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A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation

by Confalonieri, R; Bregaglio, S; Adam, M; Ruget, F; Tao Li; Hasegawa, T; Yin, Y; Zhu, Y; Boote, K; Buis, S; Fumoto, T; Gaydon, D.S; Lafarge, T; Marcaida III, M; Nakagawa, H; Ruane, A.C; Singh, B; Singh, U; Tang, L; Fulu Tao; Fugice, J; Yoshida, H; Zhao Zhang; Wilson, L.T; Baker, J; Yubin Yang; Yuji Masutomi; Wallach, D; Acutis, M; Bouman, B.
Material type: materialTypeLabelArticlePublisher: Amsterdam, Netherlands : Elsevier, 2016Subject(s): Rice | Mathematical modelsOnline resources: Click here to access online In: Environmental Modelling & Software Vol. 85, p. 332-341Summary: 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.
List(s) this item appears in: CIMMYT-SIP 2016-2017 Peer-reviewed publications list
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Journal article
CIMMYT Knowledge Center: John Woolston Library

Lic. Jose Juan Caballero Flores

 

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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.

http://diglib-cc3/Download/cis/58007.pdf

Text in English

CIMMYT Informa: 1977 (October 13, 2016)

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