Document Type : Research Article

Authors

University of Tabriz

Abstract

Introduction: Climate is one of the basic factors in nature that its change is one of the most important challenges in current century. Increasing emissions of greenhouse gases, as the most important affecting factor on climate change, lead to change of temperature, precipitation and other climatic parameters. Unlike the other sectors, agricultural sector is more vulnerable to damages caused by climate change, so that the atmosphere precipitation and temperature average patterns changing damages the horticultural and agricultural production which are the main food sources in society. Both amount and quality of production reduction arises from climate change, consequently put farmers' income at risk. In the group of cereals, wheat was the most important crop in the country during the 2013-2014 crop year with the highest cropping area of 51.22%. East Azerbaijan province with the cultivated area of 40.6% of the country's total wheat has ranked sixth among producers of this crop. Ahar, one of the largest producers of wheat, has 95.5% of the province's cultivated area. Rainfall and temperature variations show climate risks for wheat, which are the most important indicators of climate change. Usually, the highest correlation is found between wheat yield and rainfall, so that the correlation coefficient between wheat yield of Ahar and rainfall in wheat growing season (October to July) was 45%, which indicates that most of the variation of yield is explained by rainfall. Therefore, risk management in agricultural sector, particularly weather risk, is highly important; however, risk management strategy cannot be implemented properly without identifying and measuring it. Hence, with attention to the importance of weather risk measurement, in this present study we tried to measure the rainfed wheat yield risk caused by climate change in Ahar County in two base (1986-2015) and future periods (2016-2045).
Materials and Methods: We applied weather Value at Risk (VaR) and weather conditional value at risk (CVaR), aquacrop, HadCM3 general circulation models under A2 emission scenarios and downscaling Lars-wg application. For this purpose, we collected weather information between 1986 and 2015 from East Azerbaijan Province Meteorological Organization, yield and soil feature data of 1991-2015 crop years from Management of Agriculture of the city of Ahar. The concept of ‘Weather Value at Risk’ represents a method to measure non-catastrophic economic weather risks. It captures both a socio-economic indicator’s sensitivity and exposure towards weather variability. Weather-VaR (α) denotes the Value at Risk resulting from adverse weather conditions, and represents–for a given level of confidence [α] over a given period of time–the maximum expected losses. Conditional Value at Risk (CVaR) attempts to address the shortcomings of VaR model, which is a statistical technique used to measure the level of risk within a firm. Conditional Value at Risk (CVaR) also known as the expected shortfall is a risk assessment measure that quantifies the amount of tail risk an investment portfolio has. CVaR is derived by taking a weighted average of the “extreme” losses in the tail of the distribution of possible returns, beyond the value at risk (VaR) cutoff point.. AquaCrop was chosen because it is a crop water productivity model that simulates yield response to water of herbaceous crops, and can be used specifically in situations where water is a key limiting factor in crop production. AquaCrop has the additional advantage that it is relatively simple and robust and uses “a relatively small number of explicit and mostly-intuitive parameters and input variables requiring simple methods for their determination”. AquaCrop was specifically designed for “assessing water-limited, attainable crop yields at a given geographical location” and “carrying out future climate scenario analyses.
Results and Discussion: The results show that in the future period, precipitation, maximum and minimum temperature, solar radiation, maximum and minimum relative humidity, wind speed and evapotranspiration variables will shift 9.15 millimeters, 0.63 and 0.56 Celsius degrees, 0.06 MJ/m2/d, -0.61 and -0.29 percent, 0.03 m/s and 18.48 millimeters per year, respectively. Based on the weather variables change, the yield average will increase from 0.954 in the base period to 0.999 t/ha in the future period, and beta and wakeby distribution were selected for yield as the best distributions to measure risk. Compared to base period, yield risk is reduced. In three probability levels, that is 1, 5 and 10 percent, and based on VaR method the amounts of risk reduction were obtained 0.139, 0.83 and 0.61 for beta distribution, and 0.297, 0.81 and 0.31 tons per hectare, respectively for wakeby distribution, while in CVaR method for beta distribution were 0.158, 0.115 and 0.93, and for wakeby distribution were 0.403, 0.148 and 0.77 tons per hectare, respectively.
Conclusions: According to the results, evapotranspiration is one of the variables that increase the yield risk; therefore, it is recommended that farmers use the natural and artificial mulch to reduce evaporation from the soil surface.

Keywords

1- Ababaei B., Sohrabi T., Mirzaei F., Rezaverdinejad V., and Karimi B. 2010. Climate change impact on wheat yield and analysis of the related risks: (case study: Esfahan Ruddasht region). Journal of Water and Soil Science, 20(3):136-150. (In Persian with English abstract).
2- Abassi F., Babaiyan E., Habibi Nokhandan M., Goli Mokhtari L., and Malbousi Sh. 2010. Climate change assessment over Iran in the future decades using MAGICC-SCENGEN model. Journal Physical Geography Research Quarterly, 42: 91-110. (In Persian)
3- Blance E. 2012. The impact of climate change on crop yields in Sub-Saharan Africa. American Journal of Climate Change, 1:1-13.
4- Bokusheva R. 2010. Measuring the dependence structure between yield and weather variables. ETH Zurich, Institute for Environmental Decisions, 1-35. Available at https://mpra.ub.uni-muenchen.de/22786/, MPRA Paper No. 22786.
5- Boskabadi E., Kohansal M., and Ghorbani M. How does climate change affect the wheat production in Mashhad?. p. 189-208. Researches of the 8th Biennial Conference of Iranian Agricultural Economics Society, 9-10 May. 2012. Sustainable agriculture and food security., Shiraz, Iran.
6- Chen S.L., and Miranda M.J. 2008. Modeling Texas dryland cotton yields, with application to crop insurance actuarial rating. Journal of Agricultural and Applied Economics, 40(1): 239-252.
7- Department of Environment Islamic Republic of Iran. 2008. Iran's second national communication to UNFCCC: fourth part assessing vulnerability and adaptability. 1-124. (In Persian).
8- Department of Environment Islamic Republic of Iran. 2008. Master in green management: climate change. Assistance of Education and Research, Department of Participation and Public Education, 4:1-15. (In Persian).
9- Department of Meteorology of the East Azarbaijan Province. 2016. Database of weather and climate data, Tabriz.
10- Goodwin B.K. 2012. Copula-based models of systemic risk in US. Agriculture: implications for crop insurance and reinsurance contracts. The NBER Conference on Insurance Markets and Catastrophe Risk, Massachusetts United States, Boston.
11- Habibi M. 2008. Climate Modeling and Climate Change in Iran. National Center of Climatology, Available at http://www.cri.ac.ir/index.php?lang=en
12- Houghton j., Meira Filho l., Callander B., Harris N., Kattenberg A., and Maskell K. 1996. Climate change 1995-the science of climate change. Contribution of WGI to the Second Assessment Report of the IPCC. Cambridge University Press, Cambridge.
13- Intergovernmental Panel on Climate Change (IPCC). 2016. Available at https://www.ipcc.ch/.
14- Jihad Agriculture Management of Ahar County. 2016. State of agriculture in Ahar County.
15- Jihad Agriculture Organization of East Azerbaijan. 2016. State of agriculture in East Azarbaijan province.
16- Khaliliaqdam N., Mosaedi A., Soltani A., and Kamkar B. 2013. Evaluation of ability of LARS-WG model for simulating some weather parameters in Sanandaj. Journal of Water and Soil Conservation, 19(4): 85-122. (In Persian with English abstract).
17- Khorani A., and Monjazeb Marvdashti S. 2014. Investigating the effects of climate change on the number of visitors in Hengam Island. Journal Physical Geography Research Quarterly, 46(1): 930-939. (In Persian).
18- Kodra B. 2007. Risk analysis of Tilapla recirculating aquaculture systems: a Monte Carlo simulation approach. Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Master of Science.
19- Koocheki A., Nasiry M., and Kamali G. 2007. Climate indices of Iran under climate change. Iranian Journal of Field Crops Research, 5(1):133-142. (In Persian with English abstract).
20- Koocheki A., and Kamali GH. 2010. Climate change and rainfed wheat production in Iran. Iranian Journal of Field Crops Research, 8(3):508-520. (In Persian).
21- Li H., Fan X., Li Y., Zhou Y., Jin Z., and Liu Z. 2012. Approaches to VaR. Stanford University, MS&E 444 Investment Practice Project.
22- Ministry of Agriculture –Jahad. 2016. Agriculture Statistics Available at http://www.maj.ir/
23- Miranda M and Vedenov, 2001. Innovations in agricultural and natural disaster insurance. American Journal of Agricultural Economic, 83 (3):65-650.
24- Neil Bird D., Benabdallah S., Gouda N., Hummel F., Köberl J., La Jeunesse I., Meyer S., Neil Prettenthaler F., Soddu A., and Woess-Gallasch S. 2015. Modelling climate impacts on and adaptation strategies for agriculture in Sardinia and Tunisia using aquacrop and Value-at-Risk. Science of the Total Environment, 543(B):1019-1027.
25- Nelson Carl H. 1990. The influence of distributional assumptions on the calculation of crop insurance permia. North Centeral Journal of Agricultural Economics, 12(1): 71-78.
26- Palisade Corporation. (2004) Guide to Using @Risk: Risk Analysis and Simulation. Available at http://www.palisade.com/
27- Pishbahar E., GHahremanzadeh M., and Darparniyan S. 2015. Effects of climate Change on Maize yield in Iran: application of spatial econometric approach with panel data. Journal Agricultural Economics Research, 7(2): 83-106. (In Persian).
28- Pishbahar E., Abedi S., Dashti G., and Kianirad A. 2015. Weather-Based Crop Insurance (WBCI) Premium for Rainfed Wheat in Miyaneh County: D-Vine Copula Approach Application. Journal Iranian Agricultural Economics Society, 9(3): 37-62. (In Persian).
29- Pashapur H., Khalilzadeh Gh., and Abdul Azimzadeh R., 2016. Wheat and autumn agriculture in rainfed conditions of cold regions. Journal of the West Azarbaijan Jihad-e-Agriculture Organization, Agricultural Coordination Management, First Edition, 1-28.
30- Prettenthaler F., Koberl J., and Neil Bird D. 2015. Weather value at risk: A uniform approach to describe and compare sectoral income risks from climate change. Science of the Total Environment, 543(B): 10-1018
31- Raes D., Steduto P., Hsaio T.C., and Fereres E. 2011. FAO cropwater productivity model to simulate yield response to water. Reference Manual, Version 3.1, and chapter 1.
32- Rahnama Rodposhti F., and Ghandehari S. 2015. Active portfolio management with bench marking: adding a value-at-risk constraint. Journal Financial Engineering and Portfolio Management, 6(24): 91-113. (In Persian).
33- Roudier P., Sultan B., Quirion P., and Berg A. 2011. The impact of future climate change on West African crop yields: What does the recent literature say?. Global Environmental Change, 21 (3): 1073-1083.
34- Salami A. 2003. An overview of the Monte Carlo simulation method. Journal Economics Research, 3(8):117-138. (In Persian).
35- Sarykalin S., Serraino G., and Uryasev S. 2008. Value-at-risk vs. conditional value-at-risk in risk management and optimization. Tutorials in Operations Research, 270-294.
36- Sayari N., Alizadeh A., Bannayan Awal M., Farid Hossaini A., and Hesami Kermani M. 2011. Comparison of two GCM models (HadCM3 and GCM2) for the prediction of climate parameters and crop water use under climate change (case study: Kashafrood basin). Journal of Water and Soil, 25(4): 912-925. (In Persian with English abstract).
37- Semenov M.A., and Barrow E.M. 2002. LARS-WG A stochastic weather generator for use in climate impact studies, User’s manual, Version3.0. Rothamsted Research, 1-27.
38- Taei Semiromi S., Moradi H., and Khodagholi M. 2014. Simulation and prediction some of climate variable by using multi line SDSM and Global Circulation Models (case study: bar watershed Nayshabour). Journal of Human and Environment, 12(28):1-15. (In Persian).
39- Tesarova V. 2012. Value at risk: GARCH vs stochastic volatility models: empirical study. Master Thesis, Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies.
CAPTCHA Image