Mathilde Chen is one of the first #DigitAg labeled PhDs
Disease risk analysis for wheat and vine by combining regional survey data and local expert knowledge
- Start Date: january 2017
- University: Paris Saclay
- PhD School: ABIES
- Field(s): Agronomy, Statistics
- Doctoral Thesis Advisor: David Makowski (INRA)
- Co-supervisor : François Brun (Acta)
- Funding: Acta-les instituts techniques
- #DigitAg: Labeled PhD – Challenge 3 (ICT and crop protection)
Keywords: Plant disease – Downy mildew – grapevine – risk analysis – epidemiosurveillance data
In its new version of ECOPHYTO Plan, the French governement re-affirmed in 2015 its willingness to reduce pesticide uses. Nevertheless, some levels of crop protection will always be required to control diseases development, it is necessary to determine as precisely as possible where and when chemical treatments are required. This thesis aims at improving risk analysis tools in crop fields by combining different sources of information: regional survey data, epidemiologic models for diseases dynamic forecast, and local expert knowledge (RMT Modélisation, 2012 ; Brun et al., 2012, 2015).
This work will focus on two major crops, namely wheat crop at national scale and antlantical vineyard (Val de Loire, Cognac, Aquitaine et Midi Pyrénées), that are currently subject to frequent pesticide applications. With an average of 2.2 fungicide applications for the 8 last years, winter wheat is frequently treated with fungicides, notably against stripe rust and septoria. Vine represents 3.7% of French agricultural land only, but its pesticides consumption represents almost 20% of french pesticides use, mainly including fungicides against mildew and oidium, and represents a cost of 250 MEuros.In zero pesticide zones, the monitoring of disease development reveals that vine is frequently infested by mildew and oidium, which can induce more than 50% of crop loss if the vine remains untreated.
Today, many farmers apply fungicides systematically in order to avoid any yield loss. My first objective is to forecast disease evolution on short-term by combining different sources of information with different types of statistical models. I will then study how these predictive tools could be used to avoid systematic chemical treatments.
Contact: mathilde.chen [AT] acta.asso.fr ou mathilde.chen [AT] inra.fr – Tél : 01 30 81 59 06 ou 0671419020
Communications / Publications
Brun, François, & Chen, Mathilde, Michel, Lucie, Veslot, Jacques, Makowski, David & al. (2017). Valorisation des données agricoles d’épidémio-surveillance. Création d’outils pour les acteurs régionaux du Bulletin de santé du végétal. VigiCultures, Réfléxions collectives à son évolution, 22 juin 2017, Paris, Acta-INRA – DOI : https://doi.org/10.13140/rg.2.2.36295.60320
Brun, François, Chen, Mathilde, Van de Kerckhove, Simon & al, et. (2017). Epi Agro – Visualiser la santé du blé. Phttps://doi.org/10.13140/rg.2.2.36295.60320résentation du prototype. Pitch au Hackathon Api Agro – SIMA 2017, Paris, DOI : https://doi.org/10.13140/RG.2.2.28326.42568
Brun, François, Michel, Lucie, Veslot, Jacques, Chen, Mathilde, Makowski, David & al. (2017). Real-time analysis and prediction tools based on data for regional plant health monitoring: application on wheat and wine in France. EFITA 2017, Montpellier (France), July 2nd-6th (European conference dedicated to the future use of ICT in the agri-food sector, bioresource and biomass sector,