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 508007
Title A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation
Author(s) Confalonieri, Roberto; Bregaglio, Simone; Adam, Myriam; Ruget, Françoise; Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Buis, Samuel; Fumoto, Tamon; Gaydon, Donald; Lafarge, Tanguy; Marcaida, Manuel; Nakagawa, Hiroshi; Ruane, Alex C.; Singh, Balwinder; Singh, Upendra; Tang, Liang; Tao, Fulu; Fugice, Job; Yoshida, Hiroe; Zhang, Zhao; Wilson, Lloyd T.; Baker, Jeff; Yang, Yubin; Masutomi, Yuji; Wallach, Daniel; Acutis, Marco; Bouman, Bas
Source Environmental Modelling & Software 85 (2016). - ISSN 1364-8152 - p. 332 - 341.
Department(s) Crop Physiology
Publication type Refereed Article in a scientific journal
Publication year 2016
Keyword(s) Model classification - Model ensemble - Model parameterisation - Model structure - Rice - Uncertainty

For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.

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