TY  - JOUR
AU  - Wallach, Daniel
AU  - Palosuo, Taru
AU  - Thorburn, Peter
AU  - Hochman, Zvi
AU  - Andrianasolo, Fety
AU  - Asseng, Senthold
AU  - Basso, Bruno
AU  - Buis, Samuel
AU  - Crout, Neil
AU  - Dumont, Benjamin
AU  - Ferrise, Roberto
AU  - Gaiser, Thomas
AU  - Gayler, Sebastian
AU  - Hiremath, Santosh
AU  - Hoek, Steven
AU  - Horan, Heidi
AU  - Hoogenboom, Gerrit
AU  - Huang, Mingxia
AU  - Jabloun, Mohamed
AU  - Jansson, Per-Erik
AU  - Jing, Qi
AU  - Justes, Eric
AU  - Kersebaum, Kurt Christian
AU  - Launay, Marie
AU  - Lewan, Elisabet
AU  - Luo, Qunying
AU  - Maestrini, Bernardo
AU  - Moriondo, Marco
AU  - Olesen, Jørgen Eivind
AU  - Padovan, Gloria
AU  - Poyda, Arne
AU  - Priesack, Eckart
AU  - Pullens, Johannes Wilhelmus Maria
AU  - Qian, Budong
AU  - Schütze, Niels
AU  - Shelia, Vakhtang
AU  - Souissi, Amir
AU  - Specka, Xenia
AU  - Kumar Srivastava, Amit
AU  - Stella, Tommaso
AU  - Streck, Thilo
AU  - Trombi, Giacomo
AU  - Wallor, Evelyn
AU  - Wang, Jing
AU  - Weber, Tobias K. D.
AU  - Weihermüller, Lutz
AU  - de Wit, Allard
AU  - Wöhling, Thomas
AU  - Xiao, Liujun
AU  - Zhao, Chuang
AU  - Zhu, Yan
AU  - Seidel, Sabine J
TI  - Multi-model evaluation of phenology prediction for wheat in Australia
JO  - Agricultural and forest meteorology
VL  - 298-299
SN  - 0168-1923
CY  - Amsterdam [u.a.]
PB  - Elsevier
M1  - FZJ-2021-00236
SP  - 108289 -
PY  - 2021
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000610797100011
DO  - DOI:10.1016/j.agrformet.2020.108289
UR  - https://juser.fz-juelich.de/record/889347
ER  -