Process- and Data-Driven Modelling in Crop Science

This course is a hands-on course for modelling in a wide agricultural context. Besides the basic data-handling skills, this course especially focuses on the creativity-based aspects of modelling. Here, inspiration from modellers from industry and academia is provided to stimulate the develop of models. Modelling will be carried out individually, in groups and with a tutor to come up with (creative) solutions. The starting point of this course is either a raw dataset (data-driven modelling), a research question (knowledge-based modelling), or a combination of both approaches, and is always connected to a real-life problem. The aim is experience the whole modelling workflow, and to build/develop a more complex analyses throughout the course.

After successful completion of this course students are expected to be able to:

• Explain the role and key principles of data-driven and knowledge-based modelling in agriculture
• Explain how both modelling approaches can be integrated and why that is needed
• Infer the opportunities and challenges of data-driven and knowledge-based modelling
• Apply basics of data analytics (quality of data, pre-processing)
• Interpret and translate complex descriptions of biological systems into mathematical models
• Design and evaluate code for data-driven and knowledge-based models, separately or in combination
• Calibrate and validate data-driven and knowledge-based models

Assumed Knowledge:
PPS20306 Systems Analysis and Modelling;
INF22306 Programming in Python.
Understanding basic plant science concepts, as well as basics in Python, are necessary to follow this course.