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Registro Completo |
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
28/04/2016 |
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Data da última atualização: |
29/04/2016 |
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Autoria: |
PARTELLI, F. L.; GILES, J. A. D.; SILVA, M. B. da (ed.). |
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Afiliação: |
FÁBIO LUIZ PARTELLI, UFES; JOÃO ANTONIO DUTRA GILES, UFES/CEUNES; MARCELO BARRETO DA SILVA, UFES/CEUNES. |
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Título: |
Café conilon: manejo de pragas e sustentabilidade. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
São Mateus, ES: 2015. |
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Páginas: |
186 p. |
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ISBN: |
978-85-61890-66-7 |
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Idioma: |
Português |
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Conteúdo: |
Manejo integrado das pragas do café Conilon; Cochonilhas em café Conilon; Arborização do cafeeiro Conilon; Arborização do cafeeiro Conilon; A complexidade na aplicação da legislação trabalhista brasileira pelo empregador rural; Desafios da sustentabilidade na produção do Conilon; Um novo sistema de poda de renovação; Colheita semi mecanizada do Conilon; Impactos do déficit hídrico e alta temperatura no Café Conilon; Aplicação de técnicas da agricultura de precisão na cultura de café Conilon; Melhoramento genético sustentável de café Conilon; Ferrugem do café Conilon (Coffea canephora). |
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Palavras-Chave: |
Café conilon; Manejo de praga; Simpósio do produtor de conilon; Sustentabilidade. |
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Thesagro: |
Pesquisa agrícola. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01202nam a2200217 a 4500
001 2044211
005 2016-04-29
008 2015 bl uuuu 00u1 u #d
020 $a978-85-61890-66-7
100 1 $aPARTELLI, F. L.
245 $aCafé conilon$bmanejo de pragas e sustentabilidade.
260 $aSão Mateus, ES: 2015.$c2015
300 $a186 p.
520 $aManejo integrado das pragas do café Conilon; Cochonilhas em café Conilon; Arborização do cafeeiro Conilon; Arborização do cafeeiro Conilon; A complexidade na aplicação da legislação trabalhista brasileira pelo empregador rural; Desafios da sustentabilidade na produção do Conilon; Um novo sistema de poda de renovação; Colheita semi mecanizada do Conilon; Impactos do déficit hídrico e alta temperatura no Café Conilon; Aplicação de técnicas da agricultura de precisão na cultura de café Conilon; Melhoramento genético sustentável de café Conilon; Ferrugem do café Conilon (Coffea canephora).
650 $aPesquisa agrícola
653 $aCafé conilon
653 $aManejo de praga
653 $aSimpósio do produtor de conilon
653 $aSustentabilidade
700 1 $aGILES, J. A. D.
700 1 $aSILVA, M. B. da
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Registro original: |
Embrapa Unidades Centrais (AI-SEDE) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
13/10/2008 |
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Data da última atualização: |
11/04/2024 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
MENDONÇA-SANTOS, M. de L.; SANTOS, H. G. dos; DART, R. de O.; PARES, J. G. |
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Afiliação: |
MARIA DE LOURDES MENDONÇA SANTOS BREFIN, CNPS; HUMBERTO GONCALVES DOS SANTOS, CNPS; RICARDO DE OLIVEIRA DART, CNPS; JERÔNIMO GUEDES PARÉS. |
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Título: |
Digital mapping of soil classes in Rio de Janeiro State, Brazil: data, modelling and prediction. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
In: HARTEMINK, A. E.; McBRATNEY, A.; MENDONÇA-SANTOS, M. de L. (ed.). Digital soil mapping with limited data. Dordrecht: Springer, 2008. cap. 34, p. 381-396. |
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DOI: |
https://doi.org/10.1007/978-1-4020-8592-5_34 |
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Idioma: |
Inglês |
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Conteúdo: |
A soil database for Rio de Janeiro State was collated in Access, for a project on quantifying the magnitude, spatial distribution and organic carbon in the soils of Rio de Janeiro State (Projeto Carbono_RJ). The main activities were the search, selection, analysis and review of the data for each soil profile already described in the study area, the georeferencing of each soil profile (when spatial coordinates were not available) and the input of new soil profiles into a new interface. The Rio de Janeiro soil dataset now contains 731 soil profiles, 2744 soil horizons, and 48 soil attributes usually described at the soil survey process. From this soil dataset, only 431 soil profiles that were adequately geo-located have been used in this application. The dataset contains limited data for bulk density and hydraulic soil properties, among others. From this dataset, quantitative modelling and digital soil mapping have been completed experimentally at 90 m resolution, using soil data and predictor variables, such as satellite images, lithology, a prior soil map and a DEM and its derivates. This dataset, which is one of the more complete soil datasets in Brazil, is being used as a testbed for learning and teaching DSM, using a variety of methods based on the scorpan model (Embrapa, 2006). In the first instance, the soil dataset was used to predict soil classes at the Order level of the Brazilian Soil Classification System ? SiBCS (Embrapa, 2006). Five models were built and their results were compared and mapped. MenosA soil database for Rio de Janeiro State was collated in Access, for a project on quantifying the magnitude, spatial distribution and organic carbon in the soils of Rio de Janeiro State (Projeto Carbono_RJ). The main activities were the search, selection, analysis and review of the data for each soil profile already described in the study area, the georeferencing of each soil profile (when spatial coordinates were not available) and the input of new soil profiles into a new interface. The Rio de Janeiro soil dataset now contains 731 soil profiles, 2744 soil horizons, and 48 soil attributes usually described at the soil survey process. From this soil dataset, only 431 soil profiles that were adequately geo-located have been used in this application. The dataset contains limited data for bulk density and hydraulic soil properties, among others. From this dataset, quantitative modelling and digital soil mapping have been completed experimentally at 90 m resolution, using soil data and predictor variables, such as satellite images, lithology, a prior soil map and a DEM and its derivates. This dataset, which is one of the more complete soil datasets in Brazil, is being used as a testbed for learning and teaching DSM, using a variety of methods based on the scorpan model (Embrapa, 2006). In the first instance, the soil dataset was used to predict soil classes at the Order level of the Brazilian Soil Classification System ? SiBCS (Embrapa, 2006). Five models were built and their re... Mostrar Tudo |
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Palavras-Chave: |
Brasil; Mapeamento digital; Rio de Janeiro. |
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Thesagro: |
Solo. |
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Thesaurus NAL: |
Soil map. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02339naa a2200229 a 4500
001 1337609
005 2024-04-11
008 2008 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/978-1-4020-8592-5_34$2DOI
100 1 $aMENDONÇA-SANTOS, M. de L.
245 $aDigital mapping of soil classes in Rio de Janeiro State, Brazil$bdata, modelling and prediction.$h[electronic resource]
260 $c2008
520 $aA soil database for Rio de Janeiro State was collated in Access, for a project on quantifying the magnitude, spatial distribution and organic carbon in the soils of Rio de Janeiro State (Projeto Carbono_RJ). The main activities were the search, selection, analysis and review of the data for each soil profile already described in the study area, the georeferencing of each soil profile (when spatial coordinates were not available) and the input of new soil profiles into a new interface. The Rio de Janeiro soil dataset now contains 731 soil profiles, 2744 soil horizons, and 48 soil attributes usually described at the soil survey process. From this soil dataset, only 431 soil profiles that were adequately geo-located have been used in this application. The dataset contains limited data for bulk density and hydraulic soil properties, among others. From this dataset, quantitative modelling and digital soil mapping have been completed experimentally at 90 m resolution, using soil data and predictor variables, such as satellite images, lithology, a prior soil map and a DEM and its derivates. This dataset, which is one of the more complete soil datasets in Brazil, is being used as a testbed for learning and teaching DSM, using a variety of methods based on the scorpan model (Embrapa, 2006). In the first instance, the soil dataset was used to predict soil classes at the Order level of the Brazilian Soil Classification System ? SiBCS (Embrapa, 2006). Five models were built and their results were compared and mapped.
650 $aSoil map
650 $aSolo
653 $aBrasil
653 $aMapeamento digital
653 $aRio de Janeiro
700 1 $aSANTOS, H. G. dos
700 1 $aDART, R. de O.
700 1 $aPARES, J. G.
773 $tIn: HARTEMINK, A. E.; McBRATNEY, A.; MENDONÇA-SANTOS, M. de L. (ed.). Digital soil mapping with limited data. Dordrecht: Springer, 2008. cap. 34, p. 381-396.
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