Applications
Fitting
Plant structure fitting
Plant structure fitting principles
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Plant structure must be analysed in order to identify its parameters, following its development
at a specific time or step by step.
- structural parameters are fitted for each physiological age
- development parameters are to be considered first (assumed to result from a Bernoulli process)
- axis viability then has to be considered
- branching probabilities must be defined in a final step
The data sets required to fit structural parameters must be statistically significant for each axis typology.
They should be able to deliver distributions of phytomers for a given growth cycle range.
On a given species, such information is classically obtained through two types of experimental plots:
- from sets of individuals at different growth stages
- from a population of the same age
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It is understood that the fitting process is applied to each axis type.
In other words, fitting is carried out on the different physiological ages, independently.
More precisely, the parameters to be identified concern the development process, (i.e. the quantification of the production of the terminal bud building the phytomers of an axis), viability (expressed usually as the mortality of axes in relation to their age), and the branching process.
These processes are usually easier to quantify for continuous growth.
However, the approaches defined for the continuous case can usually be extended to rhythmic cases, applying the techniques on several levels, for instance within the growth unit, and from one growth unit to the next.
To sum up:
Contents
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Fitting the axis development process.
- Development is considered modelled as successive Bernoulli processes,
leading to fit binomial or negative binomial laws.
Fitting of the development process parameters is first seen in a simple continuous growth case .
Then, the case of continuous damped growth is considered.
The case of rhythmic growth is then considered.
Fitting the axis mortality process.
The mortality process is classically modelled by a mortality probability, applied to each phytomer appearance during development.
Branching.
Branching fitting can easily be related to a simple branched/(branch or unbranched) ratio, but can considerably increase in complexity if couplings have to be identified within growth units or from one phytomer to another.
In the case of young populations, crown analysis is an efficient method for retrieving development parameters on the main axis and branches, analysing phytomer distributions from the top of the crown.