Spatio-temporal Analysis of Changes in Winter Wheat Cold Damage with Meteorological Elements in Huang-Huai-Hai Plain from 2011 to 2020

doi:10.5814/j.issn.1674-764x.2022.02.003

Abstract

Abstract:

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.

Key words: cold damage, meteorological elements, Logistic regression, Pearson correlation analysis

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.

Figures/Tables 17

References 36

[1]	Campoy J, Campos I, Plaza C, et al. 2020. Estimation of harvest index in wheat crops using a remote sensing-based approach. Field Crops Research, 256: 107910. DOI: 10.1016/j.fcr.2020.107910. DOI URL
[2]	Cao Q, Yao F M, Lin E D, et al. 2011. Analysis of changing characteristics of agricultural climate resources in the main planted areas of winter wheat in China over last 50 years. Chinese Journal of Agrometeorology, 32(2): 161-166. (in Chinese)
[3]	Chu R H, Shen S H, Lv H Q, et al. 2015. Changing characteristics of agro-climatic resources in the winter wheat region of the Huang-Huai- Hai Sea under climate change scenarios. Science Technology and Engineering, 15(26): 1-10, 18. (in Chinese)
[4]	Ding Y H, Ren G Y, Shi G Y, et al. 2006. Climate change country assessment report (I): Historical and future trends of climate change in China. Advances in Climate Change Research, 2(1): 3-8. (in Chinese)
[5]	Ge Y N, Liu L, Xu X L, et al. 2015. Temporal and spatial variations of Chinese maize production potential on the background of climate change during 1960-2010. Journal of Natural Resources, 30(5): 784-795. (in Chinese)
[6]	Guo C M, Ren J Q, Wang D N, et al. 2020. Temporal and spatial characteristics of rice cold damage in Jilin from 1961 to 2018. Chinese Agricultural Science Bulletin, 36: 109-117. (in Chinese)
[7]	Guo J P. 2016. Research progress on agricultural meteorological disaster monitoring and forecasting. Journal of Applied Meteorological Science, 27(5): 620-630. (in Chinese)
[8]	Jiang H, Deng W M, Chen X Q. 2014. Analysis of wine based on Pearson coefficient and multiple kernel support vector classification. Transactions of the Chinese Society for Agricultural Machinery, 45(1): 203-208. (in Chinese)
[9]	Jiao X M, Zhang H L, Xu F G, et al. 2020. Analysis of the spatio-temporal variation in FPAR of the Tibetan Plateau from 1982 to 2015. Remote Sensing Technology and Application, 35: 950-961. (in Chinese)
[10]	Kim Y, Kimball J S, Didan K, et al. 2014. Response of vegetation growth and productivity to spring climate indicators in the conterminous United States derived from satellite remote sensing data fusion. Agricultural and Forest Meteorology, 194: 132-143. DOI URL
[11]	Li J L, Xue C Y, Zou C H, et al. 2020. Remote sensing monitoring of late frost of winter wheat in Henan Province based on land surface temperature retrieval. Meteorological and Environmental Sciences, 43(4): 11-17. (in Chinese)
[12]	Lin P. 2020. An overview of agricultural water management strategies based on ecosystem conservation. Sichuan Water Resources, 41: 129-131. (in Chinese)
[13]	Liu H J, Ni Y J, Ren D C, et al. 2018. Estimation and distribution of minimum air temperature within winter wheat canopy in prone period of late frost. Chinese Journal of Agrometeorology, 39(12): 786-795. (in Chinese)
[14]	Liu L L, Song H, Shi K J, et al. 2019. Response of wheat grain quality to low temperature during jointing and booting stages-On the importance of considering canopy temperature. Agricultural and Forest Meteorology, 278(15): 107658. DOI: 10.1016/j.agrformet.2019.107658. DOI URL
[15]	Liu M, Lv A F, Wu J J, et al. 2014. A review of impacts of drought on agro-ecosystem. Chinese Agricultural Science Bulletin, 30: 165-171. (in Chinese)
[16]	Lou W P, Ji Z W, Sun K, et al. 2013. Application of remote sensing and GIS for assessing economic loss caused by frost damage to tea plantations. Precision Agriculture, 14(6): 606-620. DOI URL
[17]	Ma S Q, Zhang B, Tang M, et al. 2019. Temporal and spatial evolution on late frost damage of winter wheat in Huaihe River Basin. Journal of Triticeae Crops, 39(1): 105-113. (in Chinese)
[18]	Meng L, Wu Y F, Hu X, et al. 2017. Using hyperspectral data for detecting late frost injury to winter wheat under different topsoil moistures. Spectroscopy and Spectral Analysis, 37(5): 1482-1488.
[19]	Nuttall J G, Perry E M, Delahunty A J, et al. 2019. Frost response in wheat and early detection using proximal sensors. Journal of Agronomy and Crop Science, 205(2): 220-234. DOI
[20]	Qian Y L, Wang J L, Zheng C L, et al. 2014. Spatial-temporal change of low temperature disaster of winter wheat in North China in last 50 years. Chinese Journal of Ecology, 33(12): 3245-3253. (in Chinese)
[21]	Tian C S, Liu X L, Wang J. 2016. Geohazard susceptibility assessment based on CF model and Logistic Regression models in Guangdong. Hydrogeology & Engineering Geology, 43(6): 154-161, 170. (in Chinese)
[22]	Wang J J, Guo M C, Jiang Z D, et al. 2010. The construction and application of an agricultural ecological-economic system coupled process model. Acta Ecologica Sinica, 30: 2371-2478. (in Chinese)
[23]	Wang L X, Qin Q M, Zhang X Y. 2003. Progress on monitor of rice low temperature disaster with remote sensing. Meteorological Monthly, 29(10): 3-7.
[24]	Wang S, Chen J, Rao Y H, et al. 2020. Response of winter wheat to spring frost from a remote sensing perspective: Damage estimation and influential factors. ISPRS Journal of Photogrammetry and Remote Sensing, 168: 221-235. DOI URL
[25]	Wang Y, Guo E X, Zhang H, et al. 2020. Analysis of urban development boundary delineation and influencing factors in Chongqing City based on Logistic regression. Journal of Chongqing Normal University (Natural Science), 37(5): 59-72. (in Chinese)
[26]	Xiao L J, Liu L L, Asseng S, et al. 2018. Estimating spring frost and its impact on yield across winter wheat in China. Agricultural and Forest Meteorology, 260-261: 154-164. DOI URL
[27]	Xu J W, Ju H, Mei X R, et al. 2015. Simulation on potential effects of drought on winter wheat in Huang-Huai-Hai Plain from 1981 to 2010. Transactions of the Chinese Society of Agricultural Engineering, 31(6): 150-158. (in Chinese)
[28]	Yang K, Chen B B, Chen H, et al. 2020. Comprehensive climatic index and grade classification of cold damage for Taiwan Green Jujube in Fujian. Journal of Applied Meteorological Science, 31(4): 427-434. (in Chinese)
[29]	Zhang J, Xie Y N, Wang M Q, et al. 2019. Estimation of daily value from PAR instantaneous value. Arid Zone Research, 36: 220-270. (in Chinese)
[30]	Zhang M T, Zhang Y J, Tong J H, et al. 2017. Evaluation of agricultural climate resources in the potential northward region of winter wheat. Journal of Meteorology and Environment, 33(6): 73-81. (in Chinese)
[31]	Zhang X R, Feng M C, Yang W D, et al. 2017. Using spectral transformation processes to estimate chlorophyll content of winter wheat under low temperature stress. Chinese Journal of Eco-Agriculture, 25(9): 1351-1359. (in Chinese)
[32]	Zhang Y, Shu X L, Zhong B, et al. 2020. Application of Logistic model combined with ROC curve and Bayes discriminant function in the diagnosis of severity of COVID-19. Chinese Journal of Disease Control & Prevention, 24(7): 851-855. (in Chinese)
[33]	Zhao H F, Pan S Q, Qiao Y F, et al. 2021. The response of winter-wheat yield to elevated temperature varied with soil types. Chinese Agricultural Science Bulletin, 37(2): 74-79. (in Chinese)
[34]	Zhao H, Wang X C, Cao T J, et al. 2011. Analysis of current state and development trends of wheat cultivars applications in the south of Huang-Huai winter wheat zone. Journal of Henan Agricultural Sciences, 40: 44-49. (in Chinese)
[35]	Zheng D X, Yang X G, Zhao J, et al. 2015. Spatial and temporal patterns of freezing injury during winter in Huang-Huai winter wheat area under climate change. Acta Ecologica Sinica, 35(13): 4338-4346. (in Chinese)
[36]	Zhu W F, Wang S L, Caldwell C D. 2010. Agro-ecosystem service and its managerial essence. Chinese Journal of Eco-Agriculture, 18: 889-896. (in Chinese) DOI URL

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

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

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	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