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Book: Methods of Introducing System Models into Agricultural Research
Published by: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America

 

This chapter in METHODS OF INTRODUCING SYSTEM MODELS INTO AGRICULTURAL RESEARCH

  1.  p. 117-154
    Advances in Agricultural Systems Modeling 2.
    Methods of Introducing System Models into Agricultural Research

    Laj R. Ahuja and Liwang Ma (ed.)

    ISBN: 978-0-89118-196-5

     

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doi:10.2134/advagricsystmodel2.c4

Special Features of the SPACSYS Modeling Package and Procedures for Parameterization and Validation

  1. L. Wu and
  2. A. Shepherd
  1. L. Wu (Lianhai.Wu@bbsrc.ac.uk) and A. Shepherd, Sustainable Soils and Grassland Systems Dep., Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB, UK.

Abstract

Process modeling and simulation are commonly used tools in both practical and research-oriented activities in agriculture. Recently released models have been developed to simulate integrated agricultural systems in one way or another, an activity which involves many processes and needs a large number of parameters and input variables. After a model is implemented and verified and before it is applied to a real scenario or used to test a hypothesis, a sensitivity analysis, calibration, and validation of the model should be performed to make sure it is workable and reliable. The SPACSYS model is an ongoing developmental, field-scale, weather-driven, and daily-step dynamic process-based simulation model. One feature of the model is its integration of the interactions among below- and aboveground plant growth with development stages, nitrogen and carbon cycling, and water and heat movements in the plant–atmosphere–soil continuum into a single package, which can avoid shortcomings of some published models that ignore the effects of one or more parts of the system. This chapter will describe the main features of the process-based SPACSYS model briefly and illustrate the procedures to parameterize and validate some state variables in it. It is concluded that data quantity and quality are limits to the parameterization of the model, especially those describing the development of root architecture of plants and the definition of organic matter pools in the soil. It is necessary to use advanced technology to determine these parameters in the future.

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