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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
03/03/2004 |
Data da última atualização: |
28/02/2024 |
Autoria: |
LEITE, M. A. de A. |
Afiliação: |
MARIA ANGELICA DE ANDRADE LEITE, CNPTIA. |
Título: |
Organização e armazenamento de arquivos de back up de banco de dados. |
Ano de publicação: |
2003 |
Fonte/Imprenta: |
Campinas: Embrapa Informática Agropecuária, 2003. |
Páginas: |
17 p. |
Série: |
(Embrapa Informática Agropecuária. Documentos, 33). |
ISSN: |
1677-9274 |
Idioma: |
Português |
Conteúdo: |
Este documento vai descrever os requisitos para a realização de procedimento de back up no projeto Agência, o esquema de atribuição de nomes, organização e armazenamento de arquivos de back up em função destes requisitos bem como os algoritmos criados para implementar este procedimento. |
Palavras-Chave: |
Agência de Informação Embrapa; Back up; Ferramenta Agência. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/CNPTIA/10060/1/doc33.pdf
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Marc: |
LEADER 00875nam a2200181 a 4500 001 1008716 005 2024-02-28 008 2003 bl uuuu u0uu1 u #d 022 $a1677-9274 100 1 $aLEITE, M. A. de A. 245 $aOrganização e armazenamento de arquivos de back up de banco de dados.$h[electronic resource] 260 $aCampinas: Embrapa Informática Agropecuária$c2003 300 $a17 p. 490 $a(Embrapa Informática Agropecuária. Documentos, 33). 520 $aEste documento vai descrever os requisitos para a realização de procedimento de back up no projeto Agência, o esquema de atribuição de nomes, organização e armazenamento de arquivos de back up em função destes requisitos bem como os algoritmos criados para implementar este procedimento. 653 $aAgência de Informação Embrapa 653 $aBack up 653 $aFerramenta Agência
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Embrapa Agricultura Digital (CNPTIA) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Arroz e Feijão. |
Data corrente: |
17/07/2022 |
Data da última atualização: |
22/08/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
HEINEMANN, A. B.; COSTA-NETO, G; FRITSCHE-NETO, R.; MATTA, D. H. da; FERNANDES, I. K. |
Afiliação: |
ALEXANDRE BRYAN HEINEMANN, CNPAF; GERMANO COSTA-NETO, Cornell University, Ithaca-NY; ROBERTO FRITSCHE-NETO, ESALQ; DAVID HENRIQUES DA MATTA, UFG; IGOR KUIVJOGI FERNANDES, UFG. |
Título: |
Enviromic prediction is useful to define the limits of climate adaptation: a case study of common bean in Brazil. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Field Crops Research, v. 286, 108628, Oct. 2022. |
ISSN: |
0378-4290 |
DOI: |
https://doi.org/10.1016/j.fcr.2022.108628 |
Idioma: |
Inglês |
Conteúdo: |
Ongoing changes in the global environmental conditions foster plant breeding research to develop climate-smart cultivars as fast as possible. Data analytics are essential for achieving this goal, especially the so-called science of enviromics (large-scale environmental characterization of crop growing conditions) that could be used to pinpoint the relevant environment impacts driving the adaptation of a certain specie in a breeding framework. Here we quantified the effects of diverse climate factors on the current adaptation of elite common bean germplasm in Brazil. To capture the non-linearity of those impacts across a wide range of environments, we developed an ?enviromic prediction? approach by combining Generalized Additive Models (GAM), environmental covariates (EC), and grain yield (GY) from 18 years of historical breeding trials. Then, we predicted the optimum limits for ECs at each production scenario (four regions, three seasons, and two grain types) and its respective predictions of GY adaptation. Our results indicate that the nonlinear influence of air temperature, solar radiation, and rainfall led to a huge interaction of the impacts among the development stages, seasons, and regions. This revealed that seasonality differently affected the vegetative and reproductive stages, which its impact drastically vary according to the region and season, which makes unfeasible the development of a breeding strategy for selecting for broad adaptation. Conversely, with our approach it was possible to pinpoint the effects of the region- or season-specific impacts, which helped identify the ?climate limits? and critical development phases for each possible production scenario. This could allow breeders to design crop ideotypes while directing efforts to develop climate-smart varieties. Furthermore, enviromics prediction is a cost-effective way to use EC as a data analytics tool to support the visualization of regional breeding gaps for specific growing conditions. MenosOngoing changes in the global environmental conditions foster plant breeding research to develop climate-smart cultivars as fast as possible. Data analytics are essential for achieving this goal, especially the so-called science of enviromics (large-scale environmental characterization of crop growing conditions) that could be used to pinpoint the relevant environment impacts driving the adaptation of a certain specie in a breeding framework. Here we quantified the effects of diverse climate factors on the current adaptation of elite common bean germplasm in Brazil. To capture the non-linearity of those impacts across a wide range of environments, we developed an ?enviromic prediction? approach by combining Generalized Additive Models (GAM), environmental covariates (EC), and grain yield (GY) from 18 years of historical breeding trials. Then, we predicted the optimum limits for ECs at each production scenario (four regions, three seasons, and two grain types) and its respective predictions of GY adaptation. Our results indicate that the nonlinear influence of air temperature, solar radiation, and rainfall led to a huge interaction of the impacts among the development stages, seasons, and regions. This revealed that seasonality differently affected the vegetative and reproductive stages, which its impact drastically vary according to the region and season, which makes unfeasible the development of a breeding strategy for selecting for broad adaptation. Conversely, with our ap... Mostrar Tudo |
Palavras-Chave: |
Generalized Additive Models (GAM). |
Thesagro: |
Clima; Feijão; Melhoramento; Phaseolus Vulgaris. |
Thesaurus NAL: |
Beans; Breeding; Climate; Environment; Plant adaptation. |
Categoria do assunto: |
-- |
Marc: |
LEADER 02916naa a2200313 a 4500 001 2144756 005 2022-08-22 008 2022 bl uuuu u00u1 u #d 022 $a0378-4290 024 7 $ahttps://doi.org/10.1016/j.fcr.2022.108628$2DOI 100 1 $aHEINEMANN, A. B. 245 $aEnviromic prediction is useful to define the limits of climate adaptation$ba case study of common bean in Brazil.$h[electronic resource] 260 $c2022 520 $aOngoing changes in the global environmental conditions foster plant breeding research to develop climate-smart cultivars as fast as possible. Data analytics are essential for achieving this goal, especially the so-called science of enviromics (large-scale environmental characterization of crop growing conditions) that could be used to pinpoint the relevant environment impacts driving the adaptation of a certain specie in a breeding framework. Here we quantified the effects of diverse climate factors on the current adaptation of elite common bean germplasm in Brazil. To capture the non-linearity of those impacts across a wide range of environments, we developed an ?enviromic prediction? approach by combining Generalized Additive Models (GAM), environmental covariates (EC), and grain yield (GY) from 18 years of historical breeding trials. Then, we predicted the optimum limits for ECs at each production scenario (four regions, three seasons, and two grain types) and its respective predictions of GY adaptation. Our results indicate that the nonlinear influence of air temperature, solar radiation, and rainfall led to a huge interaction of the impacts among the development stages, seasons, and regions. This revealed that seasonality differently affected the vegetative and reproductive stages, which its impact drastically vary according to the region and season, which makes unfeasible the development of a breeding strategy for selecting for broad adaptation. Conversely, with our approach it was possible to pinpoint the effects of the region- or season-specific impacts, which helped identify the ?climate limits? and critical development phases for each possible production scenario. This could allow breeders to design crop ideotypes while directing efforts to develop climate-smart varieties. Furthermore, enviromics prediction is a cost-effective way to use EC as a data analytics tool to support the visualization of regional breeding gaps for specific growing conditions. 650 $aBeans 650 $aBreeding 650 $aClimate 650 $aEnvironment 650 $aPlant adaptation 650 $aClima 650 $aFeijão 650 $aMelhoramento 650 $aPhaseolus Vulgaris 653 $aGeneralized Additive Models (GAM) 700 1 $aCOSTA-NETO, G 700 1 $aFRITSCHE-NETO, R. 700 1 $aMATTA, D. H. da 700 1 $aFERNANDES, I. K. 773 $tField Crops Research$gv. 286, 108628, Oct. 2022.
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