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140122 ||| eng |
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|a 9789401120869
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|a Leffelaar, P.A.
|e [editor]
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|a On Systems Analysis and Simulation of Ecological Processes with Examples in CSMP and FORTRAN
|h Elektronische Ressource
|c edited by P.A. Leffelaar
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| 250 |
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|a 1st ed. 1993
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| 260 |
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|a Dordrecht
|b Springer Netherlands
|c 1993, 1993
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| 300 |
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|a XIII, 294 p. 14 illus
|b online resource
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|a A Fundamentals of dynamic simulation -- 1 Philosophy and terminology -- 2 Basic elements of dynamic simulation -- 3 A simulation language: Continuous System Modeling Program III -- 4 The growth of yeast -- 5 Additional exercises I -- B Advanced themes in dynamic simulation -- 6 Numerical integration and error analysis -- 7 Aspects of structured programming using CSMP and FORTRAN -- 8 Modelling of ageing, development, delays and dispersion -- 9 Mass flow, numerical dispersion and diffusion -- 10 Simulation using FORTRAN -- 11 Additional exercises II -- Solutions to the exercises -- References -- Appendix 6.1 Derivation of the relative error in the rectangular integration method for a given exponential rate curve given as a function of time (i.e. integration without feedback) -- Appendix 6.2 Derivation of the relative error in the rectangular integration method for an exponential rate curve which is not known as a function of timem (i.e. integration with feedback) -- Appendix 7.1 Summary of the processing of a CSMP program -- Appendix 9.1 Derivation of the average concentration (AVC) at the boundary of two consecutive layers of different thickness (TCOM) in connection with the suppression of numerical dispersion -- Appendix 9.2 Derivation of the average conductivity of two adjacent layers of unequal thickness with different conductivity or resistance -- Appendix 10.1 The FORTRAN simulation environment consisting of main program FORSIM, the Euler and Runge-Kutta drivers, and the adapted integration routines from Press et al. (1986), including subroutine headers explaining their meaning
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|a Compilers (Computer programs)
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| 653 |
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|a Compilers and Interpreters
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|a Control theory
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| 653 |
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|a Systems Theory, Control
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| 653 |
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|a System theory
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| 653 |
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|a Ecology
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| 653 |
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|a Differential Equations
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| 653 |
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|a Ecology
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| 653 |
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|a Differential equations
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|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a Current Issues in Production Ecology
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|a 10.1007/978-94-011-2086-9
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| 856 |
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|u https://doi.org/10.1007/978-94-011-2086-9?nosfx=y
|x Verlag
|3 Volltext
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|a 003
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|a A system may be studied by distinguishing its major components, characterizing the changes in them by differential equations that form their simplified representa tions, and then interconnecting these representations to obtain a model of the original system. Developing the model is the systems synthesis phase. The behaviour of the model may now be studied and compared with experimental results obtained from the system. This research method is called systems analysis and simulation. Systems analysis and simulation can serve to make predictions, to improve the insight in systems, and to test knowledge on consistency and completeness. Predictive models are rare in ecology, simply because the underlying processes which form the basis of the models are seldom well known. A successful example of a predictive model was the work of van Keulen (1975). He showed that under semi arid conditions, where water is the main factor controlling primary production, the simulation technique could predict the production of natural grasslands. Fair predicti ons could also be made for the Sahelian pastures (Penning de Vries & Djiteye, 1982). Predictive models of populations of different pest and disease organisms are being used in biological control systems (Zadoks et aI., 1984)
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