干旱区生态环境知识资源中心(Arid): Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data

Arid

DOI	10.3389/fpls.2023.1201806
	Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data
	Sakurai, Kengo; Toda, Yusuke; Hamazaki, Kosuke; Ohmori, Yoshihiro; Yamasaki, Yuji; Takahashi, Hirokazu; Takanashi, Hideki; Tsuda, Mai; Tsujimoto, Hisashi; Kaga, Akito; Nakazono, Mikio; Fujiwara, Toru; Iwata, Hiroyoshi
通讯作者	Iwata, H
来源期刊	FRONTIERS IN PLANT SCIENCE
ISSN	1664-462X
出版年	2023
卷号	14
英文摘要	Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (Glycine max (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant's stability under drought stresses. We built a genomic prediction model ( MTRRM model ) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In 2020 and 2021,the mean prediction accuracies of MTRRM models built in the changing irrigation treatments (r = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (r = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the MTRRM model across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (r = 0.46) was 42% higher than that of the simple genomic prediction model (r =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought.
英文关键词	plant response irrigation change drought stress single environmental trial multispectral (MS) time-series random regression model (RRM) Glycine max (L.) Merr
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:001032410000001
WOS关键词	QUANTITATIVE TRAIT LOCI ; YIELD STABILITY ; MAIZE HYBRIDS ; STRESS TOLERANCE ; MULTI-TRAIT ; DROUGHT ; PARAMETERS ; GENOTYPES ; GROWTH ; SIMULATION
WOS类目	Plant Sciences
WOS研究方向	Plant Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396633
推荐引用方式 GB/T 7714	Sakurai, Kengo,Toda, Yusuke,Hamazaki, Kosuke,et al. Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data[J],2023,14.
APA	Sakurai, Kengo.,Toda, Yusuke.,Hamazaki, Kosuke.,Ohmori, Yoshihiro.,Yamasaki, Yuji.,...&Iwata, Hiroyoshi.(2023).Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data.FRONTIERS IN PLANT SCIENCE,14.
MLA	Sakurai, Kengo,et al."Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data".FRONTIERS IN PLANT SCIENCE 14(2023).