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Wheat Self-Sufficiency, Water Restriction and Virtual Water Trade in Iran

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Listed:
  • Hmaed Najafi Alamdarlo

    (Tarbiat Modares University)

  • Fariba Riyahi

    (Tarbiat Modares University)

  • Mohamad Hasan Vakilpoor

    (Tarbiat Modares University)

Abstract

Sustainable food security is one of the policy-making targets in Iran, but water scarcity has always been a challenge to its actualization. Regarding this point, offering policies to address this challenge is essential. On the other hand, wheat is the most important commodity in the food baskets of Iranians, and due to the lack of equality in the supply and demand among the regions, a large amount of wheat is displaced every year. The displacement of wheat has resulted in virtual water trade among the regions and could be a possible solution to manage the water crisis in Iran. With respect to the necessity of research, the present study investigated the effects of economic policies on virtual water trade for wheat using a spatial equilibrium model. Therefore, the aim of this study was to investigate the pattern of virtual water trade flow and welfare changes which resulted from making different economic policies in the wheat market. The results showed that the wheat self-sufficiency policy reduces the total amount of virtual water trade, and will increase pressure on the water resources of Iran. The policy of paying tax and receiving subsidies also significantly affect the flow of virtual water trade. But that happens best when tax revenues are equal to the payment of subsidies.

Suggested Citation

  • Hmaed Najafi Alamdarlo & Fariba Riyahi & Mohamad Hasan Vakilpoor, 2019. "Wheat Self-Sufficiency, Water Restriction and Virtual Water Trade in Iran," Networks and Spatial Economics, Springer, vol. 19(2), pages 503-520, June.
    • Handle: RePEc:kap:netspa:v:19:y:2019:i:2:d:10.1007_s11067-018-9383-4
    DOI: 10.1007/s11067-018-9383-4
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    2. Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Papers 2104.14286, arXiv.org.

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