Spatial Characteristics and Implications of Grey Water Footprint of Major Food Crops in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Grey Water Footprint and Total Water Footprint of Food Crops
2.2. Parameter Selection and Data Source
2.2.1. Nitrate-Nitrogen Leaching Rate
2.2.2. Average Application Rate of Nitrogen-Containing Fertilizers AR
2.2.3. Other Parameters
3. Results
3.1. Nitrogen Application Rate Per Unit Area and Yield Spatial Distribution
3.2. Grey Water Footprint and Its Spatial Distribution
3.3. The Proportion of the Total Water Footprint and the Grey Water Footprint
4. Discussion
4.1. Grey Water Footprint Influencing Factors
4.2. Total Water Footprint and Grey Water Footprint
5. Conclusions and Implications
- The average WFgrey of wheat from 2012 to 2016 in China ranked as follows: North China 1.13 > Northwest China 0.74 > East China 0.29 > Southwest China 0.09 m3/kg. WFgrey in North and Northwest China were relatively high and showed an overall increasing trend, mainly because of the high nitrogen application rates and the low yield per unit area. It is necessary to limit the nitrogen-application rate for wheat specifically in Inner Mongolia and Xinjiang, and to improve the yield per unit area in Inner Mongolia, Shanxi, Gansu, and Ningxia. The average WFgrey of maize ranked as follows: Southwest China 0.56 > Northwest China 0.54 > North China 0.44 > Northeast China 0.37 > East China 0.24 m3/kg. It was relatively high in Southwest China and Northwest China mainly because of the high nitrogen application rates and the low yield per unit area. The nitrogen application rate in the Northwest division was 35% higher than that of the cultivation divisional average. It is necessary to limit the nitrogen application rate for maize specifically in Northwest China and Yunnan, and to improve the yield per unit area in Shaanxi, Guizhou, and Gansu.
- As for WFtot of the irrigation area, wheat reached the highest value, with 2.10 m3/kg in North China, followed by Northwest China with 1.61 m3/kg. In the provincial administrative regions, Inner Mongolia, Jiangxi, Gansu, Shanxi, and Ningxia showed a high WFtot of wheat. The divisional difference in WFtot of maize was not significant. The Southwest showed the highest WFtot with 1.48 m3/kg, which was due to the high WFgreen in Tibet.
- In the provincial administrative regions where WFgrey accounted for more than 50% of WFtot, ranking for wheat from the highest to lowest was as follows: Inner Mongolia, Qinghai, Tianjin, and Ningxia, while, for maize, the corresponding ranking was Yunnan, Shaanxi, and Qinghai. It is necessary to pay attention to and effectively control fertilizer application rates. Except for the Southwestern division, where fertilization is relatively low, the WFgrey of wheat was generally 1.3 times higher than that of the WFblue, and two to three times higher than for maize in most cases. In the planning and development of agricultural water resources, it is necessary to consider the amount of water utilized for NPS pollution, such as chemical fertilizers and pesticides.
- The WFgrey is an indicator of pollution reflected by the amount of clean water required to assimilate polluting chemicals. The higher the concentration of the pollutants in the receptor water-body, the smaller the difference to the maximum allowable concentration, and the greater the amount of water required to assimilate the discharged chemical. In water deficient areas, the WFgrey will undoubtedly increase the water shortage in the region. When analyzing and planning the water demand for agricultural irrigation, it is necessary to pay attention to and analyze the amount of water required for NPS pollution, such as by fertilizers and pesticides. In current evaluations of agricultural water demand planning and water resources development and utilization, focusing on blue water (WFblue) only while neglecting WFgreen, and ignoring WFgrey could lead to a gross overestimation of the availability of agricultural water resources and failure to meet the goal of sustainable utilization of water resources.
- This study discussed the effects of nitrogen fertilizer, nitrate-nitrogen leaching rate, and yield per unit area on the magnitude of the grey water footprint of wheat and maize. Because the effects of pesticides and organic fertilizers were not considered and the natural background concentration of the receiving water body is unavailable, the resulting grey water footprint values reported herein are relatively low. It might be underestimated for assessing the negative impact by over-using fertilizer. In-depth studies over an extended duration are warranted in the future.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Division | Location | N Application Rate (kg/ha) | Nitrate-N Leaching Loss Rate (kg/ha) | Leaching Rate (%) | Reference |
---|---|---|---|---|---|
North China | Chinese Agricultural University campus test field | 120 | 22 | 18.3 | [15] |
240 | 117 | 48.8 | |||
360 | 166 | 46.1 | |||
120 | 1 | 0.8 | |||
240 | 62 | 25.8 | |||
360 | 131 | 36.4 | |||
The piedmont region of Taihang Mountains | 200 | 22 | 11.0 | [16] | |
400 | 110 | 27.5 | |||
Quzhou County, Hebei Province | 258.8 | 23.1 | 8.9 | [17] | |
341.3 | 16.5 | 4.8 | |||
354.9 | 14.4 | 4.1 | |||
303.6 | 4.2 | 1.4 | |||
Average | — | — | 19.5 | — | |
East China | Taihu Plains | 225 | 11.7 | 5.2 | [18] |
300 | 13.2 | 4.4 | |||
Changshu, Jiangsu | 150 | 5.52 | 3.7 | [19] | |
225 | 14.71 | 6.5 | |||
300 | 31.57 | 10.5 | |||
450 | 40.69 | 9.0 | |||
Average | — | — | 6.6 | — | |
Southwest China | Chongqing | 75 | 1.67 | 2.2 | [20] |
150 | 1.98 | 1.3 | |||
225 | 2.26 | 1.0 | |||
Chongqing | 150 | 4.42 | 3.0 | [21] | |
Average | — | — | 1.9 | — | |
Northwest China | Guanzhong Plain | 250 | 6.3 | 2.5 | [22] |
Yangling, Shaanxi | 250 | 4.67 | 1.9 | [23] | |
Yangling, Shaanxi | 250 | 77.5 | 31.0 | [24] | |
Hanzhong, Shaanxi | 250 | 37.5 | 15.0 | [24] | |
Yangling, Shaanxi | 250 | 5.22 | 2.1 | [25] | |
Wuwei, Gansu | 225 | 47.49 | 21.1 | [26] | |
Average | — | — | 12.3 | — |
Division | Location | N Application Rate (kg/ha) | Nitrate-N Leaching Loss Rate (kg/ha) | Leaching Rate (%) | Reference |
---|---|---|---|---|---|
North China | Chinese Agricultural University campus test field | 120 | 21 | 17.5 | [15] |
240 | 38 | 15.8 | |||
360 | 16 | 4.4 | |||
120 | 70 | 58.3 | |||
240 | 104 | 43.3 | |||
360 | 197 | 54.7 | |||
Bayannur, Inner Mongolia | 80 | 5.41 | 6.8 | [27] | |
160 | 12.9 | 8.1 | |||
240 | 14.36 | 6.0 | |||
320 | 19.85 | 6.2 | |||
Quzhou County, Hebei Province | 186.3 | 9.3 | 5.0 | [17] | |
148.3 | 11.6 | 7.8 | |||
151.8 | 2.5 | 1.7 | |||
158.7 | 1.2 | 0.8 | |||
Average | — | — | 16.9 | — | |
East China | Shandong Agricultural University | 240 | 20.44 | 8.5 | [28] |
480 | 24.54 | 5.1 | |||
Average | — | — | 6.8 | — | |
Southwest China | Yanting County | 150 | 22.18 | 14.8 | [29] |
150 | 19.25 | 12.8 | |||
Average | — | — | 13.8 | — | |
Northwest China | Guanzhong Plain | 250 | 6.3 | 2.5 | [22] |
Yangling, Shaanxi | 250 | 4.67 | 1.9 | [23] | |
Mizhi County, North Shaanxi | 250 | 102.5 | 41.0 | [24] | |
Yangling, Shaanxi | 250 | 5.22 | 2.1 | [25] | |
Wuwei, Gansu | 225 | 54.16 | 24.1 | [26] | |
Gansu Agricultural University | 150 | 4.64 | 3.1 | [30] | |
300 | 4 | 1.3 | |||
Average | — | — | 10.9 | — | |
Northeast China | Linghai City | 210 | 82.2 | 39.1 | [31] |
263 | 99.6 | 37.9 | |||
Northeast Agricultural University | 229.42 | 0.16 | 0.1 | [32] | |
290.66 | 0.21 | 0.1 | |||
Average | — | — | 19.3 | — |
Division | Provincial Administrative Region | Wheat(kg/ha) | Maize(kg/ha) | Ratio | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2012 (1) | 2013 (2) | 2014 (3) | 2015 (4) | 2016 (5) | 2012 (6) | 2013 (7) | 2014 (8) | 2015 (9) | 2016 (10) | Wheat (5)/(1) | Maize (10)/(6) | ||
North China | Beijing | 240.3 | 238.2 | 242.4 | 242.4 | 251.0 | 151.0 | 147.4 | 153.4 | 148.0 | 158.2 | 1.04 | 1.05 |
Tianjing | 240.3 | 238.2 | 242.4 | 242.4 | 251.0 | 151.0 | 147.4 | 153.4 | 148.0 | 158.2 | 1.04 | 1.05 | |
Hebei | 218.2 | 212.5 | 211.8 | 216.6 | 214.6 | 127.9 | 126.5 | 126.9 | 126.0 | 117.2 | 0.98 | 0.92 | |
Inner Mongolia | 312.4 | 323.4 | 329.1 | 333.8 | 355.6 | 182.8 | 174.9 | 179.2 | 173.7 | 203.7 | 1.14 | 1.11 | |
Shanxi | 190.4 | 178.6 | 186.2 | 176.8 | 182.8 | 142.3 | 140.8 | 154.1 | 144.4 | 153.7 | 0.96 | 1.08 | |
East China | Shandong | 163.3 | 164.2 | 167.2 | 164.4 | 156.4 | 149.7 | 168.5 | 165.7 | 147.1 | 141.5 | 0.96 | 0.94 |
Shanghai | 175.4 | 176.6 | 184.7 | 177.6 | 177.1 | 190.9 | 198.8 | 207.2 | 186.5 | 181.2 | 1.01 | 0.95 | |
Zhejiang | 175.4 | 176.6 | 184.7 | 177.6 | 177.1 | 190.9 | 198.8 | 207.2 | 186.5 | 181.2 | 1.01 | 0.95 | |
Jiangsu | 216.1 | 218.8 | 227.9 | 226.9 | 228.5 | 248.1 | 228.9 | 255.3 | 228.8 | 236.5 | 1.06 | 0.95 | |
Jiangsu | 175.4 | 176.6 | 184.7 | 177.6 | 177.1 | 190.9 | 198.8 | 207.2 | 186.5 | 181.2 | 1.01 | 0.95 | |
Fujian | 175.4 | 176.6 | 184.7 | 177.6 | 177.1 | 190.9 | 198.8 | 207.2 | 186.5 | 181.2 | 1.01 | 0.95 | |
Anhui | 146.9 | 146.9 | 158.9 | 141.5 | 146.4 | 175.1 | 199.1 | 200.7 | 183.5 | 165.6 | 1.00 | 0.95 | |
Southwest China | Sichuan | 93.0 | 90.6 | 104.6 | 99.9 | 104.6 | 163.8 | 159.7 | 155.7 | 158.4 | 157.7 | 1.12 | 0.96 |
Chongqing | 112.0 | 109.9 | 120.2 | 119.4 | 131.7 | 221.7 | 189.7 | 193.8 | 187.8 | 186.1 | 1.18 | 0.84 | |
Guizhou | 112.0 | 109.9 | 120.2 | 119.4 | 131.7 | 186.8 | 198.3 | 202.3 | 230.6 | 219.7 | 1.18 | 1.18 | |
Tibet | 112.0 | 109.9 | 120.2 | 119.4 | 131.7 | 209.9 | 207.3 | 207.8 | 213.2 | 211.7 | 1.18 | 1.01 | |
Yunnan | 131.1 | 129.3 | 135.7 | 138.8 | 158.7 | 267.4 | 281.3 | 279.3 | 276.0 | 283.3 | 1.21 | 1.06 | |
Northwest China | Shaanxi | 229.1 | 201.0 | 206.2 | 217.1 | 202.4 | 263.6 | 255.0 | 273.3 | 245.9 | 267.7 | 0.88 | 1.02 |
Xinjiang | 222.0 | 228.0 | 236.5 | 247.6 | 249.5 | 259.4 | 263.9 | 258.6 | 262.6 | 270.8 | 1.12 | 1.04 | |
Gansu | 173.9 | 163.9 | 200.6 | 189.7 | 178.2 | 262.7 | 252.2 | 265.3 | 272.6 | 273.6 | 1.02 | 1.04 | |
Ningxia | 194.6 | 220.8 | 211.6 | 220.6 | 228.2 | 234.0 | 243.7 | 253.9 | 291.4 | 259.1 | 1.17 | 1.11 | |
Qinghai | 204.9 | 203.4 | 213.7 | 218.8 | 214.6 | 254.9 | 253.7 | 262.8 | 268.1 | 267.8 | 1.05 | 1.05 | |
Northeast China | Heilongjiang | — | — | — | — | — | 114.9 | 125.2 | 133.3 | 130.9 | 139.0 | — | 1.21 |
Jilin | — | — | — | — | — | 136.7 | 129.2 | 115.6 | 104.3 | 109.0 | — | 0.80 | |
Liaoning | — | — | — | — | — | 157.2 | 155.8 | 151.3 | 124.1 | 128.1 | — | 0.82 |
Division | Provincial Administrative Region | Wheat | Maize | ||||
---|---|---|---|---|---|---|---|
WFblue (%) | WFgreen (%) | WFgrey (%) | WFblue (%) | WFgreen (%) | WFgrey (%) | ||
North China | Beijing | 40.6 | 13.5 | 45.9 | 17.4 | 42.7 | 39.9 |
Tianjin | 37.0 | 12.3 | 50.7 | 19.0 | 38.8 | 42.2 | |
Hebei | 36.1 | 15.0 | 48.9 | 18.4 | 43.5 | 38.1 | |
Inner Mongolia | 16.3 | 16.2 | 67.5 | 20.0 | 33.6 | 46.4 | |
Shanxi | 35.9 | 18.7 | 45.4 | 11.2 | 41.1 | 47.7 | |
East China | Shandong | 52.9 | 26.5 | 20.6 | 20.9 | 54.5 | 24.6 |
Shanghai | 4.8 | 56.4 | 38.9 | 24.0 | 53.3 | 22.7 | |
Zhejiang | 16.3 | 48.6 | 35.0 | 9.7 | 60.9 | 29.4 | |
Jiangsu | 18.9 | 44.8 | 36.3 | 23.3 | 44.7 | 32.1 | |
Jiangxi | 47.6 | 30.5 | 21.9 | 8.8 | 62.0 | 29.1 | |
Fujian | 32.5 | 41.6 | 25.9 | 9.7 | 61.2 | 29.1 | |
Anhui | 31.2 | 45.6 | 23.2 | 26.9 | 45.9 | 27.3 | |
Southwest China | Sichuan | 50.8 | 41.9 | 7.3 | 5.1 | 53.9 | 41.0 |
Chongqing | 28.8 | 60.7 | 10.5 | 0.6 | 51.5 | 47.9 | |
Guizhou | 28.2 | 58.6 | 13.2 | 1.0 | 50.6 | 48.4 | |
Tibet | 58.3 | 37.5 | 4.3 | 24.1 | 56.6 | 19.3 | |
Yunnan | 25.2 | 59.5 | 15.3 | 1.2 | 41.5 | 57.3 | |
Northwest China | Shaanxi | 20.1 | 30.2 | 49.7 | 12.6 | 35.1 | 52.3 |
Xinjiang | 49.8 | 7.8 | 42.4 | 45.6 | 6.8 | 47.6 | |
Gansu | 42.5 | 21.9 | 35.7 | 23.0 | 29.8 | 47.2 | |
Ningxia | 35.1 | 14.5 | 50.4 | 39.3 | 19.3 | 41.4 | |
Qinghai | 6.3 | 40.1 | 53.6 | 16.1 | 33.3 | 50.5 | |
Northeast China | Heilongjiang | — | — | — | 7.5 | 46.5 | 46.0 |
Jilin | — | — | — | 4.9 | 55.8 | 39.3 | |
Liaoning | — | — | — | 6.5 | 49.6 | 43.9 |
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Wang, L.; Zhang, Y.; Jia, L.; Yang, G.; Yao, Y.; Wang, W. Spatial Characteristics and Implications of Grey Water Footprint of Major Food Crops in China. Water 2019, 11, 220. https://doi.org/10.3390/w11020220
Wang L, Zhang Y, Jia L, Yang G, Yao Y, Wang W. Spatial Characteristics and Implications of Grey Water Footprint of Major Food Crops in China. Water. 2019; 11(2):220. https://doi.org/10.3390/w11020220
Chicago/Turabian StyleWang, Lin, Yutong Zhang, Ling Jia, Guiyu Yang, Yizhen Yao, and Weiping Wang. 2019. "Spatial Characteristics and Implications of Grey Water Footprint of Major Food Crops in China" Water 11, no. 2: 220. https://doi.org/10.3390/w11020220