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dc.contributorTittonell, Pabloen_US
dc.contributorCorbeels, Marcen_US
dc.contributorRoux, Sebastienen_US
dc.contributorMotisi, Natachaen_US
dc.contributorTixier, Philippeen_US
dc.contributorWery, Jacquesen_US
dc.creatorAffholder, Francoisen_US
dc.date2012-03-13en_US
dc.date.accessioned2018-09-18T22:06:21Z
dc.date.available2018-09-18T22:06:21Z
dc.identifierhttps://mel.cgiar.org/dspace/limiteden_US
dc.identifierhttps://dl.sciencesocieties.org/publications/aj/abstracts/104/3/735?access=0&view=pdfen_US
dc.identifier.citationFrancois Affholder, Pablo Tittonell, Marc Corbeels, Sebastien Roux, Natacha Motisi, Philippe Tixier, Jacques Wery. (13/3/2012). Ad Hoc Modeling in Agronomy: What Have We Learned in the Last 15 Years. Agronomy Journal, 104 (3), pp. 735-748.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/8393
dc.description.abstractThe “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.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherAmerican Society of Agronomyen_US
dc.rightsCC-BY-NC-4.0en_US
dc.sourceAgronomy Journal;104,(2012) Pagination 735,748en_US
dc.titleAd Hoc Modeling in Agronomy: What Have We Learned in the Last 15 Years?en_US
dc.typeJournal Articleen_US
cg.creator.idCorbeels, Marc: 0000-0002-8084-9287en_US
cg.creator.idWery, Jacques: 0000-0003-0014-4541en_US
cg.creator.ID-typeORCIDen_US
cg.creator.ID-typeORCIDen_US
cg.subject.agrovoccrop modellingen_US
cg.subject.agrovocdatabasesen_US
cg.subject.agrovocsoftware developmenten_US
cg.subject.agrovocframeworksen_US
cg.contributor.centerThe French Agricultural Research Center for International Development - CIRADen_US
cg.contributor.centerFrench National Research Institute for Agriculture, Food and Environment - INRAE Franceen_US
cg.contributor.centerMontpellier SupAgro - SupAgroen_US
cg.contributor.funderCGIAR System Organization - CGIARen_US
cg.date.embargo-end-dateTimelessen_US
cg.coverage.regionGlobalen_US
cg.contactaffholder@cirad.fren_US
cg.identifier.doihttps://dx.doi.org/10.2134/agronj2011.0376en_US
dc.identifier.statusTimeless limited accessen_US
mel.impact-factor1.897en_US


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