Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 510323
Title Set-membership estimation from poor quality data sets : Modelling ammonia volatilisation in flooded rice systems
Author(s) Nurulhuda, K.; Struik, P.C.; Keesman, K.J.
Source Environmental Modelling & Software 88 (2017). - ISSN 1364-8152 - p. 138 - 150.
Department(s) Crop Physiology
Biobased Chemistry and Technology
Publication type Refereed Article in a scientific journal
Publication year 2017
Keyword(s) Ammonia volatilisation - Bounded-error - Flooded rice - Model calibration - Parameter estimation - Set-membership approach - Uncertainty analysis

A set-membership (bounded-error) estimation approach can handle small and poor quality data sets as it does not require testing of statistical assumptions which is possible only with large informative data sets. Thus, set-membership estimation can be a good tool in the modelling of agri-environmental systems, which typically suffers from limited and poor quality observational data sets. The objectives of the paper are (i) to demonstrate how six parameters in an agri-environmental model, developed to estimate NH3 volatilisation in flooded rice systems, were estimated based on two data sets using a set-membership approach, and (ii) to compare the set-membership approach with conventional non-linear least-squares methods. Results showed that the set-membership approach is efficient in retrieving feasible parameter-vectors compared with non-linear least-squares methods. The set of feasible parameter-vectors allows the formation of a dispersion matrix of which the eigenvalue decomposition reflects the parameter sensitivity in a region.

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