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Master Thesis

Comment coupler observations et prédictions pour améliorer les prédictions d'épidémie de Septoriose sur le blé ?

Abstract : Wheat Septoria leaf blotch disease is one of the most important in France. Fungicides are more efficient just before the appearance of symptomes, thug it is challenging to find the right timing. Nowadays, the decision of treatment is based on observations made in the fields and on modelling of the severity of the disease made with the SeptoLISO model. So far no one had ever tried to fuse the modelled and the observed data. We had two aims: one was to predict whether the contamination threshold of 4/20 is reached or not on a given leaf, of a given field on a given week, the other was to predict whether this threshold will be reached on the next week or not. Finally we compared our prediction performances to the current prediction performances in 4 different scenarios. Binomial regression models and classification trees were the most efficient to predict contaminations, when precise information from the field was scarce. Fusion of observed and modeled data seems a promising approach to improve epidemics prediction performances.
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Master Thesis
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https://dumas.ccsd.cnrs.fr/dumas-00753398
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Submitted on : Monday, November 19, 2012 - 11:12:03 AM
Last modification on : Monday, July 6, 2020 - 3:38:21 PM
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Lygie Esquirol. Comment coupler observations et prédictions pour améliorer les prédictions d'épidémie de Septoriose sur le blé ?. Sciences agricoles. 2012. ⟨dumas-00753398⟩

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