气候变化领域集成服务门户(CCPortal): Harnessing enviromics to predict climate-impacted high-profile traits to assist informed decisions in agriculture

CCPortal

DOI	10.1002/fes3.544
	Harnessing enviromics to predict climate-impacted high-profile traits to assist informed decisions in agriculture
	Zhang, Bosen; Hauvermale, Amber L.; Zhang, Zhiwu; Thompson, Alison; Neely, Clark; Esser, Aaron; Pumphrey, Michael; Garland-Campbell, Kimberly; Yu, Jianming; Steber, Camille; Li, Xianran
发表日期	2024
ISSN	2048-3694
起始页码	13
结束页码	3
卷号	13 期号:3
英文摘要	Modern agriculture is a complex system that demands real-time and large-scale quantification of trait values for evidence-based decisions. However, high-profile traits determining market values often lack high-throughput phenotyping technologies to achieve this objective; therefore, risks of undermining crop values through arbitrary decisions are high. Because environmental conditions are major contributors to performance fluctuation, with the contemporary informatics infrastructures, we proposed enviromic prediction as a potential strategy to assess traits for informed decisions. We demonstrated this concept with wheat falling number (FN), a critical end-use quality trait that significantly impacts wheat market values but is measured using a low-throughput technology. Using 8 years of FN records from elite variety testing trials, we developed a predictive model capturing the general trend of FN based on biologically meaningful environmental conditions. An explicit environmental index that was highly correlated (r = 0.646) with the FN trend observed from variety testing trials was identified. An independent validation experiment verified the biological relevance of this index. An enviromic prediction model based on this index achieved accurate and on-target predictions for the FN trend in new growing seasons. Two applications designed for production fields illustrated how such enviromic prediction models could assist informed decision along the food supply chain. We envision that enviromic prediction would have a vital role in sustaining food security amidst rapidly changing climate. As conducting variety testing trials is a standard component in modern agricultural industry, the strategy of leveraging historical trial data is widely applicable for other high-profile traits in various crops. image
英文关键词	climate change; enviromic prediction; falling number; food supply chain
语种	英语
WOS研究方向	Agriculture ; Food Science & Technology
WOS类目	Agronomy ; Food Science & Technology
WOS记录号	WOS:001218681700001
来源期刊	FOOD AND ENERGY SECURITY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299917
作者单位	Washington State University; United States Department of Agriculture (USDA); Iowa State University
推荐引用方式 GB/T 7714	Zhang, Bosen,Hauvermale, Amber L.,Zhang, Zhiwu,et al. Harnessing enviromics to predict climate-impacted high-profile traits to assist informed decisions in agriculture[J],2024,13(3).
APA	Zhang, Bosen.,Hauvermale, Amber L..,Zhang, Zhiwu.,Thompson, Alison.,Neely, Clark.,...&Li, Xianran.(2024).Harnessing enviromics to predict climate-impacted high-profile traits to assist informed decisions in agriculture.FOOD AND ENERGY SECURITY,13(3).
MLA	Zhang, Bosen,et al."Harnessing enviromics to predict climate-impacted high-profile traits to assist informed decisions in agriculture".FOOD AND ENERGY SECURITY 13.3(2024).