The Art of Modelling

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The Art of Modelling

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Modelling is a crucial part of today's science. Particularly in agronomy, ecology and environmental sciences, where models are used for assessing sensitivity of systems to disturbances or changes in external factors, and for predictions of future system states. This course provides an introduction to modelling. Modelling concepts will be dealt with in detail, going through the basic steps to be taken. The main themes of this course are:

  • Modelling concepts
  • Model design
  • Model use
  • Calibration and validation
  • Sensitivity and uncertainty
  • Linearity and complexity
  • Reporting on model studies

The main part of the course focuses on systems analysis using dynamic simulation models. Systems approaches are widely used in studies of ecological systems for the purpose of increasing our understanding of ecosystems functioning and improving systems management. This course introduces the participants to the study of the behaviour of ecological systems. The course comprises four blocks:

  1. Systems dynamics with examples from crop production & population ecology
  2. Partial differential equations & modelling in space
  3. Model performance & model evaluation
  4. Reflection & reporting

The first block focuses on conceptual model formulation and quantitative model specification, using concepts such as system, model, simulation, state, rate, feedback, time-coefficient, relational diagram, analysis of dimensions or units, numerical integration methods and discontinuities in integral contents. The concepts are explained using examples from crop production, soil organic matter dynamics and population ecology. Programming is guided by the use of relational diagrams in the software package Visual Grind.
The second block introduces partial differential equations for the simulation of spatial processes in one or two dimensions. We start with simulating one dimensional processes, such as heat flow, and mass flow and diffusion of nutrients in soils. Subsequently, spatial modelling is introduced, using the example of vegetation patterning to illustrate two-dimensional modelling. Programming will be conducted in in the software package MATLAB, which is more flexible than Visual Grind.
The third block focuses on aspects related to model calibration and evaluation. Statistical means and inverse modelling are used to assess how well a model describes experimental data. Techniques for parameter estimation and sensitivity analysis are introduced and model outcomes will be discussed critically.
Lastly, some time is reserved for reflection on own research and modelling plans. On the last day, all participants will prepare and present their modelling work.

The software packages Visual Grind and Matlab will be used. These will be made available to participants. The course comprises a) lectures, b) practicals in which exercises are solved using paper and pencil and by means of a computer, c) application of the subject matter in case studies elaborated in small groups, d) reflection on own study and model(s) using the concepts and applications used, and e) presentations of how modelling is used in the participants' own work. Upon successful completion of this course, participants are expected to be able to:

  • Understand and evaluate scientific literature where models are used
  • Choose appropriate modelling methods for their own research
  • Plan and report on a modelling study in their own research
  • Follow advanced modelling courses

The programme of the last edition of this course (September 2021) can be downloaded here. This should provide you with an indication of the general build-up of this course. The precise structure of the next edition of this course, may of course differ somewhat.

General information
Target Group The course is aimed at PhD candidates and other academics
Group Size Min. 14, max. 24 participants
Course duration 2 weeks (10 working days)
Language of instruction English
Frequency of recurrence Every two years
Number of credits 3 ECTS
Lecturers Tom Schut (Plant Production Systems Group, Wageningen University) and Dr. Katrien Descheemaeker (Plant Production Systems Group, Wageningen University)
Prior knowledge No previous PE&RC postgraduate courses are required. However, basic knowledge of mathematics (in particular basic algebra, vector and matrix algebra, differentiation, integration and differential equations) is required. Participants who need to refresh their mathematics background knowledge can refer to the hand books  "Mathematics that works 2 - Analysis applied (2018)" and "Mathematics that works 3, Vectors and matrices applied (2018)", authored by M. de Gee. Both are available on-line and in the syllabus shop of the Mathematical and Statistical Methods group at Wageningen University.
Location Wageningen University Campus
Options for accommodation Accommodation is not included in the fee of the course, but there are several possibilities in Wageningen. For information on B&B's and hotels in Wageningen please visit Another option is Short Stay Wageningen. Furthermore Airbnb offers several rooms in the area. Note that besides the restaurants in Wageningen, there are also options to have dinner at Wageningen Campus.
More information

Claudius van de Vijver (PE&RC)
Phone: +31 (0) 317 485116

Registration of interest

At this moment, this course is not scheduled yet. However, if you register your interest in this activity below, we will inform you as soon as the course is scheduled and registration of participation is opened.