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Registro Completo |
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Biblioteca(s): |
Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
23/04/2001 |
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Data da última atualização: |
23/04/2001 |
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Autoria: |
EIRA, M. T. S.; WALTERS, C.; CALDAS, S.; REIS, R. B. |
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Título: |
Conservacao de sementes de coffea spp. em colecoes de germoplasma ex situ. |
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Ano de publicação: |
2000 |
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Fonte/Imprenta: |
In: SIMPOSIO DE PESQUISA DOS CAFES DO BRASIL, 1., 2000, Pocos de Caldas. Resumos expandidos. Brasilia: Embrapa Cafe/MINASPLAN, 2000. v.1, p.557-558 |
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Idioma: |
Português |
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Palavras-Chave: |
Coffee; Conservacao de germoplasma; Conservation. |
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Thesagro: |
Café; Semente. |
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Thesaurus Nal: |
germplasm; seeds. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00686nam a2200217 a 4500
001 1180007
005 2001-04-23
008 2000 bl uuuu u01u1 u #d
100 1 $aEIRA, M. T. S.
245 $aConservacao de sementes de coffea spp. em colecoes de germoplasma ex situ.
260 $aIn: SIMPOSIO DE PESQUISA DOS CAFES DO BRASIL, 1., 2000, Pocos de Caldas. Resumos expandidos. Brasilia: Embrapa Cafe/MINASPLAN, 2000. v.1, p.557-558$c2000
650 $agermplasm
650 $aseeds
650 $aCafé
650 $aSemente
653 $aCoffee
653 $aConservacao de germoplasma
653 $aConservation
700 1 $aWALTERS, C.
700 1 $aCALDAS, S.
700 1 $aREIS, R. B.
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Registro original: |
Embrapa Recursos Genéticos e Biotecnologia (CENARGEN) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
06/09/2019 |
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Data da última atualização: |
23/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
APARECIDO, L. E. de O.; MORAES, J. R. da S. C. de; ROLIM, G. de S.; MARTORANO, L. G.; MENESES, K. C. de; VALERIANO, T. T. B. |
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Afiliação: |
Lucas Eduardo de Oliveira Aparecido, IFMS; José Reinaldo da Silva Cabral de Moraes, UNESP; Glauco de Souza Rolim, UNESP; LUCIETA GUERREIRO MARTORANO, CPATU; Kamila Cunha de Meneses, UNESP; Taynara Tuany Borges Valeriano, UNESP. |
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Título: |
Neural networks in climate spatialization and their application in the agricultural zoning of climate risk for sunflower in different sowing dates. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Archives of Agronomy and Soil Science, v. 65, n. 11, p. 1477-1492, 2019. |
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DOI: |
10.1080/03650340.2019.1566715 |
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Idioma: |
Inglês |
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Conteúdo: |
Sunflower is a species that is sensitive to local climate conditions. However, studies that use artificial neural networks (ANNs) to evaluate this influence and create tools such as agricultural zoning of climate risk (ZARC) have not been conducted for this species. Due to the importance of sunflower as a human food source and for biodiesel production, and also the necessity of conducting research to evaluate the suitability of this oleaginous species under different climatic conditions. Thus, we seek to construct a ZARC for sunflower in Brazil simulating sowing on different dates and using meteorological elements spatialized by ANNs. Climate data were used: air temperature (T), rainfall (P), relative air humidity (UR), solar radiation (MJ_m−2_d−1) and wind velocity (U2). Climatic regions considered suitable for the cultivation of sunflower had average annual values for T between 20 and 28°C, P between 500 and 1.500 mm per cycle, and soil water deficit (DEF) below 140 mm per cycle. A neural network is an efficient tool that can be used in spatialization of climate variables quickly and accurately. Sunflower sowing in the spring and summer are the ones that provide the largest suitable areas in southeastern Brazil, with 58.13 and 64.36% of suitable areas, respectively |
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Thesagro: |
Clima; Girassol; Zoneamento Agrícola. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02087naa a2200229 a 4500
001 2112016
005 2020-01-23
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1080/03650340.2019.1566715$2DOI
100 1 $aAPARECIDO, L. E. de O.
245 $aNeural networks in climate spatialization and their application in the agricultural zoning of climate risk for sunflower in different sowing dates.$h[electronic resource]
260 $c2019
520 $aSunflower is a species that is sensitive to local climate conditions. However, studies that use artificial neural networks (ANNs) to evaluate this influence and create tools such as agricultural zoning of climate risk (ZARC) have not been conducted for this species. Due to the importance of sunflower as a human food source and for biodiesel production, and also the necessity of conducting research to evaluate the suitability of this oleaginous species under different climatic conditions. Thus, we seek to construct a ZARC for sunflower in Brazil simulating sowing on different dates and using meteorological elements spatialized by ANNs. Climate data were used: air temperature (T), rainfall (P), relative air humidity (UR), solar radiation (MJ_m−2_d−1) and wind velocity (U2). Climatic regions considered suitable for the cultivation of sunflower had average annual values for T between 20 and 28°C, P between 500 and 1.500 mm per cycle, and soil water deficit (DEF) below 140 mm per cycle. A neural network is an efficient tool that can be used in spatialization of climate variables quickly and accurately. Sunflower sowing in the spring and summer are the ones that provide the largest suitable areas in southeastern Brazil, with 58.13 and 64.36% of suitable areas, respectively
650 $aClima
650 $aGirassol
650 $aZoneamento Agrícola
700 1 $aMORAES, J. R. da S. C. de
700 1 $aROLIM, G. de S.
700 1 $aMARTORANO, L. G.
700 1 $aMENESES, K. C. de
700 1 $aVALERIANO, T. T. B.
773 $tArchives of Agronomy and Soil Science$gv. 65, n. 11, p. 1477-1492, 2019.
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