The future of fruit production depends on producing high-quality fruits with low environmental impact. Peach trees are highly susceptible to pathogens and pests. Current chemical control practices pose environmental and health risks and may promote the development of resistant strains. One significant disease affecting peach trees is leaf curl, caused by the fungus Taphrina deformans. Symptoms include leaf thickening and de-
formation, leading to defoliation (finally impacting fruit yield) and, in severe cases, tree death. Developing predictive models for disease risk under various climatic scenarios, as well as identifying resistant varieties, is crucial for sustainable production (see e.g. Chaloner et al. (2019)).
Previous works have led to models estimating leaf curl disease intensity based on climate conditions (Giosu`e et al., 2000; Thomidis et al., 2010). This internship will ameliorate existing modeling approaches explicitly considering plant phenology, using new techniques (possibly including deep learning see e.g. Lee and Yun (2023)) and an original dataset providing disease symptoms in different years in different French locations, characterized by different climates. The new modeling framework will be used, in combination with estimates of climate change in the 21th century, to assess which area will be suitable for peach cultivation in France (see Vanalli et al. (2021)) and which will be the expected impact of leaf curl.
Intern’s Main Activities
1. Literature review on risk diseases models
2. Conception and calibration of a risk model for peach leaf curl
3. Discussion of results, report writing, and presentation of findings to the team and
external partners.
Collaboration Opportunities
The intern will have the opportunity to collaborate with experts from INRAE’s units and University of Avignon (e.g. Davide Martinetti, Marie Launay, Florent Bonneu) and from a panel of international colleagues (Chiara Vanalli, Nik Cunniffe and Renato Casagrandi respecively from ´Ecole Polytechnique F´ed´erale de Lausanne, University of Cambridge, and Politecnico di Milano)
Required Profile
• Master’s degree in final year.
• Required Knowledge: Mathematical modeling and statistics
• Appreciated knowledge: Epidemiology, Management of natural resources, Plant
Science
• Skills: Proficiency in programming languages (R, Python or Matlab) and ability to manage datasets.
References
Chaloner, T. M., H. N. Fones, V. Varma, D. P. Bebber, and S. J. Gurr (2019). A new mechanistic model of weather-dependent Septoria tritici blotch disease risk. Philosophical Transactions of the Royal Society B: Biological Sciences 374 (1775).
Giosu`e, S., G. Spada, V. Rossi, G. Carli, and I. Ponti (2000). Forecasting Infections of the Leaf Curl Disease on Peaches Caused by Taphrina deformans. European Journal of Plant Pathology (106), 563–571.
Lee, S. and C. M. Yun (2023). A deep learning model for predicting risks of crop pests and diseases from sequential environmental data. Plant Methods 19 (1), 145.
Thomidis, T., V. Rossi, and E. Exadaktylou (2010, December). Evaluation of a disease forecast model for peach leaf curl in the Prefecture of Imathia, Greece. Crop Protection 29 (12), 1460–1465.
Vanalli, C., R. Casagrandi, M. Gatto, and D. Bevacqua (2021). Shifts in the thermal niche of fruit trees under climate change: The case of peach cultivation in France. Agricultural and Forest Meteorology 300 (108327
Commentaires récents