Spatial Heterogeneity in the Response of Winter Wheat Yield to Meteorological Dryness/Wetness Variations in Henan Province, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Region
2.2. Datasets
2.3. Methods
2.3.1. Characteristic-Based Time Series Clustering Analysis
2.3.2. Calculation of sc-PDSI
2.3.3. Calculations of Climate-Driven Winter Wheat Yield (CDWWY)
2.3.4. Relationships between the KMDWI and Atmospheric Circulation Indices
3. Results
3.1. AWWY-Based Regionalization in Henan
3.2. Spatial Differences in AWWY in Henan
3.3. Relationships between CDWWY and sc-PDSIs during the Wheat Growing Season in Henan
3.4. Relationships between the KMDWI and Atmospheric Circulation Indices in Henan
4. Discussion
5. Conclusions
- (1)
- A PCA and a K-means clustering analysis were used to partition the AWWY time series from 17 cities into four sub-regions. Although AWWY exhibited an increasing trend in all of the four sub-regions (p < 0.05), it was high and stable in Regions III and IV.
- (2)
- The sc-PDSI for a specific month could be considered the KMDWI affecting CDWWY in each sub-region, for example, the sc-PDSI in February of the current year for Regions I and II, the sc-PDSI in December of the previous year for Region III and the sc-PDSI in May of the current year for Region IV.
- (3)
- The atmospheric circulation indices had time-lag effects on the KMDWIs, and empirical KMDWI simulation models were constructed based on selected atmospheric circulation indices in the four sub-regions. Their R2 values were in the range of 0.49 to 0.65, and the corresponding RMSE values were in the range of 0.58 to 0.72.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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No. | Types | Indices | Definition |
---|---|---|---|
1 | Statistical characteristics | MIN | The minimum value of the AWWY time series |
2 | MAX | The maximum value of the AWWY time series | |
3 | AVE | The mean value of the AWWY time series | |
4 | RNG | The range value of the AWWY time series | |
5 | STD | The standard deviation of the AWWY time series | |
6 | CV | The coefficient of variation in the AWWY time series | |
7 | Time-domain characteristics | ZMK | The Z statistic of the AWWY time series |
8 | MAG | The magnitude of the AWWY time series | |
9 | H | The persistent pattern of the AWWY time series | |
10 | Frequency-domain characteristics | CIMF1 | The variance contribution of a short-period oscillation component to AWWY |
11 | CIMF2 | The variance contribution of a medium-period oscillation component to AWWY | |
12 | CIMF3 | The variance contribution of a long-period oscillation component to AWWY | |
13 | CTrend | The variance contribution of a trend component to AWWY |
Eigenvalue | Variance (%) | Cumulative (%) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PC1 | 6.64 | 51.11 | 51.11 | ||||||||||
PC2 | 3.61 | 27.80 | 78.90 | ||||||||||
PC3 | 1.42 | 10.92 | 89.83 | ||||||||||
The loadings of PC1, PC2 and PC3 on 13 indices | |||||||||||||
MIN | MAX | AVE | RNG | STD | CV | ZMK | MAG | H | CIMF1 | CIMF2 | CIMF3 | Ctrend | |
PC1 | 0.51 | 0.51 | 0.52 | 0.14 | 0.23 | −0.14 | 0.93 | 0.51 | 0.86 | −0.90 | −0.57 | −0.84 | 0.98 |
PC2 | −0.33 | 0.59 | 0.37 | 0.96 | 0.98 | 0.85 | 0.36 | 0.92 | 0.32 | −0.07 | −0.49 | 0.08 | 0.17 |
PC3 | 0.82 | 0.90 | 0.98 | 0.30 | 0.35 | −0.32 | 0.66 | 0.46 | 0.71 | −0.63 | −0.21 | −0.29 | 0.60 |
No. | Indices | Region I | Region II | Region III | Region IV |
---|---|---|---|---|---|
1 | MIN (kg/hm2) | 1861.0 | 2554.3 | 3592.8 | 2118.3 |
2 | MAX (kg/hm2) | 4812.0 | 5098.1 | 7154.0 | 6855.2 |
3 | AVE (kg/hm2) | 3536.3 | 4023.1 | 5770.9 | 5091.3 |
4 | RNG (kg/hm2) | 2951.0 | 2543.8 | 3561.2 | 4736.9 |
5 | STD | 796.0 | 710.1 | 1048.8 | 1416.1 |
6 | CV (%) | 22.5 | 17.8 | 18.3 | 27.8 |
7 | ZMK | 4.2 | 5.2 | 6.9 | 6.5 |
8 | MAG (kg/(hm2·a)) | 62.2 | 63.3 | 111.5 | 138.1 |
9 | H | 0.7 | 0.8 | 0.8 | 0.8 |
10 | CIMF1 (%) | 26.9 | 16.8 | 2.3 | 11.2 |
11 | CIMF2 (%) | 5.8 | 4.4 | 2.6 | 2.6 |
12 | CIMF3 (%) | 3.4 | 2.4 | 1.1 | 2.5 |
13 | CTrend (%) | 63.9 | 76.4 | 94.0 | 83.8 |
Lag Time | Region I | Region II | Region III | Region IV | ||||
---|---|---|---|---|---|---|---|---|
Indices | Coefficients | Indices | Coefficients | Indices | Coefficients | Indices | Coefficients | |
0 | AO | −0.08 | AO | 0.02 | AO | 0.06 | PDO | −0.45 |
1 | NAO | 0.19 | NAO | 0.10 | NAO | 0.35 | Niño 3 | 0.27 |
2 | NAO | −0.30 | NAO | −0.39 | NAO | −0.10 | Niño 3 | −0.11 |
3 | PDO | 0.30 | PDO | 0.27 | AO | 0.73 | Niño 4 | 0.92 |
4 | PDO | 0.13 | NAO | −0.11 | AO | 1.07 | Niño 3.4 | −0.62 |
5 | PDO | 0.08 | AO | 0.35 | AO | 0.44 | Niño 3 | −0.50 |
6 | Niño 4 | −0.57 | PDO | −0.05 | AO | 0.25 | Niño 3 | 1.19 |
7 | Niño 1+2 | −0.18 | AO | −0.05 | AO | 0.17 | Niño 3 | −0.97 |
8 | Niño 3 | 0.29 | NAO | 0.38 | NAO | −0.04 | Niño 3 | −0.11 |
9 | Niño 3 | −0.53 | Niño 3 | −0.04 | AO | 0.19 | Niño 3 | 1.90 |
10 | AO | −0.08 | AO | −0.13 | NAO | 0.08 | Niño 3 | −0.99 |
11 | NAO | −0.10 | Niño 3 | −0.14 | NAO | −0.29 | Niño 3 | 0.24 |
Intercept | 27.29 | 5.16 | −0.02 | −32.42 | ||||
R2 | 0.50 | 0.55 | 0.65 | 0.49 | ||||
RMSE | 0.66 | 0.64 | 0.58 | 0.72 |
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Li, C.; Gu, Y.; Xu, H.; Huang, J.; Liu, B.; Chun, K.P.; Octavianti, T. Spatial Heterogeneity in the Response of Winter Wheat Yield to Meteorological Dryness/Wetness Variations in Henan Province, China. Agronomy 2024, 14, 817. https://doi.org/10.3390/agronomy14040817
Li C, Gu Y, Xu H, Huang J, Liu B, Chun KP, Octavianti T. Spatial Heterogeneity in the Response of Winter Wheat Yield to Meteorological Dryness/Wetness Variations in Henan Province, China. Agronomy. 2024; 14(4):817. https://doi.org/10.3390/agronomy14040817
Chicago/Turabian StyleLi, Cheng, Yuli Gu, Hui Xu, Jin Huang, Bo Liu, Kwok Pan Chun, and Thanti Octavianti. 2024. "Spatial Heterogeneity in the Response of Winter Wheat Yield to Meteorological Dryness/Wetness Variations in Henan Province, China" Agronomy 14, no. 4: 817. https://doi.org/10.3390/agronomy14040817