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The number and quality of driving variables needed to model tree growth
Landsberg, J.J. (1981). The number and quality of driving variables needed to model tree growth, in: Linder, S. (Ed.) Understanding and predicting tree growth. Studia forestalia suecica, 160: pp. 43-50
In: Linder, S. (Ed.) (1981). Understanding and predicting tree growth. Studia forestalia suecica, 160. The Swedish University of Agricultural Sciences; College of Forestry: Uppsala. ISBN 91-38-06617-3. 87 pp., more
In: Studia forestalia suecica. The Swedish University of Agricultural Sciences; College of Forestry: Uppsala. ISSN 0039-3150, more
Peer reviewed article  

Keywords
    Models > Growth models
    Models > Model compounds
    Organisms > Eukaryotes > Plants > Woody plants > Trees

Event Top | Author 
  • Understanding and predicting tree growth, more

Author  Top 
  • Landsberg, J.J.

Abstract

    Tree growth may be described by simple models with a high empirical content and response times of the order of a season, but if these are to be of general value they should be consistent with shorter period growth models written in terms of the underlying physiological processes. The driving variables for the models at all levels are weather factors : radiant energy, air temperature and humidity and wind speed. Soil water and nutrient conditions modify the responses to these factors. The accuracy with which driving variables must be specified for simulation, or measured for model testing , depends on the response time of the biological processes being simulated. For short-period (low organisation level) model sdetailed, accurate values of driving variables are required; as the response time of processes increases the detail and accuracy required of driving variables decreases. Variables such as leaf temperature in low-level models and soil water balance in higher level models may have to be calculated from weather data. The relationships between in-canopy conditions and standard weather measurements must be sutdied and, for long term models, the spatial and temporal variations in weather conditions are important.


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