In this short introductory section we want to ask ourselves: what is the use of simulation modeling in crop loss understanding and analysis? But before moving into the field of crop loss modeling, we need to point out a few elements.
Much of what will be developed in the following chapters is derived, with some expansion, but also some simplifications, from the book by Rabbinge et al. (1989). As in the previous chapters, our aim is to bring forward ideas and methods that can be implemented with ease, and we shall try to provide frameworks that are as simple as possible.
Let us try, at least temporarily, to answer the question we posed at the beginning of this introduction. Simulation modeling in crop loss analysis is useful in at least two important areas: one is to produce estimates of likely crop losses caused by one (sometimes several) yield reducers; another is to assess and rank the importance of yield reducers in terms of crop losses. There are other possible applications of simulation modeling. One potential application, which was widely shared when simulation modeling was new to the field of plant protection, was that it could become a tool to guide crop protection. There has been accumulating evidence, however, that this approach was unlikely to bear fruit for a number of reasons (e.g., Butt and Jeger, 1985; Jeger, 2000). Conversely, much simpler approaches based on very solid science could be far more successful, such as the EPIPRE program (Zadoks, 1989).
The second area where simulation modeling is a powerful tool is that, being process-based, it enables one to not only address "what has been lost", but also what could be gained. In other words, the approach enables one to explore scenarios where new crop health management approaches (e.g., new genotypes, different crop rotations) would be implemented. This is possibly the most exciting reason for using simulation modeling in this area.
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