Value of forest information

Traditionally, the quality of forest inventory data has been measured with root mean square error of interesting variables. In many occasions, however, even this information has not been available for all variables of interest, as the main focus has always been on the accuracy of timber volume estimates. In recent years, the quality of data as a basis for decision-making has also been considered, using so-called cost-plus-loss analysis. In such analysis, the optimal data acquisition method is defined to be the one which minimizes the total costs of inventory, i.e., the direct inventory costs and the losses due to suboptimal decisions based on incorrect data. It would, however, be possible to go even further, and estimate the value of certain information, or even information concerning a certain forest variable/parameter in decision-making. This is possible when utilizing Bayesian decision theory. It would enable inventory researchers to concentrate on important issues, and managers to invest optimally on data acquisition process. This review presents the research carried out in valuation of information in different fields and discusses the possibilities to use it in forestry applications.