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Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models
Qi, Yanbing1,2; Huo, Zailin1; Feng, Shaoyuan3; Adeloye, Adebayo J.4; Dai, Xiaoqin5
2018-10-01
Source PublicationIRRIGATION AND DRAINAGE
ISSN1531-0353
Volume67Issue:4Pages:615-624
Corresponding AuthorHuo, Zailin(huozl@cau.edu.cn)
AbstractThe response of the water use of crops to soil moisture and salinity is complex to quantify using traditional field experiments. Based on field experimental data for 2years, artificial neural network (ANN) models with five inputs including soil moisture content, total salt content, plant height, leaf area index and crop reference evapotranspiration (ET0) were developed to estimate daily actual evapotranspiration (ET). The models were later used to simulate the response of crop water consumption to soil moisture and salinity stresses at different growth stages. The results showed that the ANN model has a high precision with root mean squared error of 0.41 and 0.52mmday(-1), relative error of 19.6 and 25.6%, and coefficient of determination of 0.87 and 0.79 for training and testing samples, respectively. Furthermore, the simulation results showed that the seed corn ET is sensitive to soil salt stress at all growth stages, although the salinity threshold at which the impact becomes felt and the extent of the impact vary for the different growth stages, with the booting and tasseling stages being the most robust. The study offers a more direct approach of evaluating actual crop evapotranspiration by considering explicitly water and salinity stresses. (c) 2018 John Wiley & Sons, Ltd.
Keywordcrop water consumption soil moisture salinity artificial neural network
DOI10.1002/ird.2270
WOS KeywordREFERENCE EVAPOTRANSPIRATION ; DEFICIT IRRIGATION ; WATER PRODUCTIVITY ; NORTHWEST CHINA ; YIELD ; WHEAT ; RIVER ; FIELD
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2016YFC0400107] ; National Natural Science Foundation of China[516390095167923691425302]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China
WOS Research AreaAgriculture ; Water Resources
WOS SubjectAgronomy ; Water Resources
WOS IDWOS:000446663100013
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52736
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHuo, Zailin
Affiliation1.China Agr Univ, Ctr Agr Water Res China, 17 Qinghua East Rd, Beijing 100083, Peoples R China
2.Beijing Water Sci & Technol Inst, Beijing, Peoples R China
3.Yangzhou Univ, Sch Hydraul Energy & Power Engn, Yangzhou, Jiangsu, Peoples R China
4.Heriot Watt Univ, Inst Infrastruct & Environm, Edinburgh, Midlothian, Scotland
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Qi, Yanbing,Huo, Zailin,Feng, Shaoyuan,et al. Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models[J]. IRRIGATION AND DRAINAGE,2018,67(4):615-624.
APA Qi, Yanbing,Huo, Zailin,Feng, Shaoyuan,Adeloye, Adebayo J.,&Dai, Xiaoqin.(2018).Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models.IRRIGATION AND DRAINAGE,67(4),615-624.
MLA Qi, Yanbing,et al."Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models".IRRIGATION AND DRAINAGE 67.4(2018):615-624.
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