A method to determine optimal stand data-acquisition policies

A method to determine optimal forest stand data acquisition policies is presented. The method is based on a proposal about how timber management planning could be performed, recognizing that stand data cannot be exactly known. It is suggested that decisions be made using probability distributions of values in the calculations instead of point estimates. Within this planning framework, using Bayesian theory, a posterior distribution can be calculated if an inventory is carried out. The probabilities for different posterior distributions can also be calculated. As different distributions may imply different optimal harvest actions, the expected inoptimality losses from erroneous harvest decisions will decrease after an inventory. However, if an inventory is to be an adequate decision, the inventory cost must not be higher than the expected decrease in losses. The numerical results showed that the profitability of inventories depends to a large extent on factors such as the interest rate, stumpage value, compartment size, stand age, and prior distribution of true stand volume. We found inventories to be most profitable if they were undertaken well ahead of calculated optimal harvests. Moreover, we also found that a series of moderately precise but cheap inventories was superior to one single very precise but expensive inventory.