Optimization of Irrigation Parameters of Peanut Under Mulched Drip Irrigation in Xinjiang Based on Yield and Water Use Efficiency
Abstract
1. Introduction
2. Materials and Methods
2.1. Experimental Field
2.2. Peanut Cropping Patterns
2.3. Experimental Design
2.4. Measurement Items and Methods
2.4.1. Methods for Determining Soil Water Content
2.4.2. Measurement and Methods of Plant Growth Indexes
2.4.3. Yield Determination and Methods
2.4.4. Calculation of Crop Water Consumption
2.4.5. Calculation of Water Use Efficiency, Nitrogen Partial Factor Productivity
2.4.6. Quality
2.4.7. Meteorological Data
2.5. Principles of Principal Component Analysis (PCA)
- (a)
- Sample parameter selection
- (b)
- Standardization of sample parameters
- (c)
- Calculation of the matrix of correlation coefficients for standardized evaluation indicators:
- (d)
- Calculate the eigenvalues of the matrix R and the eigenvectors for each sample number:
- (e)
- Calculation of contribution rates using eigenvalues (Gr) and cumulative contribution (ACr):
- (f)
- Mathematical modeling based on PCA is defined by the following equations:
- (g)
- Determine the evaluation process based on a comprehensive evaluation index (Q):
2.6. Data Processing
3. Results
3.1. Effect of Irrigation and Fertilization Technical Parameters on Main Stem Height and Aboveground Biomass of Peanuts
3.2. Effect of Technical Parameters of Irrigation and Fertilization on Peanut Yield and Yield Component Indicators
3.3. Effect of Irrigation and Fertilization Parameters on Peanut Quality
3.4. Effects of Irrigation and Fertilization Parameters on Water Consumption (a), Water Use Efficiency (b), and Nitrogen Partial Factor Productivity (c) of Peanuts
3.5. Optimization of Irrigation and Fertilization Parameters Based on the Principal Component Analysis (PCA) Method
3.5.1. Principal Component Extraction
3.5.2. Principal Component Scores and Rankings
4. Discussion
4.1. Effects of Irrigation and Fertilization Technical Parameters on Peanut Growth Index
4.2. Effect of Technical Parameters of Irrigation and Fertilization on Peanut Yield
4.3. Effects of Irrigation and Fertilization Technical Parameters on Peanut Quality
4.4. Effect of Technical Parameters of Irrigation and Fertilization on Water and Nitrogen Use Efficiency in Peanut
4.5. Experience in Optimizing Field Experiment Parameters Based on Principal Component Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Depths | Available Phosphorus | Available Potassium | Alkaline Hydrolysis | Organic Matter | Soil Bulk |
---|---|---|---|---|---|
Nitrogen | Density | ||||
(mg kg−1) | (mg kg−1) | (mg kg−1) | (g kg−1) | (g kg−1) | |
0–20 | 23.77 | 199.06 | 42.98 | 14.86 | 1.35 |
20–40 | 22.17 | 169.33 | 32.04 | 14.28 | 1.43 |
40–60 | 22.86 | 114.46 | 43.41 | 9.73 | 1.44 |
Year | Treatment | Drip | Irrigation Account | Irrigation Cycles (d) | Amount of Nitrogen Topdressing | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Discharge | (mm) | |||||||||||
(L h−1) | Seedling Water | Seedling Stage | Flower-Pegging Stage | Pod-Setting Stage | Pod-Filling Stage | Seedling Stage | Flower-Pegging Stage | Pod-Setding Stage | Pod-Filling Stage | Growth Period | ||
2022 | W1N1Q1 | 3.0 | 45 | - | 30 | 30 | 30 | - | 7 | 7 | 10 | 0.5 |
W1N2Q1 | 3.0 | 45 | - | 30 | 30 | 30 | - | 7 | 7 | 10 | 1 | |
W2N1Q1 | 3.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 0.5 | |
W2N2Q1 | 3.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 1 | |
W1N0Q1 | 3.0 | 45 | - | 30 | 30 | 30 | - | 7 | 7 | 10 | 0 | |
2023 | W2N1Q1 | 3.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 0.5 |
W2N1Q2 | 6.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 0.5 | |
W2N2Q1 | 3.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 1 | |
W2N2Q2 | 6.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 1 | |
W2N0Q1 | 3.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 0 | |
2024 | W2N1Q1 | 3.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 0.5 |
W2N1Q2 | 6.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 0.5 | |
W2N2Q1 | 3.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 1 | |
W2N2Q2 | 6.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 1 | |
W2N0Q1 | 3.0 | 45 | - | 37.5 | 37.5 | 37.5 | - | 7 | 7 | 10 | 0 |
Year | Factor | Main Stem Height | Aboveground Biomass | ||||
---|---|---|---|---|---|---|---|
Flowering- | Pod- | Pod- | Flowering- | Pod- | Pod- | ||
Pegging Stage | Setting Stage | Filling Stage | Pegging Stage | Setting Stage | Filling Stage | ||
W | ns | ** | ** | ** | ** | ** | |
2022 | N | ** | ** | ** | ** | ** | ** |
W × N | ns | ** | ns | ** | ** | ** | |
N | ** | ** | ns | ** | ** | ** | |
2023 | Q | * | * | ns | ** | ** | ** |
N × Q | ns | * | ns | ** | ** | ** | |
N | ** | ** | ns | ** | ** | ** | |
2024 | Q | * | * | ns | ** | ** | ** |
N × Q | ns | * | ns | ** | ** | ** |
Year | Treatment | Pod Yield | 100-Pod Weight | 100-Kernel Weight | Kernel Rate |
---|---|---|---|---|---|
(kg ha−1) | (g) | (g) | (%) | ||
W1N1Q1 | 3698.15 c | 165.26 c | 80.40 b | 66.43 bc | |
W1N2Q1 | 4522.74 b | 179.42 b | 81.40 b | 68.29 abc | |
2022 | W2N1Q1 | 4697.65 ab | 180.81 b | 85.29 b | 69.13 ab |
W2N2Q1 | 5297.35 a | 208.54 a | 95.42 a | 70.83 a | |
W1N0Q1 | 3398.30 c | 156.48 c | 70.33 c | 65.61 c | |
W | ** | ** | ** | * | |
N | ** | ** | ** | ns | |
W × N | ns | ns | ns | ns | |
W2N1Q1 | 5528.28 a | 227.23 a | 94.88 a | 70.91 b | |
W2N1Q2 | 5597.53 a | 230.07 a | 96.93 a | 72.38 a | |
2023 | W2N2Q1 | 5044.43 c | 226.74 a | 97.08 a | 70.89 b |
W2N2Q2 | 5266.05 b | 227.58 a | 92.66 a | 72.25 a | |
W2N0Q1 | 4418.18 d | 214.37 b | 83.82 b | 69.98 c | |
N | ** | ns | ** | ** | |
Q | ** | ns | ** | ** | |
N × Q | ns | ns | ns | ** | |
W2N1Q1 | 8668.80 ab | 234.24 ab | 88.22 bc | 72.28 a | |
W2N1Q2 | 9095.38 a | 237.18 a | 98.29 a | 72.89 a | |
2024 | W2N2Q1 | 8584.60 b | 234.40 ab | 85.92 c | 69.79 b |
W2N2Q2 | 8738.84 ab | 234.61 ab | 94.41 ab | 72.50 a | |
W2N0Q1 | 7518.83 c | 227.63 b | 84.59 c | 69.32 b | |
N | ** | ns | ** | ** | |
Q | ** | ns | ** | ** | |
N × Q | ns | ns | ns | ** |
Year | Treatment | Protein Content (%) | Oil Content (%) | Year | Treatment | Protein Content (%) | Oil Content (%) | Year | Treatment | Protein Content (%) | Oil Content (%) |
---|---|---|---|---|---|---|---|---|---|---|---|
W1N1Q1 | 28.40 a | 38.30 b | W2N1Q1 | 30.82 b | 50.16 c | W2N1Q1 | 30.65 c | 49.52 b | |||
W1N2Q1 | 27.37 b | 43.78 a | W2N1Q2 | 31.78 a | 51.51 b | W2N1Q2 | 31.00 b | 50.12 a | |||
2022 | W2N1Q1 | 25.12 c | 45.74 a | 2023 | W2N2Q1 | 30.73 b | 50.63 c | 2024 | W2N2Q1 | 30.55 c | 48.50 c |
W2N2Q1 | 24.88 c | 45.42 a | W2N2Q2 | 31.55 a | 53.42 a | W2N2Q2 | 31.44 a | 50.29 a | |||
W1N0Q1 | 26.85 b | 43.86 a | W2N0Q1 | 30.53 b | 49.53 d | W2N0Q1 | 30.84 b | 48.16 c | |||
W | ** | ** | N | ns | ** | N | ** | ** | |||
N | * | ** | Q | ** | ** | Q | ** | ** | |||
W × N | ns | ** | N × Q | ns | ** | N × Q | ** | ** |
Year | Component | Eigenvalue | Contribution Rate | Cumulative Contribution Rate |
---|---|---|---|---|
(%) | (%) | |||
1 | 3.789 | 63.152 | 63.152 | |
2 | 1.519 | 25.315 | 88.467 | |
2022 | 3 | 0.612 | 10.202 | 98.669 |
4 | 0.080 | 1.331 | 100.000 | |
5 | −5.833 × 10−17 | −9.721 × 10−16 | 100.000 | |
1 | 3.781 | 75.615 | 75.615 | |
2 | 1.047 | 20.947 | 96.562 | |
2023 | 3 | 0.167 | 3.342 | 99.904 |
4 | 0.005 | 0.096 | 100 | |
5 | 1.26 × 10−16 | 2.52 × 10−15 | 100 | |
1 | 3.683 | 73.667 | 73.667 | |
2 | 1.212 | 24.249 | 97.916 | |
2024 | 3 | 0.068 | 1.351 | 99.267 |
4 | 0.037 | 0.733 | 100.000 | |
5 | 4.139 × 10−17 | 8.279 × 10−16 | 100.000 |
Year | Function Expressions |
---|---|
2022 | Y221 = −0.384 × X1 + 0.208 × X2 + 0.293 × X3 + 0.318 × X4 + 0.112 × X5 |
Y222 = 0.187 × X1 + 0.160 × X2 + 0.036 × X3 − 0.121 × X4 − 0.453 × X5 | |
F = 0.63152/0.88467 × Y221 + 0.25315/0.88467 × Y222 | |
2023 | Y231 = 0.336 × X1 + 0.385 × X2 + 0.386×X3 − 0.076 × X4 − 0.234 × X5 Y232 = −0.046 × X1 − 0.136 × X2 − 0.137 × X3 + 0.510 × X4 + 0.670 × X5 |
F = 0.75615/0.96562 × Y231 + 0.20947/0.96562 × Y232 | |
2024 | Y241 = 0.291 × X1 + 0.310 × X2 + 0.336 × X3 − 0.179×X4 + 0.119 × X5 Y242 = −0.028 × X1 − 0.087 × X2 − 0.173 × X3 + 0.754×X4 + 0.387 × X5 |
F = 0.73667/97.916 × Y241 + 0.24249/0.97916 × Y242 |
Year | Treatment | Score | Rankings | Year | Treatment | Score | Rankings | Year | Treatment | Score | Rankings |
---|---|---|---|---|---|---|---|---|---|---|---|
W1N1Q1 | −0.39 | 4 | W2N1Q1 | 0.64 | 2 | W2N1Q1 | 0.31 | 3 | |||
W1N2Q1 | 0.47 | 2 | W2N1Q2 | 0.71 | 1 | W2N1Q2 | 0.82 | 1 | |||
2022 | W2N1Q1 | 0.24 | 3 | 2023 | W2N2Q1 | −0.11 | 4 | 2024 | W2N2Q1 | −0.24 | 4 |
W2N2Q1 | 0.82 | 1 | W2N2Q2 | 0.06 | 3 | W2N2Q2 | 0.35 | 2 | |||
W1N0Q1 | −1.13 | 5 | W2N0Q1 | −1.31 | 5 | W2N0Q1 | −1.25 | 5 |
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Zhang, Y.; Li, S.; Cui, W.; Gao, Y.; Si, Z.; Li, H.; Chen, J.; Dong, J.; Li, Q.; Shen, X.; et al. Optimization of Irrigation Parameters of Peanut Under Mulched Drip Irrigation in Xinjiang Based on Yield and Water Use Efficiency. Agronomy 2025, 15, 1302. https://doi.org/10.3390/agronomy15061302
Zhang Y, Li S, Cui W, Gao Y, Si Z, Li H, Chen J, Dong J, Li Q, Shen X, et al. Optimization of Irrigation Parameters of Peanut Under Mulched Drip Irrigation in Xinjiang Based on Yield and Water Use Efficiency. Agronomy. 2025; 15(6):1302. https://doi.org/10.3390/agronomy15061302
Chicago/Turabian StyleZhang, Yuchao, Shaofei Li, Weimin Cui, Yang Gao, Zhuanyun Si, Haiming Li, Junwei Chen, Jianshu Dong, Qiang Li, Xiaojun Shen, and et al. 2025. "Optimization of Irrigation Parameters of Peanut Under Mulched Drip Irrigation in Xinjiang Based on Yield and Water Use Efficiency" Agronomy 15, no. 6: 1302. https://doi.org/10.3390/agronomy15061302
APA StyleZhang, Y., Li, S., Cui, W., Gao, Y., Si, Z., Li, H., Chen, J., Dong, J., Li, Q., Shen, X., & Zhang, X. (2025). Optimization of Irrigation Parameters of Peanut Under Mulched Drip Irrigation in Xinjiang Based on Yield and Water Use Efficiency. Agronomy, 15(6), 1302. https://doi.org/10.3390/agronomy15061302