Keywords: GxE, crop adaptation, plant breeding, statistical models, crop growth models, genomic prediction
Role and scientific interest
I work as an assistant professor at Biometris. My main scientific interest is to understand and to predict crop adaptation across multiple environments and agronomic management conditions. This interest was already triggered during my BSc studies of Agricultural Sciences at the Universidad Austral de Chile and was deepened during my MSc in Crop Physiology at the same university. During my PhD thesis at Wageningen University, I focused on combining and developing modelling strategies for genotype by environment interaction GxE. Modelling GxE contributes to agricultural sustainability because it allows to identify which crop variety will perform best at which environmental conditions, and which physiological mechanisms contribute to adaptation. Therefore, more food can be produced with the same environmental input.
Using novel data to model crop adaptation
The availability of molecular markers and new types of phenotyping information from drones, cameras and sensors has opened new opportunities to understand and predict crop’s response to the environment. Integrating this information to make predictions is chellenging because data sets are large, and not all information is equally useful.
Other academic activities
Besides research, I’m also passionate about teaching. I enjoy the interaction with students and plant breeders. I coordinate the MSc course 'Data Science for Plant Breeding and Genetics'. I'm also involved in courses for institutions from several parts of the world. These courses include topics as:
- Design and analysis of experiments
- Modelling traits over time, with applications to field trials and phenotyping platforms
- Genotype by environment interaction
- QTL detection and genomic prediction
- EU-INVITE: is a 5-year European Union funded project, is to foster the introduction of new varieties better adapted to varying biotic and abiotic conditions and to more sustainable crop management practices.
- Digital twin of a tomato crop in a greenhouse: the goal is to develop a 3D simulation model that is fed in real-time with sensor information from a real greenhouse. These constant updates make this digital twin more advanced than the existing simulation models.
- Collaborations with plant breeding companies: these projects involve methods for environment characterization and classification, and prediction of genotype performance across multiple environments.