A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation
Roberto Confalonieri
(1)
,
Simone Bregaglio
(1)
,
Myriam Adam
(2)
,
Francoise Ruget
(3)
,
Tao Li
(4)
,
Toshihiro Hasegawa
(5)
,
Xinyou Yin
(6)
,
Yan Zhu
(7)
,
Kenneth Boote
(8)
,
Samuel Buis
(3)
,
Tamon Fumoto
(5)
,
Donald Gaydon
(9)
,
Tanguy Lafarge
(2)
,
Manuel Marcaida
(4)
,
Hiroshi Nakagawa
(10)
,
Alex C. Ruane
(11)
,
Balwinder Singh
(12)
,
Upendra Singh
(13)
,
Liang Tang
(14)
,
Fulu Tao
(15, 16)
,
Job Fugice
(13)
,
Hiroe Yoshida
(10)
,
Zhao Zhang
(17)
,
Lloyd T. Wilson
(18)
,
Jeff Baker
(19)
,
Yubin Yang
(20)
,
Yuji Masutomi
(21)
,
Daniel D. Wallach
(22)
,
Marco Acutis
(1)
,
Bas Bouman
(4)
1
Cassandra Lab
2 UMR AGAP - Amélioration génétique et adaptation des plantes méditerranéennes et tropicales
3 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
4 IRRI - International Rice Research Institute [Philippines]
5 NIAES - National Institute of Agro-Environmental Sciences
6 Centre for Crop Systems Analysis
7 National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production
8 UF - University of Florida [Gainesville]
9 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Australia]
10 NARO - National Agriculture and Food Research Organization
11 GISS - NASA Goddard Institute for Space Studies
12 CIMMYT - International Maize and Wheat Improvement Center
13 IFDC - International Fertilizer Development Center
14 NAU - Nanjing Agricultural University
15 China Academy of Chinese Medicinal Sciences
16 LUKE - Natural Resources Institute Finland
17 State Key Laboratory of Earth Surface Processes and Resource Ecology
18 Texas A
19 USDA-ARS : Agricultural Research Service
20 Texas A and M AgriLife Research
21 College of Agriculture
22 AGIR - AGroécologie, Innovations, teRritoires
2 UMR AGAP - Amélioration génétique et adaptation des plantes méditerranéennes et tropicales
3 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
4 IRRI - International Rice Research Institute [Philippines]
5 NIAES - National Institute of Agro-Environmental Sciences
6 Centre for Crop Systems Analysis
7 National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production
8 UF - University of Florida [Gainesville]
9 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Australia]
10 NARO - National Agriculture and Food Research Organization
11 GISS - NASA Goddard Institute for Space Studies
12 CIMMYT - International Maize and Wheat Improvement Center
13 IFDC - International Fertilizer Development Center
14 NAU - Nanjing Agricultural University
15 China Academy of Chinese Medicinal Sciences
16 LUKE - Natural Resources Institute Finland
17 State Key Laboratory of Earth Surface Processes and Resource Ecology
18 Texas A
19 USDA-ARS : Agricultural Research Service
20 Texas A and M AgriLife Research
21 College of Agriculture
22 AGIR - AGroécologie, Innovations, teRritoires
Myriam Adam
- Fonction : Auteur
- PersonId : 1239730
- ORCID : 0000-0002-8873-6762
Francoise Ruget
- Fonction : Auteur
- PersonId : 1203697
Toshihiro Hasegawa
- Fonction : Auteur
- PersonId : 776790
- ORCID : 0000-0001-8501-5612
Samuel Buis
- Fonction : Auteur
- PersonId : 743153
- IdHAL : samuel-buis
- ORCID : 0000-0002-8676-5447
- IdRef : 253121485
Tanguy Lafarge
- Fonction : Auteur
- PersonId : 1022269
Zhao Zhang
- Fonction : Auteur
- PersonId : 776791
- ORCID : 0000-0003-3397-3549
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.
