A Stochastic Efficiency Approach for Determining the Economic Rotation of a Forest Stand
Abstract:Existing risk-rotation models for forest stands assume risk-neutrality on the part of decision makers. The criterion by which the optimal economic rotation is chosen in such models is based on the maximum expected discounted value of the site. Economic theory suggests, however, that many individuals are risk-averse and do not make investment decisions based solely on the mean of the distribution of investment returns. Models assuming risk-neutrality do not necessarily identify the appropriate economic rotation for such investors. Stochastic dominance analysis is employed to incorporate risk-aversion into the rotation decision. An example demonstrates how to apply the technique in practice, and results are compared with alternative decision rules, these being the (1) deterministic land expectation value, (2) mean-variance rule, and (3) mean-coefficient of variation rule. Results are consistent with previous work in that the inclusion of risk identifies risk-efficient rotations that may be shorter than in the deterministic case. Unlike previous efforts, more than one risk-efficient rotation may be identified. The mean-variance rule cannot usefully be applied to the rotation decision, and the mean-coefficient of variation rule defines a much larger efficient set than does stochastic dominance analysis. A sensitivity test shows that the degree of stochastic efficiency attained for a given rotation age changes with the annual probability of fire. For. Sci. 34(2):441-457.
Document Type: Journal Article
Affiliations: Assistant Professor, School of Forestry and Alabama Agricultural Experiment Station, Auburn University, Auburn, AL 36849
Publication date: June 1, 1988
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
- Ingenta Connect is not responsible for the content or availability of external websites