The Application of Uncertainty Theory in Optimization of Cropping Pattern in Sari Goharbaran

Document Type : Original Article

Authors

1 Assistant Professor, Department of Agricultural Economics, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

2 Corresponding Author and Associate Professor, Department of Agricultural Economics, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

3 Associate Professor, Department of Agricultural Economics, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

Abstract

Selection of suitable crops for cultivation in an uncertain environment is considered as an important management topic in the agricultural sector. When faced with uncertainty, the only solution is to use subjective judgmental of persons in domain field rather than historical data. Based on the provided evidence, the quantification of subjective judgmental in the framework of probability theory and risk programming is not true and should be carried out in another theory called the uncertainty theory and uncertain programming method. With perception these conditions and considering that the agricultural sector is always faced with uncertain variables such as price of crops and weather conditions such as rainfall, in this study the optimal cropping pattern of in Goharbaran region of Sari was determined using uncertain programming in terms of uncertainty in rainfall and crops price. To elicitation the uncertainty distribution of these variables based on the subjective judgments of the farmers, 42 farmers were questioned randomly through cluster sampling in 2017. Subsequently, by calculating a causal relationship between rainfall and crops yield, uncertainty distribution of yield was also extracted and thus expected profit were calculated based on uncertainty theory. In order to calculate and minimize the uncertainty of the model, a Tail Value at Risk index was used. The results showed farmers that predict much uncertainty for prices and rainfall, it is advisable to growth the Tarom rice and tomatoes and to prevent Shiroodi rice and watermelon in order to deal with uncertainty and achieve a certain expected profit.

Keywords

References

  1. Agricultural Jihad Organization of Mazandaran Province, Office of statistics and information technology, (2016). (Persian)
  2. Artzner, Ph., Delbaen, F., Eber, J.M. and Heath, D. (1997). Thinking coherently. Risk, 10: 68-71.
  3. Cochran, C.B. (1977). Sampling techniques. New York: John Wiley.
  4. Dillon, J.L. and Hrdaker, J.B. (1993). Farm management research for small farmer development. Rome: FAO.
  5. Guo, H., Shi, H. and Wang, X. (2016). Dependent-chance goal programming for water resources management under uncertainty. Scientific Programming, Article ID 1747425: 1-7.
  6. Hardaker, J.B., Hiurne, R.B.M. and Anderson, J.R. (1997). Coping with risk in agriculture. Journal of Agricultural and Applied Economics, 29(2): 437-438.
  7. Hesamian, G., Peng, Z. and Chen. X. (2011). Goodness of fit test: a hypothesis test in uncertain statistics. Proceedings of the Twelfth Asia Pacific Industrial Engineering and Management Systems Conference, Beijing, China, October 14-16, pp. 978-982.
  8. Holly, S. and Hughes Hallett, A. (1991). Optimal control, expectation and uncertainty. The Economic Journal, 101(407): 976-978.
  9. Hosseini, S.T., Joulaei, R., Shirani Bidabadi, F. and Eshraghi, F. (2016). Investigating the uncertainty of crops price and yield with positive mathematical programming in Neka. Sixth Conference of Agricultural and Sustainable Natural Resource, Tehran. (Persian)
  10. Hosseini Yekani, S.A. and Kashiri Kolaei, F. (2017). Investigating the effect of crop price fluctuations on the optimal cropping pattern in Sari, Journal of Agricultural Economics, 11(2): 75-94. (Persian)
  11. Huang, X. and Di, H. (2016). Uncertain portfolio selection with background risk. Applied Mathematics and Computation, 276: 284-296.
  12. Huang, X. (2011). Mean-risk model for uncertain portfolio selection. Fuzzy Optimization and Decision Making, 10: 71-89.
  13. Kay, R.D. (2012). Farm management. Translated by M. Arsalanbod. Urmia: Urmia University. (Persian)
  14. Knight, FH. (1921). Risk, uncertainty, and profit. Boston: Houghton Mifflin.
  15. Liu, B. (2007). Uncertainty theory. Second Edition. Berlin: Springer-Verlag.
  16. Liu, B. (2009a). Some research problems in uncertainty theory. Journal of Uncertain Systems, 3(1): 3-10.
  17. Liu, B. (2009b). Theory and practice of uncertain programming. Second Edn. Berlin: Springer-Verlag.
  18. Liu, B. (2015). Uncertainty theory. Fourth Edition. Berlin: Springer-Verlag.
  19. MAJ (2016). Gojarbaran Region. Ministry of Agriculture-Jahad (MAJ), Agriculture-Jahadhad of Miandoroud County, Office of Statistics and Information Technology. (Persian)
  20. Moschini, G. and Hennessy, D.A. (2001). Uncertainty, risk aversion, and risk management for agricultural producers. In: B.L. Gardner, and G.C. Rausser (Eds), Handbook of Agricultural Economics, 1, Elsevier, pp. 88-153.
  21. Officer, R.R. and Anderson, J.R. (1968). Risk, uncertainty and farm management decisions. Review of Marketing and Agricultural Economics, 36(1): 3-19.
  22. Peng, J. (2013). Risk metrics of loss function for uncertain system. Fuzzy Optimization and Decision Making, 12(1): 53-64.
  23. Torkamani, J. (2006). Measuring and incorporating farmers’ personal beliefs and preferences about uncertain events in decision analysis: a stochastic programming experiment, Indian Journal of Agricultural Economics, 61(2): 185-199. (Persian)
  24. Veresnikov, G.S., Pankova, L.A. and Pronina, V.A. (2017). Uncertain programming in preliminary design of technical systems whit uncertain parameters. Procedia Computer Science, 103: 36-43.
  25. Wang, X. and Ning, Y. (2018). Uncertain chance-constrained programming model for project scheduling problem. Journal of the Operational Research Society, 69(3): 384-391.
  26. Zamani, O., Ghaderzadeh, H. and Mortazavi, S.A. (2014). Cropping pattern system with respect to sustainable agriculture and optimum use of energy: a case of Saqqez County of Kurdistan province. Agricultural Science and Sustainable Production, 24(1): 31-43. (Persian)
  27. Zhai, J. and Bai, M. (2018). Mean-risk model for uncertain portfolio selection with background risk. Journal of Computational and Applied Mathematics, 330: 59-69.

Files

Share

How to cite

Statistics