Individual tree height increment model for managed even-aged stands of ponderosa pine throughout the western United States using linear mixed effects models

Individual tree growth models for eucalyptus in northern Brazil

“…A large number of growth models with numerous combinations of variables are continually evaluated and tested for a wide variety of species (Uzoh and Oliver, 2006). It becomes more difficult, however, to select the best model to estimate growth if the modeling unit is an individual tree (Davis et al, 2005) because the high resolution of this type of modeling entails problems caused by cumulative errors (Cao, 2006).…”

“…Pg describes the maximum possible growth that a tree can attain, whereas fm describes the decrease in growth potential due to competition (Kiernan et al, 2008). In contrast, growth equations (or functions) use tree attributes (tree size, competition indices, crown ratio, vigor), stand attributes (age, site index, stand basal area) and site characteristics as predictor variables, all combined in a single equation (Uzoh and Oliver, 2006).…”

A generalized height–diameter model including random components for radiata pine plantations in northwestern Spain

“…The evaluation dataset was intended to obtain a measure of the adequacy of the calibration from different sampling designs. Since the data set is large enough, the 90-10% split used is unlikely to reduce the precision of the parameter estimates compared with those obtained with the model built from the entire dataset (Uzoh and Oliver, 2006). As we did not have available any really independent dataset (which is a basic prerequisite for accomplishing a ''real validation''), we did not carry out any validation of the model, relying heavily on the fact that a good quality of fit will reflect good quality of predictions.…”

Individual tree diameter increment model for managed even-aged stands of ponderosa pine throughout the western United States using a multilevel linear mixed effects model

“…The more complicated error structure of this type of data has often been ignored in forestry, with some exceptions (Gregoire et al, 1995). To overcome this problem, growth and yield researchers have widely proposed multilevel linear and nonlinear mixed models with both fixed and random components (Biging, 1985;Gregoire et al, 1995;Hall and Bailey, 2000;Calama and Montero, 2005;Lynch et al, 2005;Uzoh and Oliver, 2006;Gutzwiller and Riffell, 2007).…”