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Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the North China Plain

文献类型：期刊论文

作者Zhuang, Huimin1,2; Zhang, Zhao1,2; Cheng, Fei1,2; Han, Jichong2,3; Luo, Yuchuan2; Zhang, Liangliang2; Cao, Juan4; Zhang, Jing2; He, Bangke5; Xu, Jialu1,2
刊名AGRICULTURAL AND FOREST METEOROLOGY
出版日期2024-03-15
卷号347页码:14
关键词Crop modelling Data assimilation Early warning system Extreme climate Machine learning
ISSN号0168-1923
DOI10.1016/j.agrformet.2024.109909
通讯作者Zhang, Zhao(zhangzhao@bnu.edu.cn)
英文摘要Timely and reliable regional crop yield forecasting before harvest is critical for managing climate risk, adjusting agronomic management, and making food trade policy. Although various methods exist for crop yield forecasting, including process -based crop models and machine learning techniques, the potential of integrating these methods for early -season yield forecasts has not been well investigated. In this study, we proposed a hybrid framework for crop yield forecasting that firstly assimilated leaf area index and soil moisture into a crop model and then combined the data -assimilated crop model with machine learning techniques to improve the prediction skill further. The proposed framework was applied to winter wheat yield forecasting in the North China Plain during 2009-2015. We found that the assimilation significantly enhances wheat yield estimates, achieving additional ACC = 0.27, MAPE = 6.12 %. Incorporating weather forecasts enabled reliable winter wheat yield forecasts up to 1-3 months in advance, achieving ACC = 0.69, MAPE = 7.79 %. Furthermore, integrating the assimilated crop model with machine learning techniques improved the forecasting further, achieving ACC = 0.97 and MAPE = 1.74 %. The proposed framework for crop yield forecasting can be adapted to other crops and regions and has great potential in developing food security early warning system at a regional scale.
WOS关键词UNCERTAINTY ; IMPROVE ; AREA
资助项目National Natural Science Foundation of China[42061144003] ; National Natural Science Foundation of China[41977405]
WOS研究方向Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001175556800001
出版者ELSEVIER
资助机构National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/203838]  
专题中国科学院地理科学与资源研究所
通讯作者Zhang, Zhao
作者单位1.Beijing Normal Univ, Sch Natl Safety & Emergency Management, Joint Int Res Lab Catastrophe Simulat & Syst Risk, Zhuhai 519087, Peoples R China
2.Beijing Normal Univ, Sch Natl Safety & Emergency Management, Beijing 100875, Peoples R China
3.Beijing Normal Univ, Sch Syst Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China
5.Beijing Normal Univ, Jointly Sponsored Beijing Normal Univ & Aerosp Inf, Chinese Acad Sci, Fac Geog Sci, Beijing, Peoples R China
6.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhuang, Huimin,Zhang, Zhao,Cheng, Fei,et al. Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the North China Plain[J]. AGRICULTURAL AND FOREST METEOROLOGY,2024,347:14.
APA Zhuang, Huimin.,Zhang, Zhao.,Cheng, Fei.,Han, Jichong.,Luo, Yuchuan.,...&Tao, Fulu.(2024).Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the North China Plain.AGRICULTURAL AND FOREST METEOROLOGY,347,14.
MLA Zhuang, Huimin,et al."Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the North China Plain".AGRICULTURAL AND FOREST METEOROLOGY 347(2024):14.

入库方式： OAI收割

来源：地理科学与资源研究所

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