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اثرات تغییر اقلیم بر الگوی کشت محصولات زراعی (مورد مطالعه: دشت مشهد) | ||
تحقیقات اقتصاد و توسعه کشاورزی ایران | ||
مقاله 2، دوره 50، شماره 2، تیر 1398، صفحه 249-263 اصل مقاله (520.18 K) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijaedr.2019.237998.668461 | ||
نویسندگان | ||
سمانه سلیمانی نژاد1؛ آرش دوراندیش* 2؛ محمود صبوحی3؛ محمود بنایان اول4 | ||
1دانشجوی کارشناسی ارشد گروه اقتصادکشاورزی، دانشکده کشاورزی، دانشگاه فردوسی، مشهد، ایران | ||
2دانشیار گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه فردوسی، مشهد، ایران | ||
3استاد گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه فردوسی، مشهد، ایران | ||
4استاد گروه زراعت و اصلاح نباتات، دانشکده کشاورزی، دانشگاه فردوسی، مشهد، ایران | ||
چکیده | ||
اقلیم و تغییرات آن در دهههای اخیر به یکی از مسائل مهم و مطرح جهانی و بهعنوان یکی از معضلات عمده زیست محیطی تبدیل شده است. بخش کشاورزی یکی از اولین بخشهایی است که تحت تأثیر این تغییرات قرار میگیرد؛ چراکه کشاورزان قادر نیستند شرایط اقلیمی را کنترل کنند؛ اما مدیریت و تغییر در عواملی چون رقم محصول و بهینهسازی الگوی کشت مطابق با اقلیم منطقه، میتواند آثار سوء این تغییر اقلیم را بر رشد و عملکرد محصولات کشاورزی کاهش دهد و در تولید پایدار مواد غذایی نقش بسزایی داشته باشد. لذا در این پژوهش، به بررسی اثرات ناشی از تغییر اقلیم بر الگوی کشت زراعی دشت مشهد پرداخته شده است. آمار و اطلاعات مورد نیاز پژوهش از طریق سازمان جهاد کشاورزی خراسان رضوی، سازمان هواشناسی و همچنین مصاحبه حضوری با کارشناسان کشاورزی و کشاورزان دشت مشهد جمعآوری گردیده است. نتایج حاصل از این پژوهش نشان میدهد که مقادیر بارندگی، دمای بیشینه و کمینهی فصلی روند افزایشی دارد و این تغییرات دارای اثر معنیداری بر عملکرد محصولات زراعی منطقه هستند. همچنین با درنظرگرفتن سناریوهای تغییرات اقلیم (تا سال 1410) در دوره کاشت هریک از محصولات مورد مطالعه، مقادیر سطح زیرکشت آنها تغییر یافته و سود ناخالص کشاورزان نسبت به سال پایه (1393) 6/1 درصد افزایش مییابد. در نهایت نتایج پژوهش حاکی از این است که بیشترین تغییرات در عملکرد بر اثر شرایط اقلیمی مربوط به محصولات گندم و جو میباشد؛ بنابراین لازم است تا سیاستگذاران به این موضوع توجه داشتهباشند تا ریسک تولید این محصولات را کاهش دهند و از کاهش تولید این محصولات استراتژیک جلوگیری نمایند. | ||
کلیدواژهها | ||
الگوی کشت؛ برنامهریزی ریاضی مثبت؛ تغییر اقلیم؛ دشت مشهد | ||
عنوان مقاله [English] | ||
The Effects of Climate Change on Cropping Pattern (Case Study: Mashhad Plain) | ||
نویسندگان [English] | ||
Samaneh Soleymani Nejad1؛ Arash Dourandish2؛ mahmood Sabouhi3؛ Mohammad Banayan Aval4 | ||
1Msc Student of Agricultural Economics Department of Ferdowsi University, Mashhad, Iran | ||
2Associate Professor, Department of Agricultural Economics, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran | ||
3Professor, Department of Agricultural Economics, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran | ||
4Professor, Department of Agronomy Department, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran | ||
چکیده [English] | ||
In recent decades Climate and its changes have become one of the world major issues and as one of the major environmental problems. Agricultural sector is one of the first areas affected by these changes because farmers are not able to control climatic conditions; however, management and change in factors such as crop cultivar and optimization of the cultivation pattern according to area climate can reduce the adverse effects on growth and yield of agricultural products and play a significant role in the sustainable production of foods. Therefore, in this research, the effects of climate change on cropping pattern in Mashhad have been investigated. The statistics and data needed for the research were collected through Mashhad Agriculture Jihad Organization, meteorology Organization, as well as in-person interviews with agriculture specialists and farmers in Mashhad. The results of this study show that rainfall level, maximum and minimum seasonal temperatures have increasing trend and these changes have a significant effect on the yield of crops in the region. Also, considering the climate change scenarios (to 2031) during the planting period of each studied product, their crop area values have been changed and farmers' gross margin increased by 1.6 percent compared to the base year (2014). Finally, the results of this study indicate that the greatest changes in yield due to climatic conditions are related to wheat and barley; therefore, it is necessary for policy makers to pay attention to this issue in order to reduce the risk of these products production and prevent from reduced production of these strategic crops. | ||
کلیدواژهها [English] | ||
Cropping pattern, Positive mathematical programming and climate change | ||
مراجع | ||
10. Chijioke, O.B., Haile, M., and Waschkeit, C. (2011), Implication of climate change on crop yeild and food accessibility in sub-Sahran Africa. MSc Thesis, Bon University. 11. Chungi, S.O., Rodri'guez-di'az2, J. A., weatherhead, E. K., and Knox, J. W.(2011), Climate change impacts on water for irrigating paddd rice in south Korea. Journal of irrigation and drainage, 60: 263-273. 12. Connor, J., Kirby, M., Schwabe, K., Liukasiewics, A., and Kaczan, D.(2008), Impacts of Reduced Water Availability on Lower Murray Irrigation, Australia, Socio-Economics and the Environment in Discussion. CSIRO working paper series. 13. Conrads, P.A., and Roehle, E. A.(1999), Comparing Physics- Based and Neural Network Mo Simulating Salinity, Temperature and Dissolved in a Complex, Tidally Affected River Basin. Proceeding of the South Carolina Environmental Conference. March 15-16. 15. FAO, WFP, and IFAD. (2012), The state of food insecurity in the world: economic growth is necessary but not sufficient to accelerate reduction of hunger and malnutrition, food and agricultural organization of the united nations (FAO), the international fund for agricultural development (IFAD), and the world food programming (WFP), FAO, Rome, Italy. 16. Fulop, I. A., Jozsa, J., and Karamer, T. (1998), a neural network application in estimating wind induced shallow lake motion, Journal of Hydro informatics, 98: 753-757. 17. Hadley center. 2006. Effect of climate change in the developing countries.UK Meteorological Office. 18. Hashmi, M. Z., Shamseldin, A., and Melville, B. (2009), downscaling of future rainfall extreme events: a weather generator based approach. 18th World IMACS/ MODSIM Congress. Cairns. Australia. July 13–17. 19. Hazel, P., and Norton, R. D. (1986), Mathematical Programming for Economic Analysis in Agriculture. Colli MacMillan Pub. London. 20. Hosseini, A. (2009), Estimation and analysis of maximum temperatures in Ardabil using Artificial Neural Networks. Journal of Geographical Research. 25(3): 57-78. In Farsi. 21. Hung, N.Q., Babel, M. S., Weesakul, S., and Tripathi, N. K. (2008), an artificial neural network model for rainfall forecasting in Bangkok. Journal of Hydrology and Earth Sciences Discussion, 5: 183-218. 22. IPCC. (2007), Summary for policy makers Climate change: The physical science basis. Contribution of working group I to the forth assessment report. Cambridge University Press. 23. IPCC. (2013), Summary for policymakers. Fifth assessment report of the Intergovernmental Panel on Climate Change [Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V. Midgley, P.M. (Eds.)] Cambridge University Press, Cambridge, United Kingdom and New York. 24. Kaul, M., Hill, R. L., Walthall, C. (2005), Artificial neural networks for corn and soybean yield prediction. Journal of Agricultural System, 85: 1–18. 25. Kemfert, C. (2009), Climate Protection Requirements the Economic Impact of Climate Change. Handbook Utility Management, 725-739. 26. Kuchaki, A. (2015), Adaptation Approaches and Reducing Climate Change Dangers in Agriculture. Oral Collections presented at the Workshop on Climate Change and Low Carbon Technologies, May. In Farsi. 27. Mishra, A.K., and Desai, V.R. (2006), Drought forecasting using feed-forward recursive neural network International Journal on Ecological Modelling, 198:127–138. 28. Mislan, M., Haviluddin, H., Hardwinarto, S., Sumaryono, B., and Aipassa, M.(2015), Rainfall monthly prediction based on Artificial Neural Network: A case study in Tenggarong Station, East Kalimantan – Indonesia. Journal of Computer Science, 59: 142 –151. 29. Mitchell, T. (2003), Pattern Scaling: An Examination of Accuracy of the Technique for Describing Future Climates. Journal of Climatic Change, 60: 217-242. 30. Noferesti, M. (1999), Unit root and co-integration in econometrics. The first edition expressive Institute Publications, Tehran. In Farsi. 31. Ozkan, B., and Akcaoz, H. (2002), Impacts of climate factors on yields for selected crops in southern Turkey. Journal of Mitigation and Adaptation Strategies for Global Change, 7: 367–380. 32. Ranjithan, J., Eheart, J., and Garrett, J. H. (1995), Application of neural network in groundwater remediation under condition of uncertainty. New Uncertainty conception Hydrology and Water Resources, 133-140. 33. Redsma, P., Lansink, A., and Ewert, F. (2009), Economic impacts of climatic variability and subsidies on european agriculture and observed adaptition strategies. Journal of Mitigation and Adaptation Strategies for Global Change, 14:35-59. 34. Reilly, J. (1999), what does climate change mean for agriculture in developing countries? A comment on mendelsohn and dinar. Journal of World Bank, 14: 295-305. 35. Semenov, M.A. (2008), Simulation of extreme weather events by a stochastic weather generator. Climate Research, 35: 203-212. 36. Shafie, A.H., El-Shafie, A., Hasan, G., Mazoghi, A., and Mohd, R. (2011), artificial neural network technique for rainfall forecasting applied to Alexandria. International Journal of the Physical Sciences, 6: 1306-1316. 37. Statistical Yearbook of Khorasan Razavi Province; (2013). 38. Taghdisian,h., and Minapur, s.(2003), Climate change, what we need to know. Environmental Research Center Publications Environmental Protection Agency. National Weather Office, Tehran. In Farsi. 39. Terry, G. (2011), Climate, change and insecurity: Views from a Gisu hillside. Doctoral thesis, University of East Anglia. 40. Wang, Z.L., and sheng, H.H. (2010), Rainfall prediction using generalized regression neural network. International Conference on Computational and Information Sciences. December17-19. 41. Withey, P., and Kooten, C. (2011), The effect of climate change on land use and wetlands conservation in western Canada.Resource Economics & Policy Analysis. Research Group Department of Economics University of Victoria.
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