A decisionmaking framework for assessing atmospheric deposition impacts on regional forest inventory

A decisionmaking framework was developed to assess atmospheric deposition impacts on regional softwood inventory in Virginia. This decision making framework consists of three segments: a forest inventory projection model, a timber production function, and a timber consumption model. The Timber Resource Inventory Model (TRIM) was used to project future forest inventory, given initial inventory data, yield information, and harvest request. The timber production function allows the estimation of the individual effects of input variables on stand growth and yield. The timber consumption model was linked with TRIM to simulate the interactions between timber removals and inventory levels.

Algorithm analysis, sensitivity analysis, and an a priori analysis were used to examine the feasibility of TRIM for projecting atmospheric deposition impacts on inventory. Modification of growth and harvest decision variables in TRIM allows this impact estimation.

Schumacher's yield model was modified to develop the timber production function according to goodness-of-fit, minimal collinearity, and biological rationale. Crown length was used as a surrogate of a. biological factor to reflect atmospheric deposition impacts on stand growth and yield. The small variance inflation factor allows the crown length elasticity to serve as a measure of the quantitative effects on the yield table. A system of predictor equations was added to the yield equation for simulating stand dynamics.

A consumption function approach was used to develop the timber removals model. The BoxCox transformation, the stepwise regression procedure, and standard error were used to select the functional form, predictor variables, and estimates for the timber removals model. This removals model was linked with TRIM for simulating the interactions between removals and inventory levels for Forest Industry and Other Private. The existing forecasts of removals based on Forest Service projections were used for impact estimation for all ownerships.

This decisionmaking framework was applied to the softwood inventory data in Virginia to demonstrate the impact estimation. Sensitivity analysis showed that the percentage reduction of inventory and removals is directly related to the crown length reduction. The larger the crown length reduction, the greater the percentage reduction of the inventory. The percentage reduction of yield tables due to the crown length reduction is slightly less than the overall percentage reduction of the inventory but is slightly greater than the overall percentage reduction of removals. The quantitative information on atmospheric deposition impacts on crown variables is a key to the impact estimation for inventory and removals. Also, this decision making framework can be used to measure some silvicultural practice effects on regional inventory.