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Modeling and Prediction of Forest Growth Variables Based on Multilevel Nonlinear Mixed Models

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In this article, we describe estimation and prediction methods for nonlinear modeling of forest growth variables that are subject to nested sources of variability. The multilevel nonlinear mixed-effects models that we consider are useful for a variety of forestry applications, but we concentrate on the problem of estimating, and making projections from, growth curves for tree height based on longitudinal data grouped by location. Wolfinger and Lin consider estimating equation approaches to fitting more general nonlinear mixed-effects models, and we adapt their zero-expansion estimating equations to the multilevel case. We develop methods of prediction based on these models that allow predictions of future height both for individual trees and for plot averages. We illustrate these methods by fitting and making predictions from a Chapman-Richards type growth model for tree height data from a loblolly pine spacing study in Putnam County, Georgia. The mean and variance of prediction errors based on our methods are examined by means of cross-validation. We provide a more complete and unified presentation of linearization-based estimation and prediction based on multilevel nonlinear mixed-effects models than has previously appeared in the forestry literature, and we argue that these models lead to substantial advantages in growth and yield prediction over traditional forestry methods. FOR. SCI. 47(3):311–321.
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Keywords: Estimating equations; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; natural resource management; natural resources; random effects; repeated measures; site index; variance components

Document Type: Miscellaneous

Affiliations: 1: Assistant Professor Department of Statistics, University of Georgia, Athens, Georgia, 30602-1952, Phone: (706) 542-3302; Fax: (706) 542-3391 [email protected] 2: Professor Daniel B. Warnell School of Forest Resources, University of Georgia, Athens, Georgia, 30602, Phone: (706) 542-1187 [email protected]

Publication date: 2001-08-01

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.

    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
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