Document Type : Research Article

Authors

1 Sari University of Agricultural Sciences and Natural Resources

2 Tehran

Abstract

Introduction: Uncertainty existence in farmers crop production pulsed on important and necessity of science of risk management in the agricultural sector. The new risk management selects the best tools and techniques to minimize risks and consequences of decisions. Furthermore, determining the nature of the risk of crops yield can provide useful information about how to manage the risk of the agricultural sector. One of the effects of climate change is caused damage in the agricultural sector. Dependence of crops to climate change is caused that climate factors have a determinative role in the occurrence of crops damaged. Performed studies on the economic effects of climate change have shown that climate change has a significant impact on agricultural yield and its production risk. Moreover, climate change influences crop yield and the risk of crop yield. Although several studies have been carried out about the impact of climate change on crops yield in Iran, the effect of climate change on crops yield risk is infrequently considered. Therefore, this article tries to offer a new way for calculating the risk of crops yield using of CVaR in the period 2017-2047 in the zayanderud agricultural system. The innovation of this study can be stated as follows:1) This study used of Value at Risk index, as one of the most important indicators of risk measuring, to measure the risk of crops yield, 2) For calculating of Value at Risk index, different studies are used from a famous probability distribution such as normal distribution, historical data or Monte-Carlo simulation, while in this study tried to calculate VaR index based on the forecasted scenarios of crops yield, and 3) In this study, in order to produce future scenarios of crops yield is used from ANN-PSO combined method for forecasting crops yield.
Materials and Methods: The method of this study includes the following steps:
1) The production of possible scenarios of temperature and precipitation using of AOGCM models: Today, one of the best tools for the production of climate scenarios is Atmosphere-Ocean General Circulation Models (AOGCM).But the main problem in the use of the output of the AOGCM models is the large spatial scale of their computational cells toward the area under study. LARS-WG model is also one of the most famous models to small scale for outputs of AOGCM models. In this study uncertainty related to AOGCM models, is used of for scenarios of all AOGCM models(including A1B, B1 and A2).
2)The production of scenarios of selected crop yield and available water in the period 2017-2047: The production of scenarios of selected crops yield and available water is performed using of combinedmethod of ANN-PSO.To combine neural network with particles warm optimization algorithm, from particles warm optimization algorithm is instead of training the neural network using gradient-based algorithms.
3) Measuring risk of crops yield using of VaR and CVaR indexes: VaR index is one of the most important criteria to measure downside risk that it determines the maximum amount of expected losses of a variable for a certain time period and specific confidence level. In this study (according to the non-normal distribution of crops yield scenarios) is used on the historical simulation approach.
Results and Discussion: In the first phase of research methodology, for producing of climate scenarios from daily available stats related to weather stations of Isfahan, Kabuotarabad, Kuohrang, and Daranwere used. Validation results of LARS-WG model showed that this model is well able to simulate changes of climate parameters. Eventually, 44 scenarios of the maximum temperature, minimum temperature and rainfall wereproduced in each studied stations and for each year. The results of the network design using trial and error methods revealed the best forecast combination model obtained with 3 and6 neurons in the input layer and hidden layer of neural network and assuming the initial population of 200, in PSO algorithm. Results of this step showed that ANN-PSO model is well able to forecast crops yield (wheat, barley, maize and alfalfa) and available water. Furthermore, calculating VaR and CvaR criterain confidence level %95 and for future period of 2017-2047, showed that the values of these two criterions for wheat, barley, maize and alfalfa were equal to (4240, 4205), (4062, 4057), (49061,48480) and (10875,10743) kg/ha. The comparison of the values of these two criterions with the values of last period also showed that for all selected crops, VaR and CvaR criterions is bigger in future period toward last period.
Conclusions: The new offered method can calculate the risk of crops yield due to climate change. The more accurate measuring of risk using of new methods such as CVaR can be suitable guidance for policy man to better management of production risk of crops.

Keywords

1- Ahmadi M.E., Shadizadeh S.R., Rashidinia N., and Ebadi M. 2011. Prediction of asphaltene deposition using artificial neural network based on particle swarm algorithm. Journal of Exploration and Production, 87:37-40. (in Persian)
2- Arshad M., Amjath-Babu T.S., Krupnik T., Aravindakshan S., Abbas A., Kachele H., and Muller K. 2017. Climate variability and yield risk in South Asia’s rice–wheatsystems: emerging evidence from Pakistan. Journal of Paddy Water Environ, 15:249–261.
3- Azizi Khalklili T., and Zamani Gh. 2013. Farmers perception to agricultural work risk in condition climate change: case study Marvdasht city of Fars Province. Journal of Extension and Education of Iran’s Agriculture, 9(2):52-41. (in Persian)
4- Babaei H., Araghinejad Sh., and Hurfar A. 2011. Determinig period of Meteorological and hydrological droughts event in Zayanderud Watershed. Scientific Journal of Dry Canvas, 1(3):1 12. (in Persian)
5- Barak B. 2006. Consideration for the impact of climate change information on stated preferences. Ph.D. dissertation, University of Rhode Island, United States-Rhode Island, (Publication No. AAT 3248223).
6- Cabas J., and Weersink A. 2009. Crop yield response to economic site and climate variable. Journal of Climate Change, 92:1-18.
7- Campbell R., Huisman R., and Koedijk K. 2001. Optimal portfolio selection in a Value-at Risk framework, Journal of Banking and Finance, 25(9):1789-1804.
8- Coe R., and Stern, R.D. 2011. Assessing and addressing climate-induced risk in sub-saharan rainfed agriculture: lessons learned. Journal of Exploit Agriculture, 47(2):395-410.
9- Dlghandi M., Masah boani A., Ajorluo M.J., Broumand Nasab S., and Andarzian B. 2014. Risk assessment of effects of climate change on yield and and phenology of growth wheat (Case Study: Ahvaz city). Journal of Management and Irrigation, 5(2):161-175. (in Persian)
10- Falahpuor S., and Baghban M. 2014. The Using of Cupiola-CvaR in portfolio optimization andits analogical comparison Via Mean-CvaR. Journal of Research and Economic Policies, 22(7):155-172. (in Persian)
11- Ghahremanzadeh M., Golbaz M., Hayati B., and Dashti Gh. 2014. The Effect of climate parameters on risk and yield of wheat and corn in Ghazvin Province. Journal of Agricultural Economics, 8(4):107-126. (in Persian with English abstract)
12- Haupt R.L., and Haupt S.E. 2004. Practical Genetic Algorithms, Second Edition, John Wiley and Sons, Inc., Hoboken, NJ, USA.
13- Huang Y., Li Y., Chen X., and Mai Y.G. 2012. Optimization of the irrigation water resources for agriculturalsustainability in Tarim River Basin, China. Journal of Agricultural Water Management, 107(1):74-85.
14- Hull J.C. 2002. Fundamentals of Futures and Options Markets, The GARCH (1,1) Model as a Risk Predictor for International Portfolio. Journal of Prentice Hall, Fourth Edition.
15- Jason P.E. 2008. 21st century climate change in the Middle East. Journal of Climatic Change.
16- Khaleghi S., bazzazan F., and Madani Sh. 2015. Effects of climate change on agriculture sector and economics of Iran (Social Accounting Matrix Approach). Journal of Agricultural Economics Researches, 7(1):113-135. (in Persian)
17- Kim C-G., and Kan O-S. 2008. Climate change and rice productivity. Journal of Nonparametric and Semi Parametric Analysis, Korean Agricultural Economic production.
18- Kim M.K., and Pang A. 2008. Climate change impact on rice yield and production risk. Journal of Rural Development, 32(2):17-29.
19- Krokhmal P., Palmquist J., and Uryasev S. 2002. Portfolio optimization with conditional value-at-risk objective and constraints. Journal of Risk, 4:43-68.
20- Kuochaki A., and Kamali Gh. 2010. Climate changhe and wheat production in Iran. Journal of Researches of Iran’s Farming, 8(3):508-520. (in Persian)
21- Li X., Takahashi S.T., and Kaiser H. 2011. The impact of climate change on maize yields in the United States and China. Journal of Agricultural Systems, 104:348-353.
22- Ligeon C., Jolly C., Bencheva N., Delikostadinov S., and Puppala N. 2008. Production risks in Bulgarian peanut production. Journal of Agricultural Economics Review, 85:234-259.
23- Masah Boani A., Morid S., and Mohammadzadeh M. 2010. Comparison of small-scale methods and AOGCM models in survay of effect of climate changhe at the regional scale. Journal of Earth and Space Physics, 36(4): 99-110. (in Persian)
24- Maxino C.C., McAvaney B.J., Pitman A.J., Perkins S.E. 2008. Ranking the AR4 climate models over the Murray-Darling Basin using simulated maximum temperature, minimum temperature and precipitation. International Journal of Climatol, 28(8):1097–1112.
25- Mirfakhrodini S.H., Babaei Mybodi H., and Sharifabadi A. 2013. Prediction of Iran’s energy consumption using of ANN-GA hybrid model and its comparison via traditional patterns. Journal of Management Researches in Iran, 17(2):196-222. (in Persian with English abstract)
26- Moghadasi M., and Jalerajabi M. 2011. Integrated modeling approach for the prediction of agricultural product prices. Journal of Economics and Agricultural Development (Sciences and Industries of Agriculture), 5(3): 355-364. (in Persian with English abstract)
27- Pishbahar A., Darparnian S., and Ghahremanzadeh M. 2015. Examinig effects of climate change on corn yield in Iran: The application of spatial econometric approach with panel data. Journal of Agricultural Economics Researches, 7(2):83-106. (in Persian)
28- Poli R., Kennedy J., and Blackwell T. 2007. Particle swarm optimization-an overview. Journal of Swarm Intelligence, 1:33-57.
29- Reid S., Smit B., Caldwell W., and Bllivieau S. 2007. Vulnerability and adaption to climate risk in Ontario agriculture. Journal of Mitig Adapt Strat Glob Change, 12(4):609-637.
30- Resti A., and Sironi A. 2007. Risk management and shareholder's value in banking: from risk measurement models to capital allocation policies, John Wiley and Sons.
31- Roberts M.J., Schlenker W., and Eyer J. 2013. Agronomic weather measures in econometric models of crop yield with implication for climate change. American Journal of Agricultural Economics, 95(2):236–243.
32- Rostaei M., Sohrabi T., Masah Boani A., and Ahmadi M.S. 2012. Risk assessment of plant biomass yield of maize under the effect of climate change.Journal of Water Research in Agriculture, 26(4):425-438. (in Persian)
33- Saadat Joy Ordklo M., Rahimi m.A. 2014. Risk management and its application in the enterprise market using risk assessment model of Value at Risk. Journal of Industrial Management University of Human Sciences, 9:60-72. (in Persian)
34- Sabuohi M., Fahimifard S.M., and Mohades S.A. 2012. Survay of effect of guaranteed price in cereal supply response. Journal of Agricultural Economics and Development, 20(87): 39-60. (in Persian with English abstract)
35- Sadeghi H. and Biabani Khameneh K. 2015. Financial optimization in the electricity market: application of portfolio theory. Journal of Iran's Energy, 18(1):54-39. (in Persian with English abstract)
36- Saita F. 2007. Value at risk and bank capital management. Elsevier: Academic Press Advanced Finance.
37- Semenov M.A., and Barrow E.M. 2002. LARS-WG a stochastic weather generator for use in climate impact studies, User's Manual, Version 3.
38- Solomon S., Qin D., Manning M., Chen Z., Marquis M., Averyt K.B., Tignor M., and Miller H.L (eds). 2007. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
39- Stern N. 2007. The economics of climate change: The stern review. Cambridge University Press: Cambridge and New York.
40- Tahamipuor M., and Salami H. 2014. Determination of same-risk areas of potatoes yield in terms of the risk of frost in Iran: the application of spatial econometric approach. Journal of Agricultural Economics, Special Issue: 55.67 (in Persian)
41- Tari vardi Y., and Damchi Jelowdar Z. 2012. The linkage of risk management and corporation yield. Journal of Financial Accounting and Accountan, 4(15):43-62. (in Persian)
42- Tucker C.M., Eakin H., and Castellanson E.J. 2010. Perception of risk and adaption: Coffee producers, market shocks, and extreme weather in Central America and Mexico. Journal of Global Environment Change, 20(1):23-32.
43- Wang Y.J., Huang J., Wang J.X. 2014. Household and community assets and farmers’ adaptation to extreme weather event: the case of drought in China. Journal of Integrative Agriculture, 13:687-697.
44- Wilby R.L., and Harris I. 2006. A framework for assessing uncertainties in climate change impacts: low flow scenarios for the River Thames, UK. Journal of Water Resources Research.
45- Yang Y., Chen Y., Wang Y., Li C., and Li L. 2016. Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting. Journal of Applied Soft Computing, 49:663–675.
46- Zare Abyaneh H., Bayat Varkeshi M., and Ildromi A. 2011. Survay of effect of some of climate parameters and and ENSO in wheat and barley yield (Case study: Hamadan). Journal of Water Research of Iran, 5(9):181-192. (in Persian)
CAPTCHA Image