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Article Dans Une Revue Agricultural and Forest Meteorology Année : 2021

Multi-model evaluation of phenology prediction for wheat in Australia

1 AGIR - AGroécologie, Innovations, teRritoires
2 LUKE - Natural Resources Institute Finland
3 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra]
4 ARVALIS - Institut du Végétal [Boigneville]
5 UF - University of Florida [Gainesville]
6 UF|ABE - Department of Agricultural and Biological Engineering [Gainesville]
7 Michigan State University [East Lansing]
8 DEPARTMENT OF EARTH AND ENVIRONMENTAL SCIENCES MICHIGAN STATE UNIVERSITY USA
9 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
10 UON - University of Nottingham, UK
11 Université de Liège - Gembloux
12 Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech
13 UniFI - Università degli Studi di Firenze = University of Florence
14 DAGRI - Department of Agriculture, Food, Environment and Forestry
15 Universität Bonn = University of Bonn
16 INRES - Institute of Crop Science and Resource Conservation [Bonn]
17 University of Hohenheim
18 Institute of Soil Science and Land Evaluation, Soil Biology Section
19 Aalto University School of Science and Technology [Aalto, Finland]
20 WUR - Wageningen University and Research [Wageningen]
21 CSIRO - CSIRO Agriculture and Food
22 UF|IFAS - Food Systems Institute [Gainesville]
23 China Agriculture University [Beijing]
24 College of Resources and Environmental Sciences
25 KTH - KTH Royal Institute of Technology [Stockholm]
26 AAFC - Agriculture and Agri-Food
27 Ottawa Research and Development Center
28 UMR ABSys - Agrosystèmes Biodiversifiés
29 UMR SYSTEM - Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens
30 Cirad-PERSYST - Département Performances des systèmes de production et de transformation tropicaux
31 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
32 CzechGlobe - Global Change Research Centre
33 AGROCLIM - Agroclim
34 SLU - Swedish University of Agricultural Sciences = Sveriges lantbruksuniversitet
35 Hillridge Technology Pty Ltd
36 IBE | CNR - Institute for BioEconomy [Sesto Fiorentino]
37 Aarhus University [Aarhus]
38 CAU - Christian-Albrechts-Universität zu Kiel = Christian-Albrechts University of Kiel = Université Christian-Albrechts de Kiel
39 Institute of Crop Science and Plant Breeding
40 Helmholtz Zentrum München = German Research Center for Environmental Health
41 German Res Ctr Environm Hlth
42 Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)
43 TU Dresden - Technische Universität Dresden = Dresden University of Technology
44 UCAR - Université de Carthage (Tunisie)
45 INRAT - Institut National de la Recherche Agronomique de Tunisie
46 College of Resources and Environmental Sciences
47 FZJ - Forschungszentrum Jülich GmbH | Centre de recherche de Jülich | Jülich Research Centre
48 IBG - Institute of Bio- and Geosciences [Jülich]
49 Lincoln Agritech Ltd
50 NAU - Nanjing Agricultural University
51 National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production
Daniel Wallach
  • Fonction : Auteur
  • PersonId : 1202871
Marie Launay
  • Fonction : Auteur

Résumé

Highlights: • A large multi-model study predicting wheat phenology in Australia was performed. • Calibration and evaluation datasets were independently drawn from the same population. • Mean absolute prediction error ranged from 6 to 20 days (median 9 days). • Two thirds of modeling groups predicted better than a simple temperature sum. • Variability between groups using the same model structure was substantial. Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.
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hal-03119039 , version 1 (26-11-2021)

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Daniel Wallach, Taru Palosuo, Peter Thorburn, Zvi Hochman, Fety Andrianasolo, et al.. Multi-model evaluation of phenology prediction for wheat in Australia. Agricultural and Forest Meteorology, 2021, 298-299, ⟨10.1016/j.agrformet.2020.108289⟩. ⟨hal-03119039⟩
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