Multi-model evaluation of phenology prediction for wheat in Australia

Wallach Daniel, Palosuo Taru, Thorburn Peter J., Hochman Zvi, Andrianasolo Fety, Asseng Senthold, Basso Bruno, Buis Samuel, Crout Neil, Dumont Benjamin, Ferrise Roberto, Gaiser Thomas, Gayler Sebastian, Hiremath Santosh, Hoek Steven, Horan Heidi, Hoogenboom Gerrit, Huang Mingxia, Jabloun Mohamed, Jansson Per-Erik, Jing Qi, Justes Eric, Kersebaum Kurt Christian, Launay Marie, Lewan Elisabet, Luo Qunying, Maestrini Bernardo, Moriondo Marco, et al.. 2021. Multi-model evaluation of phenology prediction for wheat in Australia. Agricultural and Forest Meteorology, 298-299:108289, 10 p.

Quartile : Outlier, Sujet : AGRONOMY / Quartile : Outlier, Sujet : FORESTRY / Quartile : Q1, Sujet : METEOROLOGY & ATMOSPHERIC SCIENCES

Résumé : 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.

Mots-clés Agrovoc : Triticum, phénologie, technique de prévision, incertitude statistique, modélisation des cultures, sélection de cultivars

Mots-clés géographiques Agrovoc : Australie

Mots-clés libres : Evaluation, Phenology, Wheat, Australia, Structure uncertainty, Parameter uncertainty

Classification Agris : F40 - Écologie végétale
U10 - Informatique, mathématiques et statistiques
F01 - Culture des plantes

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Auteurs et affiliations

• Wallach Daniel, INRAE (FRA)
• Palosuo Taru, Natural Resources Institute Finland (FIN) - auteur correspondant
• Thorburn Peter J., CSIRO (AUS)
• Hochman Zvi, CSIRO (AUS)
• Andrianasolo Fety, ARVALIS Institut du végétal (FRA)
• Asseng Senthold, University of Florida (USA)
• Basso Bruno, MSU (USA)
• Buis Samuel, INRAE (FRA)
• Crout Neil, University of Nottingham (GBR)
• Dumont Benjamin, Université de Liège (BEL)
• Ferrise Roberto, University of Florence (ITA)
• Gaiser Thomas, Universität Bonn (DEU)
• Gayler Sebastian, Universität Hohenheim (DEU)
• Hiremath Santosh, Aalto University (FIN)
• Hoek Steven, Wageningen University (NLD)
• Horan Heidi, CSIRO (AUS)
• Hoogenboom Gerrit, University of Florida (USA)
• Huang Mingxia, CAU [China Agricultural University] (CHN)
• Jabloun Mohamed, University of Nottingham (GBR)
• Jansson Per-Erik, Royal Institute of Technology (SWE)
• Jing Qi, Ottawa Research and Development Center (CAN)
• Justes Eric, CIRAD-PERSYST-UMR SYSTEM (FRA) ORCID: 0000-0001-7390-7058
• Kersebaum Kurt Christian, Leibniz Centre for Agricultural Landscape Research (DEU)
• Launay Marie, INRAE (FRA)
• Lewan Elisabet, Swedish University of Agricultural Sciences (SWE)
• Luo Qunying, Hillridge Technology Pty Ltd (AUS)
• Maestrini Bernardo, MSU (USA)
• Moriondo Marco, CNR-IBE (ITA)
• et al.

Source : Cirad-Agritrop (https://agritrop.cirad.fr/597374/)

Métriques

[ Page générée et mise en cache le 2024-12-18 ]