气候变化领域集成服务门户(CCPortal): A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada

CCPortal

DOI	10.3390/agronomy14040733
	A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada
	Sarzaeim, Parisa; Munoz-Arriola, Francisco
发表日期	2024
EISSN	2073-4395
起始页码	14
结束页码	4
卷号	14 期号:4
英文摘要	Throughout history, the pursuit of diagnosing and predicting crop yields has evidenced genetics, environment, and management practices intertwined in achieving food security. However, the sensitivity of crop phenotypes and genetic responses to climate still hampers the identification of the underlying abilities of plants to adapt to climate change. We hypothesize that the PiAnosi and WagNer (PAWN) global sensitivity analysis (GSA) coupled with a genetic by environment (GxE) model built of environmental covariance and genetic markers structures, can evidence the contributions of climate on the predictability of maize yields in the U.S. and Ontario, Canada. The GSA-GxE framework estimates the relative contribution of climate variables to improving maize yield predictions. Using an enhanced version of the Genomes to Fields initiative database, the GSA-GxE framework shows that the spatially aggregated sensitivity of maize yield predictability is attributed to solar radiation, followed by temperature, rainfall, and relative humidity. In one-third of the individually assessed locations, rainfall was the primary responsible for maize yield predictability. Also, a consistent pattern of top sensitivities (Relative Humidity, Solar Radiation, and Temperature) as the main or the second most relevant drivers of maize yield predictability shed some light on the drivers of genetic improvement in response to climate change.
英文关键词	sensitivity analysis; maize yield predictability; genetic-by-environment interactions (GxE)
语种	英语
WOS研究方向	Agriculture ; Plant Sciences
WOS类目	Agronomy ; Plant Sciences
WOS记录号	WOS:001210714100001
来源期刊	AGRONOMY-BASEL
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/288394
作者单位	University of Nebraska System; University of Nebraska Lincoln; University of Nebraska System; University of Nebraska Lincoln
推荐引用方式 GB/T 7714	Sarzaeim, Parisa,Munoz-Arriola, Francisco. A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada[J],2024,14(4).
APA	Sarzaeim, Parisa,&Munoz-Arriola, Francisco.(2024).A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada.AGRONOMY-BASEL,14(4).
MLA	Sarzaeim, Parisa,et al."A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada".AGRONOMY-BASEL 14.4(2024).