Mathilde Chen defended her PhD thesis on 12 December 2019, 14h
Amphithéâtre Dumont, AgroParisTech, 16 rue Claude Bernard,Paris 5ème
Grape downy mildew risk analysis by analysing regional survey data and local expert knowledge
- Start Date: january 2017
- Defence: 12 December 2019
- 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: statistical models, plant diseases, grapevine, regional data, expert knowledge, machine learning, plant health, Downy mildew
Pesticides reduce yield losses but have negative consequences, particularly for farmers’ health. It is important to provide precise information on the epidemic risks concerning harmful organisms in order to reason the use of pesticides, in particular in the case of grape downy mildew (Plasmopara viticola), which is responsible on average for 43% of pesticides used in Bordeaux vineyards.
The objective of this work is to estimate the benefits of using downy mildew onset date to avoid unjustified sprays in the control of this disease. Based on regional observations and local expertise, we show that in Bordeaux, the first treatments are applied on average four weeks before the first symptoms appear.
We show that postponing the date of the first downy mildew spray to disease onset reduces fungicide use by an average of 56% compared to current practices in this region. For operators, our results show that combining this strategy with the use of personal protective equipment reduces exposure by more than 70%.
By using machine learning methods, we also show that, in Bordeaux, the precocity and severity of downy mildew epidemics are strongly linked. Our predictions can be used to trigger disease treatments only in high-risk cases, resulting in a reduction of more than 50% in mildew treatments compared to current practices.
These results, as well as the methods used, are discussed and compared with other methods for reducing the use of pesticides in viticulture.
Contact: mathilde.chen [AT] acta.asso.fr ou mathilde.chen [AT] inra.fr – Tél : +33 1 30 81 59 06 / +33 671419020
Communications / Publications
M. Chen, F. Brun, M. Raynal, C. Debord, D. Makowski, (2019). Use of probabilistic expert elicitation for assessing risk of appearance of grape downy mildew. Crop Protection, 126. – https://doi.org/10.1016/j.cropro.2019.104926
Chen, M., Brun, F., Raynal, M. & Makowski, D., 2018. Timing of grape downy mildew onset in Bordeaux vineyards. Phytopathology. 2018 Oct 30. https://doi.org/10.1094/PHYTO-12-17-0412-R
Chen M.a,Brun F.b,Raynal M.c,Makowski D. (2020) Forecasting severe grape downy mildew attacks using machine learning, PLoS ONE https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230254
Chen M., Brun F., Raynal M., Makowski D. (2020) Delaying the first grapevine fungicide application reduces exposure on operators by half, Scientific Reports
Chen, M., Brun, F., Raynal, M. & Makowski, D., 2018. Estimer la date d’apparition du mildiou de la vigne grâce à l’élicitation probabiliste d’experts. Végéphyl – 12ème Conférence Internationale sur les Maladies des plantes. Tours, le 11 et 12 décembre 2018.
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