A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation. (November 2016)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Environmental modelling & software. Volume 85(2016:Nov.)
• Page Start: 332
• Page End: 341
• Publication Date: 2016-11
• Subjects: Model classification -- Model parameterisation -- Model ensemble -- Model structure -- Rice -- Uncertainty; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.09.007
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: A taxonomy-based approach was used to classify AgMIP rice simulation models. Different model structures often resulted in similar outputs. Similar structures often led to large differences in outputs. User subjectivity likely hides relationships between model structure and behaviour. Shared protocols are still needed to limit the risks during calibration.