Virtual Water Transfer through Iranian Interprovincial Cereals Trade

مجله اقتصاد و توسعه کشاورزی

Article 5, Volume 33, Issue 1 - Serial Number 43, April 2019, Pages 55-74   

Document Type: Research Article

DOI: 10.22067/jead2.v0i0.76365

Authors

N. Ashktorab^* ; M. Zibaei

Shiraz university

Abstract

Introduction: freshwater resources which are essential for human life, sustainable livelihood, food security and conservation of ecosystem appear to be under increasing pressure from population growth, socio-economic development and climate changes. The largest consumer of water is agricultural sector. Hence improving productivity in agricultural sector and reducing agricultural water use hold the key to tacking water scarcity.  But over the past decades, it has been argued that international trade of agricultural crops from wet-countries to arid and semi-arid countries is one possible path to mitigate water shortage. The trade of commodities, which water has been used in their production, is generally referred to as virtual water trade. Often the terms "virtual water" and "water footprint" are usually used synonymously, while there are significant differences. The water footprint concept, however, has a wider application. In fact, the water footprint of a product is an empirical indicator of how much water is consumed, when and where, measured over the whole supply chain of the product. In other words, the water footprint is a multidimensional indicator, showing volumes but also making explicit the type of water use and the location and timing of water use. For products containing virtual water, trade is a means of transferring water resources between regions and also this virtual water trade network among provinces has a large share of domestic trade. in current study, in order to determine the inter-provincial virtual water trade network of the country, water footprint of wheat, barley and maize and also the amount of excess supply and excess demand of selected products has been calculated using data over 1395 in each province.  Therefore virtual water trade network of each product has been obtained in different provinces of the country. Then, using the transportation model, exporting provinces has been specified and the amount of exports of different products for minimizing shipping costs has been identified
Materials and Methods: In order to determine the excess supply and excess demand of the selected products in each province, firstly water footprint of each product was calculated for each province, then virtual water trade network of each product was identified and by using the transportation model, export route was determined.
The green, blue, gray and white water footprints of studied crops were estimated following the calculation frameworks of Hoekstra and Chapagain (2008) and Hoekstra et al. (2009), and modifications proposed by Ababaei and Ramezani Etedali (2014). By calculating water footprint components for different plains, their mean values were obtained for each province and then the obtained water footprint components in both irrigated and dryland were aggregated together for each product.
In this part of the study, due to lack of access to information and statistics of the amount of exports and imports between Iran’s provinces, at first per capita consumption of each Iranian person was obtained for each product. Then, total consumption of each province was obtained from the province's population by per capita consumption of each product. In order to calculate the excess demand or excess supply of each province, the total production of each province was deducted from the total consumption of each province. Finally, virtual water trade of each product in each province was acquired from water footprint in excess supply or demand.
Finally, the purpose of current study is to provide a minimum cost model for a virtual trade network from production centers to consumer centers. In the transportation model used here, the objective function is to minimize the total transportation costs between all selected agricultural production centers and consumption centers. The constraint (1) indicates that the amount of exchangeable product in each province is more than or equal to the product demanded by the province. Constraint (2) ensures that the product delivered between two centers is less than or equal to the capacity of the center. In constraint (3), the total demand for products is considered to be equal to the total amount of exchanged product. Constraint (4) provides for the positive value of exchanged items between supply and demand centers.
Results and Discussion: Based on the results of this study, the provinces of Azarbaijan sharghi, Azarbaijan gharbi, Ardebil, Ilam, Khuzestan, Zanjan, Fars, Qazvin, Kurdistan, Kermanshah, Golestan, Lorestan, Markazi and Hamedan are the wheat suppliers and so are the exporter. The provinces of Isfahan, Bushehr, Tehran, Chaharmahal and Bakhtiari, Khorasan, Semnan, Sistan and Baluchestan, Qom, Kerman, Kohgiluyeh and Boyer Ahmad, Gilan, Mazandaran, Hormozgan and Yazd have exceeded demand for wheat thus they are importer. Among all provinces of the country, Tehran has the highest wheat consumption, due to the fact that the population of this province is about 13 million (Iran's capital of history, 1395). Kayani (2018) has shown that Tehran province is the largest importer of agricultural products and virtual water in the country. According to the results of the study, after ten years, mentioned province remains the importer. Among the provinces where surplus wheat has been supplied, Golestan province has the largest wheat exports up to 1.1 million tons, and by exporting this product about 2846.6 million cubic meters of water has been exported. Based on the results, Tehran province is the destination of export of Azarbaijan sharghi, Azarbaijan gharbi, Ardebil, Ilam, Zanjan, Qazvin, Kurdistan, Kermanshah, Markazi and Hamedan provinces. In addition to the province of Tehran, Ardebil, Ilam and Markazi provinces have to export 492, 188 and 182 thousand tons of wheat to the provinces of Gilan, Isfahan and Qom, in order to minimize transportation costs.
Following the results, Ardebil, Ilam, Khorasan, Semnan, Qazvin, Kermanshah, Golestan, Lorestan, Markazi and Hamedan provinces have excess supply of barley in the country, while the provinces of Azarbaijan sharghi, Azarbaijan gharbi, Isfahan, Bushehr, Tehran, Chaharmahal va Bakhtiari, Khuzestan, Zanjan, Sistan va Baluchestan, Fars, Qom, Kurdistan, Kerman, Kohgiluyeh va Boyer Ahmad, Gilan, Mazandaran, Hormozgan and Yazd have excess demand for barley in the country. Kermanshah and Hamedan provinces are the largest exporters of barley, which export about 690 and 666 million cubic meters of virtual water through exports of barley to other provinces and foreign countries. The rainfall of these two provinces is about 475 and 334 millimeters and footprint of water production in both provinces is 2833 and 4568 m 3 / ton. Barley import of Tehran province has taken place from Semnan, Qazvin, and Kermanshah, Golestan, Lorestan, Markazi and Hamedan provinces with a mean distance of 324 kilometers. Considering that Tehran is the largest importer of barley in the country, it is justifiable that all provinces that are located near that province are the export bases..
The province of Tehran is the largest consumer and importer of maize in the country and since Tehran is not maize producer, it is not possible to calculate the water footprint. On the other hand, the province of Ilam is the smallest consumer of maize and is the sole exporter of this product which exports 21 million cubic meters of virtual water along with this product. The provinces of Khuzestan, Kermanshah and Fars are the largest maize producers in Iran, which respectively produce 351, 152, 123 thousand tons in 2016. The province of Ilam export to the provinces of Tehran and Khorasan, respectively, 634 and 99 thousand tons of maize, the cost of maize supplies is minimized. Excess demand from other provinces of the country has also been provided from imports of other countries.
Conclusion: Comparison of the results of this study, based on the statistics of 2016, and the Kayani study (2018), which was carried out in 2006, showed no significant changes in water resources management. Modifying the agricultural cropping pattern and correcting the pattern of consumption in line with the water footprint of agricultural products can be useful in improving the situation of the country's water resources in the long run. Determining the pattern of agricultural trade based on water footprint production of these products and the volume of virtual exports and imports of each product in each province could have a significant effect on reducing water losses in provinces of Iran.

Keywords

Province; Water footprint; Virtual Water Trade Network; Transportation Model; wheat; barley; Maize

References

1- Ababaei B., and Ramezani Etedali H. 2014. Estimation of water footprint components of Iran’s wheat production: comparison of global and national scale estimates. Journal Environmental Process 1: 193-205.

2- Agricultural Engineering Research Institute. 2015. Improving water use productivity. Agricultural Research, Education and Extension Organization (AREEO), Writers: Abbasi, F., Naseri, A., Sohrab, F., Baghani, G., Abbasi, N. And Akbari, M. (In Persian).

3- Aquastat. 2010. FAO Aquastat China Country Profile. Retrieved from http://www.fao.org/nr/water/aquastat/countries regions/Iran/index.stm.

4- Babazadeh H., and Sarai Tabrizi M. 2012. Evaluation of Hormozgan province agricultural situation from the point of view of virtual water. Journal of Water Resource in Agriculture 26(4):485–499. (In Persian)

5- Baghestan A.A., Mehrabi Boshrabadi H., Zare Mehrjerdi M.R., and Sherafatmand H. 2010. Application of the concept of virtual water in water resources management of Iran. Iran Water Resources Research 6(1): 28-38. (In Persian with English Abstract)

6- Chapagain A.K., Hoekstra A., and Savenije H. 2006. Water saving through international trade of agricultural products. Hydrology and Earth System Sciences 10: 455–468.

7- Chapagain A.K., and Orr S. 2009. An improved water footprint methodology linking global consumption to local water resources: A case of Spanish tomatoes. Journal of Environmental Management 90: 1219-1228.

8- Chouchane H., Krol M.S., and Hoekstra A.Y. 2018. Virtual water trade patterns in relation to environmental and socioeconomic factors: A case study for Tunisia. Science of the Total Environment 613–614: 287–297.

9- Dalin C., Hanasaki N., Qiu H., Mauzerall D.L., and Rodriguez-Iturbe I. 2014. Water resources transfer through Chinese interprovincial and foreign food trade. Proceedings of the National Academy of Sciences of the United States of America 111(27): 9774–9779.

10- Ghezel Jahromi S., and Mohammadi H. 2008. Application of transportation programming model in sugar industries in Fars Province. Sugar beet Journal 24(2): 109-128. (In Persian with English Abstract)

11- Guan D., and Hubacek K. 2007. Assessment of regional trade and virtual water flows in China. Ecological Economics 61(1): 159-170

12- Food and Agriculture Organization of United Nation (FAO). 2016. www.faosta.fao.org.

13- Hoekstra A.Y., and Chapagain A.K. 2007a. Water footprints of nations: Water use by people as a function of their consumption pattern. Water Resource Management 21: 35-48.

14- Hoekstra A.Y., and Chapagain A.K. 2007b. The water footprints of Morocco and the Netherlands: Global water use as a result of domestic consumption of agricultural commodities. Ecological Economics 6: 143-151.

15- Hoekstra A.Y., and Chapagain A.K. 2008. Globalization of water: Sharing the planet’s freshwater resources. Blackwell Publishing, Oxford, UK.

16- Hoekstra A.Y., Chapagain A.K., Aldaya M.M., and Mekonnen M.M. 2009. Water footprint manual: State of the art 2009, Water Footprint Network, Enscheda, the Netherlands.

17- Iran Meteorological Organization. 2018. Statistics and information, maps and tables and climate classification. (In Persian)

18- Iran Statistical Centre. Statistical Data and Information. 2018. (In Persian)

19- Islam S.M., Oki T., Kanae S., Hanasaki N., Agata Y., and Yoshimura K. 2007. A grid-based assessment of global water scarcity including virtual water trading. Water Resource Management 21: 19-33.

20- Jafari A., and Zarei GH. 2007. Virtual Water Trade and Its Role in Addressing the Dehydration Crisis. Second Water Resources Management Conference. Isfahan, Isfahan University of Technology: Iran Water Resources Sciences and Engineering Society. (In Persian)

21- Kiani GH. 2001. Determination of the economic pattern of wheat transportation in Iran. Master's thesis, Agricultural Economics Department, Tehran University. (In Persian)

22- Kiani GH. 2018. Study of domestic and international virtual water trade in Iran. Journal of water and soil Sciences 22(1):115-125. (In Persian with English Abstract)

23- Kumar M.D., and Singh O.P. 2005. Virtual water in global food and water policy making: Is there a need for rethinking? Water Resource Management 19: 759-789.

24- Liu J., and Savenije H.H.G. 2008. Food consumption patterns and their effect on water requirement in China. UNESCO-IHE, Value of Water Research Report Series No. 30.

25- Ma J., Hoekstra A.Y., Wang H., Chapagain A.K., and Wang D. 2006. Virtual versus real water transfers within China. PHILOS. T. R. SOC B: 19, 361: 835-842.

26- Ministry of Agricultural Jihad, Agricultural Jihad Organization of Gilan Province, Deputy Director of Plant Production Improvement of Agricultural Management Guidelines for Agriculture and Rural Development Preparation and Settlement: Javad Yahyazadeh, Agriculture Expert September, 2013. (In Persian)

27- Ministry of Agriculture Jihad, Deputy Economic and Planning Department, Information and Communication Technology Center. 2017. Agricultural Statistics, Agricultural Crop Years 2015-2016, Vol.1: Crop Production. Ministry of Agriculture publications. (In Persian)

28- Ministry of Agriculture Jihad. 2017. http://www.maj.ir. (In Persian)

29- Namakin K., Moasheri B.N., and Khosravi S. 2012. Study of food consumption pattern in students of junior high schools in Birjand. New Care, Journal of Nursing and Midwifery Faculty, Birjand University of Medical Sciences, 9 (3): 272-264. (In Persian with English Abstract)

30- Omidi F., and Homaee M. 2015. Deriving crop production functions to estimate wheat virtual water and irrigation water price. Cereal Research 5(2): 131–143. (In Persian)

31- Verma Sh., Kampman D.A., Van der Zaag P., and Hoekstra A.Y. 2009. Going against the flow: A critical analysis of inter-state virtual water trade in the context of India’s National River Linking Program. Physics and Chemistry of the Earth 34(4-5): 261–269.

32- Wang X., Haung K., Yu Y., Hu T., and Xu Y. 2016. An input–output structural decomposition analysis of changes insectoral water footprint in China. Ecological Indicators 69: 26–34.

33- World Bank. 2014. Annual freshwater withdrawals, total (billion cubic meters). Data. Retrieved from http://data.worldbank.org/indicator/ER.H2O.FWTL.K3

34- Zhao J., Liu W., and Deng H. 2005. The potential role of virtual water in solving water scarcity and food security problems in China. International Journal of Sustainable Development World Ecology 12: 419-428.