Enhanced Growth Rate and Reduced Water Demand of Crop Due to Climate Change in the Eastern Mediterranean Region

  • Jiftah Ben-AsherEmail author
  • Tomohisa Yano
  • Mehmet Aydın
  • Axel Garcia y Garcia
Part of the The Anthropocene: Politik—Economics—Society—Science book series (APESS, volume 18)


The specific objectives of this study were to: (a) test the reliability of a regional climate model (RCM) as a tool for climate change projection in the Eastern Mediterranean, (b) compare the observed yield variables of maize and wheat in the region with results of two crop models, (c) compare the models DSSAT and SWAP and (d) use DSSAT and SWAP to generate future productivity of wheat and maize under the A2 global warming scenario. Reference evapotranspiration was highly correlated with the models with average r2 = 0.98 and a unit slope. The two models accurately predicted observed dry mass production (DMP) and leaf area index (LAI) of wheat and maize. The correlations strengthen the legitimacy of DSSAT, SWAP and RCM to serve as predicting models for future climate change on a regional scale.

A simulation was carried out to describe the effects of climate change on crop growth and irrigation water requirements for a wheat-maize-wheat cropping sequence. Climate change scenarios were projected using data of three general circulation models (CGCM2, ECHAM4 and MRI) for the period of 1990–2100 and one RCM for the period of 2070–2079. Daily RCM data were consistent with actual meteorological data in the region and therefore were used for computations of present and future water balance and crop development. Predictions derived from the models about changes in irrigation and crop growth covered the period of 2070–2079 relative to a baseline period of 1994–2003. The effects of climate change on wheat and maize water requirements and yields were predicted using the detailed crop growth subroutine of the DSSAT (Decision Support System for Agrotechnology Transfer) and SWAP (Soil-Water-Atmosphere-Plant) models. Precipitation was projected to decrease by about 163, 163 and 105 mm during the period of 1990–2100 under the A2 scenario of the CGCM2, ECHAM4 and MRI models respectively (an average of about 1.3 mm/year). The models projected a temperature rise of 4.3, 5.3 and 3.1 °C, by the year 2100. An increase in temperature may result in a higher evaporative demand of the atmosphere under combined doubling CO2 concentration and temperature rise by about 2 °C for the period of 2070–2079. The temperature rise accelerated crop development and shortened the growing period by a maximum of thirteen days for wheat and nine days for maize during the period 2070–2079. When yield and available water (rain + applied irrigation) were normalised by extension of the growing period with respect to the baselines years, DMP of maize increased by 1–3 ton ha−1 and that of wheat by 3–4 ton ha−1. Consequently, water use efficiency (WUE) increased for both crops. It was concluded, therefore, that the effect of increased temperature and doubling CO2 on agro-productivity may be positive. This positive effect can be explained if elevated temperature meets the optimal level of a crop response to temperature. Effects of elevated CO2 on crop tolerance to water stress may counteract the expected negative effects of rising temperature. Increased atmospheric CO2 levels have important physiological effects on crops such as the increase in photosynthetic rate, which is associated with higher yield and WUE, at least for some cereal crops in the Eastern Mediterranean.


Climate change DSSAT CSM-CERES-Wheat CSM-CERES-Maise SWAP Atmospheric CO2 enrichment 



The research was funded by the project Impact of Climate Change on Agricultural Production in Arid Areas (ICCAP), administered by the Research Institute for Humanity and Nature (RIHN) of Japan, and the Scientific and Technological Research Council of Turkey (TÜBITAK). We are grateful to Drs. M. Koç, M. Ünlü and C. Barutçular for providing crop and meteorological data. The study was partially supported by a grant from the Ministry of Science, Israel, the Bundesministerium für Bildung und Forschung (BMBF), and State and Federal funds allocated under the GLOWA project.


  1. Adams RM, Rosenzweig C, Ritchie J, Peart R, Glyer J, McCarl B, Curry B, Jones J (1990) Global climate change and U.S. agriculture. Nature 345:219–224.CrossRefGoogle Scholar
  2. Adams RM, Hurd BH, Lenhart S, Leary N (1998) Effects of global climate change on agriculture: an interpretative review. Climate Research 11:19–30.CrossRefGoogle Scholar
  3. Ainsworth EA, Davey PA, Bernacchi CJ, Dermody OC, Heaton EA, Moore DJ, Morgan PB, Naidu SL, Ra HSY, Zhu XG (2002) A meta analysis of elevated [CO2] effects on soybean (Glycine max) physiology, growth and yield. Global Change Biology 8:695–709.CrossRefGoogle Scholar
  4. Alexandrov VA, Hoogenboom G (2000) The impact of climate variability and change on crop yield in Bulgaria. Agricultural and Forest Meteorology 104:315–327.CrossRefGoogle Scholar
  5. Asseng S, Jamieson PD, Kimball B, Pinter P, Sayre K, Bowden JW, Howden SM (2004) Simulated wheat growth affected by rising temperature, increased water deficit and elevated atmospheric CO2. Field Crop Research 85:85–102.CrossRefGoogle Scholar
  6. Aydın M (1994) Hydraulic properties and water balance of a clay soil cropped with cotton. Irrigation Science 15:17–23.Google Scholar
  7. Bernacchi CJ, Kimball BA, Quarles DR, Long SP, Ort DR (2007) Decreases in stomatal conductance of soybean under open-air elevation of [CO2] are closely coupled with decreases in ecosystem evapotranspiration. Plant Physiology 143:134–144.CrossRefGoogle Scholar
  8. Boogaard HL, van Diepen CA, Rötter RP, Cabrera JMCA, van Laar HH (1998) User’s Guide for the WOFOST 7.1 Crop Growth Simulation Model and WOFOST Control Center 1.5. DLO-Winand Staring Centre, Wageningen, Technical Document 52.Google Scholar
  9. Bunce JA (2004) Carbon dioxide effects on stomatal responses to the environment and water use by crops under field conditions. Oecologia 140:1–10.CrossRefGoogle Scholar
  10. Christensen JH, Carter T, Giorgi F (2002) PRUDENCE Employs New Methods to Assess European Climate Change. EOS 83:147.CrossRefGoogle Scholar
  11. Dhungana P, Eskridge KM, Weiss A, Baenziger PS (2006) Designing crop technology for a future climate: An example using response surface methodology and the CERES-Wheat model. Agricultural Systems 87:63–79.CrossRefGoogle Scholar
  12. Eitzinger J, Trnka M, Hösch J, Žalud Z, Dubrovský M (2004) Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions. Ecological Modelling 171:223–246.CrossRefGoogle Scholar
  13. El Maayar M, Singh B, Andre P, Bryant CR, Thouez JP (1997) The effects of climate change and CO2 fertilisation on agriculture in Quebec. Agricultural and Forest Meteorology 85:193–208.CrossRefGoogle Scholar
  14. Evrendilek F, Wali MK (2004) Changing global climate: historical carbon and nitrogen budgets and projected responses of Ohio’s cropland ecosystems. Ecosystems 7(4):381–392Google Scholar
  15. Flato GM, Boer GJ (2001) Warming asymmetry in climate change simulations. Geophysical Research Letters 28:195–198.CrossRefGoogle Scholar
  16. Francesco NT, Donatelli M, Rosenzweig C, Stockle CO (2000) Effects of climate change and elevated CO2 on cropping systems: model predictions at two Italian locations. European Journal of Agronomy 13:179–189.CrossRefGoogle Scholar
  17. Grant RF, Wall GW, Kimball BA, Frumau KFA, Pinter Jr PJ, Hunsakerb DJ, Lamorte RL (1999) Crop water relations under different CO2 and irrigation: testing of ecosys with the free air CO2 enrichment (FACE) experiment. Agricultural and Forest Meteorology 95:27–51.CrossRefGoogle Scholar
  18. IPCC (2001) The Scientific Basis. Cambridge: Cambridge University Press.Google Scholar
  19. Izaurralde RC, Rosenberg NJ, Brown RA, Thomson AM (2003) Integrated assessment of Hadley Center (HadCM2) climate-change impacts on agricultural productivity and irrigation water supply in the conterminous United States: Part II. Regional agricultural production in 2030 and 2095. Agricultural and Forest Meteorology 117:97–122.CrossRefGoogle Scholar
  20. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. European Journal of Agronomy 18:235–265.CrossRefGoogle Scholar
  21. Jones CA, Kiniry JR (1986) Ceres-Maize: A Simulation Model of Maize Growth and Development. College Station: Texas A & M Univ Press.Google Scholar
  22. Jones PG, Thornton PK (2003) The potential impacts of climate change on maize production in Africa and Latin American in 2055. Global Environmental Change 13:51–59.CrossRefGoogle Scholar
  23. Kimura F, Kitoh A (2007) Downscaling by Pseudo Global Warming Method. In the Final Report of ICCAP, Research Institute for Humanity and Nature and the Scientific and Technological Research Council of Turkey, 43–46.Google Scholar
  24. Kimura F, Kitoh A (2008) Downscaling by Pseudo Global Warning Method. Meteorological Research Institute, Japan Meteorological Agency Tsukuba, Ibaraki 305–8272, Japan.Google Scholar
  25. Kitoh A, Hosaka M, Adachi Y, Kamiguchi K (2005) Future projections of precipitation characteristics in East Asia simulated by the MRI CGCM2. Advances in Atmospheric Sciences 22: 467–478.CrossRefGoogle Scholar
  26. Mearns LO, Rosenzweig C, Goldberg R (1992) Effects of changes in interannual variability on CERES-wheat yields: sensitivity and 2 × CO2 General Circulation Model studies. Agricultural and Forest Meteorology 62:159–189.CrossRefGoogle Scholar
  27. Monteith JL, Szeicz G (1962) Radiative temperature in the heat balance of natural surfaces. Quaterly Journal of the Royal Meteorological Society 88:496–507.CrossRefGoogle Scholar
  28. Nakicenovic N, Swart R (2000) Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press.Google Scholar
  29. Olesen JE, Bindi M (2002) Consequences of climate change for European agricultural productivity, land use and policy. European Journal of Agronomy 16:239–262.CrossRefGoogle Scholar
  30. Raisanen J (2001) CO2-induced climate change in CMIP2 experiments: Quantification of agreement and role of internal variability. Journal of Climate 14(9):2088–2104.CrossRefGoogle Scholar
  31. Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Climate Models and Their Evaluation. In Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge and NewYork: Cambridge University Press.Google Scholar
  32. Ritchie JT (1991) Wheat phasic development. p. 31–54. In Hanks J, Ritchie JT (eds) Modeling plant and soil systems. Agronomy Monograph 31, ASA, CSSSA, SSSA, Madison, WI.Google Scholar
  33. Ritchie JT, Singh U, Godwin DC, Bowen WT (1998) Soil Water Balance and Plant Water Stress. In Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding Options for Agricultural Production pp 83–102. Dordrecht: Kluwer Academic Publishers.CrossRefGoogle Scholar
  34. Roeckner E, Arpe K, Bengtsson L, Christoph M, Claussen M, Dümenil L, Esch M, Gioretta M, Schlese U, Schulzweida U (1996) The Atmospheric General Circulation Model ECHAM4: Model Description and Simulation of Present-Day Climate (Report No. 218). Hamburg: Max-Planck Institute for Meteorology (MPI).Google Scholar
  35. Rosenzweig C, Hillel D (1998) Climate Change and the Global Harvest: Potential Impacts of the Greenhouse Effect on Agriculture. Oxford: Oxford University Press.Google Scholar
  36. Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn 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. Agricultural and Forest Meteorology 170:166–182.CrossRefGoogle Scholar
  37. Schulze R (2000) Transcending scales of space and time in impact studies of climate and climate change on agrohydrological responses. Agriculture, Ecosystems and Environment 82:185–212.CrossRefGoogle Scholar
  38. Southworth J, Randolph JC, Habeck M, Doering OC, Pfeifer RA, Rao DG, Johnston JJ (2000) Consequence of future climate change and changing climate variability on maize yields in the Midwestern United States. Agriculture, Ecosystems and Environment 82:139–158.CrossRefGoogle Scholar
  39. Supit I, Hooijer AA, van Diepen CA (1994) System Description of the WOFOST 6.0 Crop Simulation Model Implemented in CGMS. In Supit I, Hooijer AA, van Diepen CA (eds) Volume 1: Theory and Algorithms. Catno: CL-NA-15956-EN-C. EUR 15956, Office for Official Publications of the European Communities, Luxembourg.Google Scholar
  40. Tubiello FN, Ewert F (2002) Simulating the effects of elevated CO2 on crops: approaches and applications for climate change. European Journal of Agronomy 18:57–74.CrossRefGoogle Scholar
  41. Türkeş M (1996) Spatial and temporal analysis of annual rainfall variations in Turkey. International Journal of Climatology 16:1057–1076.CrossRefGoogle Scholar
  42. van Dam JC, Huygen J, Wesseling JG, Feddes RA, Kabat P, van Walsum PEV, Groendijk P, van Diepen CA (1997) Theory of SWAP version 2.0. Simulation of Water Flow, Solute Transport and Plant Growth in the Soil-Water-Atmosphere-Plant Environment. Technical Document 45, DLO Winand Staring Centre, Report 71, Dept. of Water Resources, Agricultural University: Wageningen.Google Scholar
  43. Wolf J, van Oijen M, Kempenaar C (2002) Analysis of the experimental variability in wheat responses to elevated CO2 and temperature. Agriculture, Ecosystems and Environment 93:227–247.CrossRefGoogle Scholar
  44. Yano T, Aydın M, Haraguchi T (2007) Impact of climate change on irrigation demand and crop growth in a Mediterranean environment of Turkey. Sensors 7(10):2,297–2,315.CrossRefGoogle Scholar
  45. Yano T, Koriyama M, Haraguchi T, Aydın M (2005) Prediction of future change of water demand following global warming in the Çukurova region of Turkey. Proceedings of International Conference on Water, Land and Food Security in Arid and Semi-Arid Regions, (in CD-ROM), Mediterranean Agronomic Institute Valenzano (Bari), CIHEAM-MAIB, Italy, September 6–11.Google Scholar
  46. Yukimoto S, Noda A, Kitoh A, Sugi M, Kitamura Y, Hosaka M, Shibata K, Maeda S, Uchiyama T (2001) The new Meteorological Research Institute coupled GCM (MRI-CGCM2). Papers in Meteorology and Geophysics 51:47–88.CrossRefGoogle Scholar
  47. Zhao C, Liu B, Piao S, Wang X, Lobell DB, Huang Y, Huang M, Yao Y, Bassu S, Ciais P, Durand JP, Elliott J, Ewert F, Janssens IA, Li T, Lin E, Liu Q, Martre P, Müller C, Peng S, Peñuelas J, Ruane AC, Wallach D, Wang T, Wu D, Liu Z, Zhu Y, Zhu Z, and Asseng S (2017) Temperature increase reduces global yields of major crops in four independent estimates. PNAS 114(35):9326–9331. Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jiftah Ben-Asher
    • 1
    Email author
  • Tomohisa Yano
    • 2
  • Mehmet Aydın
    • 3
  • Axel Garcia y Garcia
    • 4
  1. 1.Ben Gurion University Agroecology Group, The Katif R&D Center, Ministry of Science and TechnologySedot Negev Regional CouncilIsrael
  2. 2.Tottori University, Arid Land Research CenterTottoriJapan
  3. 3.Department of Soil ScienceMustafa Kemal UniversityAntakyaTurkey
  4. 4.Department of Agronomy and Plant GeneticsUniversity of Minnesota, Southwest Research and Outreach CenterLambertonUnited States

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