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Artigo de periodico
Elite germplasm introduction, training set composition, and genetic optimization algorithms effect on genomic selection‐based breeding programs (2024)
Fritsche‐Neto, Roberto ; Yassue, Rafael Massahiro; Silva, Allison Vieira da ; Prado, Melina ; DoVale, Júlio César
Source: Crop Science. Unidade: ESALQ
Subjects: ALGORITMOS GENÉTICOS, GENOMAS, GENÔMICA, GERMOPLASMA VEGETAL, MELHORAMENTO GENÉTICO VEGETAL, SELEÇÃO GENÉTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FRITSCHE‐NETO, Roberto et al. Elite germplasm introduction, training set composition, and genetic optimization algorithms effect on genomic selection‐based breeding programs. Crop Science, v. 64, p. 3323–3338, 2024Tradução . . Disponível em: https://doi.org/10.1002/csc2.21384. Acesso em: 04 maio 2026.
APA
Fritsche‐Neto, R., Yassue, R. M., Silva, A. V. da, Prado, M., & DoVale, J. C. (2024). Elite germplasm introduction, training set composition, and genetic optimization algorithms effect on genomic selection‐based breeding programs. Crop Science, 64, 3323–3338. doi:10.1002/csc2.21384
NLM
Fritsche‐Neto R, Yassue RM, Silva AV da, Prado M, DoVale JC. Elite germplasm introduction, training set composition, and genetic optimization algorithms effect on genomic selection‐based breeding programs [Internet]. Crop Science. 2024 ; 64 3323–3338.[citado 2026 maio 04 ] Available from: https://doi.org/10.1002/csc2.21384
Vancouver
Fritsche‐Neto R, Yassue RM, Silva AV da, Prado M, DoVale JC. Elite germplasm introduction, training set composition, and genetic optimization algorithms effect on genomic selection‐based breeding programs [Internet]. Crop Science. 2024 ; 64 3323–3338.[citado 2026 maio 04 ] Available from: https://doi.org/10.1002/csc2.21384
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 (2023)
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, v. 63, p. 88-100, 2023Tradução . . Disponível em: https://doi.org/10.1002/csc2.20836. Acesso em: 04 maio 2026.
APA
Yassue, R. M., Galli, G., Fritsche‐Neto, R., & Morota, G. (2023). 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, 63, 88-100. 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. 2023 ;63 88-100.[citado 2026 maio 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. 2023 ;63 88-100.[citado 2026 maio 04 ] Available from: https://doi.org/10.1002/csc2.20836
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 maio 2026.
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 2026 maio 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 2026 maio 04 ] Available from: https://doi.org/10.1002/csc2.20267
Artigo de periodico
Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum (2020)
Galli, Giovanni ; Horne, David W ; Collins, S. Delroy; Jung, Jinha; Chang, Anjin ; Fritsche‐Neto, Roberto ; Rooney, William L
Source: The Plant Phenome Journal. Unidade: ESALQ
Subjects: AERONAVES NÃO TRIPULADAS, ANÁLISE ESPECTRAL, ANTRACNOSE, FENOLOGIA, FENÓTIPOS, FUNGOS FITOPATOGÊNICOS, IMAGEM DIGITAL, SORGO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GALLI, Giovanni et al. Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum. The Plant Phenome Journal, v. 3, p. 1-14, 2020Tradução . . Disponível em: https://doi.org/10.1002/ppj2.20010. Acesso em: 04 maio 2026.
APA
Galli, G., Horne, D. W., Collins, S. D., Jung, J., Chang, A., Fritsche‐Neto, R., & Rooney, W. L. (2020). Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum. The Plant Phenome Journal, 3, 1-14. doi:10.1002/ppj2.20010
NLM
Galli G, Horne DW, Collins SD, Jung J, Chang A, Fritsche‐Neto R, Rooney WL. Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum [Internet]. The Plant Phenome Journal. 2020 ; 3 1-14.[citado 2026 maio 04 ] Available from: https://doi.org/10.1002/ppj2.20010
Vancouver
Galli G, Horne DW, Collins SD, Jung J, Chang A, Fritsche‐Neto R, Rooney WL. Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum [Internet]. The Plant Phenome Journal. 2020 ; 3 1-14.[citado 2026 maio 04 ] Available from: https://doi.org/10.1002/ppj2.20010

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