Free Access
Ann. For. Sci.
Volume 59, Number 5-6, July-October 2002
Proceedings of the Wood, Breeding, Biotechnology and Industrial Expectations Conference
Page(s) 577 - 582


  1. Clark J. d'A., Pulp technology and treatment of paper, 2nd ed., Miller Freeman Publicatoins, San Francisco, 1985.
  2. Chantre G., Rozenberg P., Baonza V., Macchioni N., Le Turcq A., Rueff M., Petit Conil M., Heois B., Genetic selection within Douglas fir (Pseudotsuga menziessi) in Europe for papermaking uses, in: Abstracts of the International Conference on: Wood, Breeding, Biotechnology and Industrial Expectations, June 11-14, 2001, Bordeaux, France, p. 38.
  3. Cockerham C.C., Wier B.S., Quadratic analysis of reciprocal crosses, Biometrics 33 (1977) 187-203.
  4. Gibson J.P., Kennedy B.W., The use of constrained selection indexes in breeding for economic merit, Theor. Appl. Genet. 80 (1990) 801-805.
  5. Harwood J.L., Managing risk in farming: concepts, research, and analysis, USDA Economic Research Service, Agricultural economic report No 774, (1999).
  6. Itoh Y., Yamada Y., Linear selection indices for non-linear profit functions, Theor. Appl. Genet. 75 (1988) 553-560.
  7. Ivkovich M., Koshy M.P., Wood density measurement: comparison of X-ray, photometric, and morphometric methods, in: Proceedings of the 26th Biannual Meeting of the Canadian Tree Improvement Association (CTIA/IUFRO), International Workshop on Wood Quality, Quebec City, Zhang S.Y., Gosselin R., Chauret G. (Eds.), 1997, pp. II 55-58.
  8. Jandel Corporation, SigmaScanPro $^{\textcircled{o}}$ Automated Image Analysis Software, User's Manual, Jandel Corporation, 1995.
  9. Kennedy R.W., Coniferous wood quality in the future: concerns and strategies, Wood. Sci. Tech. 29 (1995) 321-338.
  10. King J.N., Cartwright C., Hatton J., Yanchuk A.D., The potential of improving western hemlock pulp and paper quality I. Genetic control and interrelationships of wood and fibre traits, Can. J. For. Res. 28 (1998) 863-870.
  11. Lasdon L.S., Waren A., Jain A., Ratner M., Design and testing of a generalized reduced gradient code for non-linear programming, ACM Transactions on Mathematical Software 4 (1978) 34-50.
  12. Lasdon L.S., Smith S., Solving sparse non-linear programs using GRG, ORSA J. Comput. 4 (1992) 2-15.
  13. Magnussen S., Selection index: economic weights for maximum simultaneous genetic gain, Theor. Appl. Genet. 79 (1990) 289-293.
  14. Miettinen K., Non-linear multiobjective optimization, Kluwer Academic Publishers, 1999.
  15. Namkoong G., A multiple-index selection strategy, Silvae Genet. 25 (1976) 5-6.
  16. Namkoong G., Kang H.C., Brouard J.S., Tree breeding: principles and strategies, Springer-Verlag, NY, 1988.
  17. NIMBUS, Nondifferentiable interactive multiobjective bundle-based optimization system, University of Jyväskylä, Department of Mathematical Information Technology, Finland, 2000,
  18. Page D.H., A theory for the tensile strength of paper, TAPPI J. 52 (1969) 674-681.
  19. Page D.H., A quantitative theory of the strength of wet webs, J. Pulp. Paper. Sci. 19 (1993) 175-176.
  20. QUERCUS, Quantitative genetics Ssoftware, the University of Minnesota College of Biological Sciences, 2000, eeb/quercus.html
  21. Roberds J.H., Namkoong G., Population selection to maximize value in an environmental gradient, Theor. Appl. Genet. 77 (1989) 128-134.
  22. Rudie A.W., Morra J., St. Laurent J., Hickey K., The influence of wood and finber propertieson mechanical pulping, TAPPI J. 77 (1994) 86-89.
  23. Shaw R., Maximum likelihood approaches applied to quantitative genetics of natural populations, Evolution 41 (1987) 812-826.
  24. Zhang S.Y., Morgenstern E.K., Genetic variation and inheritance of wood density in black spruce (Picea mariana) and its relationship with growth: implications for tree breeding, Wood. Sci. Tech. 30 (1995) 63-75.
  25. Zobel B.J., vanBuijtenen J.P., Wood Variation: its causes and control, Springer-Verlag, NY, 1989.
  26. Zobel B.J., Jett J.B., Genetics of wood production, Springer-Verlag, NY, 1995.


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