Two approaches were used to evaluate multiple-use inventories. The first approach was to analyze inventory data currently being collected. Assuming that all variables being inventoried are necessary in making decisions for multiple-use management, simple correlation, multiple linear regression, and factor analysis techniques were employed. Data from the Jefferson National Forest in Virginia were analyzed but no strong association between variables currently being observed was detected.

The second approach was to define the decisions that are to be made and the variables necessary to make these decisions. Once the variables have been defined, sampling intensity must be determined. The decision-maker was not required to define the sampling intensity based on experience or a rule-of thumb, but rather a cost-loss function for multiple inventories and multiple decisions was minimized. The cost of collecting data will increase as sample size increases, while the expected monetary loss that will occur to the decision-maker from making decisions with inventory data will decrease. Minimizing the cost-loss function determines the sampling intensity that will provide the lowest total (cost plus loss) monetary cost to the decision-maker. Methods were developed to determine necessary information for the cost-loss function, and several methods of minimization were evaluated.