Author:

Abstract:

Decision support systems (DSS) are becoming more widespread in forest management, helping to shape the forests of the future. These systems allow foresters to simulate future forest performances under expected or hypothetical conditions and select the most optimal forest management strategy based on the simulations results and stakeholders’ preferences. To increase forest managers’ awareness about the long-term effects of their management, a DSS called Sim4Tree was developed for Flanders, Belgium and has been operational for almost a decade. Sim4tree is a strategic and tactical planning tool that performs simulations of forest development based on traditional empirical growth curves, which, thanks to the flexible Sim4Tree structure, can be easily replaced by other growth models. Unfortunately, empirical growth curves do not allow easily taking into account dynamic processes such as those related to changing management practices and climate change. By plugging in the mechanistic forest growth model ‘4C (FORESEE)’ these changing conditions can be accounted for, allowing to predict future biomass standing stock under different management and climate scenarios in Flanders. Our research shows the possibility of using national forest inventory (NFI) data to parameterize mechanistic models and illustrates the importance of mechanistic models in DSS by evaluating different management (business as usual, production oriented and recreation oriented) and climate scenarios and how these compare to the results from the empirical growth tables and the NFI. The results will improve the performance of Sim4Tree to support the choice for optimal future-oriented forest management scenarios.