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Analyzing Uncertainties in Experts' Opinions of Forest Plan Performance

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Multi-objective forestry requires new decision support systems to aid the forest owner and foresters in the planning of future treatment schedules. The analytic hierarchy process (AHP), based on pairwise comparison data and Saaty's eigenvector method, is one technique that has been proposed to make such qualitatively different objectives as income from timber sales and scenic beauty of forest landscape commensurable. A weak point of the methodology has been the lack of a statistical theory behind it. We have earlier shown how classical regression techniques can be used to provide a statistical assessment of the uncertainty of the estimated ratio-scales. In this paper we extend the results to a multi-level decision hierarchy commonly used in forest planning. We also provide a Bayesian extension of the regression technique. The advantage of the Bayesian approach is that it provides summaries of expert views that are easily understood by decision makers who may not have extensive understanding of statistical concepts. On the basis of the Bayesian analysis, one can calculate, for example, how likely it is that (in the view of the expert) a given forest plan is better than any other plan being compared. For. Sci. 43(4):521-528.
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Keywords: AHP; Bayes; decision analysis; planning; regression

Document Type: Journal Article

Affiliations: Station head, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, 69101 Kannus, Finland

Publication date: 1997-11-01

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    Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
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