Advertisement

The Role of Expert and Hypertext Systems in Modeling Root-Shoot Interactions and Carbon Allocation

  • H. Michael Rauscher
Part of the Basic Life Sciences book series (BLSC, volume 62)

Abstract

Access to knowledge and the ability to use it wisely has always been the hallmark of successful individuals, companies and nations. Scientific progress also depends upon accessible and organized knowledge (Bauer, 1992). Over the last 50 years, an increasing number of scientists have called attention to the deteriorating condition of our scientific knowledge infrastructure, i.e., the technical literature that supports scientific progress. Vannevar Bush (1945) was among the first influential scientists to point out that management of scientific knowledge has not essentially changed for more than 200 years. He summarized the situation as follows:

...There is a growing mountain of research. But there is increased evidence that we are being bogged down today as specialization extends. The investigator is staggered by the findings and conclusions of thousands of other workers--conclusions which he cannot find time to grasp, much less to remember, as they appear. Yet specialization becomes increasingly necessary for progress, and the effort to bridge between disciplines is correspondingly superficial....

Keywords

Expert System Knowledge Management Human Expert Diameter Growth Hybrid Poplar 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allen, T.F.H., and Hoekstra, T.W., 1992. “Toward a Unified Ecology,” Columbia Univ. Press, New York.Google Scholar
  2. Ashcraft, M.H., 1989. “Human Memory and Cognition,” Scott, Foresman and Co., Glenview.Google Scholar
  3. Bauer, H.H., 1992. “Scientific Literacy and the Myth of the Scientific Method,” Univ. of Illinois Press, Urbana.Google Scholar
  4. Beets, P.N., and Pollock, D.S., 1987, Accumulation and partitioning of dry matter in Pinus radiata as related to stand age and thinning, N.Z. J. For. Sci. 17:17.Google Scholar
  5. Berk, E., 1991, “Text-Only Hypertexts,” in: E. Berk, and J. Devlin, eds., “Hypertext/Hypermedia Handbook,” McGraw Hill, New York.Google Scholar
  6. Bloom, A.J., Chapin III, F.S., and Mooney, H.A., 1985, Resource limitation in plants — an economic analogy, Annu. Rev. Ecol. Syst. 16:16.Google Scholar
  7. Bowerman, R.G., and Glover, D.E., 1988, “Putting Expert Systems into Practice,” Van Nostrand Reinhold, New York.Google Scholar
  8. Bush, V., 1945, As we may think, Atlant. Month. 176.1:101.Google Scholar
  9. Conklin, J., 1987, “Hypertext: an Introduction and Survey,” in: IEEE Computer, Sept. 17.Google Scholar
  10. Davis, J.R., and Clark, J.L., 1989, A selective bibliography of expert systems in natural resource management, AI Applic. 3:3.Google Scholar
  11. de Wit, C.T., 1968, Plant production, Misc. Pap. Landbouw. Wag. 3:3.Google Scholar
  12. Dickmann, D.I. 1985, The ideotype concept applied to forest trees, in: “Institute of Terrestrial Ecology,” M. Cannell, and J. Jackson, eds., Huntington.Google Scholar
  13. Dickson, R.E., and Isebrands, J.G., 1991, Leaves as regulators of stress response, in: “Response of Plants to Multiple Stresses,” H.A. Mooney, W.E. Winner, and E.J. Pell, eds., Academic Press, San Diego.Google Scholar
  14. Eisenstein, E. 1979, “The Printing Press as an Agent of Change,” Cambridge Univ. Press, Cambridge.Google Scholar
  15. Fersko-Weiss, J., 1991, 3-D reading with the hypertext edge, PC Mag., May 28, p. 241.Google Scholar
  16. Forscher, B.K., 1963, Chaos in the brickyard, Science 142:142.CrossRefGoogle Scholar
  17. Gal, A., Lapalme, G., Saint-Dizier, P., and Somers, H., 1991, “PROLOG for Natural Language Processing,” John Wiley and Sons, Chichester.Google Scholar
  18. Geiger, D.R., and Servaites, J.C., 1991, Carbon allocation and response to stress, in: “Response of Plants to Multiple Stresses,” H.A. Mooney, W.E. Winner, and E.J. Pell, eds., Academic Press, San Diego.Google Scholar
  19. Giere, R.N., 1984, “Understanding Scientific Reasoning,” 2nd ed., Holt, Rinehart, and Winston, New York.Google Scholar
  20. Harmon, P., and King, D., 1985, “Expert Systems: Artificial Intelligence in Business,” Wiley and Sons, New York.Google Scholar
  21. Herring, C., 1968, Distill or drown: the need for reviews. Phys. Today 21:21.CrossRefGoogle Scholar
  22. Horn, R.E., 1989, “Mapping Hypertext: Analysis, Linkage, and Display of Knowledge for the Next Generation of On-line Text and Graphics,” Information Mapping, Inc., Waltham.Google Scholar
  23. Host, G.E., and Rauscher, H.M., 1990, Validating the regional applicability of a whole-plant ecophysiological growth process model of poplar, in: “Proc. IUFRO Forest Simulation Systems Conf.,” L.C. Wensel, and G.S. Biging, eds., Berkeley.Google Scholar
  24. Host, G.E., Rauscher, H.M., Isebrands, J.G., Dickmann, D.I., Dickson, R.E., Crow, T.R., and Michael, D.A., 1990, “The Microcomputer Scientific Software Series No. 6: The ECOPHYS User’s Manual,” Gen. Tech. Rep. NC-141, USDA Forest Service, North Central For. Exp. Sta., St. Paul.Google Scholar
  25. Isebrands, J.G., and Nelson, N.D., 1983, Distribution of 14C-labeled photosynthates within intensively cultured Populus clones during the establishment year, Physiol. Plant. 59:59.CrossRefGoogle Scholar
  26. Isebrands, J.G., Rauscher, H.M., Crow, T.R., and Dickmann, D.I., 1990, Whole-tree growth process models based on structural-functional relationships, in: “Process Modeling of Forest Growth Responses to Environmental Stress,” R.K. Dixon, R.S. Meldahl, G.A. Ruark, and W.G. Warren, eds, Timber Press, Portland.Google Scholar
  27. Jackson, P., 1986, “Introduction to Expert Systems,” Addison-Wesley Company, Inc, Reading.Google Scholar
  28. James, G., 1985, “Document Databases,” Van Nostrand Reinhold Co., New York.Google Scholar
  29. Kozlowski, T.T., Kramer, P.J., and Pallardy, S.G., 1991, “The Physiological Ecology of Woody Plants,” Academic Press, San Diego.Google Scholar
  30. Kramer, P.J., and Kozlowski, T.T., 1979, “Physiology of Woody Plants,” Academic Press, New York.Google Scholar
  31. Kurzweil, R., 1990, “The Age of Intelligent Machines,” MIT Press, Cambridge.Google Scholar
  32. Larson, P.R., and Isebrands, J.G., 1971, The plastochron index as applied to developmental studies of cottonwood, Can. J. For. Res. 1:1.CrossRefGoogle Scholar
  33. Leopold, A.C., and Kriedemann, P.E., 1975, “Plant Growth and Development,” 2nd ed., McGraw-Hill Book Co., New York.Google Scholar
  34. Linder, S., and Axelsson, B., 1982, Changes in carbon uptake and allocation patterns as a result of irrigation and fertilization in a young Pinus sylvestris stand, in: “Carbon Uptake and Allocation: Key to Management of Subalpine Forest Ecosystems,” R.H. Waring, ed., IUFRO Workshop, For. Res. Lab., Oregon State Univ., Corvallis.Google Scholar
  35. Luger, G.F., and Stubblefield, W.A., 1989, “Artificial Intelligence and the Design of Expert Systems,” Benjamin/Cummings Pub. Co., Inc., Redwood City.Google Scholar
  36. McLaughlin, S.B., McConathy, R.K., Barnes, R.L., and Edwards, N.T., 1980, Seasonal changes in energy allocation by white oak (Quereus alba), Can. J. For. Res. 10:10.CrossRefGoogle Scholar
  37. McNeill, D., and Freiburger, P., 1993. “Fuzzy Logic,” Simon and Schuster, New York.Google Scholar
  38. McRoberts, R.E., Schmoldt, D.L., and Rauscher, H.M., 1991, Enhancing the scientific process with artificial intelligence: forest science applications, AI Applic. 5(2):5.Google Scholar
  39. Mooney, H.A, 1972, The carbon balance of plants, Annu. Rev. Ecol. Syst. 3:3.CrossRefGoogle Scholar
  40. Mooney, H.A., Winner, W.E., and Pell, E.J., 1991, “Response of Plants to Multiple Stresses,” Academic Press, San Diego.Google Scholar
  41. Nielsen, J., 1989, “Hypertext and Hypermedia,” Academic Press, New York.Google Scholar
  42. Nguyen, P.V., Dickmann, D.I., Pregitzer, K.S., and Hendrick, R., 1990, Late-season changes in allocation of starch and sugar to shoots, coarse roots, and fine roots in two hybrid poplar clones, Tree Physiol. 7:7.CrossRefGoogle Scholar
  43. Parsaye, K., Chignell, M., Khoshafian, S., and Wong, H., 1989, “Intelligent Databases,” John Wiley & Sons, Inc., New York.Google Scholar
  44. Porter, H.K., 1966, Leaves as collecting and distributing agents of carbon, Aust. J. Sci. 29:29.Google Scholar
  45. Pregitzer, K.S., Dickmann, D.I., Hendrick, R., and Nguyen, P.V., 1990, Whole-tree carbon and nitrogen partitioning in young hybrid poplars, Tree Physiol. 7:7.CrossRefGoogle Scholar
  46. Price, D.J., 1963, “Little Science, Big Science,” Columbia Univ. Press, New York.Google Scholar
  47. Rauscher, H.M., and Hacker, R., 1989, Overview of artificial intelligence applications in natural resource management, J. Knowl. Eng. 2:2.Google Scholar
  48. Rauscher, H.M., and Host, G.E., 1990, Hypertext and AI: a complementary combination for knowledge management, AI Applic. 4:4.Google Scholar
  49. Rauscher, H.M., and Isebrands, J.G., 1990, Using expert systems to model tree development, in: “Proc. IUFRO Forest Simulation Systems Conf.,” L.C. Wensel, and G.S. Biging, eds., Berkeley.Google Scholar
  50. Rauscher, H.M., Alban, D.H., Johnson, D.W., A brief overview of hypertext system development, in: “Proc. IUFRO Cent. Meet.,” 1992 Aug. 31–Sept. 7, Berlin, (in press).Google Scholar
  51. Rauscher, H.M., Isebrands, J.G., Host, G.E., Dickson, R.E., Dickmann, D.I., Crow, T.R., and Michael, D.A., 1990, ECOPHYS: an ecophysiological growth process model for juvenile poplar, Tree Physiol. 7:7.CrossRefGoogle Scholar
  52. Rauscher, H.M., Bartos, D.L., Davey, S.M., Downing, K., Elmes, G.A., Gertner, G., Biing, T., Stockwell, D.R.B., Twery, M.J., and Schmoldt, D.L., 1991, The encyclopedia of AI applications to forest science, (Hypertext version, computer disk), AI Applic. 5: insert 592,080 bytes; 235 chunks; and 449 links.Google Scholar
  53. Ruark, G.A., and Bockheim, J.G., 1987, Biomass, net primary production, and nutrient distribution for an age sequence of Populus tremuloides ecosystems, Can. J. For. Res. 18:18.Google Scholar
  54. Schank, R.C., 1988, “The Cognitive Computer: On Language, Learning, and Artificial Intelligence,” Addison-Wesley Publishing Co., Inc., Reading.Google Scholar
  55. Schlumlienzer, P.C., 1989. Shadow-fusing hypertext with AI, IEEE Expert, Winter, p. 65.Google Scholar
  56. Schmoldt, D.L., and Martin, G.L., 1989, Construction and evaluation of an expert system for pest diagnosis of red pine in Wisconsin, For. Sci. 35:35.Google Scholar
  57. Schulze, E.D., 1983, Root-shoot interactions and plant life forms, Nether. J. Agric. Sci. 4:4.Google Scholar
  58. Sell, P.S., 1985, “Expert Systems: A Practical Introduction,” John Wiley & Sons, Inc., New York.Google Scholar
  59. Seyer, P., 1991, “Understanding Hypertext: Concepts and Applications,” Windcrest Books, Blue Ridge Summit.Google Scholar
  60. Sharpe, P.J.H., and Rykiel, Jr., E.J., 1991, Modelling integrated response of plants to multiple stresses, in: “Response of Plants to Multiple Stresses,” H.A. Mooney, W.E. Winner, and E.J. Pell, eds., Academic Press, San Diego.Google Scholar
  61. Shneiderman, B., and Kearsley, G., 1989, “Hypertext Hands-on! An Introduction to a New Way of Organizing and Accessing Information,” Addison-Wesley, Reading.Google Scholar
  62. Shneiderman, B., Kreitzberg, C., and Berk, E., 1991, Editing to structure a reader’s experience, in: “Hypertext/Hypermedia Handbook,” E. Berk, and J. Devlin, eds., McGraw-Hill, New York.Google Scholar
  63. Simon, K.H., Manche, A., and Uhrmacher, A., 1992, “Expertensysteme auf dem Umweltsektor,” Umweltbundesamt, Berlin.Google Scholar
  64. Slatin, J.M., 1991, Composing hypertext: A discussion for writing teachers,” in: “Hypertext/Hypermedia Handbook,” E. Berk, and J. Devlin, eds., McGraw-Hill, New York.Google Scholar
  65. Starfield, A.M., and Bleloch, A.L., 1983, Expert systems: an approach to problems in ecological management that are difficult to quantify, J. of Environ. Manag. 16:16.Google Scholar
  66. Starfield, A.M., and Bleloch, A.L., 1986, “Building models for conservation and wildlife management,” MacMillan Pub. Co., New York.Google Scholar
  67. Udell, J., 1993, Desktop CD-ROM publishing, BYTE 18:18.Google Scholar
  68. Thomson, A.J., Sutherland, J.R., and Carpenter, C., 1993, Computer-assisted diagnosis using expert systems-guided hypermedia, AI Applic. 7(1): 17.Google Scholar
  69. Waring, R.H., and Schlesinger, W.H., 1985, “Forest Ecosystems: Concepts and Management,” Academic Press, Inc., Orlando.Google Scholar
  70. Wasserman, P.D., 1989, “Neural computing,” Van Nostrand Reinhold, New York.Google Scholar
  71. Wilson, B.F., 1984, “The Growing Tree,” rev. ed., Univ. Mass. Press, Amherst.Google Scholar
  72. Wilson, J.B., 1988, Shoot competition and root competition, J. Appl Ecol. 25:25.CrossRefGoogle Scholar
  73. Ziman, J., 1976, “The Force of Knowledge: The Scientific Dimension of Society,” Cambridge Univ. Press, Cambridge.Google Scholar

Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • H. Michael Rauscher
    • 1
  1. 1.USDA Forest Service North Central Forest Experiment StationForestry Sciences LaboratoryGrand RapidsUSA

Personalised recommendations