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Artigo de periodico
Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize (2023)
Gevartosky, Raysa ; Carvalho, Humberto Fanelli; Costa-Neto, Germano; Montesinos-López, Osval A ; Crossa, José; Fritsche-Neto, Roberto
Fonte: BMC Plant Biology. Unidade: ESALQ
Assuntos: ALOCAÇÃO DE RECURSOS, INTERAÇÃO GENÓTIPO-AMBIENTE, GENÔMICA, MILHO, SELEÇÃO GENÉTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GEVARTOSKY, Raysa et al. Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize. BMC Plant Biology, v. 23, p. 1-20, 2023Tradução . . Disponível em: https://doi.org/10.1186/s12870-022-03975-1. Acesso em: 21 jul. 2024.
APA
Gevartosky, R., Carvalho, H. F., Costa-Neto, G., Montesinos-López, O. A., Crossa, J., & Fritsche-Neto, R. (2023). Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize. BMC Plant Biology, 23, 1-20. doi:10.1186/s12870-022-03975-1
NLM
Gevartosky R, Carvalho HF, Costa-Neto G, Montesinos-López OA, Crossa J, Fritsche-Neto R. Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize [Internet]. BMC Plant Biology. 2023 ; 23 1-20.[citado 2024 jul. 21 ] Available from: https://doi.org/10.1186/s12870-022-03975-1
Vancouver
Gevartosky R, Carvalho HF, Costa-Neto G, Montesinos-López OA, Crossa J, Fritsche-Neto R. Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize [Internet]. BMC Plant Biology. 2023 ; 23 1-20.[citado 2024 jul. 21 ] Available from: https://doi.org/10.1186/s12870-022-03975-1
Artigo de periodico
Enviromic prediction is useful to define the limits of climate adaptation: a case study of common bean in Brazil (2022)
Heinemann, Alexandre Bryan; Costa-Neto, Germano; Fritsche-Neto, Roberto ; Matta, David Henriques da; Fernandes, Igor Kuivjogi
Fonte: Field Crops Research. Unidade: ESALQ
Assuntos: ADAPTAÇÃO AMBIENTAL, FEIJÃO, GERMOPLASMA VEGETAL, IMPACTOS AMBIENTAIS, MELHORAMENTO GENÉTICO VEGETAL, MODELOS MATEMÁTICOS, MUDANÇA CLIMÁTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
HEINEMANN, Alexandre Bryan et al. Enviromic prediction is useful to define the limits of climate adaptation: a case study of common bean in Brazil. Field Crops Research, v. 286, p. 1-19, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.fcr.2022.108628. Acesso em: 21 jul. 2024.
APA
Heinemann, A. B., Costa-Neto, G., Fritsche-Neto, R., Matta, D. H. da, & Fernandes, I. K. (2022). Enviromic prediction is useful to define the limits of climate adaptation: a case study of common bean in Brazil. Field Crops Research, 286, 1-19. doi:10.1016/j.fcr.2022.108628
NLM
Heinemann AB, Costa-Neto G, Fritsche-Neto R, Matta DH da, Fernandes IK. Enviromic prediction is useful to define the limits of climate adaptation: a case study of common bean in Brazil [Internet]. Field Crops Research. 2022 ; 286 1-19.[citado 2024 jul. 21 ] Available from: https://doi.org/10.1016/j.fcr.2022.108628
Vancouver
Heinemann AB, Costa-Neto G, Fritsche-Neto R, Matta DH da, Fernandes IK. Enviromic prediction is useful to define the limits of climate adaptation: a case study of common bean in Brazil [Internet]. Field Crops Research. 2022 ; 286 1-19.[citado 2024 jul. 21 ] Available from: https://doi.org/10.1016/j.fcr.2022.108628
Artigo de periodico
The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data (2021)
Crossa, Jose; Fritsche-Neto, Roberto ; Montesinos-Lopez, Osval A; Costa-Neto, Germano ; Dreisigacker, Susanne; Montesinos-Lopez, Abelardo; Bentley, Alison R
Fonte: Frontiers in Plant Science. Unidade: ESALQ
Assuntos: MELHORAMENTO GENÉTICO VEGETAL, GENÔMICA, FENÓTIPOS, MUDANÇA CLIMÁTICA, CULTIVO DE PLANTAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CROSSA, Jose et al. The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data. Frontiers in Plant Science, v. 12, p. 1-6, 2021Tradução . . Disponível em: https://doi.org/10.3389/fpls.2021.651480. Acesso em: 21 jul. 2024.
APA
Crossa, J., Fritsche-Neto, R., Montesinos-Lopez, O. A., Costa-Neto, G., Dreisigacker, S., Montesinos-Lopez, A., & Bentley, A. R. (2021). The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data. Frontiers in Plant Science, 12, 1-6. doi:10.3389/fpls.2021.651480
NLM
Crossa J, Fritsche-Neto R, Montesinos-Lopez OA, Costa-Neto G, Dreisigacker S, Montesinos-Lopez A, Bentley AR. The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data [Internet]. Frontiers in Plant Science. 2021 ; 12 1-6.[citado 2024 jul. 21 ] Available from: https://doi.org/10.3389/fpls.2021.651480
Vancouver
Crossa J, Fritsche-Neto R, Montesinos-Lopez OA, Costa-Neto G, Dreisigacker S, Montesinos-Lopez A, Bentley AR. The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data [Internet]. Frontiers in Plant Science. 2021 ; 12 1-6.[citado 2024 jul. 21 ] Available from: https://doi.org/10.3389/fpls.2021.651480
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
Fonte: Frontiers in Plant Science. Unidade: ESALQ
Assuntos: 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: 21 jul. 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 jul. 21 ] 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 jul. 21 ] Available from: https://doi.org/10.3389/fpls.2021.717552

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