Risk Analysis of Maize Biomass under Climate Change Impacts(Case tudy: Pakdasht)

Document Type : Research Paper

Authors

1 MSc student, Irrigation and Reclamation Engineering Dept., University of Tehran

2 Professor of Irrigation and Reclamation Engineering Dept., University of Tehran

3 Assistant Professor of Irrigation Engineering Dept., College of Abureihan, University of Tehran

4 Project Assistant for Climate Change Enabling Activity (UNDP-GEF), Department of Environment, National Climate Change Office

Abstract

A variety of factors contributing to food insecurity should be evaluated precisely. Among these factors, climate change has the major influence. This study evaluates risk analysis of maize biomass under climate change in the period of 2010-2039, in a maize field in Pakdasht (east of Tehran, Iran) using AOGCM uncertainties. Climate Change scenarios were derived from 9 climate experiments (9 AOGCMs under A2 emission scenario) for two time periods (1971-2000 and 2010-2039). Beta probability density function (pdf) was fitted to these scenarios and 2000 samples of temperature and rainfall of climate change scenarios were produced. Also, their cumulative distribution functions (cdf) were depicted and changes of temperature and rainfall at 25%, 50%, and 75% risk levels were extracted. LARS-WG model was used for statistical downscaling of data and AquaCrop model was employed in simulation of maize biomass for future period (2010-2039).Descending trend was detected from biomass simulation in future period. At 50 % risk level, decreasing biomass was 1.21 ton per hectare in full irrigation treatment and 1.42 ton per hectare in deficit irrigation treatment. Risk analysis results showed that difference between 25% and 75% risk’s biomass amount in full and deficit irrigation were 0.7 and 0.5 ton per hectare, respectively.

Keywords

References

  • Abbasi, F., Malbusi, S., babaeian, I., Asmari, M. and Borhani, R. 2010. Climate Change Prediction of South Khorasan Province during 2010-2039 by Using Statistical Downscaling of ECHO-G Data. Journal of Water and Soil Vol. 24, No. 2, May-Jun 2010, p. 218-233 (In Farsi)
  • EL Afandi, G.E., Khalil, F.A. and Ouda, S.A. 2010. Using irrigation scheduling to increase water productivity of wheat-maize rotation under climate change conditions. Chilean Journal of Agricultural Research,. 70(3):474-484.
  • Elmahdi, A., Shahkarami, N., Morid, S. and Massah Bavani, A.R. 2009. Assessing the impact of AOGCMs uncertainty on the risk of agricultural water demand caused by climate change. 18th World IMACS / MODSIM Congress, Cairns, Australia 13-17 July 2009.
  • García-Vila, M., Fereres, E., Mateos, L., Orgaz, F. and Steduto, P. 2009. Deficit irrigation optimization of cotton with AquaCrop. Agronomy Journal,. 101(3).
  • Giglioli N. Saltelli A. 2003. SimLab 2.2, Software for Sensitivity and uncertainty Analysis, Simlab Manual. Joint Research Centre European Commission.
  • Guo, R., Lin, Z., Mo, X. and Yang, C. 2010. Responses of crop yield and water use efficiency to climate change in the North China Plain. Agricultural Water Management,. 97: 1185–1194.
  • Hsiao C.T. Heng L. Steduto P. Rojas-Lara B. Raes D. Fereres E. 2009. AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: III. Parameterization and Testing for Maize. Agronomy Journal,. 101(3).
  • Jones, P.D., and Hulme, M. 1996. Calculating regional climatic time series for temperature and precipitation: methods and illustrations. International Journal of Climatology,. 16: 361-377.
  • Jones, P.G., and Thornton, P.K. 2003. The potential impacts of climate change on maize production in Africa and Latin America in 2055. Global Environmental Change,. 13:51-59.
  • Jones, R.N. 2000. Analyzing the risk of climate change using an irrigation demand model. Climate research,. 14: 89-100.
  • Knox, J.W., Hess, T.M., Daccache, A., and Perez Ortola, M. (2011). What are the projected impacts of climate change on food crop productivity in Africa and south Asia?. DFID systematic review, Cranfield University.
  • Massah Bavani, A.R. and Morid, S. 2006. Impact of Climate Change on the Water Resources of Zayandeh Rud Basin. JWSS - Isfahan University of Technology. 2006,. 9:17-28 (In Farsi)
  • Meza, J.F. Silva, D., and Vigil H. 2008. Climate change impact on irrigated maize in Mediterranean climates: Evaluation of double cropping as an emerging adaption alternative. Agricultural System,. 98:21-30.
  • Minville, M. Brissette, F., and Leconte, R. 2008. Uncertainty of the impact of climate change on the hydrology of a Nordic watershed. Journal of Hydrology,. 358: 70-83
  • Mirsane, M. S., Sohrabi, T., Massah Bavani, A.R. and Bazrafshan, J. 2010. Risk analysis of crop water requirement under climate change (Case study: Qazvin), Master thesis, University of Tehran (In Farsi).
  • Najjar, Z., Massah Bavani, A.R. and Mashal, M. 2009. Adaptation strategies of climate change impacts on maize yield. Master thesis, University of Tehran, College of Abureyhan (In Farsi).
  • Prudhomme, Ch., Davies, H. 2005: Comparison of different sources of uncertainty in climate change impact studies in Great Britain. Center for Ecology and Hydrology, Wallingford, OXON, OX10 0QN, UK
  • Racsko, P., Szeidl, L. and Semenov, M.A. 1991. A serial approach to local stochastic weather models. Ecological Modeling,. l (57): 27–41.
  • Rowhani, P., Lobell, D.B., Linderman, M. and Ramankutty, N. 2011. Climate variability and crop production in Tanzania. Agricultural and Forest Meteorology,. 15: 449–460.
  • Semenov, M.A. and Barrow, E.M. 2002. LARS-WG: A Stochastic Weather Generator for Use in Climate Impact Studies, Version 3.0, User’s Manual.
  • Semenov, M.A., Brooks, R.J., Barrow E.M. & Richardson, C. W. 1998. Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Climatic Research,. 10 (2): 95–107.
  • Steduto, P., Raes, T., Hsiao, T.C. Fereres, E. 2009. AquaCrop-reference manual.
  • Steduto, P., Raes, T., Hsiao, T.C. Fereres, E., Heng, L., Izzi, G. and Hoogeveen, J. 2009. “AquaCrop: a new model for crop prediction under water deficit conditions”. Options Mediterranean’s,. 80:285-292
  • Tao, F., and Zhang, Z. 2010. Adaptation of maize production to climate change in North China Plain: Quantify the relative contributions of adaptation options. European Journal of Agronomy,. 10.
  • Toews M. W. and Allen D. M (2009): Evaluating different GCMs for predicting spatial recharge in an irrigated arid region. Journal of Hydrology, 374: 265-281.
  • Torriani, D.S., Calanca, P., Schmid, S., Beniston, M., and Fuhrer, J. 2007a. Potential effects of changes in mean climate and climate variability on the yield of winter and spring crops in Switzerland. Climate Research,. 34:59-69.
  • Turral, H., Burke, J., and Faures, J.M. 2011. Climate change, water and food security. Food and Agriculture Organization of the United Nations. Rome
Journal of Water Research in Agriculture
Volume 26, Issue 4
June 2013
Pages 425-438

Files

Share

How to cite

Statistics