Zoek medewerkers/organisaties DV Bustos Korts PhD
Naam
Naam DV Bustos Korts PhD
RoepnaamDaniela
Emaildaniela.bustoskorts@wur.nl

Werk
OmschrijvingUniversitair docent
OrganisatieDepartement Plantenwetenschappen
OrganisatieeenheidWiskundige en Statistische Methoden - Biometris
Reguliere werkdagen
Ma Di Wo Do Vr
Ochtend
Middag

Biografie

Keywords: GxE, crop adaptation, plant breeding, statistical models, crop growth models, genomic prediction

Role and scientific interest

I work as a researcher at Biometris, the group for statistical and mathematical methods for the quantification of biological processes in our living environment.

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 is a very useful tool that 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 have been involved in courses organized by Biometris 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

 

Projects

  • EU-INVITE: is a 5-year European Union funded project, aiming 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: 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.

 

Publications

Google Scholar, ORCID


Expertiseprofiel
Expertise

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Projecten

EU-INVITE: the overall objective of INVITE, 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. I’m involved in WP4, led by Fred van Eeuwijk, aiming at evaluating methodologies for environment classification and proposing strategies to improve the efficiency of the variety testing network.

Digital twin of a tomato crop in a greenhouse: this project, let by Jochem Evers, aims at developing 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. The interactions between the characteristics of the crop (the variety), the environmental factors and crop management are all simulated in the virtual crop. Because the model is linked to a real tomato crop in a greenhouse, it becomes possible to refine predictions more and more and thus make better choices for the real crop.

Collaborations with plant breeding companies: these projects involve methods for environment characterization and classification, and prediction of genotype performance across multiple environments.


Onderwijs
Vakken
  • MAT-33306 - Data Science for Plant Breeding and Genetics
  • MAT-70424 - MSc Internship Mathematical and Statistical Methods
  • MAT-79324 - MSc Research Practice Mathematical and Statistical Methods
  • MAT-80424 - MSc Thesis Mathematical and Statistical Methods
  • MAT-80436 - MSc Thesis Mathematical and Statistical Methods
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