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

Confalonieri Roberto, Bregaglio Simone, Adam Myriam, Ruget Françoise, Li Tao, Hasegawa Toshihiro, Yin Xinyou, Zhu Yan, Boote Kenneth J., Buis Samuel, Fumoto Tamon, Gaydon Donald, Lafarge Tanguy, Marcaida Manuel, Nakagawa Hitochi, Ruane Alex C., Singh Balwinder, Singh Upendra, Tang Liang, Tao Fulu, Fugice Job, Yoshida Hiroe, Zhang Zhao, Wilson Lloyd Ted, Baker Jeff, Yang Yubin, Masutomi Yuji, Wallach Daniel, Acutis Marco, Bouman Bas. 2016. A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation. Environmental Modelling and Software, 85 : 332-341.

Quartile : Q1, Sujet : COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS / Quartile : Q1, Sujet : ENVIRONMENTAL SCIENCES / Quartile : Q1, Sujet : ENGINEERING, ENVIRONMENTAL

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Géographie-Aménagement-Urbanisme-Architecture

Résumé : 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.

Mots-clés Agrovoc : Oryza sativa, modèle mathématique, modèle de simulation, développement biologique, taxonomie, classification, analyse en composantes (statistique)

Classification Agris : U30 - Méthodes de recherche
F62 - Physiologie végétale - Croissance et développement
U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : Axe 1 (2014-2018) - Agriculture écologiquement intensive

Auteurs et affiliations

• Confalonieri Roberto, University of Milan (ITA)
• Bregaglio Simone, University of Milan (ITA)
• Adam Myriam, CIRAD-BIOS-UMR AGAP (BFA) ORCID: 0000-0002-8873-6762
• Ruget Françoise, INRA (FRA)
• Li Tao, IRRI [International Rice Research Institute] (PHL)
• Hasegawa Toshihiro, National Institute of Agro-Environmental Sciences (JPN)
• Yin Xinyou, Wageningen University (NLD)
• Zhu Yan, National Engineering and Technology Center for Information Agriculture (CHN)
• Boote Kenneth J., University of Florida (USA)
• Buis Samuel, INRA (FRA)
• Fumoto Tamon, NIAES (JPN)
• Gaydon Donald, CSIRO (AUS)
• Lafarge Tanguy, CIRAD-BIOS-UMR AGAP (FRA)
• Marcaida Manuel, IRRI [International Rice Research Institute] (PHL)
• Nakagawa Hitochi, NARO (JPN)
• Ruane Alex C., NASA (USA)
• Singh Balwinder, CIMMYT (IND)
• Singh Upendra, IFDC (USA)
• Tang Liang, National Engineering and Technology Center for Information Agriculture (CHN)
• Tao Fulu, CAS (CHN)
• Fugice Job, IFDC (USA)
• Yoshida Hiroe, NARO (JPN)
• Zhang Zhao, Beijing Normal University (CHN)
• Wilson Lloyd Ted, Texas A&M AgriLife Research (USA)
• Baker Jeff, ARS (USA)
• Yang Yubin, Texas A&M AgriLife Research (USA)
• Masutomi Yuji, Ibaraki University (JPN)
• Wallach Daniel, INRA (FRA)
• Acutis Marco, Università degli studi di Milano (ITA)
• Bouman Bas, IRRI [International Rice Research Institute] (PHL)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/582325/)

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