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
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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: 23 ago. 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/s12870-022-03975-1NLM
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 ago. 23 ] Available from: https://doi.org/10.1186/s12870-022-03975-1Vancouver
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 ago. 23 ] Available from: https://doi.org/10.1186/s12870-022-03975-1
