Skip to main content

Effects of precipitation and temperature on crop production variability in northeast Iran

Abstract

Climate variability adversely impacts crop production and imposes a major constraint on farming planning, mostly under rainfed conditions, across the world. Considering the recent advances in climate science, many studies are trying to provide a reliable basis for climate, and subsequently agricultural production, forecasts. The El Niño-Southern Oscillation phenomenon (ENSO) is one of the principle sources of interannual climatic variability. In Iran, primarily in the northeast, rainfed cereal yield shows a high annual variability. This study investigated the role played by precipitation, temperature and three climate indices [Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and NINO 3.4] in historically observed rainfed crop yields (1983–2005) of both barley and wheat in the northeast of Iran. The results revealed differences in the association between crop yield and climatic factors at different locations. The south of the study area is a very hot location, and the maximum temperature proved to be the limiting and determining factor for crop yields; temperature variability resulted in crop yield variability. For the north of the study area, NINO 3.4 exhibited a clear association trend with crop yields. In central locations, NAO provided a solid basis for the relationship between crop yields and climate factors.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  • Alexandrov VA, Hoogenboom G (2000) Vulnerability and adaptation assessments of agricultural crops under climate change in the Southeastern USA. Theor App Climatol 67:45–63

    Article  Google Scholar 

  • Bannayan M, Hoogenboom G (2008a) Daily weather sequence prediction realization using the non-parametric nearest-neighbor re-sampling technique. Int J Climatol 28(10):1357–1368

    Article  Google Scholar 

  • Bannayan M, Hoogenboom G (2008b) Weather Analogue: A tool for real-time prediction of daily weather data realizations based on a modified k-Nearest Neighbor approach. Environ Model Softw 3:703–713

    Article  Google Scholar 

  • Bannayan M, Crout NMJ, Hoogenboom G (2003) Applying the CERES-Wheat model for real-time forecasting of winter wheat. Agron J 95(1):114–125

    Article  Google Scholar 

  • Bannayan M, Hoogenboom G, Crout NMJ (2004) Photothermal impact on maize performance: a simulation approach. Ecol Model 180(2&3):277–290

    Article  Google Scholar 

  • Berliner LM, Wikle CK, Cressie N (2000) Long-lead prediction of Pacific SSTs via bayesian dynamic modeling. J Climate 13(22):3953–3968

    Article  Google Scholar 

  • Broad K, Agrawala S (2000) The Ethiopia crisis-uses and limits of climate forecasts. Science 289:1693–1694

    CAS  Google Scholar 

  • Diaz RA (1995) Seeking practical applications for short climate predictions in the agriculture of Argentina. International Research Institute for Climate Prediction Rep. IRIP-TL-95/1, 40 pp. [Available from INTA, Instituto de Clima y Agua, 1712 Castelar, Prov. Buenos Aires, Argentina.]

  • Gouveia C, Trigo RM (2008) Infuence of climate variability on wheat production in Portugal. In: Soares A, Pereira MJ, Dimitrakopoulos R (eds) geoENV VI—Geostatistics for environmental applications. Springer, Berlin, pp 335–345

  • Gouveia C, Trigo RM, DaCamara CC, Libonati R, Pereira JMC (2008) The North Atlantic Oscillation and European vegetation dynamics. Int J Climatol 28(14):1835–1847

    Article  Google Scholar 

  • Hansen JW, Sivakumar MVK (2006) Advances in applying climate prediction to Agriculture. Clim Res 33:1–2

    Article  Google Scholar 

  • IPCC (1996) Climate Change 1995: The Science of Climate Change. In: Houghton JJ, Meiro Filho LG, Callander BA, Harris N, Kattenberg A, Maskell K (eds) Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change . Cambridge University Press, Cambridge

  • Joseph J, Laviola J (2003) Double exponential smoothing: an alternative to Kalman filter-based predictive tracking. In: Proceedings of the Immersive Projection Technology and Virtual Environments. ACM Press, New York, pp 199–206

  • Jury MR, Weeks S, Gondwe MP (1997) Satellite observed vegetation as an indicator of climate variability over southern Africa. S Afr J Sci 93:34–38

    Google Scholar 

  • Karimi M (1999) Drought during growing season of 1997-8 and its effects on wheat production in Iran (in Iranian with English abstract). Sonbloe J 30(12):1–7

    Google Scholar 

  • Lobell DB, Asner GP (2003) Climate and management contributions to recent trends in U.S. agricultural yields. Science 299:1032

    Article  CAS  Google Scholar 

  • Meinke H, Stone RC, Hammer GL (1996) SOI phases and climatic risk to peanut production: a case study for northern Australia. Int J Climatol 16:783–789

    Article  Google Scholar 

  • Messina C, Beltrán A, Ravelo A (1996a) El feno´meno ENSO: Su relación con la productividad de maı́z en la región pampeana argentina (The ENSO phenomenon: Its relationship with maize productivity in the Argentine Pampas). IV Congreso Colombiano de Meteorologı́a, Santa Fe de Bogota, Colombia, Sociedad Colombiana de Meteorologı́a, pp 236–241

  • Messina C, Beltrán A, Ravelo A (1996b) La variabilidad interanual de los rendimientos de trigo en la región pampeana y su relación con el fenómeno ENSO (El Niño/Southern Oscillation) (Interannual variability of wheat yields in the Pampas, and their association with the ENSO phenomenon). Actas del VII Congreso Argentino y VII Congreso Latinoamericano e Ibérico de Meteorologı́a, Buenos Aires, Argentina, Centro Argentino de Meteorólogos and Federacı́on Latinoamericana e Ibérica de Sociedades de Meteorologı́a, pp 55–56

  • Mjelde JW, Hill HSJ, Griffiths JF (1998) A review of current evidence on climate forecasts and their economic effects in agriculture. Am J Agric Econ 80:1089–1095

    Article  Google Scholar 

  • Monirul M, Mirza Q (2003) Climate change and extreme weather events: can developing countries adapt? Climate Policy 3:233–248

    Article  Google Scholar 

  • Moradi H (2004) NAO and its effects on climate in Iran (in Iranian with English abstract). Geographical Res 48:17–30

    Google Scholar 

  • Nazemosadat MJ, Cordery I (2000) On the relationships between ENSO and autumn rainfall in Iran. Int J Climatol 20:47–61

    Article  Google Scholar 

  • Nicholls N, Gruza GV, Jouzel J, Karl TR, Ogallo LA, Parker DE et al (1996) Observed climate variability and change. In: Houghton JT (ed) The IPCC Second Scientific Assessment. Cambridge University Press, New York, pp 133–92

    Google Scholar 

  • Phillips JG, Rajagopalan B, Cane MA, Rosenzweig C (1999) The role of ENSO in determining climate and maize yield variability in the US Cornbelt. Int J Climatol 19:877–888

    Article  Google Scholar 

  • Reilly JM (2002) Agriculture: the potential consequences of climate variability and change. Cambridge University Press, Cambridge, UK

    Google Scholar 

  • Rodo X, Baert E, Comin FA (1997) Variations in seasonal rainfall in southern Europe during the present century: relationships with the North Atlantic oscillation and the El Niño–Southern Oscillation. Climate Dyn 13:275–285

    Article  Google Scholar 

  • Rozhkov VA, Nurberdiev M, Rangavar A (2007) Agroecological bases for raising the productivity of degraded soils in Khorasan Province of Iran. Eurasian Soil Sci 40(12):1335–1342

    Article  Google Scholar 

  • Soltani S, Modarres R, Eslamian SS (2007) The use of time series modeling for the determination of rainfall climates of Iran. Int J Climatol 27:819–829

    Article  Google Scholar 

  • Talliee AA, Bahramy N (2003) The effects of rainfall and temperature on the yield of dryland wheat in Kermanshah province (in Iranian with English abstract). Soil and Water J 17(1):111–114

    Google Scholar 

  • Trenberth K (1996) El Niño 3.4–Southern Oscillation. In: Giambelluca TW, Henderson-Sellers A (eds) Climate change: developing southern hemisphere perspectives. Wiley, New York, pp 145–173

  • Trenberth K (1997) Short-term climate variations: Recent accomplishments and issues for future progress. Bull Am Meteorol Soc 78:1081–1096

    Article  Google Scholar 

  • Vanderlip RL, Hammer GL, Muchow RC (1996) Assessing planting opportunities in semiarid subtropical environments. Agric Syst 51:97–112

    Article  Google Scholar 

  • Vicente-Serrano SM, López-Moreno JI (2008) Nonstationary influence of the North Atlantic Oscillation on European precipitation. J Geophys Res 113:D20120

    Article  Google Scholar 

  • Wheeler TR, Caruford PQ, Ellis RH, Porter JR, Vara Prasad PV (2000) Temperature variability and the yield of annual crops. Agric Ecosyst Environ 82:159–167

    Article  Google Scholar 

  • Yarahmadi D, Nassiri B (2006) Rainfed yield and climate in Lorestan Province (in Iranian with English abstract). Geographical Res 8(4):175–190

    Google Scholar 

Download references

Acknowledgment

This study has been supported by the grant approval of the Ferdowsi University of Mashhad, Iran and the authors would like to appreciate it.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammad Bannayan.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Bannayan, M., Sadeghi Lotfabadi, S., Sanjani, S. et al. Effects of precipitation and temperature on crop production variability in northeast Iran. Int J Biometeorol 55, 387–401 (2011). https://doi.org/10.1007/s00484-010-0348-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00484-010-0348-7

Keywords

  • Climate change
  • Climate variability
  • Rainfed crop production
  • Weather and crops