Spatial–Temporal Evolution of Agricultural Water Use Efficiency Based on DEA Approach and Spatial Autocorrelation
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Data Sources and Literature Review
3.2. The Lasso Regression Model
3.3. Data Envelopment Analysis Model
3.4. Spatial Autocorrelation Analysis Based on GMI and GOGI
3.4.1. Global Spatial Autocorrelation
3.4.2. Local Spatial Autocorrelation
4. Results and Discussion
4.1. Descriptive Statistics and Correlation Analysis of Input–Output Indicators
4.2. Determined the Indicators of AWUE Based on the Lasso Model
4.3. AWUE Evaluation Analysis Based on DAE from the Time Scale
4.4. AWUE Autocorrelation Analysis Based on GMI and GOGI from the Spatial Scale
4.4.1. Global Spatial Autocorrelation Analysis of AWUE
4.4.2. Local Spatial Autocorrelation Analysis of AWUE
4.5. Evolution Trend and Direction Analysis of AWUE
5. Conclusions
- (1)
- On the whole, under the changing environment from 2012 to 2021, the AWUE of the Hebei Province did not reach DEA effectiveness, and the average overall efficiency was 0.828, indicating that the AWUE still has potential and room for improvement. Among them, the annual average of AWUE in the southwest region is lower than 0.8, which are invalid DEA cities that need to be improved. The average AWUE of the northeast region is between 0.77 and 0.91, which is a strong EDA efficiency.
- (2)
- From the dynamic trend, the average value of each region of AWUE in the Hebei Province shows a fluctuating development trend of first rising, then falling, and then rising with time under the changing environment during the study period. AWUE reached the maximum in the window period 2014–2016.
- (3)
- The distribution of AWUE shows a directional pattern from southwest to northeast. The AWUE in the southwest region has always been low, while the efficiency of agricultural water use in the northeast region has always been a high-value area, with the center of gravity gradually shifting towards the northeast from 2012 to 2021. The results of the hotspot centroid and standard deviation ellipse further indicate that attention should be paid to improving AWUE in the southwest region.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Indicator | Symbol | Proxy Variables | Unit |
---|---|---|---|
Output indicators | Y | Gross agricultural output value | RMB |
Land input | S | Total sown area | Hectares |
Labor input | L | Agricultural labor force | People |
Capital investment | K | Total power of agricultural machinery | Kilowatt |
Material input | C | Consumption of chemical fertilizer | Tonnes |
Agricultural water consumption input | W | Agricultural water consumption | Cubic meters |
Precipitation input | P | Precipitation | mm |
Temperature input | T | Temperature | °C |
Year | Variables | Mean | Standard | Coefficient | Min | 10th | 25th | 50th | 75th | 90th | Max |
---|---|---|---|---|---|---|---|---|---|---|---|
Deviation | Variation | ||||||||||
2017–2012 | Y | 5,179,588 | 1,841,388 | 0.36 | 2,574,006 | 3,134,141 | 3,633,196 | 4,546,801 | 6,594,927 | 7,604,342 | 9,220,393 |
S | 760,201 | 307,479 | 0.40 | 190,086 | 366,188 | 455,617 | 790,095 | 1,001,133 | 1,108,306 | 1,207,825 | |
L | 1,189,088 | 559,409 | 0.47 | 626,584 | 668,225 | 779,174 | 1,024,230 | 1,357,500 | 1,700,738 | 2,725,340 | |
K | 6,919,309 | 5,194,505 | 0.75 | 290 | 1047 | 2,613,891 | 7,404,278 | 9,839,985 | 12,864,047 | 20,360,714 | |
C | 286,365 | 136,692 | 0.48 | 100,283 | 108,290 | 140,110 | 291,890 | 429,412 | 472,825 | 487,732 | |
W | 12 | 5 | 0.41 | 5 | 6 | 7 | 12 | 16 | 18 | 20 | |
P | 550.61 | 90.24 | 0.16 | 391.91 | 426.24 | 488.53 | 552.04 | 612.48 | 680.11 | 742.35 | |
T | 12.15 | 2.67 | 0.22 | 6.32 | 7.13 | 10.97 | 13.34 | 14.13 | 14.41 | 14.63 | |
2021–2018 | Y | 3,613,351 | 2,751,687 | 0.76 | 91 | 500 | 221,881 | 3,988,795 | 5,695,937 | 6,996,701 | 9,764,508 |
S | 320,565 | 394,552 | 1.23 | 110 | 197 | 722 | 1053 | 686,247 | 954,173 | 1,095,623 | |
L | 1,676,139 | 1,096,828 | 0.65 | 94,453 | 567,031 | 916,396 | 1,341,842 | 2,499,099 | 3,492,980 | 3,928,995 | |
K | 3,052,876 | 4,046,765 | 1.33 | 104 | 176 | 806 | 1311 | 7,410,765 | 9,728,382 | 12,833,239 | |
C | 120,381 | 151,987 | 1.26 | 6 | 10 | 25 | 41 | 283,681 | 347,252 | 438,829 | |
W | 9 | 4 | 0.49 | 2 | 2 | 5 | 9 | 12 | 14 | 17 | |
P | 528.89 | 102.33 | 354.31 | 0.19 | 406.58 | 456.69 | 508.07 | 582.06 | 679.62 | 783.68 | |
T | 11.77 | 2.68 | 5.13 | 0.23 | 6.99 | 10.65 | 12.90 | 13.81 | 14.14 | 14.65 |
Value | 2012–2014 | 2015–2016 | 2017–2019 | 2020–2021 |
---|---|---|---|---|
Global Moran’ I | 0.841 | 0.832 | 0.826 | 0.813 |
Z-Score | 9.350 | 9.260 | 9.188 | 9.032 |
P | 0.030 | 0.045 | 0.045 | 0.060 |
Year | Type | Longitude (°) | Latitude (°) | Rotation Angle (°) | Displacement (km) |
---|---|---|---|---|---|
2012–2014 | Efficiency value | 117.521 | 39.494 | 44.92 | - |
2015–2016 | Efficiency value | 117.201 | 39.634 | 20.51 | 49.85 |
2017–2019 | Efficiency value | 118.071 | 39.944 | 37.74 | 26.34 |
2020–2021 | Efficiency value | 118.356 | 40.392 | 136.46 | 35.24 |
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Huo, L.; Jia, R.; Wei, S.; Chen, M.; Ma, Q.; Jia, T.; Sun, J.; Han, Z. Spatial–Temporal Evolution of Agricultural Water Use Efficiency Based on DEA Approach and Spatial Autocorrelation. Water 2025, 17, 1456. https://doi.org/10.3390/w17101456
Huo L, Jia R, Wei S, Chen M, Ma Q, Jia T, Sun J, Han Z. Spatial–Temporal Evolution of Agricultural Water Use Efficiency Based on DEA Approach and Spatial Autocorrelation. Water. 2025; 17(10):1456. https://doi.org/10.3390/w17101456
Chicago/Turabian StyleHuo, Litao, Ruitao Jia, Sa Wei, Meijing Chen, Qingqing Ma, Tengfei Jia, Jiawei Sun, and Zonglin Han. 2025. "Spatial–Temporal Evolution of Agricultural Water Use Efficiency Based on DEA Approach and Spatial Autocorrelation" Water 17, no. 10: 1456. https://doi.org/10.3390/w17101456
APA StyleHuo, L., Jia, R., Wei, S., Chen, M., Ma, Q., Jia, T., Sun, J., & Han, Z. (2025). Spatial–Temporal Evolution of Agricultural Water Use Efficiency Based on DEA Approach and Spatial Autocorrelation. Water, 17(10), 1456. https://doi.org/10.3390/w17101456