AUTOSAW simulations of lumber recovery for small-diameter Douglas-fir and Ponderosa pine from southwestern Oregon

Realistic forestry value chain simulations require accurate representations of each component. For primary processing, this is complicated by the fact that a single raw material is converted into a wide range of lumber products. The aim of this study was to develop statistical models for predicting lumber product assortment from tree size information, while taking into account the high proportion of zeros in the data. Lumber recovery was simulated from a database of 1013 laser-scanned Picea mariana (Mill.) Britton, Sterns & Poggenb. and Abies balsamea (L.) Mill. stems using the sawing simulator Optitek. The number of boards per stem of specific products was modelled with zero-inflated Poisson regression using stem diameter and height as covariates. The number of boards per stem was strongly related to both diameter and height, but also changed according to input prices for lumber products. Zero-inflated models outperformed ordinary Poisson regression in all cases. The developed models will be integrated into simulation tools designed to optimize processes along the entire forest value chain from forest to end user.

show abstract

AUTOSAW simulations of lumber recovery for small-diameter Douglas-fir and Ponderosa pine from southwestern Oregon

Cited by 3 publications

References 1 publication

Bibliography

Bibliography

Effect of utilization standard on wood supply and lumber yield

StatSAW: modelling lumber product assortment using zero-inflated Poisson regression

Contact Info

Product

Resources

About