Applications of Tree Growth Modelling in Decision Support for Sustainable Forest Management

Abstract

Multi-functional silviculture and sustainable forest management in Europe was recently defined by the Helsinki Resolution H1 (MCPFE 1993). Sustainable development (SD) is associated with abandoning the concept of even-aged forests. In view of this extended SD perception, the conventional forest planning procedure has to be revised. By relying on yield tables and the model of normative forests at the overall enterprise planning level, the conventional results become increasingly irrelevant in forest management. Such outdated planning tools do not provide the information needed for multi-criteria strategic planning. Modern strategic forest planning and decision-making require appropriate tools and technologies, such as forest growth simulators, evaluation and optimisation algorithms, geographical information systems, sample-plot data and visualisation routines, most of which are supplied by forest growth and yield science. The main aspect is to combine these tools and integrate them into decision support systems supporting the planning and decision processes at the enterprise level. In particular, the application of forest growth simulators will enable an improved SD evaluation and a more flexible adjustment between single stand and total estate planning. Simulation models can replace common indicators with aggregated dynamic long-term indicators. Management alternatives can be analysed with regard to their estate-referring and long-term consequences. In this chapter, we will point out: (1) how forest planning and decision-making can evolve from monitoring forest development to strategic planning, (2) how the Pan-European criteria can be used for monitoring and strategic planning of SD at estate level, (3) how available forest planning data can be best utilised for the planning process, and (4) what could be contributed by forest growth and yield science to developing decision support systems for multi-criteria forest enterprise planning.