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

Fronzek, S. and Pirtioja, Nina and Carter, Timothy and Bindi, Marco and Hoffmann, Holger and Palosuo, Taru and Ruiz Ramos, Margarita and Tao, Fulu and Trnka, M. and Acutis, Marco and Asseng, Senthold and Baranowski, Piotr and Basso, Bruno and Bodin, Per and Buis, Samuel and Cammarano, Davide and Deligios, Paola and Destain, Marie-France and Dumont, Benjamin and Ewert, Frank and Ferrise, Roberto and François, Louis and Gaiser, Thomas and Hlavinka, P. and Jacquemin, I. and Kersebaum, K.C. and Kollas, Chris and Krzyszczak, Jaromir and Lorite, I. J. and Minet, Julien and Minguez Tudela, Maria Ines and Montesino, Manuel and Moriondo, Marco and Müller, Christoph and Nendel, Claas and Öztürk, Isik and Perego, Alessia and Rodríguez Sánchez, Alfredo and Ruane, Alex C. and Ruget, François and Sanna, Mattia and Semenov, Mikhail A. and Slawinski, Cezary and Stratonovitch, Pierre and Supit, Iwan and Waha, Katharina and Wang, E. and Wu, Lianhai and Zhao, Z. and Röter, R.P. (2018). Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change. "Agricultural Systems", v. 159 ; pp. 209-224. ISSN 0308-521X. https://doi.org/10.1016/j.agsy.2017.08.004.

Description

Title: Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
Author/s:
  • Fronzek, S.
  • Pirtioja, Nina
  • Carter, Timothy
  • Bindi, Marco
  • Hoffmann, Holger
  • Palosuo, Taru
  • Ruiz Ramos, Margarita
  • Tao, Fulu
  • Trnka, M.
  • Acutis, Marco
  • Asseng, Senthold
  • Baranowski, Piotr
  • Basso, Bruno
  • Bodin, Per
  • Buis, Samuel
  • Cammarano, Davide
  • Deligios, Paola
  • Destain, Marie-France
  • Dumont, Benjamin
  • Ewert, Frank
  • Ferrise, Roberto
  • François, Louis
  • Gaiser, Thomas
  • Hlavinka, P.
  • Jacquemin, I.
  • Kersebaum, K.C.
  • Kollas, Chris
  • Krzyszczak, Jaromir
  • Lorite, I. J.
  • Minet, Julien
  • Minguez Tudela, Maria Ines
  • Montesino, Manuel
  • Moriondo, Marco
  • Müller, Christoph
  • Nendel, Claas
  • Öztürk, Isik
  • Perego, Alessia
  • Rodríguez Sánchez, Alfredo
  • Ruane, Alex C.
  • Ruget, François
  • Sanna, Mattia
  • Semenov, Mikhail A.
  • Slawinski, Cezary
  • Stratonovitch, Pierre
  • Supit, Iwan
  • Waha, Katharina
  • Wang, E.
  • Wu, Lianhai
  • Zhao, Z.
  • Röter, R.P.
Item Type: Article
Título de Revista/Publicación: Agricultural Systems
Date: January 2018
Volume: 159
Subjects:
Freetext Keywords: Classification; Climate change; Crop model; Ensemble; Sensitivity analysis; Wheat
Faculty: E.T.S. de Ingeniería Agronómica, Alimentaria y de Biosistemas (UPM)
Department: Producción Agraria
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (4MB) | Preview

Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7603416IMPRESSIONSNATURAL ENVIRONMENT RESEARCH COUNCILImpacts and risks from higher-end scenarios: Strategies for innovative solutions

More information

Item ID: 50809
DC Identifier: http://oa.upm.es/50809/
OAI Identifier: oai:oa.upm.es:50809
DOI: 10.1016/j.agsy.2017.08.004
Official URL: https://www.sciencedirect.com/science/article/pii/S0308521X1630498X?via%3Dihub
Deposited by: Memoria Investigacion
Deposited on: 20 Jul 2018 08:56
Last Modified: 26 Apr 2019 12:30
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM