According to the available literature, the appearance of Parana pine (Araucaria angustifolia (Bertol.) Kuntze) wood resembles that of Scots pine (Pinus sylvestris L.). The anatomy is quite different, however. There are no resin canals and fusiform rays with resin canals in Parana pine. They are abundant in Scots pine, however. The basic density of Parana pine is higher. In both species the density increases from the pith outwards, the maximum being reached at the age of 100 years. Compression wood is more common in Parana pine than in Scots pine, and this makes the longitudinal shrinkage of Parana pine greater than that of Scots pine. Otherwise the shrinkage properties do not differ. The mechanical strength is of the same magnitude with the exception of hardness, where Parana pine is superior.
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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).