# Economic Evaluation of Drought Resistance Measures for Maize Seed Production Based on TOPSIS Model and Combination Weighting Optimization

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Description of the Experimental Site

^{3}. The soil pH value within the 0–40 cm surface layer was 8.35, the organic matter content was 16.30 g/kg, and the soil available phosphorus, nitrogen, and potassium contents were 15.80, 45.32, and 125.36 mg/kg, respectively. The rainfall in 2018 was 118.2 mm and the evaporation was 1898.5 mm; in 2019, the rainfall was 207.5 mm and the evaporation was 1736.9 mm.

#### 2.2. Experimental Design and Field Management

^{2}(6 m × 22 m), and the split plot design was adopted. A drip irrigation belt produced by DAYU Water-Saving Group Co., Ltd. Jiuquan City, China was paved on the soil surface. In the first experimental year, the female seeds were sown on 17 April 2018, and the male seeds were sown in the first and second phases on 26 and 30 April, respectively, while the crops were harvested on 30 September 2018. In the second experimental year, the female seeds were sown on 12 April 2019, and the male seeds were sown in the first and second phases on 19 and 24 April, respectively, while the crops were harvested on 15 September 2019. The planting row ratio of parents was 1:4, with 1 line of father plants and 4 lines of mother plants oriented east–west. The female seeds were sown in wide-narrow rows with row spacing of 0.45 m and plant spacing of 0.2 m, while the male seeds were sown in the middle 2 lines of female parents with row spacing of 1.35 m and plant spacing of 0.2 m. Each plot was laid with 5 lines of film, and 2 lines of seeds were sown on each film. Basal fertilizer application was urea of 105 kg/ha, with P

_{2}O

_{5}of 138 kg/ha and K

_{2}O of 75 kg/ha, and the nitrogen topdressing dosage was 345 kg/ha. The topdressing was carried out at maize jointing, silking, and filling, with a topdressing ratio of 3:4:3. Drip irrigation was adopted with water and fertilizer coupling. During the growth period of maize seed in 2018, irrigation was carried out 7 times on the 51st, 65th, 73rd, 90th, 98th, 108th, and 116th day after sowing, with the irrigation quota of 400, 450, 450, 450, 500, 500, and 400 m

^{3}/ha, respectively, for a total of 3150 m

^{3}/ha. In 2019, the maize seed was irrigated 6 times during the growth period on the 46th, 62nd, 82nd, 95th, 101st, and 120th day after sowing, with the irrigation quota of 400, 550, 500, 400, 550, and 550 m

^{3}/ha, respectively, for a total of 2950 m

^{3}/ha.

#### 2.3. Measurements and Calculations

#### 2.3.1. Soil Moisture Content

#### 2.3.2. Plant Yield

#### 2.3.3. Water Consumption and Water Use Efficiency

^{3}).

#### 2.3.4. Quality

#### 2.3.5. Determination of Weights

- (1)
- Establishment of the declining hierarchical structure. The relationship and affiliating among every factors were divided into multiple levels, including criterion layer, target layer, and scheme layer, according to the different characteristics of factors.
- (2)
- Construction of pairwise judgment matrix. Pairwise comparison of factors in the criterion layer was carried out to construct a pairwise comparison matrix among factors, and a nine-point scale method was adopted (Table 2). Then, a pairwise comparison judgment matrix was formed from the quantization results $O-C={\left({\mathrm{a}}_{ij}\right)}_{n\times n}$.
- (3)
- Calculation of the relative weight of the factors. The relative weight of the factors was calculated by the judgment matrix, and the weight of all the elements in this layer in the upper layer was calculated and further synthesized by the calculation results of the weight of a single layer. By weight sorting, the optimal scheme was selected and the consistency of the judgment matrix was tested to ensure the scientific and reliable calculation.

Scaling | Meaning |
---|---|

1 | Equally important |

3 | Slightly important |

5 | Obviously important |

7 | Strongly important |

9 | Extremely important |

2, 4, 6, 8 | The median of the above two adjacent judgments |

reciprocal | A is compared to B if the scale is 3, then B is 1/3 compared to A |

- a.
- Arithmetic average method

- b.
- Geometric average method

- c.
- Feature vector method

- a.
- Calculation of consistency indicators $\left(CI\right)$:

- b.
- Calculation of the corresponding average random consistency index (RI) (Table 3).
- c.
- Calculation of consistency ratio $CR$:

- (1)
- The indexes to be calculated are formed into a numerical matrix to judge whether there is a negative number in the input matrix. If so, it should be re-normalized to a non-negative range to ensure that every element is a non-negative number to form a positive matrix $X={\left({X}_{\mathrm{ij}}\right)}_{n\times m}$.

- (2)
- The non-negative matrix Z is obtained through normalization, and the proportion of the ith sample in the jth index is calculated, which is regarded as the probability used in relative entropy calculation:

- (3)
- Calculations of the information entropy of each indicator, the information utility value, and the entropy weight of each indicator through normalization were conducted as follows:

- (a)
- Use different weighting methods (L kinds) to weight the participating indicators and construct the basic weight vector set.

- (b)
- Construction of the linear combination q of weight vectors. The linear combination of the above L vectors is:

- (c)
- where u is the weight set after linear combination, and ${\alpha}_{k}$ is the coefficient of linear combination. In order to minimize the deviation with each, the equilibrium idea of GT is used to optimize ${\alpha}_{k}$, i.e.,

- (d)
- After obtaining the optimal linear combination coefficient $\left({\alpha}_{1},{\alpha}_{2},\dots {\alpha}_{L}\right)$ according to Equation (19), it is processed with the improved normalization formula [34], i.e.,

- (e)
- By applying GT, the comprehensive weight vector is obtained by combining AHP and EWM:

#### 2.4. TOPSIS Model Evaluation Method

- (1)
- Construct the weighted evaluation matrix.

- (2)
- Determine positive and negative ideal solutions.First, the weighting matrix was forward—that is, the benefit index—and then the matrix $Z$ was obtained by normalizing and removing the dimension. Finally, the positive ideal solution set was formed by the maximum value of each participating index in the scheme, and the negative ideal solution set was formed by the minimum value.

- (3)
- Calculate Euclidean distance.For each evaluation scheme, the distance to the positive ideal solution and the distance to the negative ideal solution were calculated as follows:$${D}_{i}^{+}=\sqrt{{{\displaystyle \sum _{j=1}^{m}\left({\stackrel{\sim}{Z}}_{j}^{+}-{\stackrel{\sim}{z}}_{ij}\right)}}^{2}}$$$${D}_{i}^{-}=\sqrt{{{\displaystyle \sum _{j=1}^{m}\left({\stackrel{\sim}{Z}}_{j}^{-}-{\stackrel{\sim}{z}}_{ij}\right)}}^{2}}$$

- (4)
- Calculate the comprehensive score.According to Equation (25), the proximity Si of each scheme to the optimal scheme was first calculated, and then the comprehensive score of each evaluation scheme was obtained after normalization according to Equation (26):$${S}_{\mathrm{i}}={D}_{i}^{-}/\left({D}_{i}^{+}+{D}_{i}^{-}\right)$$$${\stackrel{\text{\u223c}}{S}}_{i}={S}_{i}/{\displaystyle \sum _{i=1}^{n}{S}_{i}}$$

#### 2.5. Statistics Analysis

## 3. Results

#### 3.1. Selection of Evaluation Indicators

^{3}/ha less than conventional open-field planting CK. Compared with CK, the WF, BF, and SA also reduced the water consumption during the whole growth period, and the decreases were 14.07%, 9.67%, 9.60%, and 9.13%, respectively. The WUE in CK was the lowest, with only 1.34 kg/m

^{3}, and the WUE was significantly increased by 36.37–65.49% by adding drought resistance measures. The WUE in BF was the highest, with 2.22 kg/m

^{3}, followed by WF, SA, and SM, which were significantly increased by 65.49%, 58.60%, and 42.11%, and 36.37%, respectively, compared with CK.

^{2}, followed by WF treatment and SA treatment with 8960.50 and 8097.92 kg/ha

^{2}, respectively. Compared with CK, the yield of these three treatments was significantly increased by 49.57%, 42.97%, and 29.21%, respectively. Although the increase rate of straw mulching treatment was lower than in other drought resistance measures, the rate was still notably increased by 17.24% compared with CK. The effect of different drought resistance measures on output was consistent with the change in yield and could significantly increase the water output by 1.30–3.74 RMB/m

^{3}, among which BF and WF treatment were the most significant, with increases of 3.74 RMB/m

^{3}and 3.24 RMB/m

^{3}, which were 49.57% and 42.97% higher than CK. Although the increase rate of SA and SM treatment was lower than that of plastic film mulching, it was still increased by 2.20 RMB and 1.30 RMB/m

^{3}compared with CK, which was significantly increased by 29.20% and 17.23%. Different drought resistance measures had different effects on quality components of maize seed production. BF treatment significantly increased starch content of maize seed, followed by SM, which was significantly increased by 10.32% and 8.07% compared with CK. Although starch content in SA and WF treatments increased by 6.89% and 3.33%, there was no significant difference (p > 0.05). The crude protein and crude fat contents of maize seed were greatly increased by different drought resistance measures, among which the BF was the highest, with 10.12 mg/g and 1.70%, respectively, followed by SM, SA, and WF. Compared with CK, the crude protein and crude fat contents in all the treatments were significantly increased by 24.48%, 15.31%, 10.70%, and 8.06% and 43.04%, 31.65%, 18.14%, and 16.03% respectively. Different drought resistance measures were beneficial to the accumulation of soluble sugar content in maize seed, among which the accumulation effect of SM was the best, with 14.21%, followed by SA, BF, and WF, which were significantly increased by 32.76%, 21.68%, 17.10%, and 11.59% compared with CK. The application of a water retaining agent could significantly increase the crude fiber content of maize seed by 28.85%, while plastic film mulching and straw mulching were not suitable for the accumulation of crude fiber content. Compared with CK, the grain crude fiber content in BF treatment decreased the most (11.17%), followed by WF treatment (9.09%), and the difference was significant. The grain crude fiber content in SM treatment decreased the least (3.46%), and the difference was not significant.

#### 3.2. Determination of the Weight of Indices in the Evaluation System

#### 3.2.1. The Analytic Hierarchy Process

- (1)
- Establishment of a hierarchy.In order to find suitable drought resistance measures for maize seed production in northwest China, the evaluation index system was constructed considering the concepts of yield, water use efficiency, quality, and economic benefits and the principles of scientific, representativeness, and consistency. In addition, an index decomposition was conducted to the four dimensions that were needed in the study of drought resistance measures. The comprehensive hierarchical evaluation model was constructed by using the principle of the analytic hierarchy process (Figure 1).

- (2)
- Construction of a judgment matrix.The weight was calculated by constructing the judgment matrix $O-C=\left({a}_{ij}\right)n\times n$ according to the 1–9 ratio scale method (Table 5).

- (3)
- Calculation of the subjective weights using the judgment matrix (wsj).In order to ensure the rationality of the results, the arithmetic average method, geometric average method, and feature vector method were adopted in this study to calculate the weights.

#### 3.2.2. Entropy Weight Method

#### 3.2.3. Combination Weights

#### 3.3. Integrated Evaluation Model Based on the Improved TOPSIS Method

#### 3.4. Results Analysis

## 4. Discussion

#### 4.1. Analysis and Evaluation of Measured Values Based on Indicators

#### 4.2. CW of Evaluation Indicators

#### 4.3. Comprehensive Evaluation Results of Drought Resistance Measures

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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Number | Treatment | Description of Treatments |
---|---|---|

SA | water retention agents | Forestry water retention agent (long-term) was selected from Gansu Hai Ruida Ecological Environmental Science and Technology Co., Ltd. Lanzhou, CN. The arable layer soil was turned over 30 cm before sowing and mixed with seed manure of 45.0 kg/ha and depth of 10–15 cm. Then, the drip irrigation belt was paved. Planter dibbling was used to sow female seeds first, and male seeds were sown in different stages. |

WF | white mulching film | The arable layer soil was turned over 30 cm before sowing. Enough fertilizer was applied, and the drip irrigation belt was paved. A 120 cm wide white mulch film was used to cover, purchased from Shanxi Dongqing Agricultural Film Co., Ltd. Datong City, CN.No space was left between the films, and the films overlapped each other by about 5 cm. Soil was compacted at the interface. Planter dibbling was used to sow female seeds first, and male seeds were sown in different stages. |

BF | black mulching film | Soil preparation, fertilizing, and drip irrigation belt pavement were the same as the WF treatment before covering the ground. A 120 cm wide black mulch film was used to cover, purchased from Shanxi Dongqing Agricultural Film Co., Ltd. No space was left between the films, and the films overlapped each other by about 5 cm. Soil was compacted at the interface. Planter dibbling was used to sow female seeds first, and male seeds were sown in different stages. |

SM | straw mulching | Soil preparation, fertilizing, and drip irrigation belt pavement were the same as the WF treatment before covering the ground. The corn straw was crushed into 5–10 cm long sections by machinery, and evenly covered the bare ground between rows totaling 3500 kg/ha after the emergence of seedlings. Planter dibbling was used to sow female seeds first, and male seeds were sown in different stages. |

CK | open-ground seed | Planter dibbling was used to sow female seeds first, and male seeds were sown in different stages without covering. |

n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

RI | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.24 | 1.36 | 1.41 | 1.46 | 1.49 | 1.52 | 1.54 | 1.56 | 1.58 | 1.59 |

Year | Treatment | Yield (kg/ha) | Starch (%) | Crude Protein (mg/g) | Crude Fat (%) | Soluble Sugar (%) | Crude Fiber (%) | Water Consumption (m ^{3}/ha) | WUE (kg/m ^{3}) | Output Value (RMB/ha) | Output Value of One Cubic Meter of Water (RMB/m ^{3}) |
---|---|---|---|---|---|---|---|---|---|---|---|

2018 | SA | 8280.75 bc | 67.57 a | 9.08 ab | 1.49 ab | 13.28 ab | 6.75 a | 4211.35 ab | 1.97 b | 31301.24 bc | 9.94 bc |

WF | 9075.44 ab | 64.96 ab | 9.12 ab | 1.33 bc | 12.04 c | 4.83 c | 4027.18 bc | 2.25 a | 34305.16 ab | 10.89 ab | |

BF | 9431.62 a | 68.69 a | 9.66 a | 1.55 a | 12.91 bc | 4.53 c | 4111.46 ab | 2.29 a | 35651.52 a | 11.32 a | |

SM | 7566.39 c | 67.90 a | 9.14 ab | 1.61 a | 14.75 a | 3.87 d | 3985.60 c | 1.90 b | 28600.95 c | 9.08 c | |

CK | 6229.51 d | 62.78 b | 8.47 b | 1.15 c | 10.37 d | 5.61 b | 4529.03 a | 1.38 c | 23547.55 d | 7.48 d | |

2019 | SA | 7915.08 b | 66.98 ab | 8.92 bc | 1.31 cd | 12.76 ab | 6.29 a | 4305.71 bc | 1.84 bc | 28098.53 b | 9.52 b |

WF | 8845.55 a | 65.11 ab | 8.45 c | 1.42 bc | 11.84 bc | 4.37 b | 4439.24 ab | 1.99 ab | 31401.70 a | 10.64 a | |

BF | 9317.17 a | 70.18 a | 10.58 a | 1.84 a | 12.15 b | 4.46 b | 4360.85 bc | 2.14 a | 33075.95 a | 11.21 a | |

SM | 7128.76 c | 68.14 ab | 9.61 ab | 1.51 b | 13.66 a | 5.90 a | 4067.93 c | 1.75 c | 25307.10 c | 8.58 c | |

CK | 6305.24 d | 63.10 b | 7.79 c | 1.22 d | 11.03 c | 4.51 b | 4843.51 a | 1.30 d | 22383.60 d | 7.59 d |

O | C1 | C2 | C3 | C4 |
---|---|---|---|---|

C1 | 1 | 2 | 3 | 5/3 |

C2 | 1/2 | 1 | 7/3 | 2 |

C3 | 1/3 | 3/7 | 1 | 2 |

C4 | 3/5 | 1/2 | 1/2 | 1 |

Method | C1 | C2 | C3 | C4 | ${\mathit{\lambda}}_{\mathbf{max}}$ | CI | CR |
---|---|---|---|---|---|---|---|

Average method | 0.4023 | 0.2754 | 0.1731 | 0.1493 | 4.2124 | 0.0708 | 0.0796 |

Geometric means method | 0.4071 | 0.2830 | 0.1674 | 0.1425 | |||

Eigenvector method | 0.4070 | 0.2774 | 0.1699 | 0.1456 |

Target Layer | Criterion Layer (Weights) | Indicator Layer | Comprehensive Weight | ||
---|---|---|---|---|---|

Index | Weights | ||||

Benefit evaluation of different drought resistance measures of maize seed | Yield C1 (0.4070) | Yield | P1 | 1 | 0.4070 |

Quality C2 (0.2774) | Starch | P2 | 0.1161 | 0.0322 | |

Crude protein | P3 | 0.2502 | 0.0694 | ||

Crude fat | P4 | 0.1269 | 0.0352 | ||

Soluble sugar | P5 | 0.4047 | 0.1123 | ||

Crude fiber | P6 | 0.1021 | 0.0283 | ||

Water use status C3 (0.1699) | Water consumption | P7 | 0.2500 | 0.0425 | |

WUE | P8 | 0.7500 | 0.1274 | ||

Economic benefits C4 (0.1456) | Output value | P9 | 0.3333 | 0.0485 | |

Output value of one cubic meter of water | P10 | 0.6667 | 0.0971 |

Year | Yield P1 | Starch P2 | Crude Protein P3 | Crude Fat P4 | Soluble Sugar P5 | Crude Fiber P6 | Water Consumption P7 | WUE P8 | Output Value P9 | Output Value of One Cubic Meter of Water P10 |
---|---|---|---|---|---|---|---|---|---|---|

2018 | 0.10168 | 0.10385 | 0.09884 | 0.10204 | 0.10440 | 0.10151 | 0.09055 | 0.09372 | 0.10168 | 0.10173 |

2019 | 0.09960 | 0.09652 | 0.10856 | 0.13416 | 0.09894 | 0.10630 | 0.07871 | 0.07796 | 0.09960 | 0.09963 |

Indicator | Subjective Weight | Objective Weight | Combined Weights | |||
---|---|---|---|---|---|---|

2018 | 2019 | 2018 | 2019 | |||

Yield | P1 | 0.4070 | 0.10168 | 0.09960 | 0.4082 | 0.3845 |

Starch | P2 | 0.0322 | 0.10385 | 0.09652 | 0.0319 | 0.0369 |

Crude protein | P3 | 0.0694 | 0.09884 | 0.10856 | 0.0693 | 0.0723 |

Crude fat | P4 | 0.0352 | 0.10204 | 0.13416 | 0.0350 | 0.0425 |

Soluble sugar | P5 | 0.1123 | 0.1044 | 0.09894 | 0.1123 | 0.1113 |

Crude fiber | P6 | 0.0283 | 0.10151 | 0.10630 | 0.0280 | 0.0340 |

Water consumption | P7 | 0.0425 | 0.09055 | 0.07871 | 0.0423 | 0.0452 |

WUE | P8 | 0.1274 | 0.09372 | 0.07796 | 0.1275 | 0.1238 |

Output value | P9 | 0.0485 | 0.10168 | 0.09960 | 0.0483 | 0.0523 |

Output value of one cubic meter of water | P10 | 0.0971 | 0.10173 | 0.09963 | 0.0971 | 0.0973 |

Year | Treatment Number | Yield | Starch | Crude Protein | Crude Fat | Soluble Sugar | Crude Fiber | Water Consumption | WUE | Output Value | Output Value of One Cubic meter of Water |
---|---|---|---|---|---|---|---|---|---|---|---|

2018 | SA | 0.1844 | 0.0145 | 0.0309 | 0.0162 | 0.0523 | 0.0000 | 0.0148 | 0.0566 | 0.0218 | 0.0439 |

WF | 0.2021 | 0.0140 | 0.0311 | 0.0145 | 0.0474 | 0.0126 | 0.0234 | 0.0646 | 0.0239 | 0.0481 | |

BF | 0.2101 | 0.0148 | 0.0329 | 0.0169 | 0.0508 | 0.0146 | 0.0195 | 0.0658 | 0.0249 | 0.0500 | |

SM | 0.1685 | 0.0146 | 0.0311 | 0.0176 | 0.0581 | 0.0189 | 0.0253 | 0.0546 | 0.0199 | 0.0401 | |

CK | 0.1387 | 0.0135 | 0.0288 | 0.0125 | 0.0408 | 0.0075 | 0.0000 | 0.0396 | 0.0164 | 0.0330 | |

Optimal vector | 0.2101 | 0.148 | 0.0329 | 0.0176 | 0.0581 | 0.0000 | 0.0000 | 0.0658 | 0.0249 | 0.0500 | |

Worst vector | 0.1387 | 0.135 | 0.0288 | 0.0125 | 0.0408 | 0.0189 | 0.0253 | 0.0396 | 0.0164 | 0.0330 | |

2019 | SA | 0.1706 | 0.0166 | 0.0316 | 0.0169 | 0.0516 | 0.0000 | 0.0214 | 0.0558 | 0.0232 | 0.0432 |

WF | 0.1906 | 0.0161 | 0.0300 | 0.0183 | 0.0478 | 0.0203 | 0.0161 | 0.0603 | 0.0259 | 0.0482 | |

BF | 0.2008 | 0.0174 | 0.0375 | 0.0237 | 0.0491 | 0.0193 | 0.0192 | 0.0649 | 0.0273 | 0.0508 | |

SM | 0.1536 | 0.0168 | 0.0341 | 0.0194 | 0.0552 | 0.0041 | 0.0309 | 0.0530 | 0.0209 | 0.0389 | |

CK | 0.1359 | 0.0156 | 0.0276 | 0.0157 | 0.0446 | 0.0188 | 0.0000 | 0.0394 | 0.0185 | 0.0344 | |

Optimal vector | 0.2008 | 0.0174 | 0.0375 | 0.0237 | 0.0552 | 0.0000 | 0.0000 | 0.0649 | 0.0273 | 0.0508 | |

Worst vector | 0.1359 | 0.0156 | 0.0276 | 0.0157 | 0.0446 | 0.0203 | 0.0309 | 0.0394 | 0.0185 | 0.0344 |

Treatment Number | 2018 | 2019 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

${\mathit{D}}^{+}$ | ${\mathit{D}}^{-}$ | ${\mathit{S}}_{\mathbf{i}}$ | ${\stackrel{\sim}{\mathit{S}}}_{\mathit{i}}$ | Ranking | ${\mathit{D}}^{+}$ | ${\mathit{D}}^{-}$ | ${\mathit{S}}_{\mathbf{i}}$ | ${\stackrel{\sim}{\mathit{S}}}_{\mathit{i}}$ | Ranking | |

SA | 0.1365 | 0.1266 | 0.4812 | 0.1632 | 4 | 0.1406 | 0.1318 | 0.4839 | 0.1719 | 4 |

WF | 0.0555 | 0.1935 | 0.7772 | 0.2636 | 2 | 0.0860 | 0.1804 | 0.6773 | 0.2405 | 2 |

BF | 0.0443 | 0.2013 | 0.8195 | 0.2779 | 1 | 0.0581 | 0.2068 | 0.7806 | 0.2772 | 1 |

SM | 0.0822 | 0.1900 | 0.6979 | 0.2367 | 3 | 0.1321 | 0.1619 | 0.5508 | 0.1956 | 3 |

CK | 0.2139 | 0.0447 | 0.1729 | 0.0586 | 5 | 0.2135 | 0.1020 | 0.3232 | 0.1148 | 5 |

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## Share and Cite

**MDPI and ACS Style**

Liang, C.; Yu, S.; Zhang, H.; Wang, Z.; Li, F.
Economic Evaluation of Drought Resistance Measures for Maize Seed Production Based on TOPSIS Model and Combination Weighting Optimization. *Water* **2022**, *14*, 3262.
https://doi.org/10.3390/w14203262

**AMA Style**

Liang C, Yu S, Zhang H, Wang Z, Li F.
Economic Evaluation of Drought Resistance Measures for Maize Seed Production Based on TOPSIS Model and Combination Weighting Optimization. *Water*. 2022; 14(20):3262.
https://doi.org/10.3390/w14203262

**Chicago/Turabian Style**

Liang, Chao, Shouchao Yu, Hengjia Zhang, Zeyi Wang, and Fuqiang Li.
2022. "Economic Evaluation of Drought Resistance Measures for Maize Seed Production Based on TOPSIS Model and Combination Weighting Optimization" *Water* 14, no. 20: 3262.
https://doi.org/10.3390/w14203262