Simulation models in general
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What it is, and is not
A simulation program is in it self no new knowledge. It is a powerful way tot structure and link loads of detailed pieces of knowledge, perform calculations with the total of this combined knowledge and see if the outcome matches the reality.
Where this way of packing knowledge leads to realistic outcomes, it is a beautiful tool for growers and advisers to improve their decisions in crop protection. Where it doe not, these logical structures can makes us clear where the gabs or critical points in our knowledge or reasoning are.
As the models that I present in RIMpro are meant to be used by growers and advisors, I am concerned that the user interface is friendly, and the presentation of the outcomes is self explaining. And also that the data of simple on-farm weather stations can be used as input.

The proper way to develop a model
In the development process models are structured, and variables are set according to the available scientific explaining information on the individual processes, the models and parameter settings are, where ever possible, not based on correlative studies on field observations.
For validation purposes the intermediate and end outcomes of the model are compared to field data. The only valid adaptations to a model to these field data are the setting of biofixes where the starting point is an outcome of a not simulated processes (eg date of full bloom), and the relative dispersion of processes that in the field situation may turn out tot be larger than in populations studies under lab conditions.
All other deviations between model outcomes and field observations should be solved by reinterpreting of the underlying process information.

Climatic data
RIMpro reads ASCII files and stores the weather data into an MDB database with a uninformed format. Regardless the format or resolution of the original ASCII file, data are stored in 30 min interval. For this the original files are interpreted and data series are split  (in case of hourly readings) or merged (in case of shorter reading intervals then 30 min) according to logic rules. For instance leaf wetness readings are presented in the datafiles in many different ways, but are in RIMpro interpreted and stored as minutes of wetness in 30 minutes.
In normal use, RIMpro looks for new data in the ASCII file, and reads only the new data. That is the data in the time interval that is not yet present in the MDB. Calculations and recalculations in RIMpro are made with the interpreted data in the MDB file, not with the original ASCII data. If for any reason (for instance after corrections in the ASCII dataset) you want to recalculate with the data in original ASCII file you must force RIMpro to clear the MDB data file, and read the ASCII file again.

Structuring stages and processes
A simulation program is made up of stages that are physically containers for the individuals in that stage. These stages are connected by processes that let the individuals in the population develop from one stage to the next.
The first step in developing a simulation program is to structure the lifecycle into a chain of biological stages. After that for each developmental process the process speed  is described as a mathematical function, in most case as a temperature driven nonlinear relationship. I principally use here Logan shaped curves. As we are simulating the development of a population not only the average developmental time but also the variance to this relationship is quite important. This variance is defined as the relative dispersion (RD) in the process. RD is the standard deviation /average process time).
Finally all kinds of triggers and other provisional conditions for the process are defined.
The data to define the program structure, processes and variables are mostly found by combining literature data from very different sources.

RIMpro uses fractional boxcartrain technique to mimic developmental processes and their dispersion.

Runtime cycles
During runtime typically the following happens at 15 minute data interval:

  • Weather data are read and interpreted
  • Drivers ( e.g. temperature, humidity) are set
  • Values for trigger and condition are set
  • Process speed is calculated for all processes
  • Process development in 15 minutes is calculated form the above values
  • With this the fractional boxcar trains are updated
  • Data are stored

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