Genomic Selection model to predict grain Zn concentrations

Discovery research

Detalles Bibliográficos
Autor principal: CGIAR Research Program on Rice
Formato: Informe técnico
Lenguaje:Inglés
Publicado: 2020
Materias:
Acceso en línea:https://hdl.handle.net/10568/122286
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author CGIAR Research Program on Rice
author_browse CGIAR Research Program on Rice
author_facet CGIAR Research Program on Rice
author_sort CGIAR Research Program on Rice
collection Repository of Agricultural Research Outputs (CGSpace)
description Discovery research
format Informe técnico
id CGSpace122286
institution CGIAR Consortium
language Inglés
publishDate 2020
publishDateRange 2020
publishDateSort 2020
record_format dspace
spelling CGSpace1222862023-03-14T11:40:42Z Genomic Selection model to predict grain Zn concentrations CGIAR Research Program on Rice models development selection rural development grain systems agrifood systems genomic selection model Discovery research 2020-12-31 2022-10-06T13:55:47Z 2022-10-06T13:55:47Z Report https://hdl.handle.net/10568/122286 en Open Access application/pdf CGIAR Research Program on Rice. 2020. Genomic Selection model to predict grain Zn concentrations. Reported in Rice Annual Report 2020. Innovations.
spellingShingle models
development
selection
rural development
grain
systems
agrifood systems
genomic selection
model
CGIAR Research Program on Rice
Genomic Selection model to predict grain Zn concentrations
title Genomic Selection model to predict grain Zn concentrations
title_full Genomic Selection model to predict grain Zn concentrations
title_fullStr Genomic Selection model to predict grain Zn concentrations
title_full_unstemmed Genomic Selection model to predict grain Zn concentrations
title_short Genomic Selection model to predict grain Zn concentrations
title_sort genomic selection model to predict grain zn concentrations
topic models
development
selection
rural development
grain
systems
agrifood systems
genomic selection
model
url https://hdl.handle.net/10568/122286
work_keys_str_mv AT cgiarresearchprogramonrice genomicselectionmodeltopredictgrainznconcentrations