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Journal of Arid Land
Journal of Arid Land  2020, Vol. 12 Issue (4): 580-593    DOI: 10.1007/s40333-020-0058-x
Research article     
Evaluating agricultural water-use efficiency based on water footprint of crop values: a case study in Xinjiang of China
Yang HAI1, Aihua LONG1,2,*(), Pei ZHANG1, Xiaoya DENG1, Junfeng LI2, Mingjiang DENG3
1 State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
2 College of Water Conservancy and Architectural Engineering, Shihezi University, Shihezi 832003, China
3 Xinjiang Water Conservancy and Hydropower Planning and Design Management Bureau, Urumqi 830000, China
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Abstract  

Efficient agricultural water use is crucial for food safety and water conservation on a global scale. To quantitatively investigate the agricultural water-use efficiency in regions exhibiting the complex agricultural structure, this study developed an indicator named water footprint of crop values (WFV) that is based on the water footprint of crop production. Defined as the water volume used to produce a unit price of crop (m3/CNY), the new indicator makes it feasible to directly compare the water footprint of different crops from an economic perspective, so as to comprehensively evaluate the water-use efficiency under the complex planting structure. On the basis of WFV, the study further proposed an indicator of structural water-use coefficient (SWUC), which is represented by the ratio of water-use efficiency for a given planting structure to the water efficiency for a reference crop and can quantitatively describe the impact of planting structure on agricultural water efficiency. Then, a case study was implemented in Xinjiang Uygur Autonomous Region of China. The temporal and spatial variations of WFV were assessed for the planting industries in 14 prefectures and cities of Xinjiang between 1991 and 2015. In addition, contribution rate analysis of WFV for different prefectures and cities was conducted to evaluate the variations of WFV caused by different influencing factors: agricultural input, climatic factors, and planting structure. Results from these analyses indicated first that the average WFV of planting industries in Xinjiang significantly decreased from 0.293 m3/CNY in 1991 to 0.153 m3/CNY in 2015, corresponding to an average annual change rate of -3.532%. WFV in 13 prefectures and cities (with the exception of Karamay) has declined significantly during the period of 1991-2015, indicating that agricultural water-use efficient has effectively improved. Second, the average SWUC in Xinjiang decreased from 1.17 to 1.08 m3/CNY in the 1990s, and then declined to 1.00 m3/CNY in 2011-2015. The value of SWUC was highly consistent with the relative value of WFV in most prefectures and cities, showing that planting structure is one of the primary factors affecting regional agricultural water-use efficiency. Third, the contribution rate of WFV variations from human factors including agricultural input and planting structure was much more significant than that from climatic factors. However, the distribution of agricultural input and the adjustment of planting structure significantly differed among prefectures and cities, suggesting regional imbalances of agricultural development. This study indicated the feasibility and effectiveness of controlling agricultural water use through increasing technical input and rational selection of crops in the face of impending climate change. Specifically, we concluded that, the rational application of chemical fertilizers, the development of the fruit industry, and the strict restriction of the cotton industry should be implemented to improve the agricultural water-use efficiency in Xinjiang.

Key words： agricultural input      climatic factors      contribution rate      planting structure      structural water-use coefficient      water footprint of crop values     
Received: 13 October 2019      Published: 10 July 2020
Corresponding Authors:
About author: *Corresponding author: LONG Aihua (E-mail: ahlong@iwhr.com)

The first and second authors contributed equally to this work.
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Yang HAI
Aihua LONG
Pei ZHANG
Xiaoya DENG
Junfeng LI
Mingjiang DENG
Cite this article:

HAI Yang, LONG Aihua, ZHANG Pei, DENG Xiaoya, LI Junfeng, DENG Mingjiang. Evaluating agricultural water-use efficiency based on water footprint of crop values: a case study in Xinjiang of China. Journal of Arid Land, 2020, 12(4): 580-593.

URL:

http://jal.xjegi.com/10.1007/s40333-020-0058-x     OR     http://jal.xjegi.com/Y2020/V12/I4/580

Fig. 1 Overview of northern Xinjiang (Altay, Tacheng, Karamay, Bortala, Changji, Urumqi, and Ili), southern Xinjiang (Kizilsu, Kashgar, Aksu, Hotan, and Bayingol), and eastern Xinjiang (Turpan and Hami)
Fig. 2 Variations in water footprint of crop values (WFV) in 14 prefectures and cities of Xinjiang at different time periods from 1991 to 2015
Region Prefecture/
city		 Agricultural machinery power per unit area of cultivated land (kw/hm2) Average annual change rate
(%)
1991-1995 1996-2000 2001-2005 2006-2010 2011-2015
Northern Altay 3.35 3.64 4.41 5.82 5.69 3.197
Xinjiang Tacheng 2.74 3.98 5.90 5.78 5.30 3.968
Karamay 2.09 2.61 2.10 3.28 3.07 4.756
Bortala 2.87 3.01 3.63 4.12 4.08 2.532
Ili 3.62 4.44 5.76 6.39 7.26 3.854
Changji 2.52 2.88 3.21 3.11 3.19 1.709
Urumqi 6.09 5.80 6.16 6.52 9.01 2.987
Eastern Turpan 7.31 8.59 7.70 6.62 6.44 -0.005
Xinjiang Hami 3.92 4.63 5.00 4.46 4.38 0.004
Southern Hotan 1.44 1.65 1.99 2.07 2.71 3.299
Xinjiang Aksu 1.49 1.99 2.32 2.58 2.89 3.637
Bayingol 2.78 3.19 3.69 3.57 4.21 2.442
Kashgar 1.90 2.18 2.96 4.19 5.32 4.940
Kizilsu 1.12 1.43 1.83 2.16 2.98 5.205
Average 2.41 2.90 3.52 3.68 4.08 2.856
Table 1 Agricultural machinery power per unit area of cultivated land in 14 prefectures and cities of Xinjiang at different time periods from 1991 to 2015
Region Prefecture/city Consumption of N fertilizers per unit area of cultivated land (kg/hm2) Average annual change rate
(%)
1991-1995 1996-2000 2001-2005 2006-2010 2011-2015
Northern Altay 185 316 272 347 378 4.367
Xinjiang Tacheng 185 308 432 514 519 5.255
Karamay 218 237 189 347 482 6.271
Bortala 211 299 303 386 418 3.846
Ili 188 312 367 460 562 6.341
Changji 140 191 224 250 274 4.058
Urumqi 200 257 353 417 562 6.000
Eastern Turpan 151 165 172 201 297 4.409
Xinjiang Hami 152 204 205 248 324 4.344
Southern Hotan 186 271 253 252 263 2.690
Xinjiang Aksu 210 320 327 351 383 3.734
Bayingol 271 395 425 446 467 3.376
Kashgar 211 226 206 283 288 2.085
Kizilsu 241 270 264 315 316 2.391
Average 198 280 304 353 387 3.754
Table 2 Consumption of N fertilizers per unit area of cultivated land in 14 prefectures and cities of Xinjiang at different time periods from 1991 to 2015
Prefecture/city Climatic factors
Altay -
Tacheng Sunshine hours (-)
Karamay -
Bortala Precipitation (+); wind speed (-)
Ili Wind speed (+)
Changji Precipitation (+); sunshine hours (-); wind speed (+)
Urumqi Sunshine hours (+); relative humidity (+)
Turpan Temperature (+); relative humidity (-)
Hami Sunshine hours (+); relative humidity (-)
Hotan Precipitation (+); sunshine hours (-); wind speed (-)
Aksu Temperature (+); relative humidity (-)
Bayingol Temperature (+); wind speed (-)
Kizilsu Temperature (+); sunshine hours (+); relative humidity (-)
Kashgar Sunshine hours (-)
Average Temperature (+)
Table 3 Climatic factors significantly (P<0.05) associated with water footprint of crop values (WFV) and their trends in 14 prefectures and cities of Xinjiang during the period 1991-2015
Cotton Wheat Rice Maize Sugar crops Soybean Oil crops Fruits Vegetables
WFV
(m3/CNY)		 0.893 0.472 0.394 0.353 0.331 0.234 0.132 0.046 0.029
Table 4 Average WFV of major crops in Xinjiang during the period 1991-2015
Region Prefecture/city SWUC (m3/CNY) Average annual change rate (%)
1991-1995 1996-2000 2001-2005 2006-2010 2011-2015
Northern Altay 0.95 0.89 0.62 0.59 0.54 -0.031
Xinjiang Tacheng 1.02 1.06 0.90 1.03 0.96 -0.004
Karamay 1.38 1.96 2.20 2.26 1.73 0.009
Bortala 2.31 1.85 1.34 1.46 1.65 -0.018
Ili 0.59 0.57 0.61 0.62 0.61 0.003
Changji 1.05 1.31 1.20 1.03 0.98 -0.007
Urumqi 0.76 0.97 1.08 1.04 0.61 -0.010
Eastern Turpan 1.04 1.01 0.73 0.56 0.68 -0.026
Xinjiang Hami 0.91 0.85 0.53 0.40 0.41 -0.048
Southern Hotan 1.11 1.29 1.07 1.00 1.02 -0.008
Xinjiang Aksu 1.22 1.33 1.09 1.21 1.19 -0.003
Bayingol 1.11 1.37 1.20 1.14 1.16 -0.002
Kashgar 1.25 1.27 1.18 1.21 1.04 -0.013
Kizilsu 1.21 1.41 1.22 1.02 1.00 -0.008
Average 1.08 1.17 1.04 1.02 1.00 -0.006
Table 5 Variations in the regional structural water-use coefficient (SWUC) in 14 prefectures and cities of Xinjiang at different time periods from 1991 to 2015
Region Prefecture/
city		 Contribution rate (%)
Agricultural machinery power Consumption of
N fertilizers		 Planting structure Climatic factors Other factors Standard error
Northern Altay 35.95 32.40 30.59 - 1.06 0.205
Xinjiang Tacheng 40.39 50.28 5.49 3.06 0.78 0.086
Karamay - - 63.09 - 36.91 0.293
Bortala 25.19 39.01 17.18 27.71 -9.09 0.209
Ili 37.10 52.08 - 18.37 -7.55 0.111
Changji 14.59 43.73 0.10 24.42 17.15 0.193
Urumqi 22.17 64.85 - 12.95 0.03 0.206
Eastern Turpan - 36.65 22.75 17.19 23.41 0.224
Xinjiang Hami - 49.97 7.81 26.16 16.06 0.165
Southern Hotan 46.29 2.68 20.23 3.23 27.57 0.092
Xinjiang Aksu 36.67 28.43 - 18.04 16.85 0.146
Bayingol 32.41 32.67 4.06 15.70 15.16 0.151
Kashgar 50.62 13.27 12.43 15.69 8.00 0.189
Kizilsu 72.77 21.26 7.80 0.85 -2.69 0.101
Table 6 Contribution rates of influence factors to reduce WFV in Xinjiang during the period 1991-2015
[1]   Aldaya M M, Munoz G, Hoekstra A Y. 2010. Water footprint of cotton, wheat and rice production in Central Asia. In: Value of Water Research Report Series Vol. 41. UNESCO-IHE Institute for Water Education. Delft, the Netherlands.
[2]   Apurupa G, Jeffrey J V, Lisa R W. 2019. Stomatal response in soybean during drought improves leaf-scale and field-scale water use efficiencies. Agricultural and Forest Meteorology, 2276-77: 107629, doi: 10.1016/j.agrformet.2019.107629.
[3]   Bocchiola D, Nana E, Soncini A. 2013. Impact of climate change scenarios on crop yield and water footprint of maize in the Po valley of Italy. Agricultural Water Management, 116: 50-61.
doi: 10.1016/j.agwat.2012.10.009
[4]   Bulsink F, Hoekstra A Y, Booij M J. 2010. The water footprint of Indonesian provinces related to the consumption of crop products. Hydrology and Earth System Sciences, 14(1): 119-128.
doi: 10.5194/hess-14-119-2010
[5]   Bureau of Statistics of Xinjiang Production and Construction Corps. 1991-2015. Xinjiang Production and Construction Corps Statistical Yearbook. Beijing: China Statistics Press. (in Chinese)
[6]   Bureau of Statistics of Xinjiang Uygur Autonomous Region. 1991-2015. Xinjiang Statistical Yearbook. Beijing: China Statistics Press. (in Chinese)
[7]   Calzadilla A, Rehdanz K, Tol R S J, et al. 2010. The economic impact of more sustainable water use in agriculture: A computable general equilibrium analysis. Journal of Hydrology, 384(3): 292-305.
doi: 10.1016/j.jhydrol.2009.12.012
[8]   Chapagain A K, Hoekstra A Y, Savenije H H G, et al. 2006. The water footprint of cotton consumption: An assessment of the impact of worldwide consumption of cotton products on the water resources in the cotton producing countries. Ecological Economics, 60(1): 186-203.
doi: 10.1016/j.ecolecon.2005.11.027
[9]   Chapagain A K, 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(2): 1219-1228.
doi: 10.1016/j.jenvman.2008.06.006
[10]   Chapagain A K, Hoekstra A Y. 2011. The blue, green and grey water footprint of rice from production and consumption perspectives. Ecological Economics, 70(4): 749-758.
doi: 10.1016/j.ecolecon.2010.11.012
[11]   Chen S Y, Shi Y Y, Guo Y Z, et al. 2010. Temporal and spatial variation of annual mean air temperature in arid and semiarid region in northwest China over a recent 46 year period. Journal of Arid Land, 2(2): 87-97.
doi: 10.3724/SP.J.1227.2010.00087
[12]   Chu Y M, Shen Y J, Yuan Z J, et al. 2017. Water footprint of crop production for different crop structures in the Hebei southern plain, North China. Hydrology and Earth System Sciences, 21(6): 3061-3069.
doi: 10.5194/hess-21-3061-2017
[13]   Du T S, Kang S Z, Zhang J H, et al. 2015. Deficit irrigation and sustainable water-resource strategies in agriculture for China's food security. Journal of Experimental Botany, 66(8): 2253-2269.
doi: 10.1093/jxb/erv034 pmid: 25873664
[14]   Fan Y B, Wang C G, Nan Z B. 2014. Comparative evaluation of crop water use efficiency, economic analysis and net household profit simulation in arid Northwest China. Agricultural Water Management, 146: 335-345.
doi: 10.1016/j.agwat.2014.09.001
[15]   Gilbert M, Hernandez M. 2019. How should crop water-use efficiency be analyzed? A warning about spurious correlations. Field Crops Research, 235: 59-67.
doi: 10.1016/j.fcr.2019.02.017
[16]   Hassan R M, Olbrich B. 1999. Comparative analysis of the economic efficiency of water use by plantation forestry and irrigation agriculture in the Crocodile River catchment. Agrekon, 38(4): 566-575.
doi: 10.1080/03031853.1999.9524870
[17]   He C Y, Huang G H, Liu L R, et al. 2020. Multi-dimensional diagnosis model for the sustainable development of regions facing water scarcity problem: A case study for Guangdong, China. Science of The Total Environment, 734: 139394, doi: 10.1016/j.scitotenv.2020.139394.
doi: 10.1016/j.scitotenv.2020.139394 pmid: 32485462
[18]   Hoekstra A Y, Hung P Q. 2002. Virtual water trade: a quantification of virtual water flows between nations in relation to international crop trade. In: Value of Water Research Report Series Vol. 11. UNESCO-IHE Institute for Water Education. Delft, the Netherlands.
[19]   Hoekstra A Y, Chapagain A K. 2008. Globalization of Water: Sharing the Planet's Freshwater Resources. Oxford: Blackwell Publishing, 1-208.
[20]   Hoekstra A Y, Chapagain A K, Aldaya M M, et al. 2009. Water Footprint Manual: State of the Art 2009, Enschede: Water Footprint Network Press, 1-127.
[21]   Howell T A. 2001. Enhancing water use efficiency in irrigated agriculture. Agronomy Journal, 93(2): 281-289.
doi: 10.2134/agronj2001.932281x
[22]   Kahramanoglu I, Usanmaz S, Alas T. 2019. Water footprint and irrigation use efficiency of important crops in Northern Cyprus from an environmental, economic and dietary perspective. Saudi Journal of Biological Sciences, 27(1), doi: 10.1016/j.sjbs.2019.06.005.
doi: 10.1016/j.sjbs.2019.11.002 pmid: 31889865
[23]   Li Q H, Chen Y N, Shen Y J, et al. 2011. Spatial and temporal trends of climate change in Xinjiang, China. Journal of Geographical Sciences, 21(6): 1007-1018.
doi: 10.1007/s11442-011-0896-8
[24]   Li X S, Chen Z Z. 2010. Correctly using SPSS software for principal components analysis. Statistical Research, 27(8): 105-108. (in Chinese)
[25]   Lovarelli D, Bacenetti J, Fiala M. 2016. Water Footprint of crop productions: a review. Science of The Total Environment, 548-549: 236-251.
doi: 10.1016/j.scitotenv.2016.01.022 pmid: 26802352
[26]   Lu Y, Zhang X Y, Chen S Y, et al. 2016. Changes in water use efficiency and water footprint in grain production over the past 35 years: a case study in the North China Plain. Journal of Cleaner Production, 116(1): 71-79.
doi: 10.1016/j.jclepro.2016.01.008
[27]   Ma W J, Christian O, Yang D W. 2020. Spatiotemporal supply-demand characteristics and economic benefits of crop water footprint in the semi-arid region. Science of The Total Environment, 738: 139502, doi: 10.1016/j.scitotenv.2020.139502.
doi: 10.1016/j.scitotenv.2020.139502 pmid: 32544702
[28]   Ma X C, Karen A S, Pete W J. 2019. Performance of direct root-zone deficit irrigation on Vitis vinifera L. cv. Cabernet Sauvignon production and water use efficiency in semi-arid southcentral Washington. Agricultural Water Management, 221: 47-57.
doi: 10.1016/j.agwat.2019.04.023
[29]   Marano R P, Filippi R A. 2015. Water Footprint in paddy rice systems. Its determination in the provinces of Santa Fe and Entre Ríos. Argentina. Ecological Indicators, 56: 229-236.
doi: 10.1016/j.ecolind.2015.03.027
[30]   Mekonnen M M, Hoekstra A Y. 2010. A global and high-resolution assessment of the green, blue and grey water footprint of wheat. Hydrology and Earth System Sciences, 14(7): 17-23.
[31]   Mekonnen M M, Hoekstra A Y. 2014. Water footprint benchmarks for crop production: A first global assessment. Ecological Indicators, 46(11): 214-223.
doi: 10.1016/j.ecolind.2014.06.013
[32]   Nana E, Corbari C, Bocchiola D. 2014. A model for crop yield and water footprint assessment: Study of maize in the Po valley. Agricultural Systems, 127: 139-149.
doi: 10.1016/j.agsy.2014.03.006
[33]   National Bureau of Statistics of China. 2015. Statistical Yearbook of China, Beijing: China Statistics Press. (in Chinese)
[34]   Pellegrini G, Ingrao C, Camposeo S, et al. 2016. Application of water footprint to olive growing systems in the Apulia region: a comparative assessment. Journal of Cleaner Production, 112(5): 2407-2418.
doi: 10.1016/j.jclepro.2015.10.088
[35]   Piao S, Ciais P, Huang Y, et al. 2010. The impacts of climate change on water resources and agriculture in China. Nature, 467(7311): 43-51.
doi: 10.1038/nature09364 pmid: 20811450
[36]   Ridoutt B G, Pfister S. 2010. A revised approach to water footprint to make transparent the impacts of consumption and production on global freshwater scarcity. Global Environmental Change, 20(1): 113-120.
doi: 10.1016/j.gloenvcha.2009.08.003
[37]   Rodriguez C I, Galarreta V A R D, Kruse E E. 2015. Analysis of water footprint of potato production in the pampean region of Argentina. Journal of Cleaner Production, 90: 91-96.
doi: 10.1016/j.jclepro.2014.11.075
[38]   Sinclair T R, Tanner C B, Bennett J M. 1984. Water-use efficiency in crop production. Bioscience, 34(1): 36-40.
doi: 10.2307/1309424
[39]   Sun S K, Wu P T, Wang Y B, et al. 2013a. The impacts of interannual climate variability and agricultural inputs on water footprint of crop production in an irrigation district of China. Science of The Total Environment, 444(2): 498-507.
doi: 10.1016/j.scitotenv.2012.12.016
[40]   Sun S K, Wu P T, Wang Y B, et al. 2013b. Temporal variability of water footprint for maize production: the case of Beijing from 1978 to 2008. Water Resources Management, 27(7): 2447-2463.
doi: 10.1007/s11269-013-0296-1
[41]   Sun S K, Wang Y B, Liu J, et al. 2016. Quantification and evaluation of water footprint of major grain crops in China. Journal of Hydraulic Engineering, 47(9): 1115-1124. (in Chinese)
[42]   Tao J, Yang D G. 2004. Analysis on factors of Xinjiang grain increase production in recent 50 years with principal components method. Arid Land Geography, 27(1): 95-99. (in Chinese)
[43]   Tan M H, Zheng L Q. 2019. Increase in economic efficiency of water use caused by crop structure adjustment in arid areas. Journal of Environmental Management, 230: 386-391.
doi: 10.1016/j.jenvman.2018.09.060 pmid: 30296676
[44]   Tang H J, Li Z M. 2012. Study on per capita grain demand based on Chinese reasonable dietary pattern. Scientia Agricultura Sinica, 45(11): 2315-2327. (in Chinese)
doi: 10.3864/j.issn.0578-1752.2012.11.022
[45]   Wang F T, Yu C, Xiong L C, et al. 2019. How can agricultural water use efficiency be promoted in China? A spatial-temporal analysis. Resources, Conservation and Recycling, 145: 411-418.
doi: 10.1016/j.resconrec.2019.03.017
[46]   Wang X, Sheng T Y. 2011. Study on the wind energy resources in Boertala prefecture. Journal of Anhui Agricultural Sciences, 39(22): 13653-313655. (in Chinese)
[47]   Wu Y F, Bake B, Zhang J S, et al. 2015. Spatio-temporal patterns of drought in North Xinjiang, China, 1961-2012 based on meteorological drought index. Journal of Arid Land, 7(4): 527-543.
doi: 10.1007/s40333-015-0125-x
[48]   Xu C C, Chen Y N, Yang Y H, et al. 2010. Hydrology and water resources variation and its response to regional climate change in Xinjiang. Journal of Geographical Sciences, 20(4): 599-612.
doi: 10.1007/s11442-010-0599-6
[49]   Yang Q, Zhu R X, Zhang J, et al. 2000. Mechanization profit portion estimation in plant products industry in Shaanxi Province. Transactions of the Chinese Society of Agricultural Engineering, 16(6): 64-67. (in Chinese)
[50]   Yoo S H, Choi J Y, Lee S H, et al. 2014. Estimating water footprint of paddy rice in Korea. Paddy & Water Environment, 12(1): 43-54.
[51]   Zahra Z, Karami E, Keshavarz M. 2020. Co-production of knowledge and adaptation to water scarcity in developing countries. Journal of Environmental Management, 262: 110283, doi: 10.1016/j.jenvman.2020.110283.
doi: 10.1016/j.jenvman.2020.110283 pmid: 32090886
[52]   Zhao C F, Chen B, Hayat T, et al. 2014. Driving force analysis of water footprint change based on extended STIRPAT model: Evidence from the Chinese agricultural sector. Ecological Indicators, 47: 43-49.
doi: 10.1016/j.ecolind.2014.04.048
[53]   Zwart S J, Bastiaanssen W G M. 2004. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agricultural Water Management, 69(2): 115-133.
doi: 10.1016/j.agwat.2004.04.007
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