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Research article - Peer-reviewed, 2012

Determining sample size in national forest inventories by cost-plus-loss analysis: an exploratory case study

Barth, Andreas; Stahl, Goran


National forest inventories provide information for strategic decisions in a large number of countries. In general, they cover a wide range of variables, from timber-related features to biodiversity and carbon sequestration. Often, it is difficult to decide the exact scope and design of this type of inventory; especially, it is difficult to decide the appropriate sample size. In planning inventories, trade-offs between cost and precision for core variables frequently are made; however, this approach does not fully acknowledge the fact that data typically are collected to form the basis for decisions. In theory, cost-plus-loss analysis provides a more holistic approach to inventory planning, since both inventory costs and losses due to information deficiencies in the decision-making processes are considered. However, whilst it is normally straightforward to determine cost functions, loss functions are difficult to establish; an important reason is that the linkages between data and decisions must be clearly understood. In this study, we explored the possibilities for using cost-plus-loss analysis in connection with determining the appropriate sample size of a national forest inventory. We used Sweden as a case and restricted the analysis to consider the use of data for determining sustainable harvesting levels. The results indicated that the number of plot clusters in Sweden should be in the order of 1,300-2,400 annually, whereas it is currently about 1,400. However, our main objective of the study was not to determine an exact answer for the case of Sweden, but rather to suggest pathways for how cost-plus-loss analysis could be used to support decisions related to determining the appropriate sample size of national forest inventories.


Forest inventory; Sampling; Sample-plot inventory; Optimum sample size

Published in

European Journal of Forest Research
2012, Volume: 131, number: 2, pages: 339-346
Publisher: SPRINGER

    UKÄ Subject classification

    Forest Science

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