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Registro Completo |
Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
Data corrente: |
18/12/2007 |
Data da última atualização: |
20/10/2022 |
Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
Autoria: |
CARDOSO, M. G. S.; SOUZA, A. da S.; VIDAL, A. M.; DANTAS, J. L. L.; MORAIS-LINO, L. S. |
Afiliação: |
Maria Gerolina Silva Cardoso; Antônio da Silva Souza, CNPMF; Ádila Melo Vidal, UFRB; Jorge Luiz Loyola Dantas, CNPMF; Lucymeire Souza Morais-Lino, UFRB. |
Título: |
Avaliação dos níveis de BAP na multiplicação in vitro do mamoeiro. |
Ano de publicação: |
2007 |
Fonte/Imprenta: |
Revista Brasileira de Horticultura Ornamental, Campinas, v. 13, 1226-1229, 2007. Suplemento. |
Idioma: |
Português |
Notas: |
Edição dos Resumos do XVI Congresso Brasileiro de Floricultura e Plantas Ornamentais; III Congresso Brasileiro de Cultura de Tecidos e Plantas; I Simpósio de Plantas Ornamentais Nativas, Goiânia, set. 2007. |
Conteúdo: |
A forma de propagação do mamoeiro mais utilizado é por sementes (Drew, 1987). Sendo uma espécie de fecundação cruzada, ocorre segregação gênica nas progênies obtidas por sementes, não permitindo a manutenção do genótipo manifestado pela planta-mãe. De acordo com Rajeevan & Pandey (1986), além da heterozigose, também constituem problemas a natureza dióica e a susceptibilidade a um garnde número de doenças viróticas, que tem impostos consideráveis limitações em trabalhos de melhoramento. Com o avanço das técnicas de biotecnologia, foram estabelecidas vários protocolos para a micropropagação massal de diversas espécies de fruteiras. |
Thesagro: |
Mamão; Micropropagação; Propagação Vegetativa; Reprodução Vegetal. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/654263/1/1676-Texto-do-Artigo-9190-7856-10-20181003.pdf
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Marc: |
LEADER 01571naa a2200229 a 4500 001 1654263 005 2022-10-20 008 2007 bl uuuu u00u1 u #d 100 1 $aCARDOSO, M. G. S. 245 $aAvaliação dos níveis de BAP na multiplicação in vitro do mamoeiro.$h[electronic resource] 260 $c2007 500 $aEdição dos Resumos do XVI Congresso Brasileiro de Floricultura e Plantas Ornamentais; III Congresso Brasileiro de Cultura de Tecidos e Plantas; I Simpósio de Plantas Ornamentais Nativas, Goiânia, set. 2007. 520 $aA forma de propagação do mamoeiro mais utilizado é por sementes (Drew, 1987). Sendo uma espécie de fecundação cruzada, ocorre segregação gênica nas progênies obtidas por sementes, não permitindo a manutenção do genótipo manifestado pela planta-mãe. De acordo com Rajeevan & Pandey (1986), além da heterozigose, também constituem problemas a natureza dióica e a susceptibilidade a um garnde número de doenças viróticas, que tem impostos consideráveis limitações em trabalhos de melhoramento. Com o avanço das técnicas de biotecnologia, foram estabelecidas vários protocolos para a micropropagação massal de diversas espécies de fruteiras. 650 $aMamão 650 $aMicropropagação 650 $aPropagação Vegetativa 650 $aReprodução Vegetal 700 1 $aSOUZA, A. da S. 700 1 $aVIDAL, A. M. 700 1 $aDANTAS, J. L. L. 700 1 $aMORAIS-LINO, L. S. 773 $tRevista Brasileira de Horticultura Ornamental, Campinas$gv. 13, 1226-1229, 2007. Suplemento.
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Embrapa Mandioca e Fruticultura (CNPMF) |
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Registro Completo
Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
26/07/2023 |
Data da última atualização: |
26/07/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
BASTOS, B. P.; PINHEIRO, H. S. K.; FERREIRA, F. J. F.; CARVALHO JUNIOR, W. de; ANJOS, L. H. C. dos. |
Afiliação: |
BLENDA PEREIRA BASTOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; HELENA SARAIVA KOENOW PINHEIRO, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; FRANCISCO JOSÉ FONSECA FERREIRA, UNIVERSIDADE FEDERAL DO PARANÁ; WALDIR DE CARVALHO JUNIOR, CNPS; LÚCIA HELENA CUNHA DOS ANJOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO. |
Título: |
Could airborne geophysical data be used to improve predictive modeling of agronomic soil properties in tropical hillslope area? |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
Remote Sensing, v. 15, n. 15, 3719, 2023. |
DOI: |
https://doi.org/10.3390/rs15153719 |
Idioma: |
Inglês |
Conteúdo: |
Airborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (uR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0-30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model's performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient of variation (CV%). The RF algorithm showed better performance with AGD (R2 values ranging from 0.15 to 0.23), as well as the SVM model (R2 values ranging from 0.08 to 0.23) when compared to RF (R2 values ranging from 0.10 to 0.20) and SVM (R2 values ranging from 0.04 to 0.10) models without AGD. Overall, the results suggest that AGD can be helpful for soil mapping. Nevertheless, it is crucial to acknowledge that the accuracy of AGD in predicting soil properties could vary depending on various common factors in DSM, such as the quality and resolution of the covariates and available soil data. Further research is needed to determine the optimal approach for using AGD in soil mapping. MenosAirborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (uR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0-30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model's performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient ... Mostrar Tudo |
Palavras-Chave: |
Digital soil mapping; Gamma-ray spectrometry data; Hillslope areas; Machine learning; Magnetic data; Mapeamento digital do solo; Parent material. |
Thesagro: |
Sensoriamento Remoto. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1155276/1/Could-airborne-geophysical-data-be-used-to-improve-predictive-modeling-2023.pdf
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Marc: |
LEADER 03095naa a2200277 a 4500 001 2155276 005 2023-07-26 008 2023 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.3390/rs15153719$2DOI 100 1 $aBASTOS, B. P. 245 $aCould airborne geophysical data be used to improve predictive modeling of agronomic soil properties in tropical hillslope area?$h[electronic resource] 260 $c2023 520 $aAirborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (uR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0-30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model's performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient of variation (CV%). The RF algorithm showed better performance with AGD (R2 values ranging from 0.15 to 0.23), as well as the SVM model (R2 values ranging from 0.08 to 0.23) when compared to RF (R2 values ranging from 0.10 to 0.20) and SVM (R2 values ranging from 0.04 to 0.10) models without AGD. Overall, the results suggest that AGD can be helpful for soil mapping. Nevertheless, it is crucial to acknowledge that the accuracy of AGD in predicting soil properties could vary depending on various common factors in DSM, such as the quality and resolution of the covariates and available soil data. Further research is needed to determine the optimal approach for using AGD in soil mapping. 650 $aSensoriamento Remoto 653 $aDigital soil mapping 653 $aGamma-ray spectrometry data 653 $aHillslope areas 653 $aMachine learning 653 $aMagnetic data 653 $aMapeamento digital do solo 653 $aParent material 700 1 $aPINHEIRO, H. S. K. 700 1 $aFERREIRA, F. J. F. 700 1 $aCARVALHO JUNIOR, W. de 700 1 $aANJOS, L. H. C. dos 773 $tRemote Sensing$gv. 15, n. 15, 3719, 2023.
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