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Artigo de periodico
Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data (2022)
Yassue, Rafael Massahiro ; Galli, Giovanni ; Fritsche‐Neto, Roberto ; Morota, Gota
Source: Crop Science. Unidade: ESALQ
Subjects: ANÁLISE ESPECTRAL, BACTÉRIAS, ESTIMULANTES DE CRESCIMENTO VEGETAL, GENÔMICA, INOCULAÇÃO, MILHO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
YASSUE, Rafael Massahiro et al. Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data. Crop Science, p. 1-13, 2022Tradução . . Disponível em: https://doi.org/10.1002/csc2.20836. Acesso em: 04 nov. 2024.
APA
Yassue, R. M., Galli, G., Fritsche‐Neto, R., & Morota, G. (2022). Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data. Crop Science, 1-13. doi:10.1002/csc2.20836
NLM
Yassue RM, Galli G, Fritsche‐Neto R, Morota G. Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data [Internet]. Crop Science. 2022 ; 1-13.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1002/csc2.20836
Vancouver
Yassue RM, Galli G, Fritsche‐Neto R, Morota G. Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data [Internet]. Crop Science. 2022 ; 1-13.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1002/csc2.20836
Artigo de periodico
Improving the identification of haploid maize seeds using convolutional neural networks (2021)
Sabadin, Felipe ; Galli, Giovanni ; Borsato, Ronaldo; Gevartosky, Raysa ; Campos, Gabriela Romêro; Fritsche-Neto, Roberto
Source: Crop Science. Unidade: ESALQ
Subjects: MILHO, SEMENTES, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SABADIN, Felipe et al. Improving the identification of haploid maize seeds using convolutional neural networks. Crop Science, p. 1-11, 2021Tradução . . Disponível em: https://doi.org/10.1002/csc2.20487. Acesso em: 04 nov. 2024.
APA
Sabadin, F., Galli, G., Borsato, R., Gevartosky, R., Campos, G. R., & Fritsche-Neto, R. (2021). Improving the identification of haploid maize seeds using convolutional neural networks. Crop Science, 1-11. doi:10.1002/csc2.20487
NLM
Sabadin F, Galli G, Borsato R, Gevartosky R, Campos GR, Fritsche-Neto R. Improving the identification of haploid maize seeds using convolutional neural networks [Internet]. Crop Science. 2021 ; 1-11.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1002/csc2.20487
Vancouver
Sabadin F, Galli G, Borsato R, Gevartosky R, Campos GR, Fritsche-Neto R. Improving the identification of haploid maize seeds using convolutional neural networks [Internet]. Crop Science. 2021 ; 1-11.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1002/csc2.20487
Artigo de periodico
The accuracy of different strategies for building training sets for genomic predictions in soybean segregating populations (2020)
Mendonça, Leandro de Freitas ; Fritsche‐Neto, Roberto
Source: Crop Science. Unidade: ESALQ
Subjects: GENÔMICA, LINHAGENS VEGETAIS, MELHORAMENTO GENÉTICO VEGETAL, POPULAÇÕES VEGETAIS, SELEÇÃO GENÉTICA, SOJA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MENDONÇA, Leandro de Freitas e FRITSCHE‐NETO, Roberto. The accuracy of different strategies for building training sets for genomic predictions in soybean segregating populations. Crop Science, p. 1-22, 2020Tradução . . Disponível em: https://doi.org/10.1002/csc2.20267. Acesso em: 04 nov. 2024.
APA
Mendonça, L. de F., & Fritsche‐Neto, R. (2020). The accuracy of different strategies for building training sets for genomic predictions in soybean segregating populations. Crop Science, 1-22. doi:10.1002/csc2.20267
NLM
Mendonça L de F, Fritsche‐Neto R. The accuracy of different strategies for building training sets for genomic predictions in soybean segregating populations [Internet]. Crop Science. 2020 ; 1-22.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1002/csc2.20267
Vancouver
Mendonça L de F, Fritsche‐Neto R. The accuracy of different strategies for building training sets for genomic predictions in soybean segregating populations [Internet]. Crop Science. 2020 ; 1-22.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1002/csc2.20267
Artigo de periodico
Single nucleotide polymorphism calling and imputation strategies for cost‐effective genotyping in a tropical maize breeding program (2020)
Oliveira, Amanda Avelar ; Guimarães, Lauro José Moreira; Guimarães, Claudia Teixeira; Guimarães, Paulo Evaristo de Oliveira; Pinto, Marcos de Oliveira; Pastina, Maria Marta; Margarido, Gabriel Rodrigues Alves
Source: Crop Science. Unidade: ESALQ
Subjects: GENÓTIPOS, MELHORAMENTO GENÉTICO VEGETAL, MILHO, POLIMORFISMO, SEQUENCIAMENTO GENÉTICO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA, Amanda Avelar et al. Single nucleotide polymorphism calling and imputation strategies for cost‐effective genotyping in a tropical maize breeding program. Crop Science, v. 60, p. 3066–3082, 2020Tradução . . Disponível em: https://doi.org/10.1002/csc2.20255. Acesso em: 04 nov. 2024.
APA
Oliveira, A. A., Guimarães, L. J. M., Guimarães, C. T., Guimarães, P. E. de O., Pinto, M. de O., Pastina, M. M., & Margarido, G. R. A. (2020). Single nucleotide polymorphism calling and imputation strategies for cost‐effective genotyping in a tropical maize breeding program. Crop Science, 60, 3066–3082. doi:10.1002/csc2.20255
NLM
Oliveira AA, Guimarães LJM, Guimarães CT, Guimarães PE de O, Pinto M de O, Pastina MM, Margarido GRA. Single nucleotide polymorphism calling and imputation strategies for cost‐effective genotyping in a tropical maize breeding program [Internet]. Crop Science. 2020 ; 60 3066–3082.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1002/csc2.20255
Vancouver
Oliveira AA, Guimarães LJM, Guimarães CT, Guimarães PE de O, Pinto M de O, Pastina MM, Margarido GRA. Single nucleotide polymorphism calling and imputation strategies for cost‐effective genotyping in a tropical maize breeding program [Internet]. Crop Science. 2020 ; 60 3066–3082.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1002/csc2.20255
Artigo de periodico
High-density SNP-based genetic diversity and heterotic patterns of tropical maize breeding lines (2020)
Silva, Karla Jorge; Guimaraes, Claudia Teixeira; Guilhen, Jose Henrique Soler; Guimaraes, Paulo Evaristo de Oliveira; Parentoni, Sidney Netto; Trindade, Roberto dos Santos; Oliveira, Amanda Avelar de ; Bernardino, Karine da Costa; Pinto, Marcos de Oliveira; Dias, Kaio Olimpio das Gracas ; Bernardes, Carolina de Oliveira; Dias, Luiz Antonio dos Santos; Guimaraes, Lauro Jose Moreira; Pastina, Maria Marta
Source: Crop Science. Unidade: ESALQ
Subjects: DIVERSIDADE GENÉTICA, HETEROSE, HIBRIDAÇÃO VEGETAL, LINHAGENS VEGETAIS, MARCADOR MOLECULAR, MELHORAMENTO GENÉTICO VEGETAL, MILHO, POLIMORFISMO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Karla Jorge et al. High-density SNP-based genetic diversity and heterotic patterns of tropical maize breeding lines. Crop Science, v. 60, p. 779-787, 2020Tradução . . Disponível em: https://doi.org/10.1002/csc2.20018. Acesso em: 04 nov. 2024.
APA
Silva, K. J., Guimaraes, C. T., Guilhen, J. H. S., Guimaraes, P. E. de O., Parentoni, S. N., Trindade, R. dos S., et al. (2020). High-density SNP-based genetic diversity and heterotic patterns of tropical maize breeding lines. Crop Science, 60, 779-787. doi:10.1002/csc2.20018
NLM
Silva KJ, Guimaraes CT, Guilhen JHS, Guimaraes PE de O, Parentoni SN, Trindade R dos S, Oliveira AA de, Bernardino K da C, Pinto M de O, Dias KO das G, Bernardes C de O, Dias LA dos S, Guimaraes LJM, Pastina MM. High-density SNP-based genetic diversity and heterotic patterns of tropical maize breeding lines [Internet]. Crop Science. 2020 ; 60 779-787.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1002/csc2.20018
Vancouver
Silva KJ, Guimaraes CT, Guilhen JHS, Guimaraes PE de O, Parentoni SN, Trindade R dos S, Oliveira AA de, Bernardino K da C, Pinto M de O, Dias KO das G, Bernardes C de O, Dias LA dos S, Guimaraes LJM, Pastina MM. High-density SNP-based genetic diversity and heterotic patterns of tropical maize breeding lines [Internet]. Crop Science. 2020 ; 60 779-787.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1002/csc2.20018
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: 04 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. 04 ] 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. 04 ] Available from: https://doi.org/10.1002/csc2.20072
Artigo de periodico
Contribution of additive and dominance effects on agronomical and nutritional traits, and multivariate selection on Urochloa spp. hybrids (2018)
Matias, Filipe Inácio ; Barrios, Sanzio Carvalho Lima; Bearari, Lucas Martins; Meireles, Karem Guimarães Xavier; Mateus, Rogério Gonçalves; Amaral, Pedro Nelson Cesar do; Alves, Geovani Ferreira; Valle, Cacilda Borges do; Fritsche-Neto, Roberto
Source: Crop Science. Unidade: ESALQ
Subjects: ANÁLISE MULTIVARIADA, BRACHIARIA, CRUZAMENTO VEGETAL, HIBRIDAÇÃO VEGETAL, MELHORAMENTO GENÉTICO VEGETAL, SELEÇÃO GENÉTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MATIAS, Filipe Inácio et al. Contribution of additive and dominance effects on agronomical and nutritional traits, and multivariate selection on Urochloa spp. hybrids. Crop Science, v. 58, p. 2444-2458, 2018Tradução . . Disponível em: https://doi.org/10.2135/cropsci2018.04.0261. Acesso em: 04 nov. 2024.
APA
Matias, F. I., Barrios, S. C. L., Bearari, L. M., Meireles, K. G. X., Mateus, R. G., Amaral, P. N. C. do, et al. (2018). Contribution of additive and dominance effects on agronomical and nutritional traits, and multivariate selection on Urochloa spp. hybrids. Crop Science, 58, 2444-2458. doi:10.2135/cropsci2018.04.0261
NLM
Matias FI, Barrios SCL, Bearari LM, Meireles KGX, Mateus RG, Amaral PNC do, Alves GF, Valle CB do, Fritsche-Neto R. Contribution of additive and dominance effects on agronomical and nutritional traits, and multivariate selection on Urochloa spp. hybrids [Internet]. Crop Science. 2018 ; 58 2444-2458.[citado 2024 nov. 04 ] Available from: https://doi.org/10.2135/cropsci2018.04.0261
Vancouver
Matias FI, Barrios SCL, Bearari LM, Meireles KGX, Mateus RG, Amaral PNC do, Alves GF, Valle CB do, Fritsche-Neto R. Contribution of additive and dominance effects on agronomical and nutritional traits, and multivariate selection on Urochloa spp. hybrids [Internet]. Crop Science. 2018 ; 58 2444-2458.[citado 2024 nov. 04 ] Available from: https://doi.org/10.2135/cropsci2018.04.0261

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