Improving predictive ability in sparse testing designs in soybean populations.

Persa, Reyna; Canella Vieira, Caio; Rios, Esteban; Hoyos-Villegas, Valerio; Messina, Carlos D; Runcie, Daniel; Jarquin, Diego

Persa, Reyna; Canella Vieira, Caio; Rios, Esteban; Hoyos-Villegas, Valerio; Messina, Carlos D; Runcie, Daniel; Jarquin, Diego.

Afiliação

Persa R; Agronomy Department, University of Florida, Gainesville, FL, United States.
Canella Vieira C; Crop, Soil, and Environmental Sciences, Bumpers College, University of Arkansas, Fayetteville, AR, United States.
Rios E; Agronomy Department, University of Florida, Gainesville, FL, United States.
Hoyos-Villegas V; Department of Plant Science, McGill University, Montreal, QC, Canada.
Messina CD; Horticultural Sciences Department, University of Florida, Gainesville, FL, United States.
Runcie D; Department of Plant Sciences, University of California Davis, Davis, CA, United States.
Jarquin D; Agronomy Department, University of Florida, Gainesville, FL, United States.

Front Genet ; 14: 1269255, 2023.

Article em En | MEDLINE | ID: mdl-38075684

Palavras-chave

experimental design; genomic prediction; genotype-by-environment interaction; plant breeding; soybean; sparse testing

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Genet Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

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