Future Shock in Forest Yield Forecasting: The Need for a New Approach

Abstract

The traditional method of predicting future yields of conventional forest products and/or biomass is based on an empirical bioassay of the growth potential of unmanaged stands, or of stands subject to one, or a small number of, management practices. The method employs the historical pattern of stem volume and/or forest biomass accumulation in the form of volume- or biomass-over-age curves. This type of yield predictor, which may be presented as a simple yield table or a more complex mensurational computer yield model, is widely considered to produce believable future yield predictions. However, the predictions will only be accurate if the future environmental conditions and management regimes are similar to those that pertained over the period during which the biomass accumulation on which the yield model is based occurred. This is unlikely because the continued growth of the human population and the resultant loss of forest land will require a great intensification of forest management. The significant changes in management that many believe await forestry in the not-too-distant future in many parts of the world will render such conventional predictions very questionable. In addition, human-induced changes in atmospheric chemistry may result in changes in the climatic (the "green-house gases" problem), canopy or soil conditions (the "acid rain" problem) that determine tree growth.Computer models of forest yield based solely on the simulation of the biological processes that determine tree growth do not at present offer a viable alternative. Either we do not yet know enough to build, or we do not have sufficient resources to develop and calibrate such process models at an adequate level of complexity.What is needed is a generation of hybrid yield models that combine traditional mensurational models with a simulation of those growth-regulating processes that are significantly altered by changing management practices and/or by changing atmospheric chemistry and climate. One such model is FORCYTE: the FORest nutrient Cycling and Yield Trend Evaluator. This is an ecologically-based forest management simulation model that can predict the long-term consequences of a wide variety of forest management practices for the future harvest yield, ecosystem nutrient budgets, economic efficiency and the energy benefit/cost ratio of management. It combines the believability of the traditional approach with the flexibility of ecological and biological process simulation. Present versions of the model focus on the consequences for future production and yield of changes in forest management. However, because of the structure of the model, it is capable of being modified to examine similar consequences of climatic change and alteration in atmospheric chemistry. Progress in the latter area must await clarification of the processes involved in acid rain damage to forests. FORCYTE is also capable, with minor modification, of being used in agriculture and mined land reclamation research and planning. Key words: Yield prediction, FORCYTE.