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Wallach, D.* ; Palosuo, T.* ; Thorburn, P.* ; Hochman, Z.* ; Andrianasolo, F.* ; Asseng, S.* ; Basso, B.* ; Buis, S.* ; Crout, N.* ; Dumont, B.* ; Ferrise, R.* ; Gaiser, T.* ; Gayler, S.* ; Hiremath, S.* ; Hoek, S.* ; Horan, H.* ; Hoogenboom, G.* ; Huang, M.* ; Jabloun, M.* ; Jansson, P.E.* ; Jing, Q.* ; Justes, É.* ; Kersebaum, K.C.* ; Launay, M.* ; Lewan, E.* ; Luo, Q.* ; Maestrini, B.* ; Moriondo, M.* ; Olesen, J.E.* ; Padovan, G.* ; Poyda, A.* ; Priesack, E. ; Pullens, J.W.M.* ; Qian, B.* ; Schütze, N.* ; Shelia, V.* ; Souissi, A.* ; Specka, X.* ; Kumar Srivastava, A.* ; Stella, T.* ; Streck, T.* ; Trombi, G.* ; Wallor, E.* ; Wang, J.* ; Weber, T.K.D.* ; Weihermüller, L.* ; de Wit, A.* ; Wöhling, T.* ; Xiao, L.* ; Zhao, C.* ; Zhu, Y.* ; Seidel, S.J.*

Multi-model evaluation of phenology prediction for wheat in Australia.

Agric. For. Meteorol. 298-299:108289 (2021)
Postprint DOI
Open Access Green
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Australia ; Evaluation ; Parameter Uncertainty ; Phenology ; Structure Uncertainty ; Wheat; Crop Model Predictions; Time; Uncertainty; Simulation; Maize; Performance; Cultivars; Maturity; Systems; Europe
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 0168-1923
e-ISSN 1873-2240
Zeitschrift Agricultural and Forest Meteorology
Quellenangaben Band: 298-299, Heft: , Seiten: , Artikelnummer: 108289 Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Research Unit Environmental Simulation (EUS)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Environmental Sciences
PSP-Element(e) G-504912-001
Förderungen Grains Research and Development Corporation (GRDC) under the "Adding Value to GRDC's National Variety Trial Network" project
German Federal Ministry of Education and Research (BMBF)
INRA ACCAF metaprogramme
EU
project BiomassWeb of the GlobeE programme - Federal Ministry of Education and Research (BMBF, Germany)
German Research Foundation (DFG) under Germany's Excellence Strategy
BonaRes project "I4S" of the Federal Ministry of Education and Research (BMBF), Germany
BonaRes project "Soil3" of the Federal Ministry of Education and Research (BMBF), Germany
Natural Resources Institute Finland (Luke) through a strategic project BoostIA
Academy of Finland through project DivCSA
Academy of Finland through project AI-CropPro
National Key Research and Development Program of China
National Science Foundation for Distinguished Young Scholars
CSIRO
JPI FACCE MACSUR2 project - Italian Ministry for Agricultural, Food, and Forestry Policies
Queensland Department of Agriculture and Fisheries
University of Southern Queensland
USDA/NIFA HATCH grant
U.S. Department of Agriculture (USDA), National Institute of Food and Agriculture
DFG
Agriculture and AgriFood Canada's Project under the Canadian Agricultural Partnership
China Scholarship Council
111 project
Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modelling Framework) - German Research Foundation (DFG)
DOI 10.1016/j.agrformet.2020.108289
WOS ID WOS:000610797100011
Scopus ID 85098953804
Erfassungsdatum 2021-03-26
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