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Registro Completo |
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Biblioteca(s): |
Embrapa Hortaliças. |
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Data corrente: |
05/05/2003 |
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Data da última atualização: |
05/05/2003 |
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Autoria: |
BUSO, J. A.; DUSI, A. N.; SILVA, D. M. da; TORRES, A. C.; ÁVILA, A. C. de; REZENDE, R.; MELO FILHO, P. A. de; FONSECA, L. L. C.; CARMO, A. F.; OLIVEIRA, N. A. |
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Afiliação: |
Embrapa Hortaliças, Brasília, 2003. |
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Título: |
Desenvolvimento de tecnologia de produção de alho-semente livre de vírus. Fase II: relatório final do projeto 05.1999.025. |
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Ano de publicação: |
2002 |
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Fonte/Imprenta: |
Brasilia: Embrapa Hortaliças, 2002. |
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Páginas: |
50 p. |
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Série: |
( Embrapa Hortaliças. Documentos, 46). |
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Idioma: |
Português |
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Palavras-Chave: |
Livre de vírus. |
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Thesagro: |
Alho; Allium Sativum; Semente; Tecnologia. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00841nam a2200289 a 4500
001 1773427
005 2003-05-05
008 2002 bl uuuu 00u1 u #d
100 1 $aBUSO, J. A.
245 $aDesenvolvimento de tecnologia de produção de alho-semente livre de vírus. Fase II$brelatório final do projeto 05.1999.025.
260 $aBrasilia: Embrapa Hortaliças$c2002
300 $a50 p.
490 $a( Embrapa Hortaliças. Documentos, 46).
650 $aAlho
650 $aAllium Sativum
650 $aSemente
650 $aTecnologia
653 $aLivre de vírus
700 1 $aDUSI, A. N.
700 1 $aSILVA, D. M. da
700 1 $aTORRES, A. C.
700 1 $aÁVILA, A. C. de
700 1 $aREZENDE, R.
700 1 $aMELO FILHO, P. A. de
700 1 $aFONSECA, L. L. C.
700 1 $aCARMO, A. F.
700 1 $aOLIVEIRA, N. A.
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Registro original: |
Embrapa Hortaliças (CNPH) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Algodão. Para informações adicionais entre em contato com cnpa.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Algodão. |
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Data corrente: |
08/08/2017 |
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Data da última atualização: |
15/01/2018 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
BRANDAO, Z. N.; GREGO, C. R.; MANJOLIN, R. C. |
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Afiliação: |
ZIANY NEIVA BRANDAO, CNPA; CELIA REGINA GREGO, CNPM; RODOLFO CORREA MANJOLIN, ESTAGIÁRIO - CNPM. |
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Título: |
Geoestatistical tools and spectral measurements from AWiFs data for evaluation of N and P contents in cotton leaves. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais ... Santos: INPE, 2017. |
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Páginas: |
p. 2408-2415 |
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Idioma: |
Inglês |
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Notas: |
SBSR. |
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Conteúdo: |
Satellite images and geostatistics are useful tools to assess the nutritional status of plants, and thus, understanding the variability of cotton yield in farmers' fields. The resulting kriged maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and understanding the factors that led to crop yield. To support decisions on improving cotton yield, this study combines the conventional statistic analysis, spatial regression modeling of georreferenced data and AWiFs' vegetations indices assessment. The experiments were carried out in a 47.4 ha commercial field of Goiás state, Brazil. Multispectral satellite images at 56 m spatial resolution were collected in a rainfed cotton field in two dates, on 04/01/2011 and 04/10/2012, from AWiFS sensor during the flowering cotton stage. Measures of leaf nitrogen (N) and phosphorus (P) contents were determined over previously georreferenced central points of 70 plots, each one measuring 80X80 m. Data were analyzed using descriptive statistics and geostatistical analyses by building and setting semivariograms and kriging interpolation. Best correlation was found between IVs and nitrogen contents of cotton leaves. Results indicated that NDVI, MSAVI and SAVI were the best indices to estimate P contents at cotton peak flowering. Identifications of spatial differences were possible using geostatistical methods with remote sensing data obtained from medium resolution satellite images, allowing to identify distinct nutritional needs and growth status of canopy to cotton plants. MenosSatellite images and geostatistics are useful tools to assess the nutritional status of plants, and thus, understanding the variability of cotton yield in farmers' fields. The resulting kriged maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and understanding the factors that led to crop yield. To support decisions on improving cotton yield, this study combines the conventional statistic analysis, spatial regression modeling of georreferenced data and AWiFs' vegetations indices assessment. The experiments were carried out in a 47.4 ha commercial field of Goiás state, Brazil. Multispectral satellite images at 56 m spatial resolution were collected in a rainfed cotton field in two dates, on 04/01/2011 and 04/10/2012, from AWiFS sensor during the flowering cotton stage. Measures of leaf nitrogen (N) and phosphorus (P) contents were determined over previously georreferenced central points of 70 plots, each one measuring 80X80 m. Data were analyzed using descriptive statistics and geostatistical analyses by building and setting semivariograms and kriging interpolation. Best correlation was found between IVs and nitrogen contents of cotton leaves. Results indicated that NDVI, MSAVI and SAVI were the best indices to estimate P contents at cotton peak flowering. Identifications of spatial differences were possible using geostatistical methods with remote sensing data obtained from medium resolution satellite images, allowing to identify dist... Mostrar Tudo |
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Palavras-Chave: |
Ìndices de vegetação; Spatial variability. |
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Thesagro: |
Agricultura de Precisão; Algodão; Sensoriamento remoto. |
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Thesaurus NAL: |
Cotton; Precision agriculture; Remote sensing. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02391nam a2200253 a 4500
001 2073757
005 2018-01-15
008 2017 bl uuuu u01u1 u #d
100 1 $aBRANDAO, Z. N.
245 $aGeoestatistical tools and spectral measurements from AWiFs data for evaluation of N and P contents in cotton leaves.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais ... Santos: INPE$c2017
300 $ap. 2408-2415
500 $aSBSR.
520 $aSatellite images and geostatistics are useful tools to assess the nutritional status of plants, and thus, understanding the variability of cotton yield in farmers' fields. The resulting kriged maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and understanding the factors that led to crop yield. To support decisions on improving cotton yield, this study combines the conventional statistic analysis, spatial regression modeling of georreferenced data and AWiFs' vegetations indices assessment. The experiments were carried out in a 47.4 ha commercial field of Goiás state, Brazil. Multispectral satellite images at 56 m spatial resolution were collected in a rainfed cotton field in two dates, on 04/01/2011 and 04/10/2012, from AWiFS sensor during the flowering cotton stage. Measures of leaf nitrogen (N) and phosphorus (P) contents were determined over previously georreferenced central points of 70 plots, each one measuring 80X80 m. Data were analyzed using descriptive statistics and geostatistical analyses by building and setting semivariograms and kriging interpolation. Best correlation was found between IVs and nitrogen contents of cotton leaves. Results indicated that NDVI, MSAVI and SAVI were the best indices to estimate P contents at cotton peak flowering. Identifications of spatial differences were possible using geostatistical methods with remote sensing data obtained from medium resolution satellite images, allowing to identify distinct nutritional needs and growth status of canopy to cotton plants.
650 $aCotton
650 $aPrecision agriculture
650 $aRemote sensing
650 $aAgricultura de Precisão
650 $aAlgodão
650 $aSensoriamento remoto
653 $aÌndices de vegetação
653 $aSpatial variability
700 1 $aGREGO, C. R.
700 1 $aMANJOLIN, R. C.
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