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Within-season yield prediction with different nitrogen inputs under rain-fed condition using CERES-Wheat model in the northwest of China
Li, Zhengpeng2,3; Song, Mingdan2,3; Feng, Hao2,3,4; Zhao, Ying1,3
2016-06-01
Source PublicationJOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
ISSN0022-5142
Volume96Issue:8Pages:2906-2916
Corresponding AuthorFeng, Hao(nercwsi@vip.sina.com) ; Zhao, Ying(yzhaosoils@gmail.com)
AbstractBACKGROUND: Yield prediction within season is of great use to improve agricultural risk management and decision making. The objectives of this study were to access the yield forecast performance with increasing nitrogen inputs and to determine when the acceptable predicted yield can be achieved using the CERES-Wheat model. RESULTS: the calibrated model simulated wheat yield very well under various water and nitrogen conditions. Long-term simulation demonstrated that nitrogen input enlarged the annual variability of wheat yield generally. Within-season yield prediction showed that, regardless of nitrogen inputs, yield forecasts in the later growing season improved the accuracy and reduced the uncertainty of yield prediction. In a low-yielding year (2011-2012) and a high-yielding year (1991-1992), the date of acceptable predicted yield was achieved 62 and 65 days prior to wheat maturity, respectively. In a normal-yielding year (1983-1984), inadequate precipitation after the jointing stage in most historical years led to the underestimation of wheat yield and the date of accurate yield prediction was delayed to 235-250 days after simulation (7-22 days prior to maturity) for different N inputs. CONCLUSION: Yield prediction was highly influenced by the distribution of meteorological elements during the growing season and may show great improvement if future weather can be reliably forecast early. (c) 2015 Society of Chemical Industry
Keywordyield forecast nitrogen precipitation distribution forecast accuracy CERES-Wheat model
DOI10.1002/jsfa.7467
WOS KeywordSUBTROPICAL ENVIRONMENT ; SIMULATION-MODEL ; CROP ; IRRIGATION ; FORECASTS ; RESPONSES ; MAIZE
Indexed BySCI
Language英语
Funding ProjectNational 863 Research Program[2013AA102904] ; National 863 Research Program[2011AA100503] ; Chinese State Key Laboratory Fund[K318009902-1427] ; 111 Project of Chinese Education Ministry[B12007]
Funding OrganizationNational 863 Research Program ; Chinese State Key Laboratory Fund ; 111 Project of Chinese Education Ministry
WOS Research AreaAgriculture ; Chemistry ; Food Science & Technology
WOS SubjectAgriculture, Multidisciplinary ; Chemistry, Applied ; Food Science & Technology
WOS IDWOS:000377203800038
PublisherWILEY-BLACKWELL
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/66838
Collection中国科学院地理科学与资源研究所
Corresponding AuthorFeng, Hao; Zhao, Ying
Affiliation1.Northwest A&F Univ, Colleage Nat Resources & Environm, Yangling 712100, Shaanxi Provinc, Peoples R China
2.Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi Provinc, Peoples R China
3.Northwest A&F Univ, Inst Water Saving Agr Arid Areas China, Yangling 712100, Shaanxi Provinc, Peoples R China
4.Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, Yangling 712100, Shaanxi Provinc, Peoples R China
Recommended Citation
GB/T 7714
Li, Zhengpeng,Song, Mingdan,Feng, Hao,et al. Within-season yield prediction with different nitrogen inputs under rain-fed condition using CERES-Wheat model in the northwest of China[J]. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE,2016,96(8):2906-2916.
APA Li, Zhengpeng,Song, Mingdan,Feng, Hao,&Zhao, Ying.(2016).Within-season yield prediction with different nitrogen inputs under rain-fed condition using CERES-Wheat model in the northwest of China.JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE,96(8),2906-2916.
MLA Li, Zhengpeng,et al."Within-season yield prediction with different nitrogen inputs under rain-fed condition using CERES-Wheat model in the northwest of China".JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 96.8(2016):2906-2916.
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