Effects of Climate Change on Maize Yield in Iran: Application of Spatial Econometric Approach with Panel Data

Document Type : Research Paper

Authors

Abstract

In this study the impact of climate variables such as precipitation and temperature fluctuations and seed, urea and phosphate fertilizers on the yield of maize was studied in three different climate zones of Iran. For this purpose, Ricardian and spatial econometric models were used. The results showed the severity of climate change fluctuations, in all three climates, has been enough to identify as systematical risk factors. The results showed that, the extreme heat in the growing season of September, and lack of precipitation during the growing season (October) in warm climate zone were  the main factors which caused decline in maize yield. In moderate climate, overheating during growing season (August) and harvest (October) considered as systematical risk factors. Also in the cold climate, heat in growing and flowering seasons (June and August) and harvest season (November) had negative effect on crop yield.

Keywords


  1. اسماعیلی ع. و واثقی ا. (1387) بررسی اثر اقتصادی تغییر اقلیم بر بخش کشاورزی ایران: روش ریکاردین (مطالعه موردی: گندم). مجله علوم و فنون کشاورزی و منابع طبیعی،  45 (12): 685 الی 695.
  2. آمارنامه کشاورزی (1391)، آمارنامه‌های کشاورزی سال‌های مختلف. سازمان جهاد کشاورزی، معاونت برنامه­ریزی و امور اقتصادی، دفتر آمار و فن‌آوری اطلاعات.
  3. بازگیر س. و کمالی غ، ع. (1387) پیش‌بینی عملکرد گندم دیم با استفاده از شاخص‌های هواشناسی کشاورزی در برخی از مناطق غرب کشور. مجله علوم کشاورزی و منابع طبیعی، 15 (2): 14 الی 27.
  4. سازمان بورس. (1391) بورس کالای ایران «الزامات موردنیاز برای موفقیت قراردادهای آتی». گزارش شماره 90321.
  5. سازمان توسعه تجارت ایران (1391) کتاب مقررات صادرات و واردات سال 1390.
  6. سبزی‌وری ا. ترکمان م. و مریانجی ز. (1391) بررسی تأثیر شاخص‌های و متغیرهای هواشناسی کشاورزی در عملکرد بهینه گندم، مطالعه موردی: استان همدان. نشریه آب و خاک. علوم و صنایع کشاورزی، 26 (2): 1554 الی 1567.
  7. Amiraslany, A. (2010) The impact of climate change on Canadian agriculture: A Ricardian approach. Saskatoon, Saskatchewan: Unpublished Thesis, University of Saskatchewan. Available at: http://library2.usask.ca/theses/available/etd-05252010-102012/
  8. Baltagi BH. (2005) Econometric analysis of panel data, 3rd edition. Wiley.
  9. Belotti, F. G Hughes, and A. Mortari. (2013) A command to estimate spatial panel models in Stata. University of Rome Tor Vergat School of Economics, University of Edinburg.
  10. Elhorst JP. (2008) Serial and spatial autocorrelation. Economics Letters. 100 (3): 422-424
  11. FAO, (2015) Food and Agriculture Organization of the United Nations. Available at: http://faostat3.fao.org/faostat-gateway/go/to/browse/Q/*/E
  12. Intergovernmental Panel on Climate Change )IPCC(, (2007) Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  13. Lesage, J. (1999) Spatial Econometrics. Department of Economics University of Toledo.
  14. McMillan, D.P. (1996) One hundred fifty years of land values in Chicago: a nonparametric approach, Journal of Urban Economics. 40 : 100-124
  15. Mendelsohn, R. and Nordhaus, W. D. and Shaw, D. (1994) The impact of global warming on agriculture: A Ricardian analysis. American Economic Review. 84 (4): 753-771.
  16. Reidsma P., Ewert F., Boogaard H., and Diepen K. (2009) Regional crop modeling in Europe: The impact of climatic conditions and farm characteristics on maize yields. Agricultural Systems, 100: 51-60
  17. Travis J. Lybbert, Daniel A. (2012) Sumner, Agricultural technologies for climate change in developing countries: Policy options for innovation and technology diffusion, Food Policy. 37:114–123.