气候变化领域集成服务门户(CCPortal): Projection of future drought impacts on millet yield in northern Shanxi of China using ensemble machine learning approach

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DOI	10.1016/j.compag.2024.108725
	Projection of future drought impacts on millet yield in northern Shanxi of China using ensemble machine learning approach
	Zhou, Shiwei; Wu, Yangzhong; Wang, Chu; Lu, Huayu; Zhang, Zecheng; Liu, Zijin; Lei, Yongdeng; Chen, Fu
发表日期	2024
ISSN	0168-1699
EISSN	1872-7107
起始页码	218
卷号	218
英文摘要	Foxtail millet (or millet) is an important food crop in the northern Shanxi province of China (SXN). In the SXN, drought limits millet yield, which could be exacerbated by climate change. However, little is known about the impacts of future climate change on drought and its induced millet yield reduction in the SXN. This study investigated climate change in the future (2021-2060) relative to the baseline (1981-2020) and evaluated its impacts on drought under two emission scenarios (SSP245 and SSP585). We developed a new ensemble machine learning model to quantify the impacts of future climate change on drought -induced yield reduction for millet production. The results indicated that temperature and precipitation both show an increasing trend under future climate change. Drought intensity in most regions of the SXN was projected to be higher but drought frequency to be lower in the future relative to the baseline. The northeast parts of the SXN generally have a higher drought frequency and intensity than other regions. There are non -ignorable spatial differences in drought adaptability in the SXN, with lower drought adaptability in the southeast and southwest regions. Among these regions with different drought adaptability, the difference in yield reduction rate under the same drought intensity can reach up to 15.8 %. Therefore, when constructing models to quantify the relationship between drought intensity and millet yield reduction based on multi -site data, the spatial differences in drought adaptability should be considered. The ensemble machine learning model (Random Forest + Light Gradient Boosting Machine + Deep Forwarded Neural Network) using monthly drought intensity and drought adaptability index as predictors demonstrates high accuracy and regional applicability. According to simulation results, the millet production in the SXN generally has a lower yield reduction frequency but a higher yield reduction rate in the future relative to the baseline. Specifically, the yield reduction frequency decreases by 8.27 % under SSP245 and 11.28 % under SSP585, while the yield reduction rate increases by 3.64 % under SSP245 and 8.95 % under SSP585. Our findings provide important information for guiding agricultural water management to mitigate drought risk and its induced yield reduction under a changing climate.
英文关键词	Climate change; Drought; Drought adaptability; Yield reduction; Ensemble machine learning
语种	英语
WOS研究方向	Agriculture ; Computer Science
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001184738100001
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/307006
作者单位	China Agricultural University; Ministry of Agriculture & Rural Affairs
推荐引用方式 GB/T 7714	Zhou, Shiwei,Wu, Yangzhong,Wang, Chu,et al. Projection of future drought impacts on millet yield in northern Shanxi of China using ensemble machine learning approach[J],2024,218.
APA	Zhou, Shiwei.,Wu, Yangzhong.,Wang, Chu.,Lu, Huayu.,Zhang, Zecheng.,...&Chen, Fu.(2024).Projection of future drought impacts on millet yield in northern Shanxi of China using ensemble machine learning approach.COMPUTERS AND ELECTRONICS IN AGRICULTURE,218.
MLA	Zhou, Shiwei,et al."Projection of future drought impacts on millet yield in northern Shanxi of China using ensemble machine learning approach".COMPUTERS AND ELECTRONICS IN AGRICULTURE 218(2024).