Appropriate Water-Nitrogen Regulation Mode to Improve Productivity of Mixed-Sowing Grassland of Bromus inermis and Alfalfa
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Experimental Site
2.2. Experimental Design and Field Management
2.3. Indicators and Methods for Measurement
2.3.1. Soil Moisture Content (%)
2.3.2. Water Consumption (mm)
2.3.3. Plant Height (PH, cm)
2.3.4. Leaf Area Index (LAI)
2.3.5. Yield (Y, kg·ha−1)
2.3.6. Quality
- (1)
- CP content (%)
- (2)
- Relative feeding value (RFV)
2.3.7. Water-Nitrogen Use Efficiency
- (1)
- WUE (kg·m−3)
- (2)
- Irrigation water use efficiency (IWUE, kg·m−3)
- (3)
- PFPN (kg·kg−1)
- (4)
- Water use efficiency of crude protein (CPWUE, kg·m−3)
2.4. Evaluation Methods
2.4.1. Principal Component Analysis (PCA) [32,33]
- (1)
- Data Standardization
- (2)
- Calculating the correlation coefficient matrix;
- (3)
- Calculating factor loads for principal component analysis, eigenvalue, and variance contribution rate;
- (4)
- Calculating the composite scores and sorting them by composite scores.
2.4.2. TOPSIS
- (1)
- Calculating the matrix Mij of each evaluation index under different water and nitrogen regulations [34];
- (2)
- Calculating the best set Mi+ and the worst set Mi−, calculating the distance between different indicators and the optimal and worst values according to Mij, Mi+, and Mi−;
- (3)
- Calculating the relative proximity Ci and performing a comprehensive sort according to the Ci value (the closer the Ci value is to 1, the higher the comprehensive ranking).
2.4.3. The Combined Evaluation
- (1)
- The Spearman rank correlation coefficient was used for the consistency test to obtain the R-value;
- (2)
- The results obtained by principal component analysis and TOPSIS comprehensive evaluation were calculated for membership degree (Uij);
- (3)
- The fuzzy frequencies, Phi and Whi, were calculated;
- (4)
- The ranking was converted into a score Qhi, and the score Bi was calculated using the fuzzy Borda method and ranked according to the score value.
2.5. Data Analysis
3. Results
3.1. Herbage Growth
3.1.1. Plant Height, Leaf Area Index, and Yield
3.1.2. Quality
3.1.3. Water-Nitrogen Use Efficiency
3.2. Comprehensive Evaluation
3.2.1. Principal Component Analysis (PCA)
- (1)
- Correlation coefficient matrix
- (2)
- Factor load, eigenvalue, and variance contribution rate of principal component analysis
- (3)
- Comprehensive score.
3.2.2. TOPSIS Comprehensive Evaluation
3.2.3. Combination Evaluation
4. Discussion
4.1. Effects of the Water-Nitrogen Regulation on Growth, Yield, and Quality of Herbage
4.2. Effects of the Water-Nitrogen Regulation on the Water-Nitrogen Use Efficiency of Herbage
4.3. Water-Nitrogen Regulation and Planting Pattern for Herbage Growth
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Soil Properties | Index Value |
---|---|
Soil Texture | Sandy Loam |
Bulk Density (g·cm−3) | 1.44 |
Field Capacity (% Volumetric Moisture) | 33.0 |
pH | 7.4 |
Total Nitrogen (g·kg−1) | 0.21 |
Available Phosphorus (mg·kg−1) | 3.16 |
Available Potassium (g·kg−1) | 0.17 |
Treatments | Planting Patterns | Low Irrigation Limit of Squaring and Initial Flowering Stage/%θf | Nitrogen Application Rate/(kg·ha−1) |
---|---|---|---|
D1W1N1 | D1 | 65~85 (W1) | 60 (N1) |
D1W1N2 | 120N2) | ||
D1W2N1 | 55~85 (W2) | 60 (N1) | |
D1W2N2 | 120 (N2) | ||
D1W3N1 | 45~85 (W3) | 60 (N1) | |
D1W3N2 | 120 (N2) | ||
D2W1N1 | D2 | 65~85 (W1) | 60 (N1) |
D2W1N2 | 120 (N2) | ||
D2W2N1 | 55~85 (W2) | 60 (N1) | |
D2W2N2 | 120 (N2) | ||
D2W3N1 | 45~85 (W3) | 60 (N1) | |
D2W3N2 | 120 (N2) |
Planting Patterns | Treatment | Water Use Efficiency (WUE, kg·m−3) | Irrigation Water Use Efficiency (IWUE, kg·m−3) | Nitrogen Partial Factor Productivity (PFPN, kg·kg−1) | Water Use Efficiency of Crude Protein, (CPWUE, kg·m−3) |
---|---|---|---|---|---|
Alfalfa and bromus inermis mixed-sowing | D1W1N1 | 4.29ab | 5.14b | 388.03a | 0.81ab |
D1W1N2 | 4.05c | 4.82c | 217.09d | 0.85a | |
D1W2N1 | 4.38ab | 5.66a | 369.74b | 0.76ab | |
D1W2N2 | 4.17bc | 5.26ab | 198.11e | 0.81ab | |
D1W3N1 | 4.48a | 5.16b | 305.41c | 0.70c | |
D1W3N2 | 4.41ab | 5.37ab | 176.69f | 0.73c | |
W | * | ** | ** | ** | |
N | * | ns | ** | * | |
W × N | ns | * | ** | ns | |
Bromus inermis mono-sowing | D2W1N1 | 2.18b | 2.74ab | 183.94a | 0.30bc |
D2W1N2 | 2.21ab | 2.86ab | 101.55d | 0.36a | |
D2W2N1 | 2.23ab | 2.69b | 166.69b | 0.26de | |
D2W2N2 | 2.34a | 2.84ab | 93.49e | 0.33ab | |
D2W3N1 | 2.33ab | 2.75ab | 146.54c | 0.24e | |
D2W3N2 | 2.38a | 2.89a | 84.90f | 0.28cd | |
W | ns | ns | ** | ** | |
N | * | * | ** | ** | |
W × N | ns | ns | ** | ns |
Component | Load Factor | |
---|---|---|
Principal Component 1 | Principal Component 2 | |
PH | 0.978 | 0.163 |
LAI | 0.976 | 0.205 |
Y | 0.982 | 0.141 |
CP | 0.886 | 0.462 |
RFV | −0.428 | 0.877 |
WUE | 0.966 | −0.220 |
IWUE | 0.972 | −0.174 |
PFPN | 0.762 | −0.265 |
CPWUE | 0.996 | 0.052 |
Eigenvalue | 7.292 | 1.223 |
Variance contribution rate /% | 81.026 | 13.593 |
Accumulating contribution rate /% | 81.026 | 94.618 |
Treatment | Score | Ranking | Treatment | Score | Ranking |
---|---|---|---|---|---|
D1W1N1 | 0.3790 | 2 | D2W1N1 | −0.2102 | 9 |
D1W1N2 | 0.5381 | 1 | D2W1N2 | 0.0006 | 7 |
D1W2N1 | 0.2503 | 4 | D2W2N1 | −0.3707 | 10 |
D1W2N2 | 0.3580 | 3 | D2W2N2 | −0.2097 | 8 |
D1W3N1 | 0.0326 | 6 | D2W3N1 | −0.5206 | 12 |
D1W3N2 | 0.1404 | 5 | D2W3N2 | −0.3879 | 11 |
Treatment | PH | LAI | Y | CP | RFV | WUE | IWUE | PFPN | CPWUE |
---|---|---|---|---|---|---|---|---|---|
D1W1N1 | 0.344 | 0.405 | 0.382 | 0.343 | 0.287 | 0.360 | 0.353 | 0.498 | 0.397 |
D1W1N2 | 0.376 | 0.443 | 0.428 | 0.386 | 0.307 | 0.340 | 0.331 | 0.279 | 0.416 |
D1W2N1 | 0.341 | 0.357 | 0.353 | 0.321 | 0.249 | 0.367 | 0.389 | 0.474 | 0.372 |
D1W2N2 | 0.360 | 0.385 | 0.390 | 0.351 | 0.262 | 0.350 | 0.361 | 0.254 | 0.397 |
D1W3N1 | 0.300 | 0.308 | 0.301 | 0.282 | 0.220 | 0.376 | 0.355 | 0.392 | 0.343 |
D1W3N2 | 0.317 | 0.331 | 0.348 | 0.304 | 0.233 | 0.370 | 0.369 | 0.227 | 0.358 |
D2W1N1 | 0.222 | 0.186 | 0.181 | 0.248 | 0.334 | 0.183 | 0.188 | 0.236 | 0.147 |
D2W1N2 | 0.246 | 0.212 | 0.200 | 0.296 | 0.389 | 0.185 | 0.197 | 0.130 | 0.176 |
D2W2N1 | 0.223 | 0.151 | 0.164 | 0.215 | 0.283 | 0.187 | 0.185 | 0.214 | 0.127 |
D2W2N2 | 0.234 | 0.166 | 0.184 | 0.250 | 0.327 | 0.196 | 0.195 | 0.120 | 0.162 |
D2W3N1 | 0.200 | 0.111 | 0.144 | 0.186 | 0.250 | 0.195 | 0.189 | 0.188 | 0.118 |
D2W3N2 | 0.221 | 0.132 | 0.167 | 0.210 | 0.279 | 0.200 | 0.199 | 0.109 | 0.137 |
Treatment | Di+ | Di− | Ci | Comprehensive Ranking |
---|---|---|---|---|
D1W1N1 | 0.1375 | 0.6939 | 0.8346 | 1 |
D1W1N2 | 0.2441 | 0.6579 | 0.7294 | 3 |
D1W2N1 | 0.2018 | 0.6458 | 0.7619 | 2 |
D1W2N2 | 0.2892 | 0.5901 | 0.6711 | 4 |
D1W3N1 | 0.3121 | 0.5284 | 0.6287 | 5 |
D1W3N2 | 0.3613 | 0.5079 | 0.5841 | 6 |
D2W1N1 | 0.6254 | 0.2030 | 0.2451 | 8 |
D2W1N2 | 0.6294 | 0.2454 | 0.2805 | 7 |
D2W2N1 | 0.6783 | 0.1358 | 0.1668 | 10 |
D2W2N2 | 0.6753 | 0.1537 | 0.1854 | 9 |
D2W3N1 | 0.7339 | 0.0854 | 0.1042 | 12 |
D2W3N2 | 0.7226 | 0.0793 | 0.0784 | 11 |
Treatment | Score | Ranking | Treatment | Score | Ranking |
---|---|---|---|---|---|
D1W1N1 | 14.9106 | 2 | D2W1N1 | 0.8298 | 9 |
D1W1N2 | 15.8187 | 1 | D2W1N2 | 3.7014 | 7 |
D1W2N1 | 9.7953 | 3 | D2W2N1 | 0.3516 | 10 |
D1W2N2 | 9.7760 | 4 | D2W2N2 | 0.8307 | 8 |
D1W3N1 | 3.9825 | 6 | D2W3N1 | 0.0143 | 12 |
D1W3N2 | 4.6218 | 5 | D2W3N2 | 0.0303 | 11 |
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Kang, Y.; Qi, G.; Jia, Q.; Wang, A.; Yin, M.; Ma, Y.; Wang, J.; Jiang, Y.; Tang, Z. Appropriate Water-Nitrogen Regulation Mode to Improve Productivity of Mixed-Sowing Grassland of Bromus inermis and Alfalfa. Water 2023, 15, 1124. https://doi.org/10.3390/w15061124
Kang Y, Qi G, Jia Q, Wang A, Yin M, Ma Y, Wang J, Jiang Y, Tang Z. Appropriate Water-Nitrogen Regulation Mode to Improve Productivity of Mixed-Sowing Grassland of Bromus inermis and Alfalfa. Water. 2023; 15(6):1124. https://doi.org/10.3390/w15061124
Chicago/Turabian StyleKang, Yanxia, Guangping Qi, Qiong Jia, Aixia Wang, Minhua Yin, Yanlin Ma, Jinghai Wang, Yuanbo Jiang, and Zhongxia Tang. 2023. "Appropriate Water-Nitrogen Regulation Mode to Improve Productivity of Mixed-Sowing Grassland of Bromus inermis and Alfalfa" Water 15, no. 6: 1124. https://doi.org/10.3390/w15061124