Descriptive and causal models show mutual advantages and drawbacks.

Descriptive model advantages

• Such models are often simple to define and calibrate
• Their behaviour is usually easy to analyse
• In constant contexts, they show high predictive values


Descriptive model drawbacks

• Extrapolations are limited to very low input changes
• They usually cannot integrate new conditions
• They reflect few or none of the mechanisms that cause the behaviour of the system; they are therefore not explaining any result.



Explanatory (causal) model advantages

• They are based on physiological, mechanistic or structural concepts
• They deliver quantitative indicators of the underlying mechanism and process output
• Their genericity allows extrapolation, varying input values
• They are modular, processes can be studied and developed independently
• They provide insights into plant reactions, illustrating emerging behaviour


Explanatory (causal) model drawbacks

• Model behaviour analysis is difficult and complex, and reversibility also
• Their developments is complex, very time-consuming, often requiring multiple disciplines (team of researchers)
• Only known effects can be modelled.