Skip to main content

The Application of Bayesian Model Averaging in Compatibility of Stand Basal Area for Even-Aged Plantations in Southern China

Buy Article:

$21.50 plus tax (Refund Policy)

Stand growth-and-yield models include whole-stand models, individual-tree models, and diameter distribution models. Based on the growth data of Chinese fir (Cunninghamia lanceolata [Lamb.] Hook.) in Fenyi County, Jiangxi Province, in southern China, Bayesian model averaging (BMA) was used to forecast stand basal areas by combining these three types of models into a single predictive model. BMA is a statistical method that infers consensus predictions by weighting individual predictions based on their posterior probabilities, with the better performing predictions getting higher weights than the poorer performing ones. Furthermore, BMA accounts for model uncertainty as reflected by the variance. The variance of BMA can be decomposed into a between-model variance that reflects the model's consistency and a within-model variance that reflects the data variability. Results showed that the between-model variance was much greater than the within-model variance for all the stand basal area predictions. The resulting model produced accurate and reliable predictions, and the 95% confidence interval of BMA predictions encompassed the observations very well. The BMA method provided a consistent prediction of stand basal area from three types of models, thus improving compatibility among these models.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: Bayesian model averaging; compatibility; stand basal area; uncertainty

Document Type: Research Article

Publication date: 2014-08-08

More about this publication?
  • Important Notice: SAF's journals are now published through partnership with the Oxford University Press. Access to archived material will be available here on the Ingenta website until March 31, 2018. For new material, please access the journals via OUP's website. Note that access via Ingenta will be permanently discontinued after March 31, 2018. Members requiring support to access SAF's journals via OUP's site should contact SAF's membership department for assistance.

    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
    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 content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free 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