In this study we developed a dynamic growth model for Scots pine (Pinus sylvestris L.) plantations in Galicia (north-western Spain). The data used to develop the model were obtained from a network of permanent plots, of between 10 and 55-yearold, which the Unidade de Xesti ´on Forestal Sostible (Sustainable Forest Management Unit) of the University of Santiago de Compostela has set up in pure plantations of this species of pine in its area of distribution in Galicia. In this model, the initial stand conditions at any point in time are deﬁned by three state variables (number of trees per hectare, stand basal area and dominant height), and are used to estimate stand volume, classiﬁed by commercial classes, for a given projection age. The model uses three transition functions expressed as algebraic difference equations of the three corresponding state variables used to project the stand state at any point in time. In addition, the model incorporates a function for predicting initial stand basal area, which can be used to establish the starting point for the simulation.
This alternative should only be used when the stand is not yet established or when no inventory data are available. Once the state variables are known for a speciﬁc moment, a distribution function is used to estimate the number of trees in each diameter class, by recovering the parameters of the Weibull function, using the moments of ﬁrst and second order of the distribution (arithmetic mean diameter and variance, respectively). By using a generalized height–diameter function to estimate the height of the average tree in each diameter class, combined with a taper function that uses the above predicted diameter and height, it is then possible to estimate total or merchantable stand volume.
Forest growth models predict growth of a target forest stand using site characteristics and management options as input variables, and constitute important tools for decision-making in sustainable forest management. Most of these models are empirical and can be organized around three types representing a broad continuum of model classes: whole stand models, size class models and individual tree models (Davis et al., 2001). The choice of the type of model to develop depends on both the purposes of its application and the resources available (Vanclay, 1994). These factors also determine the data needed and the resolution of the estimates. Although individual tree models provide more detailed estimates, the complexity and the amount of input data increase from whole stand models to individual tree models (Gadow and Hui, 1999; Davis et al., 2001).
Among the three model types described, whole stand models are generally recommended for the management of forest plantations (Garc´ıa, 1988; Vanclay, 1994), because they represent a good compromise between generality and accuracy of the estimates. In addition, the large state vectors that individual tree models require are likely to contain mostly redundant information, with consequent losses in precision (overparametrization), as well as being costly or the values being impossible to obtain accurately from routine ﬁeld sampling (Garc´ıa, 1994). Nevertheless, some whole stand models provide rather limited information about the forest stand (in some cases only stand volume) (Vanclay, 1994; Porte´ and Bartelink, 2002).
Considering that forest management decisions require more detailed information about stand structure and volume, as classiﬁed by merchantable products, whole single-species, evenaged stand models can be disaggregated mathematically using a diameter distribution function, which may be combined with a generalized height–diameter equation and with a taper function to estimate commercial volumes that depend on certain speciﬁed log dimensions. Similar methodologies have been used by Cao et al. (1982), Burk and Burkhart (1984), Knoebel et al. (1986), Uribe (1997), R´ıo (1999), Kotze (2003), and Castedo Dorado (2004) in the development of forest growth models, mainly for plantations. Generally, th
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