Chapter 10 - Putting It All Together in a Case Study

Abstract

In the previous chapters, methods for working with dynamic system models were presented using various examples, chosen to illustrate the principles of the methods. What is missing there is the way that the methods interact within a single modeling project. In fact, there is, to a large extent, a logical progression in a modeling project, from an exploration of the data to a preliminary test of the model to uncertainty analysis and sensitivity analysis to model calibration, then to another round of evaluation and finally to application of the model. In many cases, one step uses information from the previous steps. It is this progression and interaction that we illustrate in this chapter. Doing a case study also requires us to choose when several different approaches are possible. This may also be helpful because each modeler will be faced with similar choices. The case study uses the simple maize model, and has as its objective to map maize yield and interannual variability over Europe. Only the most important parts of the R code to apply the methods are shown. The main results are shown and discussed. All the steps can be easily rerun using demonstration R scripts (demos) in the R package ZeBook.