Skip to main content

Specification of a Size-Classified Matrix Model: The Effects on Growth Predictions and Economically Optimal Harvesting Regimes

Buy Article:

$29.50 plus tax (Refund Policy)

Abstract:

Two aims of forest economic modeling are to find optimal stand management and harvesting regimes and to set the optimal policy instruments. We studied the effect of specification of a size-class model on the solution for economically optimal forest management. We first focused on the choice of the conversion method to convert the growth data of individual trees into size-class structure; i.e., we tested two alternative estimators (proportion and increment). Next we studied the effects of specification of size classes with different numbers of diameter classes and partitionings within. Growth description was based on the MOTTI stand-level simulator built on data from extensive field measurements in managed forests in Finland. Optimal forest management with the size-classified matrix model included thinning and clear-cutting and was studied for even-aged forest stands. We found that the proportion estimator better captured growth dynamics of small-diameter trees, whereas for larger-diameter trees the difference between estimators was less evident. The effect of the number and partitioning of size classes depended on the estimator method. With the proportion estimator, the number of size classes did not have systematic effects on timber yield nor land expectation value. With the increment estimator, yield and land expectation value systematically decreased when the number of size classes was increased. The results show that specification of a size-class model has clear effects on growth predictions as well as on optimized economic indicators. Therefore, it is important to check the robustness of results with different numbers of size classes, different partitionings, and alternative conversion methods and their combinations.

Keywords: diameter distribution; economically optimal forest management; increment estimator; proportion estimator; size-classified matrix model

Document Type: Research Article

DOI: https://doi.org/10.5849/forsci.11-011

Publication date: 2012-12-02

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
X
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