The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data (2021)
- Authors:
- USP affiliated authors: FRITSCHE NETO, ROBERTO - ESALQ ; COSTA NETO, GERMANO MARTINS FERREIRA - ESALQ
- Unidade: ESALQ
- DOI: 10.3389/fpls.2021.651480
- Subjects: MELHORAMENTO GENÉTICO VEGETAL; GENÔMICA; FENÓTIPOS; MUDANÇA CLIMÁTICA; CULTIVO DE PLANTAS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Frontiers in Plant Science
- ISSN: 1664-462X
- Volume/Número/Paginação/Ano: v. 12, art. 651480, p. 1-6, 2021
- Status:
- Artigo publicado em periódico de acesso aberto (Gold Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
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: 31 mar. 2026. -
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 2026 mar. 31 ] 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 2026 mar. 31 ] Available from: https://doi.org/10.3389/fpls.2021.651480 - Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials
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- A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes
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- Multi-trait genomic prediction for nitrogen response indices in tropical maize hybrids
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