Modeling Stand Level Growth and Yield Response to Silvicultural Treatments
Abstract:As plantation growth and yield research lead to new, more intensive, and site-specific silvicultural prescriptions, there is a perpetual need to adjust existing yield prediction models to reflect the response to these practices. A height growth model that includes a treatment response term to accommodate different expected response patterns is presented. This model is shown to provide an accurate description of the observed average dominant height growth response of slash pine plantations to several silvicultural treatments, relative to standard practice. An existing per-acre basal area prediction model for unthinned plantations that includes average dominant height as a predictor variable, in addition to age and trees per acre, also required an adjustment term to adequately reflect treatment effects on stand basal area growth. No such adjustment appeared to be necessary, to account for different silvicultural practices, to an existing stand level volume prediction model that includes both average dominant height and basal area per acre as predictor variables, in addition to age and trees per acre. For. Sci. 41(3):629-638.
Document Type: Journal Article
Affiliations: Danid B. Warnell School of Forest Resources, The University of Georgia, Athens, GA 30602-2152
Publication date: August 1, 1995
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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.
2015 Impact Factor: 1.702
Ranking: 16 of 66 in forestry
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