Modeling the longitudinal variation in wood specific gravity of planted loblolly pine (Pinus taeda) in the United States

Author:

Antony F.123,Schimleck L. R.123,Daniels R. F.123,Clark A.123,Hall D. B.123

Affiliation:

1. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA.

2. USDA Forest Service, Southern Research Station, Athens, GA 30602, USA.

3. Department of Statistics, University of Georgia, Athens, GA 30602, USA.

Abstract

Loblolly pine (Pinus taeda L.) is a major plantation species grown in the southern United States, producing wood having a multitude of uses including pulp and lumber production. Specific gravity (SG) is an important property used to measure the quality of wood produced, and it varies regionally and within the tree with height and radius. SG at different height levels was measured from 407 trees representing 135 plantations across the natural range of loblolly pine. A three-segment quadratic model and a semiparametric model were proposed to explain the vertical and regional variations in SG. Both models were in agreement that a stem can be divided into three segments based on the vertical variation in SG. Based on the fitted models, the mean trend in SG of trees from the southern Atlantic Coastal Plain and Gulf Coastal Plain was observed to be higher than in other physiographical regions (Upper Coastal Plain, Hilly Coastal Plain, northern Atlantic Coastal Plain, and Piedmont). Maps showing the regional variation in disk SG at a specified height were also developed. Maps indicated that the stands in the southern Atlantic Coastal Plain and Gulf Coastal Plain have the highest SG at a given height level.

Publisher

Canadian Science Publishing

Subject

Ecology,Forestry,Global and Planetary Change

Reference26 articles.

1. Clark, A., and Daniels, R.F. 2002. Modeling the effect of physiographic region on wood properties of planted loblolly pine in southeastern United States. In Connection between Forest Resources and Wood Quality: Modeling Approaches and Simulation Software. Fourth Workshop IUFRO Working Party S5.01-04. INRA – Centre de Researches de Nancy, France, Harrison Hot Springs, B.C. pp. 54–60.

2. Cressie, N. 1993. Statistics for spatial data. Wiley, New York.

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