Both aggregative and disaggregative strategies were used to develop additive nonlinear biomass equations for slash pine (Pinus elliottii Engelm. var. elliottii) trees in the southeastern United States. In the aggregative approach, the total tree biomass equation was specified by aggregating the expectations of component biomass models, and their parameters were estimated by jointly fitting all component and total biomass equations using weighted nonlinear seemingly unrelated regression (NSUR) (SUR1) or by jointly fitting component biomass equations using weighted NSUR (SUR2). In an alternative disaggregative approach (DRM), the biomass component proportions were modeled using Dirichlet regression, and the estimated total tree biomass was disaggregated into biomass components based on their estimated proportions. There was no single system to predict biomass that was best for all components and total tree biomass. The ranking of the three systems based on an array of fit statistics followed the order of SUR2 > SUR1 > DRM. All three systems provided more accurate biomass predictions than previously published equations.

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Equations are needed to estimate the green weight of loblolly pine (Pinus taeda L.) trees across the commercial range in the South. Thus, a study was conducted to derive models for predicting green weight of total and merchantable bole portions. Equations were fitted to sectional tree stem observations where a serial correlation was discerned among the data, indicating an intratree relationship. Linear equations utilizing a correction for serial correlation did not outperform an uncorrected nonlinear equation form. Three data sets were combined, and regionwide prediction models for total green weight, green weight to any upper merchantable diameter, and green weight to any upper merchantable height were developed for loblolly pine trees. Implicit taper functions were derived from the green weight prediction equations to enable estimation of upper stem diameters and heights. South. J. Appl. For. 27(2):153–159.

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In recent Eucalyptus cold-tolerance trials, E. benthamii has shown good growth rates as well as cold tolerance for USDA Plant Hardiness Zones 8 and 9. This study developed growth and yield models for E. benthamii in the southeastern United States. A network of 182 temporary sample plots of E. benthamii ranging in age from 1.5 to 13.3 years was established, and inventory data were collected. Site quality was determined by fitting a polymorphic site index curve, whereas a function for stand basal area based on age, dominant height, and site occupancy was fitted. Stand-level volume and dry-weight biomass prediction equations were fitted as a function of dominant height and basal area. Based on the growth and yield model results, mean annual increments ranged from 26.4 m3 ha–1 year–1 at rotation age 6 years on the best sites to 13.7 m3 ha–1 year–1 at rotation age 10 years on the poorest sites. This is the first published set of management-oriented models for land managers considering planting E. benthamii in the southeastern United States.

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