Theoretical and Applied Climatology

, Volume 129, Issue 1–2, pp 503–519 | Cite as

Effects of diurnal temperature range and drought on wheat yield in Spain

  • S. Hernandez-Barrera
  • C. Rodriguez-Puebla
  • A. J. Challinor
Original Paper

Abstract

This study aims to provide new insight on the wheat yield historical response to climate processes throughout Spain by using statistical methods. Our data includes observed wheat yield, pseudo-observations E-OBS for the period 1979 to 2014, and outputs of general circulation models in phase 5 of the Coupled Models Inter-comparison Project (CMIP5) for the period 1901 to 2099. In investigating the relationship between climate and wheat variability, we have applied the approach known as the partial least-square regression, which captures the relevant climate drivers accounting for variations in wheat yield. We found that drought occurring in autumn and spring and the diurnal range of temperature experienced during the winter are major processes to characterize the wheat yield variability in Spain. These observable climate processes are used for an empirical model that is utilized in assessing the wheat yield trends in Spain under different climate conditions. To isolate the trend within the wheat time series, we implemented the adaptive approach known as Ensemble Empirical Mode Decomposition. Wheat yields in the twenty-first century are experiencing a downward trend that we claim is a consequence of widespread drought over the Iberian Peninsula and an increase in the diurnal range of temperature. These results are important to inform about the wheat vulnerability in this region to coming changes and to develop adaptation strategies.

Keywords

Climate change impact Empirical wheat yield model Partial least square regression Climate variability 

Supplementary material

704_2016_1779_MOESM1_ESM.docx (1.1 mb)
(DOCX 1.13 MB)

References

  1. Abdi H (2010) Partial least squares regression and projection on latent structure regression (pls regression). WIREs Comput Stat:1–10. doi:10.1002/wics.051
  2. Angulo C, Rotter 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. Agric For Meteorol 170:32–46. doi:10.1016/j.agrformet.2012.11.017 CrossRefGoogle Scholar
  3. Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn PJ, Rotter RP, Cammarano D, Brisson N, Basso B, Martre P, Aggarwal PK, Angulo C, Bertuzzi P, Biernath C, Challinor AJ, Doltra J, Gayler S, Goldberg R, Grant R, Heng L, Hooker J, Hunt LA, Ingwersen J, Izaurralde RC, Kersebaum KC, Muller C, Kumar SN, Nendel C, O’Leary G, Olesen JE, Osborne TM, Palosuo T, Priesack E, Ripoche D, Semenov MA, Shcherbak I, Steduto P, Stockle C, Stratonovitch P, Streck T, Supit I, Tao F, Travasso M, Waha K, Wallach D, White JW, Williams JR, Wolf J (2013) Uncertainty in simulating wheat yields under climate change. Nat Clim Chang 3(9):827–832. doi:10.1038/nclimate1916 CrossRefGoogle Scholar
  4. Atkinson MD, Kettlewell PS, Hollins PD, Stephenson DB, Hardwick NV (2005) Summer climate mediates UK wheat quality response to winter north atlantic oscillation. Agric For Meteorol 130(1-2):27–37. doi:10.1016/j.agrformet.2005.02.002 CrossRefGoogle Scholar
  5. Bannayan M, Lotfabadi SS, Sanjani S, Mohamadian A, Aghaalikhani M (2011) Effects of precipitation and temperature on crop production variability in northeast Iran. Int J Biometeorol 55(3):387–401. doi:10.1007/s00484-010-0348-7 CrossRefGoogle Scholar
  6. Barlow KM, Christy BP, O’Leary GJ, Riffkin PA, Nuttall JG (2015) Simulating the impact of extreme heat and frost events on wheat crop production: A review. Field Crop Res 171:109–119. doi:10.1016/j.fcr.2014.11.010 CrossRefGoogle Scholar
  7. Begueria S, Vicente-Serrano SM, Reig F, Latorre B (2014) Standardized precipitation evapotranspiration index (spei) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int J Climatol 34(10):3001–3023. doi:10.1002/joc.3887 CrossRefGoogle Scholar
  8. Bristow KL, Campbell GS (1984) On the relationship between incoming solar-radiation and daily maximum and minimum temperature. Agric For Meteorol 31(2):159–166. doi:10.1016/0168-1923(84)90017-0 CrossRefGoogle Scholar
  9. Brown I (2013) Influence of seasonal weather and climate variability on crop yields in Scotland. Int J Biometeorol 57(4):605–614. doi:10.1007/s00484-012-0588-9 CrossRefGoogle Scholar
  10. Capa-Morocho M, Rodriguez-Fonseca B, Ruiz-Ramos M (2014) Crop yield as a bioclimatic index of el nino impact in Europe: Crop forecast implications. Agric For Meteorol 198:42–52. doi:10.1016/j.agrformet.2014.07.012 CrossRefGoogle Scholar
  11. Capparelli V, Franzke C, Vecchio A, Freeman M P, Watkins N W, Carbone V (2013) A spatiotemporal analysis of US station temperature trends over the last century. J Geophys Res - Atmos 118 (14):7427–7434. doi:10.1002/jgrd.50551 CrossRefGoogle Scholar
  12. Carter TR (2013) Agricultural impacts multi-model yield projections. Nat Clim Chang 3(9):784–786CrossRefGoogle Scholar
  13. Centre JR (2014) Mars bulletin vol.21. no6, 2013 and vol.20 n06, 2012. crop monitoring in Europe., http://mars.jrc.ec.europa.eu/mars/Bulletins-Publications
  14. Challinor AJ, Wheeler TR, Craufurd PQ, Slingo JM (2005) Simulation of the impact of high temperature stress on annual crop yields. Agric For Meteorol 135(1-4):180–189CrossRefGoogle Scholar
  15. Challinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N (2014) A meta-analysis of crop yield under climate change and adaptation. Nat Clim Chang 4(4):287–291. doi:10.1038/nclimate2153 10.1038/nclimate2153 CrossRefGoogle Scholar
  16. Chen C, Zhou GS, Pang YM (2015) Impacts of climate change on maize and winter wheat yields in China from 1961 to 2010 based on provincial data. J Agric Sci 153(5):825–836. doi:10.1017/s0021859614001154 CrossRefGoogle Scholar
  17. Chen X, Wang M, Zhang Y, Feng Y, Wu Z, Huang N E (2013) Detecting signals from data with noise: theory and applications. J Atmos Sci 70(5):1489–1504. doi:10.1175/jas-d-12-0213.1 CrossRefGoogle Scholar
  18. Ciscar J, Soria A, Lavalle C, Raes F, Perry M, Nemry F, Demirel H, Rozsai M, Dosio A, Donatelli M, Srivastava A, Fumagalli D, Niemeyer S, Shrestha S, Ciaian P, Himics M, VanDoorslaer B, Barrios S, Ibanez N, Bianchi A, Dowling P, Camia A, Liberta G, San Miguel J, de Rigo D, Caudullo G, Barredo J, Paci D, Pycroft J, Saveyn B, VanRegemorter D, Revesz T, Vandyck T, Vrontisi Z, Baranzelli C, Vandecasteele I, BatistaSilva F, Ibarreta D (2014) Climate impacts in Europe. The jrc peseta ii project. Report, http://www.jrc.ec.europa.eu
  19. Colominas MA, Schlotthauer G, Torres ME (2014) Improved complete ensemble EEMD: A suitable tool for biomedical signal processing. Biomedical Signal Processing and Control 14:19–29. doi:10.1016/j.bspc.2014.06.009 CrossRefGoogle Scholar
  20. Coumou D, Rahmstorf S (2012) A decade of weather extremes. Nat Clim Chang 2(7):491–496. doi:10.1038/nclimate1452 Google Scholar
  21. Dai A (2011) Drought under global warming: a review. Wiley Interdiscip Rev Clim Chang 2(1):45–65. doi:10.1002/wcc.81 CrossRefGoogle Scholar
  22. Dalla Marta A, Grifoni D, Mancini M, Zipoli G, Orlandini S (2011) The influence of climate on durum wheat quality in Tuscany, central Italy. Int J Biometeorol 55(1):87–96. doi:10.1007/s00484-010-0310-8 CrossRefGoogle Scholar
  23. Deryng D, Conway D, Ramankutty N, Price J, Warren R (2014) Global crop yield response to extreme heat stress under multiple climate change futures. Environ Res Lett 9(3). doi:10.1088/1748-9326/9/3/034011
  24. Eitzinger J, Thaler S, Schmid E, Strauss F, Ferrise R, Moriondo M, Bindi M, Palosuo T, Rotter R, Kersebaum KC, Olesen JE, Patil RH, Saylan L, Caldag B, Caylak O (2013) Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria. J Agric Sci 151(6):813–835. doi:10.1017/s0021859612000779 CrossRefGoogle Scholar
  25. FAO (2014) Food agriculture organization of the united nations (FAO)., http://faostat3.fao.org/compare/E
  26. Feng J, Wu Z, Liu G (2014) Fast multidimensional ensemble empirical mode decomposition using a data compression technique. J Clim 27(10):3492–3504. doi:10.1175/jcli-d-13-00746.1 CrossRefGoogle Scholar
  27. Flandrin P, Rilling G, Goncalves P (2004) Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 11(2):112–114. doi:10.1109/lsp.2003.821662 CrossRefGoogle Scholar
  28. Franzke C (2010) Long-range dependence and climate noise characteristics of Antarctic temperature data. J Clim 23(22):6074–6081. doi:10.1175/2010jcli3654.1 CrossRefGoogle Scholar
  29. Franzke C (2015) Local trend disparities of european minimum and maximum temperature extremes. Geophys Res Lett (in press). doi:10.1002/2015GL065011 Google Scholar
  30. Gimeno L, Ribera P, Iglesias R, de la Torre L, Garcia R, Hernandez E (2002) Identification of empirical relationships between indices of ENSO and NAO and agricultural yields in Spain. Clim Res 21(2):165–172. doi:10.3354/cr021165 CrossRefGoogle Scholar
  31. Gonsamo A, Chen JM (2015) Winter teleconnections can predict the ensuing summer European crop productivity. Proc Natl Acad Sci USA 112(18):E2265–E2266. doi:10.1073/pnas.1503450112 CrossRefGoogle Scholar
  32. Gonzalez-Reviriego N, Rodriguez-Puebla C, Rodriguez-Fonseca B (2015) Evaluation of observed and simulated teleconnections over the Euro-Atlantic region on the basis of partial least squares regression. Clim Dyn 44 (11–12):2989–3014. doi:10.1007/s00382-014-2367-2 CrossRefGoogle Scholar
  33. Gouache D, Bouchon AS, Jouanneau E, Le Bris X (2015) Agrometeorological analysis and prediction of wheat yield at the departmental level in France. Agric For Meteorol 209:1–10. doi:10.1016/j.agrformet.2015.04.027 CrossRefGoogle Scholar
  34. Guan BT (2014) Ensemble empirical mode decomposition for analyzing phenological responses to warming. Agric For Meteorol 194:1–7. doi:10.1016/j.agrformet.2014.03.010 CrossRefGoogle Scholar
  35. Hansen JW, Jones JW, Irmak A, Royce F (2001) El Nino-Southern Oscillation impacts on crop production in the southeast United States, pp 55–76. Asa Special PublicationGoogle Scholar
  36. Haylock MR, Hofstra N, Tank A, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res - Atmos 113 (D20):55–76. doi:10.1029/2008jd010201.D20119CrossRefGoogle Scholar
  37. Herrera S, Gutierrez JM, Ancell R, Pons MR, Frias MD, Fernandez J (2012) Development and analysis of a 50-year high-resolution daily gridded precipitation dataset over Spain (spain02). Int J Climatol 32 (1):74–85. doi:10.1002/joc.2256 CrossRefGoogle Scholar
  38. Huang NE, Shen Z, Long SR, Wu MLC, Shih HH, Zheng QN, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Series A Math Phys Eng Sci 454(1971):903–995CrossRefGoogle Scholar
  39. Iglesias A, Quiroga S (2007) Measuring the risk of climate variability to cereal production at five sites in Spain. Clim Res 34(1):47–57CrossRefGoogle Scholar
  40. Iizumi T, Luo JJ, Challinor AJ, Sakurai G, Yokozawa M, Sakuma H, Brown ME, Yamagata T (2014) Impacts of El Nino Southern oscillation on the global yields of major crops. Nat Commun:5. doi:10.1038/ncomms4712
  41. IPCC (2012) IPCC, 2012: Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups i and ii of the intergovernmental panel on climate change. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds). Report, IPCCGoogle Scholar
  42. IPCC (2013) Climate Change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  43. IPCC (2014) Climate Change 2014: impacts, adaptation, and vulnerability. part a: global and sectoral aspects. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  44. Jarlan L, Abaoui J, Duchemin B, Ouldbba A, Tourre YM, Khabba S, Le Page M, Balaghi R, Mokssit A, Chehbouni G (2014) Linkages between common wheat yields and climate in Morocco (1982–2008). Int J Biometeorol 58(7):1489–1502. doi:10.1007/s00484-013-0753-9 Google Scholar
  45. Ji F, Wu Z, Huang J, Chassignet EP (2014) Evolution of land surface air temperature trend. Nat Clim Chang 4(6):462–466. doi:10.1038/nclimate2223 CrossRefGoogle Scholar
  46. Knutti R, Sedlacek J (2013) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Chang 3(4):369–373. doi:10.1038/nclimate1716 CrossRefGoogle Scholar
  47. Li K, Yang X, Tian H, Pan S, Liu Z, Lu S (2015) Effects of changing climate and cultivar on the phenology and yield of winter wheat in the north China plain. Int J Biometeorol (in press). doi:10.1007/s00484-015-1002-1 Google Scholar
  48. Lobell DB (2007) Changes in diurnal temperature range and national cereal yields. Agric For Meteorol 145 (3–4):229–238. doi:10.1016/j.agrformet.2007.05.002 CrossRefGoogle Scholar
  49. Lobell DB (2013) Errors in climate datasets and their effects on statistical crop models. Agric For Meteorol 170:58–66. doi:10.1016/j.agrformet.2012.05.013 CrossRefGoogle Scholar
  50. Lobell DB, Burke MB (2010) On the use of statistical models to predict crop yield responses to climate change. Agric For Meteorol 150(11):1443–1452. doi:10.1016/j.agrformet.2010.07.008 CrossRefGoogle Scholar
  51. Lobell DB, Gourdji SM (2012) The influence of climate change on global crop productivity. Plant Physiol 160(4):1686–1697. doi:10.1104/pp.112.208298 CrossRefGoogle Scholar
  52. Lobell DB, Schlenker W, Costa-Roberts J (2011a) Climate trends and global crop production since 1980. Science 333(6042):616–620. doi:10.1126/science.1204531
  53. Lobell DB, Torney A, Field CB (2011b) Climate extremes in California agriculture. Clim Chang 109:355–363. doi:10.1007/s10584-011-0304-5
  54. Lorenzo-Lacruz J, Vicente-Serrano SM, Gonzalez-Hidalgo JC, Lopez-Moreno JI, Cortesi N (2013) Hydrological drought response to meteorological drought in the Iberian peninsula. Clim Chang 58(2):117–131. doi:10.3354/cr01177 Google Scholar
  55. Luo Q, Wen L (2015) The role of climatic variables in winter cereal yields: a retrospective analysis. Int J Biometeorol 59(2):181–192. doi:10.1007/s00484-014-0834-4 CrossRefGoogle Scholar
  56. Moghtaderi A, Flandrin P, Borgnat P (2013) Trend filtering via empirical mode decompositions. Comput Stat Data Anal 58:114–126. doi:10.1016/j.csda.2011.05.015 CrossRefGoogle Scholar
  57. Moore FC, Lobell DB (2014) Adaptation potential of European agriculture in response to climate change. Nat Clim Chang 4(7):610–614. doi:10.1038/nclimate2228 CrossRefGoogle Scholar
  58. Moore FC, Lobell DB (2015) The fingerprint of climate trends on European crop yields. Proc Natl Acad Sci USA 112(9):2670–2675. doi:10.1073/pnas.1409606112 CrossRefGoogle Scholar
  59. Nicholls N (1997) Increased Australian wheat yield due to recent climate trends. Nature 387(6632):484–485CrossRefGoogle Scholar
  60. Olesen JE, Trnka M, Kersebaum KC, Skjelvag AO, Seguin B, Peltonen-Sainio P, Rossi F, Kozyra J, Micale F (2011) Impacts and adaptation of European crop production systems to climate change. Eur J Agron 34(2):96–112. doi:10.1016/j.eja.2010.11.003 CrossRefGoogle Scholar
  61. Oteros J, Garcia-Mozo H, Botey R, Mestre A, Galan C (2015) Variations in cereal crop phenology in Spain over the last twenty-six years (1986–2012). Clim Chang 130(4):545–558. doi:10.1007/s10584-015-1363-9 CrossRefGoogle Scholar
  62. Otkin J, Mark Shafer M, Svoboda M, Wardlow B, Anderson M, Hain C, Basara J (2015) Facilitating the use of drought early warning information through interactions with agricultural stakeholders. Bull Am Meteorol Soc 7:1073–1078. doi:10.1175/BAMS-D-14-00219.1 10.1175/BAMS-D-14-00219.1 CrossRefGoogle Scholar
  63. Palosuo T, Kersebaum KC, Angulo C, Hlavinka P, Moriondo M, Olesen JE, Patil RH, Ruget F, Rumbaur C, Takac J, Trnka M, Bindi M, Caldag B, Ewert F, Ferrise R, Mirschel W, Saylan L, Siska B, Rotter R (2011) Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur J Agron 35(3):103–114. doi:10.1016/j.eja.2011.05.001 CrossRefGoogle Scholar
  64. Pirttioja N, Carter TR, Fronzek S, Bindi M, Hoffmann H, Palosuo T, Ruiz-Ramos MR, Tao F, Trnka M, Acutis M, Asseng S, Baranowski P, Basso B, Bodin P, Buis S, Cammarano D, Deligios P, Destain M, Dumont B, Ewert F, Ferrise R, Franois L, Gaiser T, Hlavinka P, Jacquemin I, Kersebaum KC, Kollas C, Krzyszczak J, Lorite IJ, Minet J, Minguez MI, Montesino M, Moriondo M, Mller CC, Nendel IO, Perego A, Rodrguez A, Ruane AC, Ruget F, Sanna M, Semenov MA, Slawinski C, Stratonovitch P, Supit I, Waha K, Wang E, Wu L, Zhao Z, Rötter RP (2011) Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces. Clim Res 65:87105. doi:10.3354/cr01322 Google Scholar
  65. Podesta G, Letson D, Messina C, Royce F, Ferreyra RA, Jones J, Hansen J, Liovet I, Grondona M, O’Brien JJ (2002) Use of ENSO-related climate information in agricultural decision making in Argentina: a pilot experience. Agric Syst 74(3):371–392. doi:10.1016/s0308-521x(02)00046-x CrossRefGoogle Scholar
  66. Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, Kindermann G, Nakicenovic N, Rafaj P (2011) Rcp 8.5-a scenario of comparatively high greenhouse gas emissions. Clim Chang 109(1–2):33–57. doi:10.1007/s10584-011-0149-y CrossRefGoogle Scholar
  67. Rodriguez-Puebla C, Ayuso SM, Frias MD, Garcia-Casado LA (2007) Effects of climate variation on winter cereal production in Spain. Clim Res 34(3):223–232CrossRefGoogle Scholar
  68. Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburne P, Antle JM, Nelson GC, Porter C, Janssen S, Asseng S, Basso B, Ewert F, Wallach D, Baigorria G, Winter JM (2013) The agricultural model intercomparison and improvement project (agmip): Protocols and pilot studies. Agric For Meteorol 170:166–182. doi:10.1016/j.agrformet.2012.09.011 CrossRefGoogle Scholar
  69. Rotter R, Hohn J (2015) Chapter 4: an overview of climate change impact on crop production and its variability in Europe, related uncertainties and research challenges, Food Agriculture Organization of the United Nations (FAO), Rome, 201, RomeGoogle Scholar
  70. Rotter R, Ewert F, Palosuo T, Bindi M, Kersebaum K, Olesen J, Trnka M, van Ittersum M, Janssen S, Rivington M, Semenov M, Wallach D, Porter J, Stewart D, Verhagen J, Angulo C, Gaiser T, Nendel C, Martre P, de Wit A (2013) Challenges for agro-ecosystem modelling in climate change risk assessment for major European crops and farming systems. In: Impacts World 2013 Conference Proceedings, pp 555–564, DOI 10.2312/pik.2013.001, (to appear in print)
  71. Rotter RP (2014) Agricultural impacts robust uncertainty. Nat Clim Chang 4(4):251–252CrossRefGoogle Scholar
  72. Rotter RP, Carter TR, Olesen JE, Porter JR (2011) Crop-climate models need an overhaul. Nat Clim Chang 1(4):175–177CrossRefGoogle Scholar
  73. Royce FS, Fraisse CW, Baigorria GA (2011) ENSO classification indices and summer crop yields in the southeastern USA. Agric For Meteorol 151(7):817–826. doi:10.1016/j.agrformet.2011.01.017 CrossRefGoogle Scholar
  74. Ruiz-Ramos M, Sanchez E, Gallardo C, Minguez MI (2011) Impacts of projected maximum temperature extremes for C21 by an ensemble of regional climate models on cereal cropping systems in the Iberian peninsula. Nat Hazards Earth Syst Sci 11(12):3275–3291. doi:10.5194/nhess-11-3275-2011 CrossRefGoogle Scholar
  75. Schulzweida U (2015) CDO climate data operators, users guide., https://code.zmaw.de/projects/cdo
  76. Sen PK (1968) Estimates of regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63(324):1379–1389CrossRefGoogle Scholar
  77. Smoliak BV, Wallace JM, Stoelinga MT, Mitchell TP (2010) Application of partial least squares regression to the diagnosis of year-to-year variations in Pacific Northwest snowpack and atlantic hurricanes. Geophys Res Lett:37. doi:10.1029/2009gl041478.L03801
  78. Smoliak BV, Wallace JM, Lin P, Fu Q (2015) Dynamical adjustment of the Northern Hemisphere surface air temperature field: methodology and application to observations. J Clim 28(4):1613–1629. doi:10.1175/jcli-d-14-00111.1 CrossRefGoogle Scholar
  79. Supit I, van Diepen C A, de Wit AJW, Wolf J, Kabat P, Baruth B, Ludwig F (2012) Assessing climate change effects on European crop yields using the crop growth monitoring system and a weather generator. Agric For Meteorol 164:96–111. doi:10.1016/j.agrformet.2012.05.005 CrossRefGoogle Scholar
  80. Tao F, Zhang S, Zhang Z (2012) Spatiotemporal changes of wheat phenology in China under the effects of temperature, day length and cultivar thermal characteristics. Europ J Agronomy 43:201–212CrossRefGoogle Scholar
  81. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res - Atmos 106(D7):7183–7192CrossRefGoogle Scholar
  82. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498. doi:10.1175/bams-d-11-00094.1 CrossRefGoogle Scholar
  83. Tian D, Asseng S, Martinez CJ, Misra V, Cammarano D, Ortiz BV (2015) Does decadal climate variation influence wheat and maize production in the Southeast USA? Agric For Meteorol 204:1–9. doi:10.1016/j.agrformet.2015.01.013 CrossRefGoogle Scholar
  84. Trenberth KE (2012) Framing the way to relate climate extremes to climate change. Clim Chang 115 (2):283–290. doi:10.1007/s10584-012-0441-5 CrossRefGoogle Scholar
  85. Trenberth KE, Dai AG, van der Schrier G, Jones PD, Barichivich J, Briffa KR, Sheffield J (2014) Global warming and changes in drought. Nat Clim Chang 4(1):17–22. doi:10.1038/nclimate2067 CrossRefGoogle Scholar
  86. Trnka M, Eitzinger J, Semeradova D, Hlavinka P, Balek J, Dubrovsky M, Kubu G, Stepanek P, Thaler S, Mozny M, Zalud Z (2011a) Expected changes in agroclimatic conditions in central Europe. Clim Chang 108(1–2):261–289. doi:10.1007/s10584-011-0025-9
  87. Trnka M, Olesen JE, Kersebaum KC, Skjelvag AO, Eitzinger J, Seguin B, Peltonen-Sainio P, Rotter R, Iglesias A, Orlandini S, Dubrovsky M, Hlavinka P, Balek J, Eckersten H, Cloppet E, Calanca P, Gobin A, Vucetic V, Nejedlik P, Kumar S, Lalic B, Mestre A, Rossi F, Kozyra J, Alexandrov V, Semeradova D, Zalud Z (2011b) Agroclimatic conditions in Europe under climate change. Glob Chang Biol 17(7):2298–2318. doi:10.1111/j.1365-2486.2011.02396.x
  88. Trnka M, Roetter RP, Ruiz-Ramos M, Kersebaum KC, Olesen JE, Zalud Z, Semenov MA (2014) Adverse weather conditions for European wheat production will become more frequent with climate change. Nat Clim Chang 4(7):637–643. doi:10.1038/nclimate2242 10.1038/nclimate2242 CrossRefGoogle Scholar
  89. UCAR/NCAR (2015) The NCAR command language (software,version 6.3.0). doi:10.5065/D6WD3XH5
  90. Vicente-Serrano SM, Cuadrat-Prats JM, Romo A (2006) Early prediction of crop production using drought indices at different time-scales and remote sensing data: application in the Ebro valley (north-east spain). Int J Remote Sens 27(3):511–518CrossRefGoogle Scholar
  91. Vicente-Serrano SM, Begueria S, Lopez-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23(7):1696–1718. doi:10.1175/2009jcli2909.1 CrossRefGoogle Scholar
  92. Vicente-Serrano SM, Lopez-Moreno JI, Begueria S, Lorenzo-Lacruz J, Sanchez-Lorenzo A, Garcia-Ruiz JM, Azorin-Molina C, Moran-Tejeda E, Revuelto J, Trigo R, Coelho F, Espejo F (2014) Evidence of increasing drought severity caused by temperature rise in Southern Europe. Environ Res Lett 9(4):9. doi:10.1088/1748-9326/9/4/044001 CrossRefGoogle Scholar
  93. Wallace JM, Fu Q, Smoliak BV, Lin P, Johanson CM (2012) Simulated versus observed patterns of warming over the extratropical Northern Hemisphere continents during the cold season. Proc Natl Acad Sci USA 109(36):14:337–14:342. doi:10.1073/pnas.1204875109 CrossRefGoogle Scholar
  94. Watson J, Challinor AJ, Fricker TE, Ferro CAT (2015) Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model. Clim Chang 132(1):93–109. doi:10.1007/s10584-014-1264-3 CrossRefGoogle Scholar
  95. White JW, Hoogenboom G, Kimball BA, Wall GW (2011) Methodologies for simulating impacts of climate change on crop production. Field Crop Res 124(3):357–368. doi:10.1016/j.fcr.2011.07.001 CrossRefGoogle Scholar
  96. WMO (2012) Standardized precipitation index. User Guide. Weather Climate Water, CH 1211 Geneva 2, SwitzerlandGoogle Scholar
  97. WMO (2013) The global climate 2001-2010. A decade of climatic extremes. summary report. ReportGoogle Scholar
  98. Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58(2):109–130. doi:10.1016/s0169-7439(01)00155-1 CrossRefGoogle Scholar
  99. Wu J, Liu M, Lu A, He B (2014) The variation of the water deficit during the winter wheat growing season and its impact on crop yield in the north China plain. Int J Biometeorol 58(9):1951–1960. doi:10.1007/s00484-014-0798-4 CrossRefGoogle Scholar
  100. Wu Z, Huang NE, Long SR, Peng CK (2007) On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc Natl Acad Sci USA 104(38):14:889–14:894. doi:10.1073/pnas.0701020104 CrossRefGoogle Scholar
  101. Wu ZH, Huang NE, Wallace JM, Smoliak BV, Chen XY (2011) On the time-varying trend in global-mean surface temperature. Clim Dyn 37(3–4):759–773. doi:10.1007/s00382-011-1128-8 CrossRefGoogle Scholar
  102. Xiao D, Tao F, Liu Y, Shi W, Wang M, Liu F, Zhang S, Zhu Z (2013) Observed changes in winter wheat phenology in the north China plain for 1981-2009. Int J Biometeorol 57(2):275–285. doi:10.1007/s00484-012-0552-8 CrossRefGoogle Scholar
  103. Xiao DP, Tao FL (2014) Contributions of cultivars, management and climate change to winter wheat yield in the north China plain in the past three decades. Eur J Agron 52:112–122. doi:10.1016/j.eja.2013.09.020 CrossRefGoogle Scholar
  104. Yu Q, Li L, Luo Q, Eamus D, Xu S, Chen C, Wang E, Liu J, Nielsen D C (2014) Year patterns of climate impact on wheat yields. Int J Climatol 34(2):518–528CrossRefGoogle Scholar
  105. Yue S, Wang CY (2004) The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resour Manag 18(3):201–218. doi:10.1023/b:warm.0000043140.61082.60 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • S. Hernandez-Barrera
    • 1
  • C. Rodriguez-Puebla
    • 1
  • A. J. Challinor
    • 2
  1. 1.Department of Fundamental PhysicsUniversity of SalamancaSalamancaSpain
  2. 2.School of Earth and EnvironmentUniversity of LeedsLeedsUK

Personalised recommendations