气候变化领域集成服务门户(CCPortal): Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia

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DOI	10.1016/j.agrformet.2019.05.018
	Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia
	Feng, Puyu; Wang, Bin; Liu, De Li; Waters, Cathy; Yu, Qiang
发表日期	2019-09-15
ISSN	0168-1923
EISSN	1873-2240
卷号	275 页码:100-113
英文摘要	Accurately assessing the impacts of extreme climate events (ECEs) on crop yield can help develop effective agronomic practices to deal with climate change impacts. Process-based crop models are useful tools to evaluate climate change impacts on crop produ
关键词	Extreme climate events Wheat yield APSIM Random forest Hybrid model
学科领域	Agronomy;Forestry;Meteorology & Atmospheric Sciences
语种	英语
WOS记录号	WOS:000480376400010
来源期刊	AGRICULTURAL AND FOREST METEOROLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/80883
作者单位	Northwest A&F Univ, State Key Lab Soil Eros & Dryland Fanning Loess P, Yangling 712100, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Feng, Puyu,Wang, Bin,Liu, De Li,et al. Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia[J],2019,275:100-113.
APA	Feng, Puyu,Wang, Bin,Liu, De Li,Waters, Cathy,&Yu, Qiang.(2019).Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia.AGRICULTURAL AND FOREST METEOROLOGY,275,100-113.
MLA	Feng, Puyu,et al."Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia".AGRICULTURAL AND FOREST METEOROLOGY 275(2019):100-113.