A crop model ensemble analysis of temperature and precipitation effects on wheat yield across a European transect using impact response surfaces
Abstract
Impact response surfaces (IRSs) of spring and winter wheat yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect in Europe. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of 1981–2010 baseline weather.
In spite of large differences in simulated yield responses to both baseline and changed climate between models, sites, crops and years, several common messages emerged. Ensemble average yields decline with higher temperatures (3–7% per 1°C) and decreased precipitation (3–9% per 10% decrease), but benefit from increased precipitation (0-8% per 10% increase). Yields are more sensitive to temperature than precipitation changes at the Finnish site while sensitivities are mixed at the German and Spanish sites. Precipitation effects diminish under higher temperature changes. Inter-model variability is highest for baseline climate at the Spanish site, but relatively insensitive to changed climate. Modelled responses diverge most at the Finnish and German sites for winter wheat under temperature change. The IRS pattern of yield reliability tracks average yield levels. Inter-annual yield variability is more sensitive to precipitation than temperature, except at the Spanish site for spring wheat.
Optimal temperatures for present-day cultivars are close to the baseline under Finnish conditions but below the baseline at the German and Spanish sites. This suggests that adoption of later maturing cultivars with higher temperature requirements might already be advantageous, and increasingly so under future warming.
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Abeledo LG, Savin R, Slafer GA (2008) Wheat productivity in the Mediterranean Ebro
Valley: Analyzing the gap between attainable and potential yield with a simulation
model. European Journal of Agronomy 28:541-550.
Angulo C, Rötter R, Lock R, Enders A, Fronzek S, Ewert F (2013) Implication of crop model
calibration strategies for assessing regional impacts of climate change in Europe.
Agricultural and Forest Meteorology 170:32-46.
Asseng S, Ewert F, Martre P, Rötter RP, Lobell DB, Cammarano D, et al. (2014) Rising
temperatures reduce global wheat production. Nature Climate Change.
Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, et al. (2013)
Uncertainty in simulating wheat yields under climate change. Nature Climate Change
:827-832.
Asseng S, Foster I, Turner NC (2011) The impact of temperature variability on wheat yields.
Global Change Biology 17:997-1012.
Becker R, Chambers J, Wilks A (1988) The New S Language. Wadsworth & Brooks/Cole,
Pacific Grove CA
Bouman B, Van Keulen H, Van Laar H, Rabbinge R (1996) The ‘School of de Wit’crop
growth simulation models: a pedigree and historical overview. Agric Syst 52:171-198.
Børgesen CD, Olesen JE (2011) A probabilistic assessment of climate change impacts on
yield and nitrogen leaching from winter wheat in Denmark. Natural Hazards and Earth
System Science 11:2541-2553.
Cartelle J, Pedró A, Savin R, Slafer GA (2006) Grain weight responses to post-anthesis
spikelet-trimming in an old and a modern wheat under Mediterranean conditions.
European Journal of Agronomy 25:365-371.
Challinor A, Martre P, Asseng S, Thornton P, Ewert F (2014a) Making the most of climate
impacts ensembles. Nature Climate Change 4:77-80.
Challinor A, Wheeler T, Craufurd P, Slingo J (2005) Simulation of the impact of high
temperature stress on annual crop yields. Agricultural and Forest Meteorology
:180-189.
Challinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N (2014b) A metaanalysis of crop yield under climate change and adaptation. Nature Climate Change
:287–291.
Cleveland WS (1993) Visualizing data. Hobart Press, Summit, NJ
Craufurd PQ, Vadez V, Krishna Jagadish SV, Vara Prasad PV, Zaman-Allah M (2013) Crop
science experiments designed to inform crop modeling. Agricultural and Forest
Meteorology 170:8-18.
Diaz-Nieto J, Wilby RL (2005) A comparison of statistical downscaling and climate change
factor methods: impacts on low flows in the River Thames, United Kingdom. Climatic
Change 69:245-268.
Easterling WE, Aggarwal PK, Batima P, Brander KM, Erda L, Howden SM, et al. (2007)
Food, fibre and forest products. In: Parry ML, et al. (eds) Climate Change 2007:
Impacts, Adaptation and Vulnerability Contribution of Working Group II to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge
University Press, Cambridge, UK, 273-313
EUROSTAT (2014) Crop products yields by NUTS 2 regions. In:
http://eppeurostateceuropaeu/portal/page/portal/statistics/search_database
Ewert F, Rötter R, Bindi M, Webber H, Trnka M, Kersebaum K, et al. (2014) Crop modelling
for integrated assessment of risk to food production from climate change.
Environmental Modelling & Software:1-17. FAOSTAT (2014) Production. In: http://faostat3faoorg/faostat-gateway/go/to/home/E
Ferrise R, Moriondo M, Bindi M (2011) Probabilistic assessments of climate change impacts
on durum wheat in the Mediterranean region. Natural Hazards and Earth System
Sciences 11:1293-1302. ://WOS:000291089900007
Fronzek S (2013) Climate change and the future distribution of palsa mires: ensemble
modelling, probabilities and uncertainties. Monographs of the Boreal Environmental
Research No 44, ISBN 978-952-11-4204-8, pp 35. http://hdl.handle.net/10138/40184
Fronzek S, Carter TR, Luoto M (2011) Evaluating sources of uncertainty in modelling the
impact of probabilistic climate change on sub-arctic palsa mires. Natural Hazards and
Earth System Sciences 11:2981-2995.
Fronzek S, Carter TR, Raisanen J, Ruokolainen L, Luoto M (2010) Applying probabilistic
projections of climate change with impact models: a case study for sub-arctic palsa
mires in Fennoscandia. Climatic Change 99:515-534.
ISI>://WOS:000275704500009
Gitay H, Brown S, Easterling W, Jallow B, Antle J, Apps M, et al. (2001) Ecosystems and
their goods and services. In: McCarthy JJ, et al. (eds) Climate Change 2001: Impacts,
Adaptation, and Vulnerability Contribution of Working Group II to the Third
Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge
University Press, Cambridge, 235-242
Hanasaki N, Masutomi Y, Takahashi K, Hijioka Y, Harasawa H, Matsuoka Y (2007)
Development of a global water resources scheme for climate change policy support
models. Environmental Systems Research, Japan Society of Civil Engineers 35:367-
(in Japanese).
Harris GR, Collins M, Sexton DMH, Murphy JM, Booth BBB (2010) Probabilistic
projections for 21st century European climate. Natural Hazards and Earth System
Sciences 10:2009-2020. ://WOS:000282427300023
IPCC (2013a) Annex I: Atlas of Global and Regional Climate Projections [van Oldenborgh
GJ, Collins M, Arblaster J, Christensen JH, Marotzke J, Power SB, Rummukainen M,
Zhou T (eds.)]. In: Stocker T, et al. (eds) Climate Change 2013: The Physical Science
Basis Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 1311-1393
IPCC (2013b) Annex II: Climate System Scenario Tables [Prather M, Flato G, Friedlingstein
P, Jones C, Lamarque J-F, Liao H, Rasch P (eds.)]. In: Stocker T, et al. (eds) Climate
Change 2013: The Physical Science Basis Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,
-1445
Jamieson PD, Porter JR, Goudriaan J, Ritchie JT, van Keulen H, Stol W (1998) A comparison
of the models AFRCWHEAT2, CERES-Wheat, Sirius, SUCROS2 and SWHEAT
with measurements from wheat grown under drought. Field Crops Research 55:23-44.
Jones RJA, Thomasson AJ (1985) An Agroclimatic Databank for England and Wales. Soil
Survey Technical Monograph No 16, 45 pp.
Lobell DB, Field CB (2007) Global scale climate–crop yield relationships and the impacts of
recent warming. Environmental research letters 2.
Luo Q, Bellotti W, Williams M, Cooper I, Bryan B (2007) Risk analysis of possible impacts
of climate change on South Australian wheat production. Climatic Change 85:89-101.
MAGRAMA (2010) Anuario de Estadística. Ministerio de Agricultura, Alimentación y
Medio Ambiente, Madrid (MAGRAMA), Spain.
http://www.magrama.gob.es/en/estadistica/temas/publicaciones/anuario-de-estadistica/
Metzger M, Bunce R, Jongman R, Mücher C, Watkins J (2005) A climatic stratification of the
environment of Europe. Global ecology and biogeography 14:549-563.
Palosuo T, Kersebaum KC, Angulo C, Hlavinka P, Moriondo M, Olesen JE, et al. (2011)
Simulation of winter wheat yield and its variability in different climates of Europe: A
comparison of eight crop growth models. European Journal of Agronomy 35:103-114.
Peltonen-Sainio P, Jauhiainen L, Hakala K (2011) Crop responses to temperature and
precipitation according to long-term multi-location trials at high-latitude conditions.
The Journal of Agricultural Science 149:49-62.
Porter J (1993) AFRCWHEAT2: a model of the growth and development of wheat
incorporating responses to water and nitrogen. European Journal of Agronomy
(France).
Porter JR, Gawith M (1999) Temperatures and the growth and development of wheat: a
review. European Journal of Agronomy 10:23-26.
Porter JR, Xie L, Challinor A, Cochrane K, Howden M, Iqbal MM, et al. (2014) Food
security and food production systems. In: Field CB, et al. (eds) Climate Change 2014:
Impacts, Adaptation and Vulnerability Contribution of Working Group II to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge
University Press, Cambridge
R Core Team (2013) R: A language and environment for statistical computing. R Foundation
for Statistical Computing, Vienna, Austria. http://www.R-project.org/
Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, et al. (2014) Assessing
agricultural risks of climate change in the 21st century in a global gridded crop model
intercomparison. Proceedings of the National Academy of Sciences 111:3268–3273.
Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn P, et al. (2013) The
Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols
and pilot studies. Agricultural and Forest Meteorology 170:166-182.
Royston P (1995) Remark AS R94: A remark on Algorithm AS 181: The W test for normality
Applied Statistics 44:547-551.
Ruane AC, McDermid S, Rosenzweig C, Baigorria GA, Jones JW, Romero CC, et al. (2014)
Carbon–Temperature–Water change analysis for peanut production under climate
change: a prototype for the AgMIP Coordinated Climate-Crop Modeling Project
(C3MP). Global change biology 20:394-407.
Rötter RP (2014) Agricultural impacts: robust uncertainty. Nature Climate Change 4:251-252.
Rötter RP, Carter TR, Olesen JE, Porter JR (2011) Crop-climate models need an overhaul.
Nature Climate Change 1:175-177. ://WOS:000293849500004
Rötter RP, Ewert F, Palosuo T, Bindi M, Kersebaum KC, Olesen JE, et al. (2013) Challenges
for agro-ecosystem modelling in climate change risk assessment for major European
crops and farming systems. In: Impacts World 2013 Conference Proceedings,
Potsdam, Potsdam Institute for Climate Impact Research, pp: 555-564
Rötter RP, Palosuo T, Kersebaum KC, Angulo C, Bindi M, Ewert F, et al. (2012) Simulation
of spring barley yield in different climatic zones of Northern and Central Europe: a
comparison of nine crop models. Field Crops Research 133:23-36.
Trnka M, Olesen JE, Kersebaum KC, Skjelvåg AO, Eitzinger J, Seguin B, et al. (2011)
Agroclimatic conditions in Europe under climate change. Global Change Biology
:2298-2318. http://dx.doi.org/10.1111/j.1365-2486.2011.02396.x
Trnka M, Rötter RP, Ruiz-Ramos M, Kersebaum KC, Olesen JE, Žalud Z, et al. (2014)
Adverse weather conditions for European wheat production will become more
frequent with climate change. Nature Climate Change 4:637-643.
van Ittersum M, Rabbinge R (1997) Concepts in production
ecology for analysis and
quantification of agricultural input-output combinations. Field Crops Research
:197-208.
van Ittersum MK, Leffelaar PA, Van Keulen H, Kropff MJ, Bastiaans L, Goudriaan J (2003)
On approaches and applications of the Wageningen crop models. European Journal of
Agronomy 18:201-234.
van Keulen H, Wolf J (1986) Modelling of agricultural production: weather, soils and crops.
Pudoc
Wallach D, Makowski D, Jones JW, Brun F (2013) Working with Dynamic Crop Models:
Methods, Tools and Examples for Agriculture and Environment. Academic Press,
London, UK/San Diego, USA
Watson J, Challinor AJ, Fricker TE, Ferro CA (2014) Comparing the effects of calibration
and climate errors on a statistical crop model and a process-based crop model.
Climatic Change:1-17.
Wetterhall F, Graham LP, Andreasson J, Rosberg J, Yang W (2011) Using ensemble climate
projections to assess probabilistic hydrological change in the Nordic region. Natural
Hazards and Earth System Sciences 11:2295-2306.
ISI>://WOS:000294438700017
White JW, Hoogenboom G, Kimball BA, Wall GW (2011) Methodologies for simulating
impacts of climate change on crop production. Field Crops Research 124:357-368.
Wu L, Kersebaum KC (2008) Modeling water and nitrogen interaction responses and their
consequences in crop models. In: Ahuja LR, et al. (eds) Response of crops to limited
water: understanding and modeling water stress effects on plant growth processes.
ASA-CSSA-SSSA, Madison, WI
Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals.
Weed research 14:415-421.
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