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Using Probabilistic-Risky Programming Models in Identifying Optimized Pattern of Cultivation under Risk Conditions (Case Study: Shoshtar Region)

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  • Kalashami, Mohammad Kavoosi
  • Sabbagi, Mohammad Aghapour
  • Alipour, Hamid Reza

Abstract

Using Telser and Kataoka models of probabilistic-risky mathematical programming, the present research is to determine the optimized pattern of cultivating the agricultural products of Shoshtar region under risky conditions. In order to consider the risk in the mentioned models, time period of agricultural years 1996-1997 till 2004-2005 was taken into account. Results from Telser and Kataoka models showed that due to accepting the risk amounts, most of the optimized amounts suggest the tomato cultivation during the cultivation period of fall, and watermelon cultivation during the cultivation period of spring. On the basis of results, due to allocation of agricultural lands of Shoshtar to tomato and watermelon cultivation and specializing the farming activity in this province, gross profit of agricultural production system can be increased to 6116047000 and 727782272 thousand Rials, respectively. The results of understudy models were investigated under different income scenarios and probabilistic levels of risk acceptance. Correct policy making in order to offer the suitable equipments for adjusting the effects of lack of certainty and risks due to the climatic unwanted conditions in production process of agricultural products of Shoshtar region improve the life situation of farmers of the mentioned region.

Suggested Citation

  • Kalashami, Mohammad Kavoosi & Sabbagi, Mohammad Aghapour & Alipour, Hamid Reza, 2011. "Using Probabilistic-Risky Programming Models in Identifying Optimized Pattern of Cultivation under Risk Conditions (Case Study: Shoshtar Region)," International Journal of Agricultural Management and Development (IJAMAD), Iranian Association of Agricultural Economics, vol. 1(1), pages 1-9, March.
    • Handle: RePEc:ags:ijamad:143489
    DOI: 10.22004/ag.econ.143489
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