The Effects of Climate Change on Cropping Pattern (Case Study: Mashhad Plain)

Document Type : Research Paper

Authors

1 Msc Student of Agricultural Economics Department of Ferdowsi University, Mashhad, Iran

2 Associate Professor, Department of Agricultural Economics, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran

3 Professor, Department of Agricultural Economics, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran

4 Professor, Department of Agronomy Department, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran

Abstract

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.

Keywords


  1. Agricultural Organization of Khorasan Razavi (www. koaj.ir). In Farsi.
  2. Aksorn, P., and Srinilta, Ch. (2011), Statistical Downscaling for rainfall and temperature prediction in Thailand. Proceedings of the international multi conference of engineers and computer scientists. MARCH 16 – 18, Hong Kong.
  3. Asghari Moghadam, A., Nourani, v. and Nadiri, a. (2008), Modeling rainfall of Tabriz plain using artificial neural networks. Journal of Agricultural Science, 18(1): 1-15. In Farsi.
  4. Ashrafi, B., Mousavi Baghi, M., Kamali, Gh. and daavari, k.(2011), Forecast seasonal variations of climate parameters over the next 20 years using the exponential downscaling data of the HADCM3 model. Water and Soil Journal, 25(4): 945-957. In Farsi.
  5. Azuara, J., Howitt, R., MacEwan, D., and Lund, J. (2011), Economic impacts of climate-related changes to California agriculture. Journal of Climatic Change, 109: 387-405.
  6. Blanco, M., Cortignani, R., and Severini, S.(2007), Evaluating changes in cropping patterns due to the 2003 CAP reform, an ex-post analysis of different pmp approaches considering new activities. Presentation at the 107th EAAE Seminar Modelling of Agricultural and Rural Development Policies.
  7. Bodri, L., and Cermak, V. (2000), Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia. International Journal of Advances in Software Engineering Research Methodology, 31: 311–321.
  8. Bodri, L., and Cermak,V.(2003), prediction of surface air temperatures by neural  network. Journal of Studia Geophysica ET Geodaetica, 47: 173-184.
  9. Bustami, R., Bessaih, N., Bong, Ch., and Suhaili, S. (2007), Artificial neural network for precipitation and water level predictions of Bedup River.  IAENG International Journal of Computer Science, 34:2-10.

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.

14. Diaz-Robles, L. A., Ortega, J. C., Fu, J. S., Reed, G. D., Chow, J. C., Watson, J. G., and Moncada-Herrera, J. A. (2008), a hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas. Journal of Atmospheric Environment, 42: 8331-8340.

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.