The paper presents a simple model of long-term forest management planning in tree plantations. The model is particularly suitable for developing countries where the research resources are limited. The management plan is prepared in two steps. First, one or several treatment schedules are simulated for each calculation unit (age class, compartment, etc.) over the selected planning period. Second, an optimal combination treatment schedules according to the selected objectives and constraints is searched by mathematical programming. The simulation of growth is based on the prediction of the diameter distribution at the desired time point. All stand characteristics are derived from this distribution. The models needed in the yield simulation can be estimated from temporary sample plots. A case study management plan for 13,000 ha of Pinus kesiya (Royle ex Gordon) plantations in Zambia is presented to demonstrate the system.
The PDF includes an abstract in Finnish.
The purpose of this study was to prepare a comprehensive, computerized teak (Tectona grandis L.f) plantation yield model system that can be used to describe the forest dynamics, predict growth and yield and support forest planning and decision-making. Extensive individual tree and permanent sample plot data were used to develop tree-level volume models, taper curve models and stand-level yield models for teak plantations in Panama. Tree volume models were satisfactorily validated against independent measurement data and other published models. Tree height as input parameter improved the stem volume model marginally. Stand level yield models produced comparable harvest volumes with models published in the literature. Stand level volume product outputs were found like actual harvests with an exception that the models marginally underestimate the share of logs in very large diameter classes. The kind of comprehensive model developed in this study and implemented in an easy to use software package provides a very powerful decision support tool. Optimal forest management regimes can be found by simulating different planting densities, thinning regimes and final harvest ages. Forest practitioners can apply growth and yield models in the appropriate stand level inventory data and perform long term harvest scheduling at property level or even at an entire timberland portfolio level. Harvest schedules can be optimized using the applicable financial parameters (silviculture costs, harvesting costs, wood prices and discount rates) and constraints (market size and operational capacity).