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IPSIM-Web, An Online Resource for Promoting Qualitative Aggregative Hierarchical Network Models to Predict Plant Disease Risk: Application to Brown Rust on Wheat.
Robin, Marie-Hélène; Bancal, Marie-Odile; Cellier, Vincent; Délos, Marc; Felix, Irène; Launay, Marie; Magnard, Adèle; Olivier, Axel; Robert, Corinne; Rolland, Bernard; Sache, Ivan; Aubertot, Jean-Noël.
Afiliação
  • Robin MH; AGIR, Université Toulouse, INPT-Purpan, INRA, F-31320 Castanet-Tolosan, France.
  • Bancal MO; INRA AgroParisTech, UMR ECOSYS, F-78850 Thiverval-Grignon, France.
  • Cellier V; INRA, Domaine expérimental d'Epoisses UE 0115, F-21110 Bretenière, France.
  • Délos M; DRAAF- SRAl, F31074 Toulouse cedex, France.
  • Felix I; ARVALIS Institut du végétal, Service Agronomie Economie Environnement, F-18570 Le Subdray, France.
  • Launay M; INRA, UMR Agroclim, F-84914 Avignon, France.
  • Magnard A; AGIR, Université Toulouse, INRA.
  • Olivier A; ASFIS-GNIS, 44 rue du Louvre, F-75001 Paris, France.
  • Robert C; INRA AgroParisTech, UMR ECOSYS.
  • Rolland B; INRA, UMR APBV, F-35653 Le Rheu, France.
  • Sache I; INRA AgroParisTech, UMR BIOGER, F-78850 Thiverval-Grignon, France.
  • Aubertot JN; AGIR, Université Toulouse, INRA.
Plant Dis ; 102(3): 488-499, 2018 Mar.
Article em En | MEDLINE | ID: mdl-30673480
A qualitative pest modeling platform, named Injury Profile Simulator (IPSIM), provides a tool to design aggregative hierarchical network models to predict the risk of pest injuries, including diseases, on a given crop based on variables related to cropping practices as well as soil and weather environment at the field level. The IPSIM platform enables modelers to combine data from various sources (literature, survey, experiments, and so on), expert knowledge, and simulation to build a network-based model. The overall structure of the platform is fully described at the IPSIM-Web website ( www6.inra.fr/ipsim ). A new module called IPSIM-Wheat-brown rust is reported in this article as an example of how to use the system to build and test the predictive quality of a prediction model. Model performance was evaluated for a dataset comprising 1,788 disease observations at 13 French cereal-growing regions over 15 years. Accuracy of the predictions was 85% and the agreement with actual values was 0.66 based on Cohen's κ. The new model provides risk information for farmers and agronomists to make scientifically sound tactical (within-season) decisions. In addition, the model may be of use for ex post diagnoses of diseases in commercial fields. The limitations of the model such as low precision and threshold effects as well as the benefits, including the integration of different sources of information, transparency, flexibility, and a user-friendly interface, are discussed.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças das Plantas / Basidiomycota / Triticum / Modelos Estatísticos / Internet / Suscetibilidade a Doenças Tipo de estudo: Etiology_studies / Prognostic_studies / Qualitative_research / Risk_factors_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças das Plantas / Basidiomycota / Triticum / Modelos Estatísticos / Internet / Suscetibilidade a Doenças Tipo de estudo: Etiology_studies / Prognostic_studies / Qualitative_research / Risk_factors_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article