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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. |
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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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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: |
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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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Registro original: |
Embrapa Amazônia Oriental (CPATU) |
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Biblioteca |
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Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
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URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Agroindústria Tropical. |
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Data corrente: |
26/08/2014 |
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Data da última atualização: |
22/06/2017 |
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Tipo da produção científica: |
Circular Técnica |
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Autoria: |
CARVALHO, A. C. P. P. de; ARAÚJO, J. D. M.; BERTINI, C. H. C. de M.; BEZERRA, A. M. E.; PAULA PESSOA, P. F. A. |
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Afiliação: |
ANA CRISTINA PORTUGAL P DE CARVALHO, CNPAT; JOSÉ DIONIS MATOS ARAÚJO, Engenheiro-agrônomo, M.Sc.; CÂNDIDA HERMÍNIA CAMPOS de MAGALHÃES BERTINI, Profa. D.Sc. Fitotecnia - UFC; ANTONIO MARCOS ESMERALDO BEZERRA, Prof. D. Sc. Fitotecnia - UFC; PEDRO FELIZARDO ADEODATO P PESSOA, CNPAT. |
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Título: |
Redução de custos na produção de mudas micropropagadas de bananeira cv. Williams. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Fortaleza: Embrapa Agroindústria Tropical, 2014. |
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Páginas: |
18 p. |
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Série: |
(Embrapa Agroindústria Tropical. Circular Técnica, 45). |
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Idioma: |
Português |
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Palavras-Chave: |
Cost reduction; Produção de mudas; Redução de custos; Williams. |
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Thesagro: |
Banana. |
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Thesaurus NAL: |
seedling production. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/107313/1/CIT14002.pdf
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Marc: |
LEADER 00783nam a2200241 a 4500
001 1993465
005 2017-06-22
008 2014 bl uuuu u0uu1 u #d
100 1 $aCARVALHO, A. C. P. P. de
245 $aRedução de custos na produção de mudas micropropagadas de bananeira cv. Williams.$h[electronic resource]
260 $aFortaleza: Embrapa Agroindústria Tropical$c2014
300 $a18 p.
490 $a(Embrapa Agroindústria Tropical. Circular Técnica, 45).
650 $aseedling production
650 $aBanana
653 $aCost reduction
653 $aProdução de mudas
653 $aRedução de custos
653 $aWilliams
700 1 $aARAÚJO, J. D. M.
700 1 $aBERTINI, C. H. C. de M.
700 1 $aBEZERRA, A. M. E.
700 1 $aPAULA PESSOA, P. F. A.
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Embrapa Agroindústria Tropical (CNPAT) |
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