Annikki Mäkelä


Modelling stand growth for optimal management under climate change: experiences from Scots pine stands in Finland


In forest management planning, growth models are used for predictions of forest growth under different management practices, and for the comparison of the profitability of the alternative management options from the point of view of prescribed objectives. The models therefore need to be responsive to the management options, and they need to provide information about the variables that determine the value of the product. Lately, it has become increasingly evident that the models also need to be responsive to changing climatic and site factors, in order to be applicable throughout the stand rotation in a changing environment.

Models in use in practical forest management planning systems have usually been statistically fitted to extensive data sets but without an explicit link to climatic factors. On the other hand, so-called process-based models aim for increasing our understanding of the physiological processes related to growth and yield, but often without considering the outputs central for management planning. In addition, they generally require very detailed inputs that would not be available for regional management planning systems.

This paper presents an approach to forest growth modelling that aims for combining the crucial characteristics of the empirical and ecological models, so as to make the models responsive to both management actions and the environment, and to provide outputs useful for economic assessment using inputs that are feasible at the national scale. The modelling work is based on a theoretical analysis of the forest as a complex hierarchical system, and it utilises a modular approach to represent the real system at an appropriate level of detail.

The approach has been applied to economic optimisation of thinning and rotation schedules of pine and spruce stands in Finland.This paper will focus on the pine stands both with respect to model development and results on management optimisation and environmenal impact. The opportunities and current challenges of the approach will be discussed, with special reference to the objective of developing models with a sound mechanistic basis while aiming at being robust and utilising the exisisting empirical knowledge efficiently.

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