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Predicting Growth and "Success" of Coppice-Regenerated Oak Stems

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Logistic regression models were derived from data collected in the Missouri Ozarks to estimate the probabilities that 5-year-old stems of red oaks and white oaks in thinned and unthinned sprout clumps would attain a specified dbh at ages 12 and 30. In deriving each model, several success criteria based on selected future dbh were specified. A stem was considered "successful" for a particular criterion if it attained that dbh or larger, but considered a "failure" if it did not achieve that criterion because of either slow growth or mortality. Consequently, each model accounted for both growth and mortality probabilistically. Growth of survivors was also estimated using linear regression models. These models were developed for two species groups using data for stems that were alive at both the beginning (age 5) and end of a measurement period (ages 12 and 30). The logistic models indicated that height at age 5 was a significant predictor of competitive success of stems in subsequent years in stands with either thinned or unthinned clumps. Diameter of the parent stump was also significantly correlated with success, but only in stands where clumps were thinned. Results indicate that under the conditions of this study, which was conducted on a single site, stems with high success probabilities could be identified early in the life of this coppice-regenerated stand, and that early clump thinning in this stand increased success probabilities and diameter growth. For. Sci. 33(3):740-749.
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Keywords: Quercus; logistic regression; regenerator; silviculture

Document Type: Journal Article

Affiliations: Professor, University of Missouri

Publication date: 1987-09-01

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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.

    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
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