Plant Growth Analysis

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Functional Plant Growth Analysis

The expression Crop Growth Analysis (CGA) generally refers to useful set of quantitative methods describing and interpreting the behaviour of whole plant/crop systems under natural, semi-natural or controlled conditions. CGA can provide an explanatory, holistic, and integrative approach to interpreting plant morphology, physiology, phenology and functions often required in research and education activities.
The bases for CGA are primary data such as weights, areas, volumes and contents/components of plants/crops elaborated dynamically for describing the entire system behaviour.
Two distinct approaches to CGA have evolved: classical (or intervals) and functional one.

Classical (or intervals) approach

In the past, the interval method was utilized to compute the growth analysis components (e.g. RGR, NAR and LAR), by using average plant weight, leaf weight, and leaf area.
The problems with the interval method are that it computes the growth analysis indices using two sampling dates and therefore assumes linear growth between the two sampling dates and that sampling errors can have a relevant effect on the value of indices itself, so a high number of replication is required.

Functional approach

Functional approach typically is more robust functional approach, even if some small time scale effect can be hidden because original values of crop data are substituted by their interpolated values. Thanks to the availability of powerful software and hardware tools and numerical methods, Functional Growth Analysis (FGA) is now the commonly accepted method for growth analysis. For FGA the first step is to fit (by varying numerical technique) a function to plant growth over the season. Through this approach, a function must be fit for root, shoot, leaf biomass and leaf area over time. Once obtained a mathematical dynamic expression of an appropriate growth model, it is possible to calculate the instantaneous value of growth rate at any (and all) time, in terms of Relative Growth Rate (RGR) and derive different parameters required for crop growth models.

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Last Update 01.08.2015