Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters

Abstract The application of remote sensing in plant breeding can provide rich information about the growth processes of plants, which leads to better understanding concerning crop yield. It has been shown that traits measured by remote sensing were also beneficial for genomic prediction (GP) because...

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Autores principales:	Yusuke Toda, Akito Kaga, Hiromi Kajiya‐Kanegae, Tomohiro Hattori, Shuhei Yamaoka, Masanori Okamoto, Hisashi Tsujimoto, Hiroyoshi Iwata
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/bdd7dc631d744d38ab75396284539f26
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spelling	oai:doaj.org-article:bdd7dc631d744d38ab75396284539f262021-12-05T07:50:12ZGenomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters1940-337210.1002/tpg2.20157https://doaj.org/article/bdd7dc631d744d38ab75396284539f262021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20157https://doaj.org/toc/1940-3372Abstract The application of remote sensing in plant breeding can provide rich information about the growth processes of plants, which leads to better understanding concerning crop yield. It has been shown that traits measured by remote sensing were also beneficial for genomic prediction (GP) because the inclusion of remote sensing data in multitrait models improved prediction accuracies of target traits. However, the present multitrait GP model cannot incorporate high‐dimensional remote sensing data due to the difficulty in the estimation of a covariance matrix among the traits, which leads to failure in improving its prediction accuracy. In this study, we focused on growth models to express growth patterns using remote sensing data with a few parameters and investigated whether a multitrait GP model using these parameters could derive better prediction accuracy of soybean [Glycine max (L.) Merr.] biomass. A total of 198 genotypes of soybean germplasm were cultivated in experimental fields, and longitudinal changes of their canopy height and area were measured continuously via remote sensing with an unmanned aerial vehicle. Growth parameters were estimated by applying simple growth models and incorporated into the GP of biomass. By evaluating heritability and correlation, we showed that the estimated growth parameters appropriately represented the observed growth curves. Also, the use of these growth parameters in the multitrait GP model contributed to successful biomass prediction. We conclude that the growth models could describe the genetic variation of soybean growth curves based on several growth parameters. These dimension‐reduction growth models will be indispensable for extracting useful information from remote sensing data and using this data in GP and plant breeding.Yusuke TodaAkito KagaHiromi Kajiya‐KanegaeTomohiro HattoriShuhei YamaokaMasanori OkamotoHisashi TsujimotoHiroyoshi IwataWileyarticlePlant cultureSB1-1110GeneticsQH426-470ENThe Plant Genome, Vol 14, Iss 3, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Plant culture SB1-1110 Genetics QH426-470
spellingShingle	Plant culture SB1-1110 Genetics QH426-470 Yusuke Toda Akito Kaga Hiromi Kajiya‐Kanegae Tomohiro Hattori Shuhei Yamaoka Masanori Okamoto Hisashi Tsujimoto Hiroyoshi Iwata Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters
description	Abstract The application of remote sensing in plant breeding can provide rich information about the growth processes of plants, which leads to better understanding concerning crop yield. It has been shown that traits measured by remote sensing were also beneficial for genomic prediction (GP) because the inclusion of remote sensing data in multitrait models improved prediction accuracies of target traits. However, the present multitrait GP model cannot incorporate high‐dimensional remote sensing data due to the difficulty in the estimation of a covariance matrix among the traits, which leads to failure in improving its prediction accuracy. In this study, we focused on growth models to express growth patterns using remote sensing data with a few parameters and investigated whether a multitrait GP model using these parameters could derive better prediction accuracy of soybean [Glycine max (L.) Merr.] biomass. A total of 198 genotypes of soybean germplasm were cultivated in experimental fields, and longitudinal changes of their canopy height and area were measured continuously via remote sensing with an unmanned aerial vehicle. Growth parameters were estimated by applying simple growth models and incorporated into the GP of biomass. By evaluating heritability and correlation, we showed that the estimated growth parameters appropriately represented the observed growth curves. Also, the use of these growth parameters in the multitrait GP model contributed to successful biomass prediction. We conclude that the growth models could describe the genetic variation of soybean growth curves based on several growth parameters. These dimension‐reduction growth models will be indispensable for extracting useful information from remote sensing data and using this data in GP and plant breeding.
format	article
author	Yusuke Toda Akito Kaga Hiromi Kajiya‐Kanegae Tomohiro Hattori Shuhei Yamaoka Masanori Okamoto Hisashi Tsujimoto Hiroyoshi Iwata
author_facet	Yusuke Toda Akito Kaga Hiromi Kajiya‐Kanegae Tomohiro Hattori Shuhei Yamaoka Masanori Okamoto Hisashi Tsujimoto Hiroyoshi Iwata
author_sort	Yusuke Toda
title	Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters
title_short	Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters
title_full	Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters
title_fullStr	Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters
title_full_unstemmed	Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters
title_sort	genomic prediction modeling of soybean biomass using uav‐based remote sensing and longitudinal model parameters
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/bdd7dc631d744d38ab75396284539f26
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_version_	1718372580903092224

Genomic prediction modeling of soybean biomass using UAV‐based remote sensing and longitudinal model parameters

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