Effects of Optimized Water–Fertilizer Management on Common Bean Performance in High-Latitude, High-Altitude Regions
Abstract
1. Introduction
2. Materials and Methods
2.1. Test Site and Materials
2.2. Experimental Design
2.3. Measurements and Calculations
2.3.1. Soil Moisture Content
- (1)
- Irrigation rate (I, m3) was calculated as follows [39]:
- (2)
- Evapotranspiration (ET, mm) was calculated as follows using the water balance method [40]:
- (3)
- Water use efficiency (WUE, kg/m3) was calculated as follows [41]:
- (4)
- Soil water storage (SWS, mm) was calculated as follows:
2.3.2. Fresh Pod Yield and Nutritional Quality
2.3.3. Fertilizer Partial Factor Productivity (PFP, kg/kg) Was Calculated as Follows [42]:
2.4. Comprehensive Evaluation
2.4.1. Principal Component Analysis (PCA)
2.4.2. Membership Function Method (MFM)
2.4.3. Weighted TOPSIS Method (W-TOPSIS)
2.4.4. Gray Relational Analysis Method (GRA)
2.4.5. Rank-Sum Ratio (RSR)
2.4.6. Comprehensive Evaluation Model
2.5. Data Analysis
3. Results
3.1. Effect of Water–Fertilizer Coupling Regulation on Water Consumption During the Growth Period of Common Bean
3.2. Effect of Water–Fertilizer Coupling Regulation on Fresh Common Bean Yield
3.3. Effect of Water–Fertilizer Coupling Regulation on Common Bean Quality
3.4. Effect of Water–Fertilizer Coupling Regulation on Water Use Efficiency
3.5. Effect of Water–Fertilizer Coupling Regulation on Fertilizer Partial Factor Productivity
3.6. Multi-Objective Decision-Making and Evaluation Based on the Overall Difference Combination Evaluation Model
4. Discussion
4.1. Effects of Water–Fertilizer Optimization Strategies on Yield and Quality of Common Beans
4.2. Effect of Water–Fertilizer Optimization Strategies on WUE and PFP
4.3. Evaluating the Adaptability of Water–Fertilizer Optimization Strategies and Comprehensive Evaluation Model in Common Bean Cultivation at High-Altitude and High-Latitude Regions
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
| PFP | Fertilizer partial factor productivity |
| FC | Field capacity |
| WUE | Water use efficiency |
| VC | Vitamin C |
| SP | Soluble protein |
| SSC | Soluble solids content |
| NC | Nitrate content |
| SSDI | Subsurface drip irrigation |
| I | Irrigation rate |
| SWC | Soil water content |
| ET | Evapotranspiration |
| SWS | Soil water storage |
| CF | Crude fiber |
| PCA | Principal component analysis |
| W-TOPSIS | Weighted TOPSIS method |
| GRA | Gray Relational Analysis method |
| RSR | Rank-sum ratio |
| MFM | Membership function method |
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| Year | Organic Matter (g/kg) | Total N (g/kg) | Nitrate N (mg/kg) | Available P (mg/kg) | Available K (mg/kg) | Soil Bulk Density (Mg/cm3) | pH |
|---|---|---|---|---|---|---|---|
| 2022 | 18.53 | 1.09 | 20.23 | 34.10 | 76.63 | 1.44 | 8.08 |
| 2023 | 18.25 | 1.03 | 18.19 | 33.19 | 73.37 | 1.39 | 7.98 |
| Date | 2022 Irrigation Rate (mm) | Date | 2023 Irrigation Rate (mm) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| W1 | W2 | W3 | W4 | W5 | W6 | W7 | W8 | W9 | ||
| 06/04 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 06/08 | 25.00 | 25.00 | 25.00 |
| 07/15 | 63.50 | 51.07 | 28.80 | 21.50 | 0.00 | 0.00 | 07/17 | 68.93 | 44.65 | 22.25 |
| 07/28 | 58.95 | 47.24 | 41.09 | 30.55 | 9.71 | 9.62 | 07/29 | 58.00 | 42.00 | 23.70 |
| 08/10 | 65.42 | 57.26 | 49.91 | 28.81 | 14.82 | 11.49 | 08/07 | 72.52 | 46.52 | 29.26 |
| 08/23 | 47.84 | 40.70 | 18.09 | 6.95 | 0.00 | 0.00 | 08/19 | 67.26 | 52.81 | 26.67 |
| Total | 260.71 | 221.27 | 162.89 | 112.82 | 49.53 | 46.11 | Total | 291.71 | 210.98 | 126.88 |
| Year | Treatments | Water Source (mm) | Soil Water Storage (mm) | Actual Evapotranspiration (mm) | |
|---|---|---|---|---|---|
| Irrigation Rate | Rainfall | ||||
| 2022 | W1 | 265.71 | 258.40 | 70.86 ± 5.34 a | 453.25 ± 21.12 a |
| W2 | 226.27 | 258.40 | 62.89 ± 7.00 ab | 421.79 ± 25.20 b | |
| W3 | 167.89 | 258.40 | 61.64 ± 7.76 ab | 364.65 ± 11.80 c | |
| W4 | 117.82 | 258.40 | 62.02 ± 1.35 ab | 314.20 ± 8.18 d | |
| W5 | 57.61 | 258.40 | 50.80 ± 3.77 bc | 262.13 ± 4.90 e | |
| W6 | 51.11 | 258.40 | 48.39 ± 0.82 c | 261.11 ± 1.25 e | |
| 2023 | W7F1 | 291.71 | 162.15 | 12.29 ± 2.00 de | 441.57 ± 19.32 a |
| W7F2 | 291.71 | 162.15 | 15.22 ± 1.69 abc | 438.64 ± 1.69 a | |
| W7F3 | 291.71 | 162.15 | 17.00 ± 1.47 a | 436.86 ± 18.59 a | |
| W8F1 | 210.98 | 162.15 | 13.18 ± 1.06 cde | 359.95 ± 5.43 b | |
| W8F2 | 210.98 | 162.15 | 15.20 ± 1.29 abc | 357.93 ± 3.16 b | |
| W8F3 | 210.98 | 162.15 | 16.31 ± 0.58 ab | 356.82 ± 0.58 b | |
| W9F1 | 126.88 | 162.15 | 10.50 ± 0.49 e | 278.53 ± 9.18 c | |
| W9F2 | 126.88 | 162.15 | 14.09 ± 1.49 bcd | 274.94 ± 6.34 c | |
| W9F3 | 126.88 | 162.15 | 14.92 ± 1.64 abcd | 274.11 ± 4.27 c | |
| Year | Treatments | PCA | MFM | W-TOPSIS | GRA | RSR | Comprehensive Evaluation Model | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Score | Rank | Score | Rank | Score | Rank | Score | Rank | Score | Rank | Score | Rank | ||
| 2022 | W1 | −2.919 | 6 | 0.000 | 6 | 0.457 | 6 | 0.967 | 1 | 0.396 | 6 | 0.200 | 6 |
| W2 | −1.483 | 5 | 0.296 | 5 | 0.550 | 2 | 0.952 | 2 | 0.635 | 2 | 0.614 | 2 | |
| W3 | −0.372 | 4 | 0.501 | 4 | 0.605 | 1 | 0.884 | 3 | 0.667 | 1 | 0.742 | 1 | |
| W4 | 0.748 | 3 | 0.650 | 3 | 0.503 | 3 | 0.852 | 4 | 0.594 | 4 | 0.600 | 3 | |
| W5 | 1.900 | 2 | 0.829 | 2 | 0.461 | 5 | 0.754 | 5 | 0.625 | 3 | 0.564 | 4 | |
| W6 | 2.126 | 1 | 0.859 | 1 | 0.467 | 4 | 0.748 | 6 | 0.583 | 5 | 0.552 | 5 | |
| 2023 | W7F1 | 0.851 | 7 | 0.702 | 3 | 0.531 | 2 | 0.775 | 9 | 0.500 | 7 | 0.373 | 6 |
| W7F2 | 1.153 | 4 | 0.497 | 5 | 0.446 | 5 | 0.826 | 5 | 0.542 | 4 | 0.443 | 5 | |
| W7F3 | 1.124 | 5 | 0.173 | 9 | 0.367 | 8 | 0.818 | 6 | 0.472 | 8 | 0.239 | 8 | |
| W8F1 | 1.353 | 3 | 0.940 | 1 | 0.668 | 1 | 0.831 | 4 | 0.694 | 1 | 0.842 | 1 | |
| W8F2 | 1.658 | 2 | 0.578 | 4 | 0.510 | 4 | 0.894 | 1 | 0.694 | 1 | 0.818 | 2 | |
| W8F3 | 1.724 | 1 | 0.380 | 7 | 0.414 | 6 | 0.885 | 2 | 0.542 | 4 | 0.575 | 3 | |
| W9F1 | 1.016 | 6 | 0.716 | 2 | 0.528 | 3 | 0.797 | 7 | 0.528 | 6 | 0.461 | 4 | |
| W9F2 | 0.796 | 8 | 0.463 | 6 | 0.397 | 7 | 0.784 | 8 | 0.556 | 3 | 0.302 | 7 | |
| W9F3 | 0.290 | 9 | 0.289 | 8 | 0.266 | 9 | 0.853 | 3 | 0.472 | 8 | 0.161 | 9 | |
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Li, C.; Li, S.; An, C.; Wu, Z.; Wang, H.; Wang, X.; Jin, S.; Sun, R.; Zhang, B.; Chen, X.; et al. Effects of Optimized Water–Fertilizer Management on Common Bean Performance in High-Latitude, High-Altitude Regions. Horticulturae 2025, 11, 1487. https://doi.org/10.3390/horticulturae11121487
Li C, Li S, An C, Wu Z, Wang H, Wang X, Jin S, Sun R, Zhang B, Chen X, et al. Effects of Optimized Water–Fertilizer Management on Common Bean Performance in High-Latitude, High-Altitude Regions. Horticulturae. 2025; 11(12):1487. https://doi.org/10.3390/horticulturae11121487
Chicago/Turabian StyleLi, Changqing, Shuting Li, Chuanjing An, Ziyi Wu, Haixu Wang, Xiaojing Wang, Shuyue Jin, Ruixue Sun, Baoyue Zhang, Xiuling Chen, and et al. 2025. "Effects of Optimized Water–Fertilizer Management on Common Bean Performance in High-Latitude, High-Altitude Regions" Horticulturae 11, no. 12: 1487. https://doi.org/10.3390/horticulturae11121487
APA StyleLi, C., Li, S., An, C., Wu, Z., Wang, H., Wang, X., Jin, S., Sun, R., Zhang, B., Chen, X., & Wang, J. (2025). Effects of Optimized Water–Fertilizer Management on Common Bean Performance in High-Latitude, High-Altitude Regions. Horticulturae, 11(12), 1487. https://doi.org/10.3390/horticulturae11121487
