Prediction of Wood Structural Patterns in Trees by Using Ecological Models of Plant Water Relations

Gartner, Barbara L.

doi:10.1002/9780470999714.ch4

Cited by 12 publications

(15 citation statements)

References 44 publications

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“…Cell perimeter has a strong influence on water transport (i.e., xylem conductivity and vulnerability to cavitation under water stress) and mechanical support (Dean and Long, 1986;Gartner, 2006;Baas et al, 2004) and correlates with basic wood density. Reme and Helle (2002) found that cell wall perimeters of Scandinavian grown Scots pine and Norway spruce increased with distance from the pith, with most of the increase occurring during the first 20 years of age, and that the perimeter gradually decreased from earlywood to latewood.…”

Section: Cell Perimetermentioning

confidence: 96%

“…The distribution of cell types, cell dimensions, and chemical compositions determine wood quality attributes such as shrink, warp, strength, stiffness and pulp and paper quality such as smoothness, and tear strength (Cannell et al, 1988;Downes et al, 2002;Zobel and Van Buijtenen, 1989;Oliver and Larson, 1996;Gartner, 2006). Genetics and environment act in concert to drive variability in wood cell anatomical and chemical properties (Zobel and Van Buijtenen, 1989;Downes and Drew, 2008;Downes et al, 2002Downes et al, , 2009.…”

Section: Wood Anatomy and Its Radial And Axial Variationsmentioning

confidence: 96%

“…Wood density is strongly influenced by tree age (juvenile vs. mature), the seasonal timing, rate, and duration of secondary growth, and plays a key role in plant-level hydraulic efficiency, protection from cavitation during drought, and mechanical support (Baas et al, 2004;Hacke et al, 2001;Roderick and Berry, 2001;Gartner, 2006;Swenson and Enquist, 2007). Basic wood density has been shown to vary within and among species, geographically, and with both short-(daily) and long-term (seasonal) variations in temperature and precipitation (Swenson and Enquist, 2007).…”

Section: Basic Wood Densitymentioning

confidence: 99%

“…Cell coarseness (g/m) is defined as the ratio of cell wall mass (g) to cell unit length (m) (Gartner, 2006). Theoretical models indicate that cell coarseness is tightly coupled to drought resilience (Hacke et al, 2001;Gartner, 2006).…”

Section: Cell Coarsenessmentioning

confidence: 99%

See 3 more Smart Citations

Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review

Leeuwen

Hilker

Coops

et al. 2011

Forest Ecology and Management

101

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Section: Cell Perimetermentioning

confidence: 96%

Section: Wood Anatomy and Its Radial And Axial Variationsmentioning

confidence: 96%

Section: Basic Wood Densitymentioning

confidence: 99%

Section: Cell Coarsenessmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review

Leeuwen

Hilker

Coops

et al. 2011

Forest Ecology and Management

101

View full text Add to dashboard Cite

“…There are several hypotheses that have been advanced to explain the functional characteristics of the outerwood, including cambial maturity, the selective pressure for increased resistance to cavitation, or selection for increased mechanical support [57]. From a physiological perspective, Gartner [58] argues that many properties that vary from pith to bark (e.g., density, fibre length) can be explained based on the need for trees to maintain hydraulic conductivity by producing long tracheids with narrow diameters and thick cell walls in order to resist cavitation as they mature. It is further hypothesized that radial changes will stabilize when the effect of cambial maturity is overwhelmed by environmental constraints [57].…”

Section: Discussionmentioning

confidence: 99%

Modeling Wood Fibre Length in Black Spruce (Picea mariana (Mill.) BSP) Based on Ecological Land Classification

Townshend

Pokharel

Groot

et al. 2015

Forests

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Effective planning to optimize the forest value chain requires accurate and detailed information about the resource; however, estimates of the distribution of fibre properties on the landscape are largely unavailable prior to harvest. Our objective was to fit a model of the tree-level average fibre length related to ecosite classification and other forest inventory variables depicted at the landscape scale. A series of black spruce increment cores were collected at breast height from trees in nine different ecosite groups within the boreal forest of northeastern Ontario, and processed using standard techniques for maceration and fibre length measurement. Regression tree analysis and random forests were used to fit hierarchical classification models and find the most important predictor variables for the response variable area-weighted mean stem-level fibre length. Ecosite group was the best predictor in the regression tree. Longer mean fibre-length was associated with more productive ecosites that supported faster growth. The explanatory power of the model of fitted data was good; however, random forests simulations indicated poor generalizability. These results suggest the potential to develop localized models linking wood fibre length in black spruce to landscape-level attributes, and improve the sustainability of forest management by identifying ideal locations to harvest wood that has desirable fibre characteristics.

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Models for predicting the within-tree and regional variation of tracheid length and width for plantation loblolly pine

Dahlen

Nabavi

Auty

et al. 2020

Forestry: An International Journal of Forest Research

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Loblolly pine is a major fibre source for the pulp and paper industry. Here we developed the first nonlinear models to predict the within-tree and regional variation of tracheid length and width for planted loblolly pine. Data were obtained from macerated tracheids and near-infrared spectroscopy calibration models from trees sampled in 109 stands across the southeastern United States. The fixed effects for the final tracheid length model, which included cambial age, height of disk within tree, and physiographic region, explained 71 percent of the variation with root mean square error (RMSE) of 0.28 mm, while the fixed effects for the final tracheid width model explained 57 percent of the variation with RMSE of 1.4 μm. There was significant variation in tracheid properties across the growing regions. Tree maps showing within-tree variability in tracheid properties were produced. Five simulated scenarios were compared using the models developed, with mean tracheid dimensions calculated on a whole-tree basis at a first and second thinnings, and at final harvest. Also from the final harvest, the tops of trees, and outerwood chips produced during lumber manufacturing were also simulated. For the whole tree scenarios, both mean tracheid length and width increased with age, increasing from 2.24 mm and 40.5 μm (age 12), to 2.51 mm and 41.3 μm (age 18), and to 2.73 and 41.8 μm at age 25, respectively. The tops of the trees at age 25 had a mean tracheid length of 2.46 mm and a mean width of 41.0 μm, while the chips had a mean tracheid length of 3.13 mm and a mean width of 42.5 μm. Due to the models representing samples collected from across the southeastern United States, and their relatively high precision, they are suitable for incorporation into growth and yield systems allowing for prediction of tracheid properties.

show abstract

Prediction of Wood Structural Patterns in Trees by Using Ecological Models of Plant Water Relations

Cited by 12 publications

References 44 publications

Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review

Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review

Modeling Wood Fibre Length in Black Spruce (Picea mariana (Mill.) BSP) Based on Ecological Land Classification

Models for predicting the within-tree and regional variation of tracheid length and width for plantation loblolly pine

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