Project Description:
Fungal contamination comprises a major food-borne threat with severe impacts on agricultural production , human and livestock health, and eventually,
significant impacts for the economy (Johns et al., 2022). One of the main fungal pathogens relevant to short grains (e.g. wheat, oats) is Fusarium, which under
specific environmental conditions can produce toxic secondary metabolites called mycotoxins. Mycotoxins can pose a direct threat for the plant itself but also
affect human health after consumption.
Mycotoxin production is strongly influenced by climatic factors, particularly temperature and relative humidity (Garcia et al., 2025). In the context of increasing
human-caused climate change, quantifying the relationship between environmental conditions and mycotoxin production is essential for predicting and
mitigating future contamination risks (Infantino et al., 2023). In this context, a major challenge lies in translating laboratory-derived models into accurate
predictions under real-world conditions.
The student will work as part of the Ambrosia Horizon Europe research project, within the Institute of Climate and Atmospheric Science, School of Earth and
Environment. The aim of the Ambrosia project is to quantitatively assess the impacts of climate and climate change on food safety across Europe. For this, we
invite an enthusiastic and creative student with data analysis and Python programming experience to participate in our effort.
Data:
The data to be used are regional climate model simulations performed in the context of the Euro-CORDEX initiative, available on 12 km horizontal resolution.
Temperature and humidity data will be extracted from the Euro-CORDEX ensemble for historical simulations and future projections across Europe. These data
will be fed into probabilistic models simulating mycotoxin growth.
Methods:
The student will apply methods of statistical and geostatistical analysis, with the aim of translating the work of Hjelkrem et al. (2018), describing the
probability of mycotoxin production, into maps of future mycotoxin risk across Europe. This will involve processing the Euro-CORDEX data in a format
suitable to provide the necessary temperature and humidity input information to the mycotoxin production model for historical and future simulations under
certain climate change scenarios. This work will be performed in Python programming environment allowing for its future reproducibility.
Tools:
Programming in Python language will be used.
Benefits:
The student will participate in the Ambrosia project meetings and will be able to present and discuss their results with scientists from the fields of climate
science and food microbiology. The results of this 6-week project will be used by the University of Leeds Ambrosia group to further develop their
methodological approach. The student will acquire significant knowledge on computer programming and statistical and geostatistical analysis, develop
communication skills with scientists from various fields and obtain a hands-on experience on data-handling of real-world concerns, such as food-borne
diseases.
Pre-requisites:
N/A
Supervisory Team:
Amanda Maycock
Maria Karypidou
Contact:
Contacts:
Professor Amanda Maycock ([email protected])
Dr Maria Chara Karypidou ([email protected])