Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials (2020)
- Authors:
- USP affiliated authors: FRITSCHE NETO, ROBERTO - ESALQ ; COSTA NETO, GERMANO MARTINS FERREIRA - ESALQ
- Unidade: ESALQ
- DOI: 10.1038/s41437-020-00353-1
- Subjects: GENOMAS; GENÔMICA; INTERAÇÃO GENÓTIPO-AMBIENTE; MILHO; MODELOS MATEMÁTICOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Heidelberg
- Date published: 2020
- Source:
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: hybrid
- Licença: cc-by
-
ABNT
COSTA NETO, Germano Martins Ferreira e FRITSCHE NETO, Roberto e CROSSA, José. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity, p. 1-15, 2020Tradução . . Disponível em: https://doi.org/10.1038/s41437-020-00353-1. Acesso em: 28 dez. 2025. -
APA
Costa Neto, G. M. F., Fritsche Neto, R., & Crossa, J. (2020). Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity, 1-15. doi:10.1038/s41437-020-00353-1 -
NLM
Costa Neto GMF, Fritsche Neto R, Crossa J. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials [Internet]. Heredity. 2020 ; 1-15.[citado 2025 dez. 28 ] Available from: https://doi.org/10.1038/s41437-020-00353-1 -
Vancouver
Costa Neto GMF, Fritsche Neto R, Crossa J. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials [Internet]. Heredity. 2020 ; 1-15.[citado 2025 dez. 28 ] Available from: https://doi.org/10.1038/s41437-020-00353-1 - Enviromics: bridging different sources of data, building one framework
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Informações sobre o DOI: 10.1038/s41437-020-00353-1 (Fonte: oaDOI API)
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