Journal of Resources and Ecology ›› 2022, Vol. 13 ›› Issue (2): 196-209.DOI: 10.5814/j.issn.1674-764x.2022.02.003
• Ecosystem and Climate Change • Previous Articles Next Articles
ZHENG Xintong1,2(), XIE Chuanjie1,*(
), HE Wei1,2, LIU Gaohuan1
Received:
2021-02-05
Accepted:
2021-06-30
Online:
2022-03-30
Published:
2022-03-09
Contact:
XIE Chuanjie
About author:
ZHENG Xintong, E-mail: zhengxt.18s@igsnrr.ac.cn
Supported by:
ZHENG Xintong, XIE Chuanjie, HE Wei, LIU Gaohuan. Spatio-temporal Analysis of Changes in Winter Wheat Cold Damage with Meteorological Elements in Huang-Huai-Hai Plain from 2011 to 2020[J]. Journal of Resources and Ecology, 2022, 13(2): 196-209.
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URL: http://www.jorae.cn/EN/10.5814/j.issn.1674-764x.2022.02.003
Developmental stage | Critical temperature (℃) |
---|---|
Sowing-Tillering | 2 |
Tillering-Overwintering | 0 |
Overwintering-Greening | -17 to -13 |
Greening-Plucking | 0 |
Plucking-Spurting | 2 |
Spurting-Milk | 9 |
Milk-Ripe | 12 |
Table 1 The limits of minimum daily temperature in different fertility periods of winter wheat in Huang-Huai-Hai Plain
Developmental stage | Critical temperature (℃) |
---|---|
Sowing-Tillering | 2 |
Tillering-Overwintering | 0 |
Overwintering-Greening | -17 to -13 |
Greening-Plucking | 0 |
Plucking-Spurting | 2 |
Spurting-Milk | 9 |
Milk-Ripe | 12 |
Fig. 6 Distribution of the slopes of six different meteorological elements from 2011 to 2020 Note: (a) Distance from limit of low temperature; (b) Annual negative cumulative temperature; (c) Annual mean temperature; (d) Annual extreme drop of mean temperature; (e) Sunshine accumulation (/10); (f) Precipitation accumulation (/10).
Variables | ALT | DLT | NLDT | AFD | DVF | DRF | AND | DVN | DRN |
---|---|---|---|---|---|---|---|---|---|
ALT | 1 | 0.608** | 0.470 | 0.672** | 0.221 | 0.416* | 0.446* | 0.503* | 0.430* |
DLT | 0.608** | 1 | 0.277 | 0.396 | 0.165 | 0.480* | 0.205 | 0.532** | 0.411 |
NLDT | 0.470 | 0.277 | 1 | 0.219 | 0.056 | 0.089 | 0.351 | 0.395 | 0.234 |
AFD | 0.672** | 0.396 | 0.219 | 1 | 0.471* | 0.639** | 0.889** | 0.132 | 0.590** |
DVF | 0.221 | 0.165 | 0.056 | 0.471* | 1 | 0.433* | 0.603** | 0.811** | 0.321 |
DRF | 0.416* | 0.480* | 0.089 | 0.639** | 0.433* | 1 | 0.632** | 0.021 | 0.743** |
AND | 0.446* | 0.205 | 0.351 | 0.889** | 0.603** | 0.632** | 1 | 0.421* | 0.705** |
DVN | 0.503* | 0.532** | 0.395 | 0.132 | 0.811** | 0.021 | 0.421* | 1 | 0.159 |
DRN | 0.430* | 0.411 | 0.234 | 0.590** | 0.321 | 0.743** | 0.705** | 0.159 | 1 |
Table 2 The Pearson’s correlation coefficients between features
Variables | ALT | DLT | NLDT | AFD | DVF | DRF | AND | DVN | DRN |
---|---|---|---|---|---|---|---|---|---|
ALT | 1 | 0.608** | 0.470 | 0.672** | 0.221 | 0.416* | 0.446* | 0.503* | 0.430* |
DLT | 0.608** | 1 | 0.277 | 0.396 | 0.165 | 0.480* | 0.205 | 0.532** | 0.411 |
NLDT | 0.470 | 0.277 | 1 | 0.219 | 0.056 | 0.089 | 0.351 | 0.395 | 0.234 |
AFD | 0.672** | 0.396 | 0.219 | 1 | 0.471* | 0.639** | 0.889** | 0.132 | 0.590** |
DVF | 0.221 | 0.165 | 0.056 | 0.471* | 1 | 0.433* | 0.603** | 0.811** | 0.321 |
DRF | 0.416* | 0.480* | 0.089 | 0.639** | 0.433* | 1 | 0.632** | 0.021 | 0.743** |
AND | 0.446* | 0.205 | 0.351 | 0.889** | 0.603** | 0.632** | 1 | 0.421* | 0.705** |
DVN | 0.503* | 0.532** | 0.395 | 0.132 | 0.811** | 0.021 | 0.421* | 1 | 0.159 |
DRN | 0.430* | 0.411 | 0.234 | 0.590** | 0.321 | 0.743** | 0.705** | 0.159 | 1 |
Variables | Coefficient | Standard error | Wald | Degree of freedom | Significance | OR value |
---|---|---|---|---|---|---|
Low temperature extreme | 1.222 | 0.263 | 21.520 | 1 | 0.000 | 3.395 |
(NDVI) Maximum decrease | 7.416 | 3.472 | 4.562 | 1 | 0.033 | 1662.335 |
(FRAR) Low-value area | 0.085 | 0.027 | 9.972 | 1 | 0.002 | 1.089 |
Constants | -5.730 | 1.221 | 22.016 | 1 | 0.000 | 0.003 |
Table 3 Variables in the binary Logistic regression equations
Variables | Coefficient | Standard error | Wald | Degree of freedom | Significance | OR value |
---|---|---|---|---|---|---|
Low temperature extreme | 1.222 | 0.263 | 21.520 | 1 | 0.000 | 3.395 |
(NDVI) Maximum decrease | 7.416 | 3.472 | 4.562 | 1 | 0.033 | 1662.335 |
(FRAR) Low-value area | 0.085 | 0.027 | 9.972 | 1 | 0.002 | 1.089 |
Constants | -5.730 | 1.221 | 22.016 | 1 | 0.000 | 0.003 |
Variable | Area under the ROC curve | Standard error | Progressive significance | Asymptotic 95% confidence interval | |
---|---|---|---|---|---|
Lower limit | Upper limit | ||||
Value | 0.956 | 0.015 | 0.000 | 0.928 | 0.985 |
Table 4 The result of the ROC curve
Variable | Area under the ROC curve | Standard error | Progressive significance | Asymptotic 95% confidence interval | |
---|---|---|---|---|---|
Lower limit | Upper limit | ||||
Value | 0.956 | 0.015 | 0.000 | 0.928 | 0.985 |
Variable | DLLP | ETP | NCT | AT | PA | SA |
---|---|---|---|---|---|---|
PO in each county | 0.303** | -0.024 | 0.044** | -0.180** | -0.102** | -0.047** |
PO in counties exhibiting cold damage | 0.407** | -0.011 | -0.095** | -0.073** | -0.012 | -0.019 |
Table 5 Correlation analysis between the probability of cold damage occurrence and meteorological factors in each county and the counties exhibiting cold damage from 2011 to 2020
Variable | DLLP | ETP | NCT | AT | PA | SA |
---|---|---|---|---|---|---|
PO in each county | 0.303** | -0.024 | 0.044** | -0.180** | -0.102** | -0.047** |
PO in counties exhibiting cold damage | 0.407** | -0.011 | -0.095** | -0.073** | -0.012 | -0.019 |
Province | DLLP | ETP | NCT | AT | PA | SA |
---|---|---|---|---|---|---|
Hebei | 0.418** | 0.058* | -0.137** | -0.185** | 0.007 | -0.029 |
Shandong | 0.192** | -0.008 | -0.035 | -0.083** | 0.015 | 0.037 |
Henan | 0.353** | -0.031 | -0.010 | -0.202** | 0.117** | -0.010 |
Jiangsu | 0.213** | 0.015 | 0.012 | -0.248** | 0.085 | -0.130** |
Anhui | 0.324** | 0.021 | -0.011 | -0.045 | 0.174** | -0.104** |
Table 6 Correlation analysis between the occurrence probability of cold damage and meteorological factors in each county of the major grain provinces from 2011 to 2020
Province | DLLP | ETP | NCT | AT | PA | SA |
---|---|---|---|---|---|---|
Hebei | 0.418** | 0.058* | -0.137** | -0.185** | 0.007 | -0.029 |
Shandong | 0.192** | -0.008 | -0.035 | -0.083** | 0.015 | 0.037 |
Henan | 0.353** | -0.031 | -0.010 | -0.202** | 0.117** | -0.010 |
Jiangsu | 0.213** | 0.015 | 0.012 | -0.248** | 0.085 | -0.130** |
Anhui | 0.324** | 0.021 | -0.011 | -0.045 | 0.174** | -0.104** |
Year | Province or region | |||||
---|---|---|---|---|---|---|
Hebei | Shandong | Henan | Anhui | Jiangsu | Huang-Huai- Hai Plain | |
2011 | 39.8 | 20.8 | 23.7 | 145.2 | 3.1 | 232.6 |
2012 | 254.4 | 0 | 0 | 1.3 | 0 | 255.9 |
2013 | 158.6 | 56.0 | 71.8 | 248.8 | 56.0 | 591.2 |
2014 | 105.2 | 0.2 | 9.9 | 41.0 | 0.5 | 146.8 |
2015 | 57.1 | 43.1 | 19.9 | 58.1 | 58.8 | 237.0 |
2016 | 15.4 | 26.5 | 0.1 | 19.3 | 4.1 | 65.4 |
2017 | 41.4 | 1.3 | 0.7 | 0.1 | 0 | 43.5 |
2018 | 121.1 | 103.2 | 99.1 | 36.9 | 99.4 | 459.7 |
2019 | 10.2 | 0.3 | 0 | 0 | 0.2 | 10.7 |
Slope | -14.0417 | 2.4083 | 0.8416 | 16.5517 | 2.9700 | -24.2167 |
Table 7 The affected areas of freezing and snow disasters in each province from 2011 to 2019 (Unit: ha)
Year | Province or region | |||||
---|---|---|---|---|---|---|
Hebei | Shandong | Henan | Anhui | Jiangsu | Huang-Huai- Hai Plain | |
2011 | 39.8 | 20.8 | 23.7 | 145.2 | 3.1 | 232.6 |
2012 | 254.4 | 0 | 0 | 1.3 | 0 | 255.9 |
2013 | 158.6 | 56.0 | 71.8 | 248.8 | 56.0 | 591.2 |
2014 | 105.2 | 0.2 | 9.9 | 41.0 | 0.5 | 146.8 |
2015 | 57.1 | 43.1 | 19.9 | 58.1 | 58.8 | 237.0 |
2016 | 15.4 | 26.5 | 0.1 | 19.3 | 4.1 | 65.4 |
2017 | 41.4 | 1.3 | 0.7 | 0.1 | 0 | 43.5 |
2018 | 121.1 | 103.2 | 99.1 | 36.9 | 99.4 | 459.7 |
2019 | 10.2 | 0.3 | 0 | 0 | 0.2 | 10.7 |
Slope | -14.0417 | 2.4083 | 0.8416 | 16.5517 | 2.9700 | -24.2167 |
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