Predictability and Nonlinear Modelling in Natural Sciences and Economics

  • J. Grasman
  • G. van Straten

Table of contents

  1. Front Matter
    Pages i-ix
  2. Introduction

    1. J. Grasman, G. Van Straten
      Pages 1-5
  3. Geophysics

  4. Agriculture

  5. Population Biology

    1. Bruce E. Kendall, William M. Schaffer, Lars F. Olsen, Charles W. Tidd, Bodil L. Jorgensen
      Pages 184-203
    2. Ira B. Schwartz, Ioana Triandaf
      Pages 216-227
    3. Alan A. Berryman, Mikael Munster-Swendsen
      Pages 228-231
    4. S. A. L. M. Kooijman
      Pages 232-247
    5. F. Van Den Bosch, J. C. Zadoks, J. A. J. Metz
      Pages 274-281
    6. Jens G. Balchen
      Pages 282-293
  6. Systems sciences

  7. Environmental Sciences

    1. J.-P. Hettelingh, M. Posch
      Pages 439-446
    2. Matti P. Johansson, Peter H. M. Janssen
      Pages 447-459
    3. U. Hommen, U. Dülmer, H. T. Ratte
      Pages 460-470
    4. A. Tiktak, F. A. Swartjes, R. Sanders, P. H. M. Janssen
      Pages 471-484
    5. P. R. G. Kramer, A. C. M. De Nijs, T. Aldenberg
      Pages 485-494
    6. Andrzej Kraszewski, Rodolfo Soncini-Sessa
      Pages 505-515
    7. J. Kros, P. S. C. Heuberger, P. H. M. Janssen, W. De Vries
      Pages 541-553
  8. Economics

    1. F. J. Henk Don
      Pages 568-580
    2. Th. Fliegner, H. Nijmeijer, Ü. Kotta
      Pages 581-590

About this book


Researchers in the natural sciences are faced with problems that require a novel approach to improve the quality of forecasts of processes that are sensitive to environmental conditions. Nonlinearity of a system may significantly complicate the predictability of future states: a small variation of parameters can dramatically change the dynamics, while sensitive dependence of the initial state may severely limit the predictability horizon. Uncertainties also play a role.
This volume addresses such problems by using tools from chaos theory and systems theory, adapted for the analysis of problems in the environmental sciences. Sensitive dependence on the initial state (chaos) and the parameters are analyzed using methods such as Lyapunov exponents and Monte Carlo simulation. Uncertainty in the structure and the values of parameters of a model is studied in relation to processes that depend on the environmental conditions. These methods also apply to biology and economics.
For research workers at universities and (semi)governmental institutes for the environment, agriculture, ecology, meteorology and water management, and theoretical economists.


Atmospheric circulation Chaos Greenhouse gas Meteorology Regression Scale complex systems ecosystem model modeling simulation systems theory wheat

Editors and affiliations

  • J. Grasman
    • 1
  • G. van Straten
    • 2
  1. 1.Department of MathematicsAgricultural UniversityWageningenThe Netherlands
  2. 2.Department of Agricultural Engineering and PhysicsAgricultural UniversityWageningenThe Netherlands

Bibliographic information