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Artigo de periodico
EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture (2021)
Costa-Neto, Germano ; Galli, Giovanni ; Carvalho, Humberto Fanelli ; Crossa, José ; Fritsche-Neto, Roberto
Source: G3: Genes | Genomes | Genetics. Unidade: ESALQ
Subjects: AGRICULTURA, GENÉTICA QUANTITATIVA, GENÔMICA, INTERAÇÃO GENÓTIPO-AMBIENTE, SOFTWARES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COSTA-NETO, Germano et al. EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3: Genes | Genomes | Genetics, v. 11, n. 4, p. 1-20, 2021Tradução . . Disponível em: https://doi.org/10.1093/g3journal/jkab040. Acesso em: 16 nov. 2024.
APA
Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3: Genes | Genomes | Genetics, 11( 4), 1-20. doi:10.1093/g3journal/jkab040
NLM
Costa-Neto G, Galli G, Carvalho HF, Crossa J, Fritsche-Neto R. EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture [Internet]. G3: Genes | Genomes | Genetics. 2021 ; 11( 4): 1-20.[citado 2024 nov. 16 ] Available from: https://doi.org/10.1093/g3journal/jkab040
Vancouver
Costa-Neto G, Galli G, Carvalho HF, Crossa J, Fritsche-Neto R. EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture [Internet]. G3: Genes | Genomes | Genetics. 2021 ; 11( 4): 1-20.[citado 2024 nov. 16 ] Available from: https://doi.org/10.1093/g3journal/jkab040
Artigo de periodico
Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize (2021)
Costa-Neto, Germano; Crossa, Jose; Fritsche-Neto, Roberto
Source: Frontiers in Plant Science. Unidade: ESALQ
Subjects: MILHO, GENÔMICA, INTERAÇÃO GENÓTIPO-AMBIENTE, GENÉTICA QUANTITATIVA, FENÓTIPOS, CLIMA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COSTA-NETO, Germano e CROSSA, Jose e FRITSCHE-NETO, Roberto. Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize. Frontiers in Plant Science, v. 12, p. 1-21, 2021Tradução . . Disponível em: https://doi.org/10.3389/fpls.2021.717552. Acesso em: 16 nov. 2024.
APA
Costa-Neto, G., Crossa, J., & Fritsche-Neto, R. (2021). Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize. Frontiers in Plant Science, 12, 1-21. doi:10.3389/fpls.2021.717552
NLM
Costa-Neto G, Crossa J, Fritsche-Neto R. Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize [Internet]. Frontiers in Plant Science. 2021 ; 12 1-21.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3389/fpls.2021.717552
Vancouver
Costa-Neto G, Crossa J, Fritsche-Neto R. Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize [Internet]. Frontiers in Plant Science. 2021 ; 12 1-21.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3389/fpls.2021.717552
Artigo de periodico
Genomic prediction enables early but low-intensity selection in soybean segregating progenies (2020)
Mendonça, Leandro de Freitas ; Galli, Giovanni ; Malone, G; Fritsche-Neto, Roberto
Source: Crop Science. Unidade: ESALQ
Subjects: GENÉTICA QUANTITATIVA, GENÔMICA, MELHORAMENTO GENÉTICO VEGETAL, SELEÇÃO GENÉTICA, SOJA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MENDONÇA, Leandro de Freitas et al. Genomic prediction enables early but low-intensity selection in soybean segregating progenies. Crop Science, p. 1-16, 2020Tradução . . Disponível em: https://doi.org/10.1002/csc2.20072. Acesso em: 16 nov. 2024.
APA
Mendonça, L. de F., Galli, G., Malone, G., & Fritsche-Neto, R. (2020). Genomic prediction enables early but low-intensity selection in soybean segregating progenies. Crop Science, 1-16. doi:10.1002/csc2.20072
NLM
Mendonça L de F, Galli G, Malone G, Fritsche-Neto R. Genomic prediction enables early but low-intensity selection in soybean segregating progenies [Internet]. Crop Science. 2020 ; 1-16.[citado 2024 nov. 16 ] Available from: https://doi.org/10.1002/csc2.20072
Vancouver
Mendonça L de F, Galli G, Malone G, Fritsche-Neto R. Genomic prediction enables early but low-intensity selection in soybean segregating progenies [Internet]. Crop Science. 2020 ; 1-16.[citado 2024 nov. 16 ] Available from: https://doi.org/10.1002/csc2.20072

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