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Hauhs, Michael ; Knauft, Falk-Juri ; Lange, Holger:
Algorithmic and interactive approaches to stand growth modelling.
In: Amaro, Ana
(Hrsg.):
Modelling forest systems. -
Wallingford
: CABI Publ.
,
2003
. - S. 51-62
ISBN 0851996930
Abstract
The rationale for stand growth modelling is often either grounded in a search for improved scientific understanding or in support for management decisions. The ultimate goal under the first task is seen in mechanistic models, i.e. models that represent the stand structure realistically and predict future growth as a function of the current status of the stand. Such mechanistic models tend to be over-parameterised with respect to the data actually available for a given stand. Calibration of these models may lead to non-unique representations and unreliable predictions. Empirical models, the second major line of growth modelling, typically match available data sets equally well than process-based models. They have less degrees of freedom, hence mitigate the problem of non-unique calibration results, but they employ often parameters without physiological or physical meaning. That is why empirical models cannot be extrapolated beyond the existing conditions of observations. Here we argue that this widespread dilemma can be overcome by using interactive models as an alternative approach to mechanistic models. Interactive models can be used at two levels: a) the interactions among trees of a species or ecosystem and b) the interactions between forest management and a stand structure, e.g. in thinning trials. In such a model data from a range of sources (scientific, administrative, empirical) can be brought into consistent growth reconstructions. Interactive selection among such growth reconstructions is theoretically more powerful than algorithmic/functional selection. It is suggested that growth models should hence be equipped with interactive visualization interfaces that can be utilized as input devices for silvicultural expertise. Interactive models will not affect the difficulties of predicting forest growth, but may be at their best in documenting and disseminating silvicultural competence in forestry.