A system identification approach for developing and parameterising an agroforestry system model under constrained availability of data

  • Authors:

  • Karel J. Keesman;Anil Graves;Wopke van der Werf;Paul J. Burgess;Joao Palma;Christian Dupraz;Herman van Keulen

  • Affiliations:

  • Wageningen University, Systems & Control Group, P.O. Box 17, 6700 AA Wageningen, The Netherlands;School of Applied Sciences, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK;Wageningen University, Centre for Crop Systems Analysis, Crop & Weed Ecology Group, P.O. Box 430, 6700 AK Wageningen, The Netherlands;School of Applied Sciences, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK;ForChange - Forest Ecosystems Management under Global Change, Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade Técnica de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, ...;INRA, UMR-System, 2 Place Viala, Bít. 27, 34060 Montpellier Cedex, France;Wageningen University, Plant Production Systems, P.O. Box 430, 6700 AK Wageningen, The Netherlands

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2011

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Abstract

This paper introduces a system identification approach to overcome the problem of insufficient data when developing and parameterising an agroforestry system model. Typically, for these complex systems the number of available data points from actual systems is less than the number of parameters in a (process-based) model. In this paper, we follow a constrained parameter optimization approach, in which the constraints are found from literature or are given by experts. Given the limited a priori systems knowledge and very limited data sets, after decomposition of the parameter estimation problem and after model adaptation, we were able to produce an acceptable correspondence with validation data from a real-world agroforestry experiment.