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Article Dans Une Revue Agricultural Systems Année : 2017

Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

1 SYKE - Finnish Environment Institute
2 UniFI - Università degli Studi di Firenze = University of Florence = Université de Florence
3 Rheinische Friedrich-Wilhelms-Universität Bonn
4 INRES - Institut für Nutzpflanzenwissenschaften und Ressourcenschutz
5 LUKE - Natural Resources Institute Finland
6 CEIGRAM - Centro de Estudios e Investigación para la Gestión de Riesgos Agrarios y Medioambientales
7 Institute of Agrosystems and Bioclimatology
8 CzechGlobe - Global Change Research Centre
9 IRD [Nouvelle-Calédonie] - Institut de Recherche pour le Développement
10 UF - University of Florida [Gainesville]
11 Institute of Agrophysics,
12 Michigan State University [East Lansing]
13 Skane University Hospital [Lund]
14 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
15 The James Hutton Institute
16 UNISS - Università degli Studi di Sassari = University of Sassari [Sassari]
17 Université de Liège
18 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
19 PIK - Potsdam Institute for Climate Impact Research
20 IFAPA - Instituto Andaluz de Investigación y Formación Agraria y Pesquera
21 UCPH - University of Copenhagen = Københavns Universitet
22 IBIMET - Istituto di Biometeorologia [Firenze]
23 GISS - NASA Goddard Institute for Space Studies
24 Rothamsted Research
25 WUR - Wageningen University and Research [Wageningen]
26 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra]
27 CAU - China Agricultural University
28 Tropical Plant Production and Agricultural Systems Modelling (TROPAGS)
29 Centre for Biodiversity and Sustainable Land Use (CBL)
Stefan Fronzek
Davide Cammarano
  • Fonction : Auteur
  • PersonId : 968228
Mikhail A. Semenov
Lianhai Wu
  • Fonction : Auteur

Résumé

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern.The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

Dates et versions

hal-01606946 , version 1 (02-10-2017)

Identifiants

Citer

Stefan Fronzek, Nina Pirttioja, Timothy R. Carter, Marco Bindi, Holger Hoffmann, et al.. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change. Agricultural Systems, 2017, 159, ⟨10.1016/j.agsy.2017.08.004⟩. ⟨hal-01606946⟩
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