| The uncertainty about climate change (CC) complicates impact adaptation and risk management evaluation at the regional level. Approaches for managing this uncertainty and for simulating and communicating climate change impacts and adaptation opportunities are required. Here we apply an ensemble of crop models for adapting rainfed winter wheat at Lleida (NE Spain), constructing adaptation response surfaces (ARS).
Our methodology has been adapted from Pirttioja et al. (2015). Impact response surfaces (IRS) are plotted surfaces showing the response of an impact variable (here crop yield Y) to changes in two explanatory variables (here precipitation P and temperature T). By analyzing adaptation variables such as changes in crop yield (ΔY) when an adaptation option is simulated, these can be interpreted as the adaptation response to potential changes of P and T, i.e. ARS. To build these ARS, we explore the sensitivity of an ensemble of wheat models to changes in T and P. Baseline (1981-2010) T and P were modified using a delta change approach with changes in the seasonal patterns. Three levels of CO2 (representing future conditions until 2050) and two actual soil profiles are considered. Crop models were calibrated with field data from Abeledo et al. (2008) and Cartelle et al. (2006). Most promising adaptation options to be analyzed by the ARS approach are identified in a pilot stage with the models DSSAT4.5 and SiriusQuality v.2, subsequently simulating the selected adaptation combinations by the whole ensemble of 11 crop models.
The adaptation options identified from pilot stage were: a cultivar with no vernalisation requirements, shortening or extending a 10 % the crop cycle of the standard cultivar, sowing 15 days earlier and 30 days later than the standard date, supplementary irrigation with 40 mm at flowering and full irrigation. These options and those of the standard cultivar and management resulted in 54 combinations and 450.000 runs per crop model. Our preliminary ARSs show some adaptation options allow recover up to ca. 2000 kg/ha. Compared to the historical yields recorded at Lleida province (2550 kg/ha in 1981-2010) our results indicate that adaptation is feasible and may help to reduce detrimental effects of CC.
Our analysis evaluates if the explored adaptations fulfill the biophysical requirements to become a practical adaptive solution. This study exemplifies how adaptation options and their impacts can be analyzed, evaluated and communicated in a context of high regional uncertainty for current and future conditions and for short to long-term perspective.
This work was funded by MACSUR project within FACCE-JPI.
References
Abeledo, L.G., R. Savin and G.A. Slafer (2008). European Journal of Agronomy 28:541-550.
Cartelle, J., A. Pedró, R. Savin, G.A. Slafer (2006) European Journal of Agronomy 25:365-371.
Pirttioja, N., T. Carter, S. Fronzek, M. Bindi, H. Hoffmann, T. Palosuo, M. Ruiz-Ramos, F. Tao, M. Acutis, S. Asseng, P. Baranowski, B. Basso, P. Bodin, S. Buis, D. Cammarano, P. Deligios, M.-F. Destain, B. Dumont, R. Ewert, R. Ferrise, L. François, T. Gaiser, P. Hlavinka, I. Jacquemin, K.C. Kersebaum, C. Kollas, J. Krzyszczak, I.J. Lorite, J. Minet, M.I. Minguez, M. Montesino, M. Moriondo, C. Müller, C. Nendel, I. Öztürk, A. Perego, A. Rodríguez, A.C. Ruane, F. Ruget, M. Sanna, M.A. Semenov, C. Slawinski, P. Stratonovitch, I. Supit, K. Waha, E. Wang, L. Wu, Z. Zhao, and R.P. Rötter, 2015: A crop model ensemble analysis of temperature and precipitation effects on wheat yield across a European transect using impact response surfaces. Clim. Res., 65, 87-105, doi:10.3354/cr01322.
IRS2 TEAM:
Alfredo Rodríguez(1), Ignacio J. Lorite(3), Fulu Tao(4), Nina Pirttioja(5), Stefan Fronzek(5), Taru Palosuo(4), Timothy R. Carter(5), Marco Bindi(2), Jukka G Höhn(4), Kurt Christian Kersebaum(6), Miroslav Trnka(7,8), Holger Hoffmann(9), Piotr Baranowski(10), Samuel Buis(11), Davide Cammarano(12), Yi Chen(13,4), Paola Deligios(14), Petr Hlavinka(7,8), Frantisek Jurecka(7,8), Jaromir Krzyszczak(10), Marcos Lana(6), Julien Minet(15), Manuel Montesino(16), Claas Nendel(6), John Porter(16), Jaime Recio(1), Françoise Ruget(11), Alberto Sanz(1), Zacharias Steinmetz(17,18), Pierre Stratonovitch(19), Iwan Supit(20), Domenico Ventrella(21), Allard de Wit(20) and Reimund P. Rötter(4).
1 Universidad Politecnica de Madrid, ETSIAgrónomos,28040 Madrid, Spain, margarita.ruiz.ramos@upm.es
2 University of Florence, 50144 Florence, Italy
3 IFAPA Junta de Andalucia, 14004 Córdoba, Spain
4 Natural Resources Institute (LUKE), 01370 Vantaa, Finland
5 Finnish Environment Institute (SYKE), 00250 Helsinki, Finland
6 Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany
7 Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Brno 613 00, Czech Republic
8 Global Change Research Institute CAS, 603 00 Brno, Czech Republic
9 INRES, University of Bonn, 53115 Bonn, Germany
10 Institute of Agrophysics Polish Academy of Sciences, Lublin, Poland
11 INRA, UMR 1114 EMMAH, F-84914 Avignon, France
12 James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland
13 State Key Laboratory of Earth Surface Processes and Resource Ecology, Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
14 University of Sassari, 07100 Sassari, Italy
15 Université de Liège, 4000 Liège, Belgium
16 University of Copenhagen, 2630 Taastrup, Denmark
17 RIFCON GmbH, 69493 Hirschberg, Germany
18 Group of Environmental and Soil Chemistry, Institute for Environmental Sciences, University of Koblenz-Landau, 76829 Landau, Germany
19 Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK
20 Wageningen University, 6700AA Wageningen, The Netherlands
21 Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria. CRA-SCA |