How well do crop modeling groups predict wheat phenology, given calibration data from the target population?
Daniel Wallach
(1)
,
Taru Palosuo
(2)
,
Peter Thorburn
(3)
,
Emmanuelle Gourdain
(4)
,
Senthold Asseng
(5)
,
Bruno Basso
(6)
,
Samuel Buis
(7)
,
Neil Crout
(8)
,
Camilla Dibari
(9)
,
Benjamin Dumont
(10)
,
Roberto Ferrise
(9)
,
Thomas Gaiser
(11, 12)
,
Cécile Garcia
(4)
,
Sebastian Gayler
(13, 14)
,
Afshin Ghahramani
(15)
,
Zvi Hochman
(16)
,
Steven Hoek
(17)
,
Gerrit Hoogenboom
(18)
,
Heidi Horan
(3)
,
Mingxia Huang
(19, 20)
,
Mohamed Jabloun
(8)
,
Qi Jing
(21)
,
Eric Justes
(22, 23)
,
Kurt Christian Kersebaum
(24)
,
Anne Klosterhalfen
(25)
,
Marie Launay
(26)
,
Bernardo Maestrini
(17)
,
Budong Qian
(27)
,
Qunying Luo
(28)
,
Henrike Mielenz
(29)
,
Marco Moriondo
(30)
,
Hasti Nariman Zadeh
(31)
,
Jørgen Eivind Olesen
(32)
,
Arne Poyda
(33)
,
Eckart Priesack
(34)
,
Johannes Wilhelmus Maria Pullens
(32)
,
Niels Schütze
(35)
,
Vakhtang Shelia
(36)
,
Amir Souissi
(37)
,
Xenia Specka
(38)
,
Amit Kumar Srivastava
(11)
,
Tommaso Stella
(39)
,
Thilo Streck
(13)
,
Giacomo Trombi
(9)
,
Evelyn Wallor
(39)
,
Jing Wang
(40)
,
Tobias K.D. Weber
(13)
,
Lutz Weihermüller
(41)
,
Allard de Wit
(17)
,
Thomas Wöhling
(35, 42)
,
Liujun Xiao
(43)
,
Chuang Zhao
(43)
,
Yan Zhu
(44)
,
Sabine Seidel
(12)
1
AGIR -
AGroécologie, Innovations, teRritoires
2 LUKE - Natural Resources Institute Finland
3 CSIRO - CSIRO Agriculture and Food
4 ARVALIS - Institut du végétal [Paris]
5 TUM - Technische Universität Munchen - Technical University Munich - Université Technique de Munich
6 Michigan State University [East Lansing]
7 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
8 UON - University of Nottingham, UK
9 DAGRI - Department of Agriculture, Food, Environment and Forestry
10 Gembloux Agro-Bio Tech [Gembloux]
11 Universität Bonn = University of Bonn
12 INRES - Institute of Crop Science and Resource Conservation [Bonn]
13 University of Hohenheim
14 Institute of Soil Science and Land Evaluation, Soil Biology Section
15 USQ - University of Southern Queensland
16 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra]
17 WUR - Wageningen University and Research [Wageningen]
18 UF - University of Florida [Gainesville]
19 China Agriculture University [Beijing]
20 College of Resources and Environmental Sciences
21 Agriculture and Agri-Food Canada Eastern Cereal and Oilseed Research Centre
22 UMR SYSTEM - Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens
23 Cirad-PERSYST - Département Performances des systèmes de production et de transformation tropicaux
24 ZALF - Leibniz-Center for Agricultural Landscape Research Muencheberg
25 Inst Bio & Geosci IBG Agrosphere 3
26 AGROCLIM - Agroclim
27 Agriculture and Agri-Food Canada, Saskatoon Research Centre
28 Hillridge Technology Pty Ltd
29 JKI - Julius Kühn-Institut - Federal Research Centre for Cultivated Plants
30 CNR-IBE
31 Aalto University
32 Aarhus University [Aarhus]
33 CAU - Christian-Albrechts-Universität zu Kiel = Christian-Albrechts University of Kiel = Université Christian-Albrechts de Kiel
34 German Res Ctr Environm Hlth
35 TU Dresden - Technische Universität Dresden = Dresden University of Technology
36 FAMU - Florida Agricultural and Mechanical University
37 UCAR - Université de Carthage (Tunisie)
38 Inst Landscape Biogeochem, Leibniz Ctr Agr Landscape Res, Muncheberg, Germany
39 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
40 CAU - China Agricultural University
41 Agrosphere, IBG-3
42 Lincoln Agritech Ltd
43 UF|ABE - Department of Agricultural and Biological Engineering [Gainesville]
44 NAU - Nanjing Agricultural University
2 LUKE - Natural Resources Institute Finland
3 CSIRO - CSIRO Agriculture and Food
4 ARVALIS - Institut du végétal [Paris]
5 TUM - Technische Universität Munchen - Technical University Munich - Université Technique de Munich
6 Michigan State University [East Lansing]
7 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
8 UON - University of Nottingham, UK
9 DAGRI - Department of Agriculture, Food, Environment and Forestry
10 Gembloux Agro-Bio Tech [Gembloux]
11 Universität Bonn = University of Bonn
12 INRES - Institute of Crop Science and Resource Conservation [Bonn]
13 University of Hohenheim
14 Institute of Soil Science and Land Evaluation, Soil Biology Section
15 USQ - University of Southern Queensland
16 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra]
17 WUR - Wageningen University and Research [Wageningen]
18 UF - University of Florida [Gainesville]
19 China Agriculture University [Beijing]
20 College of Resources and Environmental Sciences
21 Agriculture and Agri-Food Canada Eastern Cereal and Oilseed Research Centre
22 UMR SYSTEM - Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens
23 Cirad-PERSYST - Département Performances des systèmes de production et de transformation tropicaux
24 ZALF - Leibniz-Center for Agricultural Landscape Research Muencheberg
25 Inst Bio & Geosci IBG Agrosphere 3
26 AGROCLIM - Agroclim
27 Agriculture and Agri-Food Canada, Saskatoon Research Centre
28 Hillridge Technology Pty Ltd
29 JKI - Julius Kühn-Institut - Federal Research Centre for Cultivated Plants
30 CNR-IBE
31 Aalto University
32 Aarhus University [Aarhus]
33 CAU - Christian-Albrechts-Universität zu Kiel = Christian-Albrechts University of Kiel = Université Christian-Albrechts de Kiel
34 German Res Ctr Environm Hlth
35 TU Dresden - Technische Universität Dresden = Dresden University of Technology
36 FAMU - Florida Agricultural and Mechanical University
37 UCAR - Université de Carthage (Tunisie)
38 Inst Landscape Biogeochem, Leibniz Ctr Agr Landscape Res, Muncheberg, Germany
39 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
40 CAU - China Agricultural University
41 Agrosphere, IBG-3
42 Lincoln Agritech Ltd
43 UF|ABE - Department of Agricultural and Biological Engineering [Gainesville]
44 NAU - Nanjing Agricultural University
Daniel Wallach
- Fonction : Auteur
- PersonId : 801198
- ORCID : 0000-0003-3500-8179
- IdRef : 079093493
Samuel Buis
- Fonction : Auteur
- PersonId : 743153
- IdHAL : samuel-buis
- ORCID : 0000-0002-8676-5447
- IdRef : 253121485
Résumé
Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.