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Determining agricultural drought for spring wheat with statistical models in a semi-arid climate
Zhao, Funian1,2,3; Lei, Jun4; Wang, Runyuan1; Wang, Heling1; Zhang, Kai1; Yu, Qiang5,6
2018-10-01
Source PublicationJOURNAL OF AGRICULTURAL METEOROLOGY
ISSN0021-8588
Volume74Issue:4Pages:162-172
Corresponding AuthorZhao, Funian(zhaofn@iamcma.cn)
AbstractAgricultural drought frequently occurs and results in major grain yield loss in semi-arid climate region, but determining it is difficult. This study was conducted to determine agricultural drought for spring wheat (Triticum aestivum L.) in the western Loess Plateau of China. Several statistical models were established and evaluated by long-term data, including soil water in soil layer of 50 cm depth at sowing day, air temperature, precipitation, pan evaporation during spring wheat growing season, and two groups of spring wheat yield (one from field experiments during 1987-2011 and the other from statistical Bureau during 1980-2013). Even though each of water supply factors, precipitation during growing season and the soil water at sowing day, could separately explain no more than 30% variation of the yield, both of them could explain > 55% yield variation under dry condition. Average air temperature and precipitation during growing season that displayed two apparent yield categories (drought and normal) could be used to determine agricultural drought by pattern recognition when years with the soil water at sowing day of > 98.4 mm were eliminated. Based on long-term meteorological data and the relationship between soil water at sowing day and yield under different growing season moisture conditions, the probability of agricultural drought occurrence in Dingxi for spring wheat was speculated, which nearly corresponds with the observational data during 1980-2013.
KeywordPattern recognition Precipitation Regression analysis Soil water content Yield
DOI10.2480/agrmet.D-18-00011
WOS KeywordSTANDARDIZED PRECIPITATION INDEX ; LOESS PLATEAU ; WINTER-WHEAT ; YIELD VARIABILITY ; WATER-CONTENT ; CROP YIELDS ; CHINA ; IRRIGATION ; MANAGEMENT ; IMPACT
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41375019] ; China Special Fund for Meteorological Research in the Public Interest (Major projects)[GYHY201506001-2] ; Natural Science Foundation of Gansu Province[145RJYA284] ; Meteorological Research Program of Gansu Provincial Meteorological Service[GSMAMs2018-14]
Funding OrganizationNational Natural Science Foundation of China ; China Special Fund for Meteorological Research in the Public Interest (Major projects) ; Natural Science Foundation of Gansu Province ; Meteorological Research Program of Gansu Provincial Meteorological Service
WOS Research AreaAgriculture ; Meteorology & Atmospheric Sciences
WOS SubjectAgriculture, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS IDWOS:000447068400004
PublisherSOC AGRICULTURAL METEOROLOGY JAPAN
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52756
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhao, Funian
Affiliation1.China Meteorol Adm, Lanzhou Inst Arid Meteorol, Key Lab Arid Climate Change & Disaster Reduct CMA, Key Lab Arid Climat Change & Disaster Reduct Gans, Lanzhou 730020, Gansu, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Dingxi Meteorol Bur, Dingxi 743000, Peoples R China
5.Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China
6.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Funian,Lei, Jun,Wang, Runyuan,et al. Determining agricultural drought for spring wheat with statistical models in a semi-arid climate[J]. JOURNAL OF AGRICULTURAL METEOROLOGY,2018,74(4):162-172.
APA Zhao, Funian,Lei, Jun,Wang, Runyuan,Wang, Heling,Zhang, Kai,&Yu, Qiang.(2018).Determining agricultural drought for spring wheat with statistical models in a semi-arid climate.JOURNAL OF AGRICULTURAL METEOROLOGY,74(4),162-172.
MLA Zhao, Funian,et al."Determining agricultural drought for spring wheat with statistical models in a semi-arid climate".JOURNAL OF AGRICULTURAL METEOROLOGY 74.4(2018):162-172.
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