Skip to main content

Optimal Policies for Managed Stands: An Infinite Horizon Markov Decision Process Approach

Buy Article:

$29.50 plus tax (Refund Policy)


The approach presented optimizes investment in timber production when the manager is faced with uncertainties both in future product markets and in the response of stands to management actions, and when the objective is to maximize total discounted expected returns over an unending time horizon. The uncertainties are formulated in a Markovian decision process with the state of each stand described by average tree size, stocking level, and market condition. Solution of a functional equation gives a "stationary" management policy in which the optimal action to apply to any stand depends only on the observed state and not on the decision time, past states, or past actions. The approach is applied to young growth Douglas-fir. Forest Sci. 21:109-122.

Keywords: Decisionmaking under uncertainty; forest management; probabilistic models; timber production

Document Type: Journal Article

Affiliations: Assistant Professor, Dept. of Forest Engineering, Oregon State University, Corvallis, OR 97331

Publication date: 1975-06-01

More about this publication?
  • 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.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
  • Submit a Paper
  • Membership Information
  • Author Guidelines
  • Podcasts
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free ContentFree content
  • Partial Free ContentPartial Free content
  • New ContentNew content
  • Open Access ContentOpen access content
  • Partial Open Access ContentPartial Open access content
  • Subscribed ContentSubscribed content
  • Partial Subscribed ContentPartial Subscribed content
  • Free Trial ContentFree trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more