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Estimating Uncertainty Of Silage Maize Harvesting Model

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Computers in Agriculture and Natural Resources, 4th World Congress Conference, Proceedings of the 24-26 July 2006 (Orlando, Florida USA) Publication Date 24 July 2006  701P0606.(doi:10.13031/2013.21906)
Authors:   Reet Põldaru, Jüri Roots
Keywords:   Uncertainty, sensitivity analysis, nonlinear stochastic models, metamodels, silage maize harvesting

Modelling of real systems is often complicated by the presence of uncertainties. A quantitative uncertainty/sensitivity analysis can provide conclusions about key sources of uncertainty. This paper attempts to explain how uncertainty is introduced to a model through the parameterisation process, what steps can be undertaken to recognise this uncertainty, and how to incorporate it into model predictions. The paper gives a brief exposition of sensitivity analysis for a simple nonlinear stochastic model namely, a silage maize harvesting model. For this sensitivity analysis a metamodel approach is used. The metamodel parameters are estimated using design of experiment methods. Sensitivity analysis efficiently selects more important and less important model parameters. First-order polynomial metamodels may be sufficient to describe sensitivity characteristics even for nonlinear problems. For the detection of highly nonlinear effects and estimation of parameters interaction sensitivity the second-order polynomial model approach should be recommended. The results of this case study are discussed.

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