Assessing Factors Driving the Change of Irrigation Water-Use Efficiency in China Based on Geographical Features
Abstract
:1. Introduction
2. Materials and Methods
2.1. Irrigation Water-Use Efficiency Calculation at Provincial Scale
2.2. Driving Factors of Irrigation Water-Use Efficiency Change
2.3. Indicators Selection and Data Collection
2.4. Cluster Analysis
- (1)
- Error sum of squares: , here the individual observations for each variable are compared against the cluster means for that variable. When the error sum of squares is small, the data are close to their cluster means.
- (2)
- Total sum of squares: , here the individual observations for each variable are compared against the grand mean for that variable.
- (3)
- R-Square: , this value is interpreted as the proportion of variation explained by a particular clustering of the observations.
3. Driving Force and Mechanism of Irrigation Water-Use Efficiency Change
3.1. Driving Factors Analysis of Irrigation Water-Use Efficiency Change in 31 Provinces of China
3.2. Influence Analysis of Irrigation Water Efficiency Change Driving Force
3.2.1. Impacts of Agricultural Economic Development on Irrigation Water-Use Efficiency
3.2.2. Influence of Water-Saving Irrigation Technology on Irrigation Water-Use Efficiency
3.2.3. Effects of Water Resources Factor on Irrigation Water-Use Efficiency
4. Regional Comparative Analysis of Driving Factors of Irrigation Water-Use Efficiency Change in China
4.1. Agricultural Economy Strong Driving Type Provinces
4.2. Agricultural Economy Dominant Provinces
4.3. Industrial Economy Dominant Provinces
4.4. Agriculture Strong Development Provinces
4.5. Coordinated Driving Type Provinces
- (1)
- Under different economic development modes, the driving force of irrigation water-use efficiency change is significantly different. The provinces of the first category have a high degree of agricultural economy development, and the economic development of the third category is in a high level of industrialization. These two kinds of provinces promote water-saving irrigation technology using different development modes. It can be argued that the provinces with a high level agricultural economy improve irrigation water-use efficiency through strong agricultural development; and the irrigation water efficiency of provinces with high-level industrialization is mainly promoted by economic development and structural adjustment. In addition, the industrial economy’s driving force for the development of water-saving irrigation technology is weak.
- (2)
- Even in provinces with similar agricultural development levels, there are differences between the driving forces of irrigation water-use efficiency changes because of the effect of local conditions. For the provinces of the second and forth categories, although they are all in the stage of strong agricultural development, there is a natural distinction in terms of resource endowments, resulting in distinct farming methods. Therefore, the driving force characteristics of the irrigation water efficiency changes are not consistent. The cultivated land scale of the provinces of the second category is more extensive, with larger populations and a high proportion of modern agriculture, their initial industries are growing faster, and their water-saving irrigation technology is advanced. The scale of the fourth category is larger, but the population is relatively small. The population of the traditional agriculture in fourth category is relatively high, and its development of water-saving irrigation technology is lagging behind.
5. Discussion
5.1. Implications for Similar Research
5.2. Future Research Directions
5.3. Suggestions for Typical Provinces
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Pute, W.; Hao, F.; Wenquan, N.; Jianen, G.; Dingsheng, J.; Youke, W.; Xingke, F.; Peng, Q. Analysis of developmental tendency of water distribution and water-saving strategies. Trans. Chin. Soc. Agric. Eng. 2003, 19, 1–6. [Google Scholar]
- Wang, G.F.; Chen, J.C.; Wu, F.; Li, Z.H. An integrated analysis of agricultural water-use efficiency: A case study in the Heihe River Basin in Northwest China. Phys. Chem. Earth 2015, 89, 3–9. [Google Scholar] [CrossRef]
- People’s Republic of China Ministry of water resources. China Water Resources Bulletin in 2015; People’s Republic of China Ministry of water resources: Beijing, China, 2016.
- Kang, S.; Hao, X.; Du, T.; Tong, L.; Su, X.; Lu, H.; Li, X.; Huo, Z.; Li, S.; Ding, R. Improving agricultural water productivity to ensure food security in China under changing environment: From research to practice. Agric. Water Manag. 2017, 179, 5–17. [Google Scholar] [CrossRef]
- Pereira, L.S.; Cordery, I.; Iacovides, I. Improved indicators of water use performance and productivity for sustainable water conservation and saving. Agric. Water Manag. 2012, 108, 39–51. [Google Scholar] [CrossRef]
- Xue, J.; Ren, L. Assessing water productivity in the Hetao Irrigation District in Inner Mongolia by an agro-hydrological model. Irrig. Sci. 2017, 35, 357–382. [Google Scholar] [CrossRef]
- Abu-Madi, M.O. Farm-level perspectives regarding irrigation water prices in the Tulkarm district, Palestine. Agric. Water Manag. 2009, 96, 1344–1350. [Google Scholar] [CrossRef]
- Zdruli, P.; Lamaddalena, N.; Todorovic, M.; Scardigno, A.; Calabrese, J.; Ladisa, G.; Verrastro, V.; Pereira, L.S.; Coppola, A.; Marino, M. White Paper, Priority 1: Sustainable Natural Resources Management; Euro-Mediterranean network on research and innovation for food security: Bari, Italy, 2013. [Google Scholar]
- Ringler, C.; Bhaduri, A.; Lawford, R. The nexus across water, energy, land and food (WELF): Potential for improved resource use efficiency? Curr. Opin. Environ. Sustain. 2013, 5, 617–624. [Google Scholar] [CrossRef]
- Qin, C.; Zhao, Y.; Pei, Y. Study on utility of generalized water resources utilization by adjustment of agricultural water price. J. Hydraul. Eng. 2010, 41, 1094–1100. [Google Scholar]
- Gao, J.; Christensen, P.; Li, W. Application of the WEAP model in strategic environmental assessment: Experiences from a case study in an arid/semi-arid area in China. J. Environ. Manag. 2017, 198, 363–371. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.; Yao, N.; Chau, H.W. Influences of removing linear and nonlinear trends from climatic variables on temporal variations of annual reference crop evapotranspiration in Xinjiang, China. Sci. Total Environ. 2017, 592, 680–692. [Google Scholar] [CrossRef] [PubMed]
- Li, H.; Shao, D.; Yin, X.; Chen, S.; Xu, B. Evaluation method for irrigation-water-use efficiency based on principle component analysis and Copula function. Trans. Chin. Soc. Agric. Eng. 2015, 31, 96–102. [Google Scholar]
- Feng, B. Study on the Evaluation and Management of Irrigation Water-Use Efficiency for Different Scales in Countrywide; Hydrology & Water Resources Engineering: Beijing, China, 2013. [Google Scholar]
- Ahmadzadeh, H.; Morid, S.; Delavar, M.; Srinivasan, R. Using the SWAT model to assess the impacts of changing irrigation from surface to pressurized systems on water productivity and water saving in the Zarrineh Rud catchment. Agric. Water Manag. 2015, 175, 15–28. [Google Scholar] [CrossRef]
- Tao, F.; Zhang, Z. Impacts of climate change as a function of global mean temperature: Maize productivity and water use in China. Clim. Chang. 2011, 105, 409–432. [Google Scholar] [CrossRef]
- Zhang, Y.-L.; Wang, F.-X.; Shock, C.C.; Yang, K.-J.; Kang, S.-Z.; Qin, J.-T.; Li, S.-E. Effects of plastic mulch on the radiative and thermal conditions and potato growth under drip irrigation in arid Northwest China. Soil Tillage Res. 2017, 172, 1–11. [Google Scholar] [CrossRef]
- Manjunatha, A.V.; Anik, A.R.; Speelman, S.; Nuppenau, E.A. Impact of land fragmentation, farm size, land ownership and crop diversity on profit and efficiency of irrigated farms in India. Land Use Policy 2013, 31, 397–405. [Google Scholar] [CrossRef]
- Sadras, V.O.; Cassman, K.G.; Grassini, P.; Hall, A.J.; Bastiaanssen, W.G.M.; Laborte, A.G.; Milne, A.E.; Sileshi, G.; Steduto, P. Yield gap analysis of field crops: Methods and case studies. In FAO Water Reports; FAO: Rome, Italy, 2015; Volume 41. [Google Scholar]
- Chen, X.; Cui, Z.; Fan, M.; Vitousek, P.; Zhao, M. Producing more grain with lower environmental costs. Nature 2014, 514, 486. [Google Scholar] [CrossRef] [PubMed]
- Chen, H.; Huang, J.; Wu, J.; Yang, J. Review of scale effect on the irrigation water-use efficiency. Adv. Water Sci. 2011, 22, 872–880. [Google Scholar]
- Willardson, L.S.; Allen, R.G.; Federiksen, H.D. Eliminating Irrigation Efficiencies. In Proceedings of the Paper presented at the 13th Technical Conference of the US Committee on Irrigation and Drainage, Denver, CO, USA, 19–22 October 1994. [Google Scholar]
- Keller, A.A.; Keller, J. Effective Efficiency: A water-use efficiency Concept for Allocating Freshwater Resources; Discussion Paper 22; Winrock International; Water resources and irrigation division: Arlington, VA, USA, 1995; p. 19. [Google Scholar]
- Cai, S.; Zhang, Z.; Zhang, D. Modified index system for utilization efficiency of irrigation water. J. Hydraul. Eng. 2004, 5, 111–115. [Google Scholar]
- Perry, C.J. Efficient irrigation; inefficient communication; flawed recommendations. Irrig. Drain. 2007, 56, 367–378. [Google Scholar] [CrossRef]
- Moore, W.C.; Meyers, D.A.; Wenzel, S.E.; Teague, W.G.; Li, H.; Li, X.; D’Agostino, R., Jr.; Castro, M.; Curran-Everett, D.; Fitzpatrick, A.M.; et al. Identification of Asthma Phenotypes Using Cluster Analysis in the Severe Asthma Research Program. Am. J. Respir. Crit. Care Med. 2010, 181, 315–323. [Google Scholar] [CrossRef] [PubMed]
- Tran, T.J.; Bruening, J.M.; Bunn, A.G.; Salzer, M.W.; Weiss, S.B. Cluster analysis and topoclimate modeling to examine bristlecone pine tree-ring growth signals in the Great Basin, USA. Environ. Res. Lett. 2017, 12, 014007. [Google Scholar] [CrossRef]
- Su, H. Study on the Selection of Effective Water-Saving Irrigation Techniques for Xinjiang Agriculture; Liu, J., Ed.; Shihezi University: Shihezi, China, 2013. (In Chinese) [Google Scholar]
- Liu, Z. Research on Current Situation and Agriculture Industrialization Countermeasures of Henan; Wang, X., Ed.; Henan Agricultural University: Zhengzhou, China, 2012. (In Chinese) [Google Scholar]
- Jia, R.F.; Fang, S.B.; Tu, W.R.; Sun, Z.L. Driven Factors Analysis of China’s Irrigation Water-use efficiency by Stepwise Regression and Principal Component Analysis. Discret. Dyn. Nat. Soc. 2016, 2016, 8957530. [Google Scholar] [CrossRef]
Index Explanation of Indices | |
---|---|
Annual rainfall (mm) | The direct or indirect effects of natural precipitation on irrigation water-use efficiency. |
Annual average temperature (°C) | Impacts of the climate on regional irrigation water use. |
Precipitation frequency (%) | Determining the rainfall distribution, which is the input to water resources. |
Drought index | Quantifying the drought risk due to meteorological, socio-economic and technological changes. |
Ratio of agricultural population of total population | Representing the agricultural labor force in total population. |
Cultivated land area (103 ha) | A direct relationship between the cultivated land area and the utilization of irrigation water. |
Per capita cultivated land area (ha) | Based on the index of cultivated land area, it has the function of characterizing population and resources. |
Local water resources (108 m3) | Indicating the differences in water resource endowments between regions. |
Per capita income of farmers (yuan) | Comparable conversion of the consumption price index of rural residents. |
Per capita water consumption (m3) | Reflecting the ability to obtain water resources and the allocation of water resources. |
Per capita GDP (yuan) | Reflecting the economic development of the region, and affecting the government’s attention and input to water conservation measures. |
Ratio of first industrial output of GDP (%) | Explaining the contribution of agricultural development to local economic development. |
Ratio of paddy rice of total planting area (%) | The difference of irrigation water-use efficiency caused by various regional grain planting structures. |
Ratio of crops of total sown area (%) | Irrigation water-use efficiency change caused by regional crop area. |
Grain yield (104 t) | Macroscopically stating the development degree of regional agricultural economy. |
Ratio of technological expenditure of financial cost (%) | Characterization of the industrialization process of water-saving irrigation equipment. |
Water-saving irrigation area (ha) | Area of the channel anti-seepage, low-pressure pipe irrigation, sprinkler irrigation, and micro-irrigation. |
Irrigated area over 1000 ha | Characterizing the size of large irrigation areas. |
Effective irrigation area (ha) | Characterization of the effective use of irrigation water. |
Ratio of agricultural expenditure to financial cost (%) | Characterization of government investment in water conservation. |
Agricultural water price (yuan) | Water price is an effective factor to stimulate water conservation, indicating that government price controls will influence the water-saving consciousness of farmers. |
Index F1 F2 F3 F4 F5 | |||||
---|---|---|---|---|---|
Annual rainfall X1 (mm) | −0.305 | −0.322 | −0.199 | −0.434 | 0.088 |
Precipitation frequency X2 (%) | 0.033 | 0.137 | 0.133 | −0.507 | 0.044 |
Drought index X3 (%) | 0.291 | 0.318 | 0.750 | −0.155 | 0.030 |
Agricultural population’s ratio X4 (%) | 0.830 | −0.324 | −0.163 | 0.027 | 0.120 |
Cultivated land area X5 (103 ha) | 0.682 | 0.518 | 0.454 | 0.189 | 0.141 |
Per capita cultivated land area X6 (ha) | 0.722 | 0.112 | 0.122 | 0.008 | −0.123 |
Local water resources X7/108 (m3) | 0.268 | −0.341 | 0.159 | 0.677 | 0.080 |
Per capita water consumption X8 (m3) | 0.352 | 0.117 | 0.810 | 0.382 | −0.024 |
Per capita income of farmers X9 (yuan) | −0.863 | 0.373 | 0.175 | −0.028 | −0.069 |
Per capita GDP X10 (yuan) | −0.855 | 0.211 | 0.211 | −0.074 | −0.061 |
Ratio of first industrial output of GDP X11 (%) | 0.810 | −0.233 | −0.128 | 0.161 | 0.041 |
Grain yield X12 (104 t) | 0.497 | 0.618 | −0.548 | 0.312 | −0.210 |
Ratio of technological expenditure of financial cost X13 (%) | −0.855 | 0.515 | 0.233 | 0.163 | 0.478 |
Water-saving irrigation area X14 (ha) | 0.500 | 0.811 | 0.034 | −0.110 | −0.088 |
Irrigated area over 1000 ha X15 (104 ha) | 0.455 | 0.683 | 0.105 | 0.018 | 0.007 |
Effective irrigation area X16 (ha) | 0.520 | 0.771 | −0.313 | 0.191 | 0.017 |
Ratio of agricultural expenditure to financial cost X17 (%) | 0.750 | −0.587 | 0.051 | −0.028 | −0.069 |
Agricultural water price X18 (yuan) | −0.147 | 0.620 | −0.303 | 0.329 | −0.005 |
Main Factor | Eigen Value | Contribution Rate/% | Cumulative Contribution Rate/% |
---|---|---|---|
First factor (F1) | 6.801 | 34.660 | 34.660 |
Second factor (F2) | 4.408 | 22.715 | 57.375 |
Third factor (F3) | 2.640 | 13.662 | 71.037 |
Fourth factor (F4) | 1.677 | 7.789 | 78.826 |
Fifth factor (F5) | 1.384 | 5.911 | 84.737 |
Province | F1 First Factor | F2 Second Factor | F3 Third Factor | F4 Fourth Factor | F5 Fifth Factor | Comprehensive Factor | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Score | Ranking | Score | Ranking | Score | Ranking | Score | Ranking | Score | Ranking | Score | Ranking | |
Beijing | −2.1723 | 30 | 0.6362 | 9 | 1.0132 | 5 | −0.1011 | 18 | 0.5922 | 10 | −0.4184 | 27 |
Tianjin | −1.9655 | 29 | 0.7252 | 8 | 0.1078 | 11 | −1.3125 | 27 | 0.767 | 7 | −0.5449 | 30 |
Hebei | 0.5317 | 8 | 1.2803 | 5 | −0.8195 | 29 | 0.2204 | 14 | 0.7696 | 6 | 0.4188 | 4 |
Shanxi | −0.0503 | 21 | −0.063 | 16 | −0.3487 | 19 | −1.7389 | 30 | 0.1053 | 13 | −0.2196 | 23 |
Inner Mongolia | 0.854 | 3 | 1.1257 | 6 | 0.3285 | 7 | −0.6138 | 23 | 1.6977 | 2 | 0.644 | 3 |
Liaoning | −0.449 | 25 | 0.2978 | 11 | −0.059 | 12 | −0.3789 | 21 | 0.671 | 9 | −0.079 | 16 |
Jilin | 0.3328 | 14 | −0.0315 | 15 | −0.2841 | 16 | −0.5339 | 22 | 1.0429 | 4 | 0.093 | 10 |
Heilongjiang | 1.3213 | 2 | 1.3177 | 3 | −0.2672 | 14 | −0.0544 | 17 | 1.2429 | 3 | 0.7773 | 2 |
Shanghai | −2.5579 | 31 | 0.6199 | 10 | 1.2956 | 3 | 0.8065 | 8 | −0.2956 | 20 | −0.497 | 29 |
Jiangsu | −0.4259 | 24 | 1.3152 | 4 | −0.3706 | 21 | 0.8562 | 6 | −0.0144 | 17 | 0.1662 | 8 |
Zhejiang | −1.2752 | 28 | 0.2789 | 12 | 0.1366 | 9 | 0.4935 | 11 | −0.6969 | 25 | −0.3547 | 24 |
Anhui | 0.4529 | 11 | 0.069 | 13 | −0.6771 | 26 | 0.6534 | 10 | −1.1124 | 29 | 0.0525 | 12 |
Fujian | −0.7834 | 26 | −0.4407 | 21 | 0.1727 | 8 | 0.1366 | 16 | −0.8112 | 27 | −0.3806 | 26 |
Jiangxi | 0.0615 | 20 | −0.6943 | 26 | −0.2394 | 13 | 0.902 | 5 | −0.5045 | 23 | −0.1223 | 20 |
Shandong | 0.4362 | 12 | 1.608 | 2 | −0.9906 | 30 | 0.8127 | 7 | −0.2533 | 19 | 0.4143 | 5 |
Henan | 0.7327 | 4 | 1.0042 | 7 | −1.6874 | 31 | 0.1656 | 15 | −0.4676 | 22 | 0.216 | 7 |
Hubei | 0.3228 | 15 | −0.1832 | 18 | −0.3335 | 18 | 0.6873 | 9 | −0.8132 | 28 | 0.0245 | 13 |
Hunan | 0.2157 | 16 | −0.4219 | 20 | −0.5123 | 24 | 1.074 | 4 | −0.617 | 24 | −0.0415 | 14 |
Guangdong | −1.0226 | 27 | −0.0159 | 14 | 0.1349 | 10 | 1.5664 | 2 | −0.2483 | 18 | −0.2118 | 22 |
Guangxi | 0.4258 | 13 | −0.9029 | 27 | −0.3202 | 17 | 0.3767 | 12 | −0.7268 | 26 | −0.1171 | 19 |
Hainan | 0.1215 | 18 | −1.7476 | 30 | −0.3487 | 20 | −0.7059 | 24 | −2.7121 | 31 | −0.6388 | 31 |
Chongqing | −0.4084 | 23 | −0.6154 | 23 | −0.449 | 23 | −1.0793 | 26 | −0.3029 | 21 | −0.4466 | 28 |
Sichuan | 0.4777 | 10 | −0.1063 | 17 | −0.7318 | 28 | 1.4008 | 3 | 0.1007 | 14 | 0.1634 | 9 |
Guizhou | 0.5049 | 9 | −1.0088 | 28 | −0.5342 | 25 | −0.1848 | 20 | 0.7129 | 8 | −0.091 | 18 |
Yunnan | 0.6215 | 7 | −0.6392 | 24 | −0.6866 | 27 | 0.3319 | 13 | 0.7853 | 5 | 0.0577 | 11 |
Tibet | 0.723 | 5 | −2.6245 | 31 | 1.9192 | 2 | 2.0444 | 1 | 2.4033 | 1 | 0.2735 | 6 |
Shaanxi | 0.0645 | 19 | −0.2261 | 19 | −0.3955 | 22 | −0.7753 | 25 | 0.1779 | 12 | −0.1359 | 21 |
Gansu | 0.6474 | 6 | −0.4864 | 22 | −0.2745 | 15 | −1.8361 | 31 | 0.0042 | 15 | −0.0833 | 17 |
Qinghai | −0.162 | 22 | −1.1663 | 29 | 0.3904 | 6 | −1.4337 | 28 | 0.0018 | 16 | −0.3784 | 25 |
Ningxia | 0.1865 | 17 | −0.6419 | 25 | 1.0281 | 4 | −1.6078 | 29 | 0.3332 | 11 | −0.051 | 15 |
Xinjiang | 2.2382 | 1 | 1.7379 | 1 | 3.8029 | 1 | −0.1719 | 19 | −1.8314 | 30 | 1.511 | 1 |
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Fang, S.; Jia, R.; Tu, W.; Sun, Z. Assessing Factors Driving the Change of Irrigation Water-Use Efficiency in China Based on Geographical Features. Water 2017, 9, 759. https://doi.org/10.3390/w9100759
Fang S, Jia R, Tu W, Sun Z. Assessing Factors Driving the Change of Irrigation Water-Use Efficiency in China Based on Geographical Features. Water. 2017; 9(10):759. https://doi.org/10.3390/w9100759
Chicago/Turabian StyleFang, Shibiao, Renfu Jia, Wenrong Tu, and Zhilin Sun. 2017. "Assessing Factors Driving the Change of Irrigation Water-Use Efficiency in China Based on Geographical Features" Water 9, no. 10: 759. https://doi.org/10.3390/w9100759