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

Canadian Journal of Forest Research

Volumn 40, Issue 12, 2010, Pages 2439-2451

  Antony, F ^a   Schimleck, L R ^a   Daniels, R F ^a   Clark III, A ^b   Hall, D B ^a  

^a UNIVERSITY OF GEORGIA   (United States)

^b USDA FOREST SERVICE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COASTAL PLAIN; DIFFERENT HEIGHTS; LOBLOLLY PINE; LONGITUDINAL VARIATIONS; LUMBER PRODUCTION; PINUS TAEDA; PLANTATION SPECIES; QUADRATIC MODELS; REGIONAL VARIATION; SEMIPARAMETRIC MODELS; SPECIFIC GRAVITY; VERTICAL VARIATION;

DENSITY (SPECIFIC GRAVITY);

FORESTRY;

CONIFEROUS FOREST; FORESTRY PRODUCTION; GRAVITY; LONGITUDINAL GRADIENT; PLANT ARCHITECTURE; TREE PLANTING; WOOD;

DENSITY; FORESTRY; GROWTH; LUMBER; MAPPING; PINUS TAEDA; PLANTATIONS; SPECIES IDENTIFICATION; UNITED STATES; WOOD;

ATLANTIC COASTAL PLAIN; GULF COASTAL PLAIN; UNITED STATES;

PINUS TAEDA;

EID: 78650450937     PISSN: 00455067     EISSN: 12086037     Source Type: Journal    
DOI: 10.1139/X10-187     Document Type: Article

References (27)

1. Burdon, R.D., Kibblewhite, R.P., John, C.F.W., Robert, A.M., Robert, E., and David, J.C. 2004. Juvenile versus mature wood: a new concept, orthogonal to corewood versus outerwood, with special reference to Pinus radiata and P. taeda. For. Sci. 50: 399-415.
2. 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.
3. Clark, A., and Saucier, J.R. 1989. Influence of initial planting density, geographic location, and species on juvenile wood formation in southern pine. For. Prod. J. 39(7-8): 42-48.
4. Cressie, N. 1993. Statistics for spatial data. Wiley, New York.
5. Fox, T.R., Jokela, E.J., and Allen, H.L. 2007. The development of pine plantation silviculture in the southern United States. J. For. 105(7): 337-347.
6. Gallant, A.R., and Fuller, W.A. 1973. Fitting segmented polynomial regression models whose join points have to be estimated. J. Am. Stat. Assoc. 68(341): 144-147.
7. He, R. 2004. Mixed effects modeling of wood properties of loblolly pine in the southeastern United States. Ph.D. dissertation, University of Georgia, Athens, Georgia.
8. Jordan, L., Clark, A., Schimleck, L.R., Hall, D.B., and Daniels, R.F. 2008. Regional variation in wood specific gravity of planted loblolly pine in the United States. Can. J. For. Res. 38: 698-710.
9. Kamman, E.E., and Wand, M.P. 2003. Geoadditive models. Appl. Stat. 52: 1-18.
10. Larson, P.R., Kretschmann, D.E., Clark, A., and Isebrands, J.G. 2001. Formation and properties of juvenile wood in southern pines: a synopsis. USDA Forest Service, General Technical Report FPL-GTR-129.
11. Max, T.A., and Burkhart, H.E. 1976. Segmented polynomial regression applied to taper equations. For. Sci. 22: 283-289.
12. Megraw, R.A. 1985. Wood quality factors in loblolly pine. TAPPI Press, Atlanta, Georgia.
13. Mitchell, H. 1964. Patterns of variation in specific gravity of southern pines and other coniferous species. TAPPI (Tech. Assoc. Pulp Pap. Ind.) J. 47: 276-283.
14. Ngo, L., and Wand, M.P. 2004. Smoothing with mixed model software. J. Stat. Softw. 9(1): 1-54.
15. Panshin, A.J., and de Zeeuw, C. 1980. Textbook of wood technology. 4th ed. McGraw-Hill Book Co., New York.
16. Phillips, K.M. 2002. Modeling within tree changes in wood specific gravity and moisture content for loblolly pine in Georgia. M.S. thesis, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia.
17. Pinheiro, C.J., and Bates, D.M. 2000. Mixed effects models in S and S-PLUS. Springer-Verlag, New York.
18. Pinheiro, C.J., Bates, M.D., DebRoy, S., Sarkar, D., and R Core Team. 2009. nlme: Linear and nonlinear mixed effects models. R package version 3. Available at http://cran.r-project.org/web/packages/nlme/index.html.
19. Ruppert, D., Wand, M.P., and Carrol, R.J. 2003. Semiparametric regression. 1st ed. Cambridge University Press, New York.
20. Tasissa, G., and Burkhart, H.E. 1998a. Juvenile-mature wood demarcation in loblolly pine trees. Wood Fiber Sci. 30(2): 119-127.
21. Tasissa, G., and Burkhart, H.E. 1998b. Modeling thinning effects on ring specific gravity of loblolly pine (Pinus taeda L.). For. Sci. 44(2): 212-223.
22. Vonesh, E.F., and Chinchilli, V.M. 1997. Linear and nonlinear models for the analysis of repeated measurements. Marcel Dekker, New York.
23. Wahlgren, H.D., and Schumann, D. 1975. Properties of major southern pines: Part I. Wood density survey. USDA Forest Service, Forest Product Laboratory, Madison, Wisconsin, Research Paper FPL-176.
24. Wand, M.P., Coull, B., French, J., Ganguli, B., Kamman, E.E., Staudenmeier, J., and Zanobetti, A. 2005. SemiPar 1.0, R package [online]. Available at http://cran.r-project.org.
25. Wear, D.N., and Greis, J.G. 2002. The Southern Forest Resource Assessment: summary report. USDA Forest Service, Southern Research Station Report No. 53.
26. Zobel, B. 1972. Three rings-per-inch, dense southern pine - can it be developed? J. For. 70: 333-336.
27. Zobel, B.J., and van Buijtenen, J.P. 1989. Wood variation: its causes and control. Springer-Verlag, Berlin.