Quantitative Research of China Agricultural Virtual Water Trade Based on CROPWAT Model

Abstract:

The virtual water trade theory is one of the hot topics in the field of water demand management research today, which is considered as an important method for water resources constraints to ensure food security. And the introduction of the virtual water concept provides a new approach to analyze and study water resources. We used the CLIMWAT crop, meteorological databases and China's actual data to calculate virtual water content of major food crops and food virtual water trade in China's major grain producing areas by CROPWAT8.0 model recommended by the United Nations Food and Agriculture Organization (FAO).

• Periodical: Advanced Materials Research (Volumes 726-731)
• Pages: 3463-3468
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.726-731.3463
• Online since: August 2013
• Authors: Gui Liang Tian, Xi Wu, Xing Bo Sun, Wen Tao, Hui Xiang Zi Zheng
• Keywords: Agricultural Structure Adjustment, CROPWAT Model, Food Security, Virtual Water Trade

References

[2] is the virtual water demand of the reference crop ignoring other impact factors. is calculated according to the standard formula of Penman. is crop coefficient, which reflects the aerodynamic resistance covering the crop and the reference crop and the differences of physiological and physical feature. FAO has developed prototype software of crops WAT based on the modified standard formula of Penman-Monteith. By inputting related climate parameters of different places, pulsing Crop coefficient and modifying the data, we will get the amount of actual daily evaporation and transpiration of crops. Then, by counting the crops yield per unit, we will calculate the amount of virtual water in this crop per unit mass. The calculation flow chart is Fig.1 as follows: climate parameter crop output C y crop coefficient K c water consumption of reference crops ET0 amount of virtual water in crops per unit weight water demand of crops in the area water consumption of specific crops ET c trade volumes of crops trade volumes of virtual water in crops Fig.1 The flow chart of calculating the virtual water in crops The data are from:

[1] Crop and Meteorological Database from CLIMWAT in FAO and CROPWAT Database（www.fao.org）

[2] Statistical Yearbook of China, China Agricultural Yearbook, Report of Chinese Agricultural Development over the Years

[3] The Website of China's Ministry of Agriculture, the Website of the Chinese Ministry of Water Resources and the related websites of agriculture in some provinces

[4] A large number of research papers, information and so on concerning the virtual water in and abroad. The result of quantifying the trade volume of virtual water. Rice, wheat, corn and soybean are the main crops in China. So we took them for examples to calculate and research. The amount of virtual water in these four main crops from 2004 to 2011 is showed in Table 2. The major grain producing areas include Henan, Anhui, Shandong, Jiangxi, Hunan, Hubei, and Hebei. Crops play an important role in production and economy in these provinces. So we took them for example to calculate the amount of virtual water in these four crops. And the results are showed in Table 3. Besides, we calculated the trade volume of virtual water since 2003 which is shown in Table4. And the volume of virtual water trade is showed in Table 5. Table 2 The amount of virtual water in main crops in China over the years [m3/t] 2004 2005 2006 2007 2008 2009 2010 2011 average value wheat 1005.4 1118.6 1096.1 990.8 982.5 907.7 883.4 868.2 989.0 rice 1005.4 1038.1 1035.8 1021.6 1014.7 1017.6 1002.4 982.5 1014.8 corn 822.4 789.4 883.4 793.3 734.5 689.5 755.3 702.4 771.3 soybean 2258.3 1761.4 2902.3 2016 2415.1 2253.1 2316.3 1977.6 2237.5 Table 3 The amount of virtual water in crops per unit in some provinces [m3/t] Anhui Henan Shandong Jiangxi Hunan Hubei wheat 970 950 780 1050 1350 1350 rice 1050 990 1020 1180 950 950 corn 760 870 740 860 940 940 soybean 2200 2240 2420 2430 2650 2650 Table 4 The total volume of virtual water in importing and exporting main crops in China during 2003 and 2010 [100 million m3] 2003 2004 2005 2006 2007 2008 2009 2010 general import 329.52 209.64 609.65 487.78 682.66 656.44 723.18 748.95 general export 81.65 129.12 208.02 45.548 86.35 56.72 88.76 23.73 net import 247.87 80.52 401.63 442.23 596.31 599.72 634.42 725.22 Table 5 The volume of trading virtual water of main crops in China [100 million m3] trade volume of importing percentage trade volume of exporting percentage wheat 20.3 4% 11.2 11% rice 4.45 1% 17.9 17% corn 0.0711 0% 68.1 65% soybean 433 95% 7.22 7% Conclusions The characteristics of food crops' virtual water content are showed in Table 2 and Table 3:

[1] There are spatial differences of food crops' virtual water content. And the virtual water content of the same food crops is obvious different in different origins, which is related to product categories of food crops. Regional difference is the fundamental characteristics of virtual water, and it is also the basic reason why this concept with comparative advantage has generated. So China should plant crops with lower virtual water in water-scarce areas to save water.

DOI: 10.1002/jsfa.5911

[2] The virtual water content of crops varies in different areas, and even in the same area, the virtual water content of different food crops is significantly different. For example, soybean has the most virtual water content of all the major crops, reaching up to 2237.5, and corn has only 771.3. It shows that soybean consumes more water if producing the same unit of soybean and corn, which is disadvantageous to ease the tensions of agriculture water resources.

DOI: 10.31830/2348-7542.2019.133

[3] There are time differences in the virtual water content of food crops. The virtual water content is also different in different years of the same crop. The main reason is that the production of food crops is affected by the weather or technology. So trying to improve the production of food crop will help ease the tensions of the water resources. The results are obvious different in different methods to calculate, but the trend of different crops' virtual water content is consistent. To China, we have a comparative advantage in production of rice and corn, and relative disadvantage of wheat and soybean. Food trade structure of China is showed in Table 4 and Table 5: On the whole, China's crop is in an imported situation. Let's examine the matter from the angle of virtual water, the country import a large number of food corps with high virtual water content (such as soybean), and export a great quantity of food corps with lower virtual water content (such as corn). Bur due to the high domestic demand for food, corps (such as rice) with lower unit virtual water content will be restricted from export. Besides, for land use, urbanization reduces the farmland with economic developing, which leads the production hard to meet demand even in some regions with better planting conditions. From the respect of area configuration, Northwest and Southwest have a poor export conditions, poor production and self-pressure, so they still need to fill crop vacancy couples through imports. Therefore, each region of China should adjust its layout of the food production in order to give full play to the comparative advantage of China's grain product, avoid planting disadvantage of the food products with high water consumption, and optimize the structure of grain industry. The country has a comparative advantage in producing rice and corn, so it should be encouraged to expand the scale of planting, broaden planting area and implement specialized production to ensure food security, forming a supply chain of food products with comparative advantage. Because China doesn't have a comparative advantage in producing soybean and wheat with higher water consumption, it should form a complementary with other closer countries and regions that produce these crops in low cost in order to improve efficiency of water resources' allocation utilization, make the structure of the country's grain industry rationalization and promote the optimization of Chinese food trade structure. Acknowledgements This work was financially supported by the China Natural Science Foundation (41001377, 51279058). References