Journal of Resources and Ecology >
Spatio-temporal Analysis of Changes in Winter Wheat Cold Damage with Meteorological Elements in Huang-Huai-Hai Plain from 2011 to 2020
ZHENG Xintong, E-mail: zhengxt.18s@igsnrr.ac.cn |
Received date: 2021-02-05
Accepted date: 2021-06-30
Online published: 2022-03-09
Supported by
The National Key Research and Development Program of China(2017YD0300403)
The Huang-Huai-Hai Plain is one of the typical agri-ecosystems in China, which suffers from cold damage frequently resulting in substantial economic losses. In order to monitor the changes in the occurrence of cold damage in an effective and large-scale manner, and to determine their meteorological causes, this paper collected low temperature data from the agricultural meteorological stations and remote sensing data of MODIS from 2005 to 2015, and constructed a monitoring model of cold damage to winter wheat in Huang-Huai-Hai Plain based on the Logistic regression model. This model was used to analyze the spatio-temporal changes of cold damage of winter wheat in Huang-Huai-Hai Plain from 2011 to 2020, and correlation analysis was performed with the spatio-temporal changes of meteorological factors to ascertain how they affect cold damage. The results show that the harm from cold damage in winter wheat has been gradually decreasing from 2011 to 2020, and the cold damage areas with high probability and high frequency are moving from north to south. The meteorological elements with the greatest impacts on the degree of cold damage from stronger to weaker are heat, precipitation and sunshine duration, whose influence has spatial variability.
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 . DOI: 10.5814/j.issn.1674-764x.2022.02.003
Fig. 1 Overview of the study area |
Fig. 2 The distribution of meteorological stations in the Huang-Huai-Hai Plain |
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. 3 The characteristic values of the minimum temperature curve |
Fig. 4 The characteristic values of the NDVI curve |
Fig. 5 Changes in meteorological elements in the Huang-Huang-Hai Plain from 2011 to 2020 |
Fig. 6 Distribution of the slopes of six different meteorological elements from 2011 to 2020Note: (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). |
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 |
Note: ALT: Accumulated low temperature; DLT: Distance from the limit of temperature; NLDT: Number of low temperature days; AFD: Area of FPAR down; DVF: Decreasing value of FPAR; DRF: Decreasing rate of FPAR; AND: Area of NDVI down; DVN: Decreasing value of NDVI; DRN: Decreasing rate of NDVI; * indicates significant correlation at the 0.05 level (two tailed); ** indicates significant correlation at the 0.01 level (two tailed). |
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 |
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 |
Fig. 7 The ROC curve of the cold damage identification model |
Fig. 9 Distribution of annual frequency of cold damage by county in each year from 2011—2020 |
Fig. 10 Number of cold damage counties in the Huang-Huai-Hai Plain and major provinces from 2011-2020 |
Fig. 8 Distribution of the maximum probability of the occurrence of cold damage in the counties in each year from 2011-2020 |
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 |
Note: DLLP: Distance from the limit of low temperature; ETP: Extreme temperature drop; NCT: Negative cumulative temperature, AT: Average temperature, PO: Probability of occurrence, PA: Precipitation accumulation; SA: Sunshine accumulation. *: Correlation is significant at the 0.05 level (two-tailed); **: Correlation is significant at the 0.01 level (two-tailed). |
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** |
Note: Abbreviations are the same as in Table 5. * and ** mean the significant level are at the 0.05 and 0.01, respectively. |
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 |
Note: Data are from Chinese Statistical Yearbook (2011-2019). |
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