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.).

TR Number: 
Daniel B. Hall and Michael Clutter
Key Words: 
Clustered Data, Growth, Prediction, Random Effects, Repeated Measures, Volume

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