Models for Predicting Specific Gravity and Ring Width for Loblolly Pine from Intensively Managed Plantations, and Implications for Wood Utilization

Abstract

Abstract:Loblolly pine (Pinus taeda L.) is increasingly grown on intensively managed plantations that yield high growth rates. Wood properties, including specific gravity (SG), change with cambial age, and thus intensively managed trees contain a high proportion of low density corewood when harvested because of reduced rotation lengths. This study was undertaken to develop models of ring-level properties (SG and width) in intensively managed loblolly pine plantations. Ninety-three trees from five stands aged from 24 to 33 years were harvested, and 490 disks were obtained from in between the 5.2-m logs that were cut, and at the merchantable top. The disks were cut into pith-to-bark radial strips that were scanned on an X-ray densitometer, and the resultant data analyzed using non-linear mixed-effects models. The fixed effects of the models, which included cambial age and for some models disk height and ring width, were able to explain 56, 46, 54, 16, and 46 percent of the within-tree variation for ring SG, ring width, latewood SG, earlywood SG, and latewood percent, respectively. To assess implications for wood utilization, a modeled tree was built by using height, diameter, and taper equations and these models were linked with the developed ring SG model to produce a tree properties map. The linked information was also used to generate tree and log SG and proportion of corewood values for different rotation ages. The results from this study are a step towards integrating wood quality models into growth-and-yield modeling systems that are important for loblolly pine plantation management.

Models to predict whole-disk specific gravity and moisture content in planted longleaf pine from cutover and old field sites

“…2008; Dahlen et al 2018). The lack of genetic improvement in longleaf pine combined with the rapid growth has also resulted in trees having a high amount of defects .…”

“…Given the rapid growth rate and high defect rate, there are questions about the quality of planted longleaf pine and how it relates to current perceptions of longleaf pine, which are based on slow growing and naturally regenerated longleaf pine. A significant amount of information is available on the wood and fiber quality of planted loblolly pine (Mora et al 2007;Jordan et al 2008;Antony et al 2015;Dahlen et al 2018;Schimleck et al 2018), and to a lesser extent slash pine (Eberhardt et al 2017), however, there is little information available on the wood quality of planted longleaf pine. The wood quality information on planted longleaf pine that does exist (Baldwin and Saucier 1983;Gibson et al 1986;Clark and Schmidtling 1989) was measured prior to the recent restoration efforts and had much higher planting densities (2470 trees ha -1 to 3700 trees ha -1 ) (Baldwin and Saucier 1983;Gibson et al 1986) compared to current practices for establishing planted longleaf pine (1235 trees ha -1 ) (South 2006).…”

“…D r a f t D r a f t natural stands, and thus have a relatively high proportion of corewood (juvenile wood) which has low SG (Burdon et al 2004;Mora et al 2007;Moore and Cown 2017;Dahlen et al 2018).…”

Mapping and modeling within-tree variation for loblolly pine pulp yield and lignin content

“…For pulp yield, a three-parameter logistic function [48] was selected. This model form has been successfully used to model latewood specific gravity in loblolly pine [8] and tracheid width measured on macerated samples of loblolly pine [9]:…”

“…where a i and a ij represent the nested random effects of the asymptote parameter β0 at the site i and tree j levels, respectively. For lignin content, a variation on the three-parameter logistic function [48] was used which has previously been used to model earlywood specific gravity in loblolly pine [8]:…”