30023-3_6
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DOI: https://doi.org/10.1007/978-3-642-30023-3_6
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