Forest managers commonly use growth and yield models to predict the impact of different combinations of site, genetics, and silviculture on volume and log product outturn (Weiskittel et al. 2011).
A stochastic run of 20,000 iterations for four harvests and six log products takes about 30 s using a 2.9 GHz Intel Core I9 iMac computer.
The resultant algorithm enabled the evaluation of the rotational consequences of competing density management regimes in terms of overall productivity, log-product distributions, biomass production and carbon yields, recoverable end-products and their associated monetary value, economic efficiency, duration of optimal site occupancy, structural stability, and wood fibre attributes.
The distributions of the log volumes by log product again follow that used in Ferguson (2005).
The modules responsible for predicting log product distributions, and end-product volumes and values, were developed employing relationships derived from taper and sawmill simulation studies.
The models developed to explain these trends in wood density have been coupled together and linked to a growth and yield simulator which also predicts branching characteristics to estimate the impact of different factors, primarily site, on the wood density distribution of log product assortments.
The models developed to explain these trends in wood density have been coupled together and linked to a growth and yield simulator which enables forest managers to estimate the impact of different factors, primarily site, on the wood density distribution of log product assortments.
Up to six different log products can be accommodated.
In this tree-log-product study across differing silvicultural treatments, the product under examination is veneer.
Pooling of log products with similar means and coefficients of variation may, therefore, be preferable.
