Introduction
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Statistical vs mechanistic models
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An *explanatory* model consists of a quantitative description of the
mechanisms and processes that cause the behavior of a system (a limited
part of reality that contains interrelated elements).

To create an explanatory model, the system is analyzed and its processes
and mechanisms are quantified separately. The model is built by
integrating these descriptions for the entire system and crop growth is
then a consequence of these underlying processes. If there are
discrepancies between the model and the real system, the model may be
adjusted by tuning variables to obtain better agreement.

The model is *explanatory* because the calculations involving rate
variables are based on knowledge of the underlying physical,
physiological and biochemical processes.

Not all mechanistic models are explanatory — they may just have a few
simple rules.

A *statistical* or *descriptive* model may reflect very little of the
mechanisms that are causing the behavior.

The models presented here are *deterministic*