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A Taxonomy-Based Approach to Shed Light on the Babel of Mathematical Models for Rice SimulationFor 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.
Document ID
20160011521
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Confalonieri, Roberto
(Milan Univ. Italy)
Bregaglio, Simone
(Milan Univ. Italy)
Adam, Myriam
(Centre de Cooperation Internationale en Recherche Agronomique pour le Developpement Montpellier, France)
Ruget, Francoise
(Institut National de la Recherche Agronomique Avignon, France)
Li, Tao
(International Rice Research Inst. Manila, Philippines)
Hasegawa, Toshihiro
(National Inst. for Agro-Environmental Sciences Ibaraki, Japan)
Yin, Xinyou
(Wageningen Univ. Wageningen, Netherlands)
Zhu, Yan
(Nanjing Agricultural Univ. China)
Boote, Kenneth
(Florida Univ. Gainesville, FL, United States)
Buis, Samuel
(Institut National de la Recherche Agronomique Avignon, France)
Ruane, Alex C.
(NASA Goddard Inst. for Space Studies New York, NY, United States)
Date Acquired
September 27, 2016
Publication Date
September 16, 2016
Publication Information
Publication: Environmental Modelling & Software
Publisher: Elsevier
Volume: 85
ISSN: 1364-8152
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN35964
Distribution Limits
Public
Copyright
Other
Keywords
model parameterisation
model classification
uncertainty
model ensemble
rice
model structure

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