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Multivariate Multilevel Nonlinear Mixed Effects Models for Timber Yield Predictions
Author(s) -
Hall Daniel B.,
Clutter Michael
Publication year - 2004
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2004.00163.x
Subject(s) - multivariate statistics , yield (engineering) , plot (graphics) , nonlinear system , tree (set theory) , slash pine , volume (thermodynamics) , mixed model , econometrics , pinus <genus> , statistics , mathematics , computer science , forestry , geography , biology , quantum mechanics , metallurgy , mathematical analysis , materials science , physics , botany
Summary. Nonlinear mixed effects models have become important tools for growth and yield modeling in forestry. To date, applications have concentrated on modeling single growth variables such as tree height or bole volume. Here, we propose multivariate multilevel nonlinear mixed effects models for describing several plot‐level timber quantity characteristics simultaneously. We describe how such models can be used to produce future predictions of timber volume (yield). The class of models and methods of estimation and prediction are developed and then illustrated on data from a University of Georgia study of the effects of various site preparation methods on the growth of slash pine ( Pinus elliottii Engelm.).