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Application of Virtual Water Trade to Evaluate Cropping Pattern in Arid Regions

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  • Fatemeh Karandish
  • Samira Salari
  • Abdullah Darzi-Naftchali

Abstract

Access to water and food security in arid and limited water resources regions requires optimal cropping pattern. In this research, virtual water (VW) trade was implemented to find the best cropping pattern in Sistan and Blouchestan province of Iran. Forty-four common cultivated productions in the province were classified in 6 classes including cereals, legumes, vegetables, oil crops, fruits, and potato. Required data were collected during 2000–2011. The improper distribution of crops in the current cropping pattern led to a high mean value of TVW both at the county (0.53–4.35 m 3 kg −1 ) and province levels, where blue VW accounted for 91.3–98.5 % of TVW. By considering three indices including VW, unit blue water value, and the ratio of required blue water to the total water allocation for the agriculture, the optimal cropping pattern was proposed as potato followed by vegetables, legumes, fruits and cereals. Based on the results, production of vegetables and potato both at the county and province levels might be a target for improvements and might contribute significantly for reducing national water use for irrigation. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Fatemeh Karandish & Samira Salari & Abdullah Darzi-Naftchali, 2015. "Application of Virtual Water Trade to Evaluate Cropping Pattern in Arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 4061-4074, September.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:11:p:4061-4074
    DOI: 10.1007/s11269-015-1045-4
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    References listed on IDEAS

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    Cited by:

    1. Hossein Mikhak & Mehdi Rahimian & Saeed Gholamrezai, 2022. "Implications of changing cropping pattern to low water demand plants due to climate change: evidence from Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(8), pages 9833-9850, August.
    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," Agriculture, MDPI, vol. 11(5), pages 1-13, May.
    3. Nastaran Chitsaz & Ali Azarnivand, 2017. "Water Scarcity Management in Arid Regions Based on an Extended Multiple Criteria Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 233-250, January.
    4. 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.
    5. Karandish, Fatemeh & Šimůnek, Jiří, 2016. "A field-modeling study for assessing temporal variations of soil-water-crop interactions under water-saving irrigation strategies," Agricultural Water Management, Elsevier, vol. 178(C), pages 291-303.
    6. Dong Yan & Zhiwei Jia & Jie Xue & Huaiwei Sun & Dongwei Gui & Yi Liu & Xiaofan Zeng, 2018. "Inter-Regional Coordination to Improve Equality in the Agricultural Virtual Water Trade," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    7. S. Dutta & B.C. Sahoo & Rajashree Mishra & S. Acharya, 2016. "Fuzzy Stochastic Genetic Algorithm for Obtaining Optimum Crops Pattern and Water Balance in a Farm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4097-4123, September.

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