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| Registros recuperados : 117 | |
| 21. |  | BRANDÃO, Z. N.; RODRIGUES, H. M.; MEDEIROS, E. P.; SANA, R. S.; SHIRATSUCHI, L. S. Técnicas de agricultura de precisão e machine learning na estimativa de atributos físicos e químicos do solo. In: SILVA-MATOS, R. R. S. da; DOIHARA, I. P.; LINHARES, S. C. (org.). Medio ambiente: Agricultura, desarrollo y sustentabilidad 2. Ponta Grossa, PR: Atena, 2023. c. 6. p. 55-64.| Biblioteca(s): Embrapa Algodão. |
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| 22. | | AZEVEDO, D. M. P. de; BELTRÃO, N. E. de M.; SEVERINO, L. S.; BRANDÃO, Z. N. Manejo del cultivo. In: SEVERINO, L. S.; MILANI, M.; BELTRÃO, N. E. de M. Ricino : el productor pregunta, la Embrapa responde. Brasília, DF : Embrapa Información Tecnológica, 2007. p. 15-31 (Colección 500 preguntas, 500 respuestas)| Biblioteca(s): Embrapa Algodão. |
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| 23. | | RESENDE, A. V. de; BRANDAO, Z. N.; GREGO, C. R.; BORGHI, E.; WILDA, L. R. M. Manejo do solo sob o enfoque da agricultura de precisão. In: BERTOL, I.; DE MARIA, I. C.; SOUZA, L. da S. (Ed.). Manejo e conservação do solo e da água. Viçosa, MG: Sociedade Brasileira de Ciência do Solo, 2019. p. 1219-1270.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Algodão; Embrapa Milho e Sorgo. |
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| 24. | | MEGNA, P. R.; SANTOS, F. D.; BRANDAO, Z. N.; ZONTA, J. H. O Algodão arbóreo (Gossypium l) e sua aptidão edáfica mapeada para o estado da Paraíba. In: FURTADO, D. A.; BARACUHY, J. G. DE V.; FRANCISCO, P. R. M.; FERNANES NETO, S.; SOUSA, V. A. DE (Org.). Tecnologias adaptadas para o desenvolvimento sustentável do semiárido brasileiro. Campina Grande: EPGRAF, 2014. cap. 22| Biblioteca(s): Embrapa Algodão. |
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| 25. | | SPERANZA, E. A.; QUEIROS, L. R.; RABELLO, L. M.; GREGO, C. R.; BRANDÃO, Z. N. Armazenamento e recuperação de dados georreferenciados de condutividade elétrica do solo na Rede de Agricultura de Precisão da Embrapa. In: INAMASU, R. Y.; NAIME, J. de M.; RESENDE, Á. V. de; BASSOI, L. H.; BERNARDI, A. C. de C. (Ed.). Agricultura de precisão: um novo olhar. São Carlos, SP: Embrapa Instrumentação, 2011. p. 46-50.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Algodão; Embrapa Instrumentação; Embrapa Territorial. |
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| 34. | | BRANDAO, Z. N.; SOFIATTI, V.; BEZERRA, J. R. C.; FERREIRA, G. B. Estimativa da produtividade do algodoeiro através de imagens de satélite. In: CONGRESSO BRASILEIRO DO ALGODÃO, 8.; COTTON EXPO, 1., 2011, São Paulo. Evolução da cadeia para construção de um setor forte: Anais. Campina Grande, PB: Embrapa Algodão, 2011. p.1602-1609| Biblioteca(s): Embrapa Algodão. |
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| 38. | | ZONTA, J. H.; BRANDAO, Z. N.; MEDEIROS, J. da C.; SANA, R. S.; SOFIATTI, V. Agricultura de precisão para o manejo da fertilidade do solo em algodoeiro cultivado no cerrado brasileiro. In: BERNARDI, A. C. DE C.; NAIME, J. DE M.; RESENDE, A. V. DE; BASSOI, L. H.; INAMASU, R. Y. (Ed.). Agricultura de precisão: resultados de um novo olhar. Brasília, DF : Embrapa, 2014. p. 315-321| Biblioteca(s): Embrapa Algodão. |
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| 40. | | ZONTA, J. H.; MANIÇOBA, R. M.; BRANDAO, Z. N.; CARRILLO, M. A.; BEZERRA, J. R. C. Canopy temperatures and accumulated degree days on cotton plants under water deficit. Irriga, v. 23, n. 4, p. 741-755, outubro-dezembro, 2018.| Biblioteca(s): Embrapa Algodão. |
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| Registros recuperados : 117 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Algodão. |
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Data corrente: |
22/03/2016 |
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Data da última atualização: |
22/03/2016 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
BRANDAO, Z. N.; SOFIATTI, V.; BEZERRA, J. R. C.; FERREIRA, G. B.; MEDEIROS, J. C. |
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Afiliação: |
ZIANY NEIVA BRANDAO, CNPA; VALDINEI SOFIATTI, CNPA; JOSE RENATO CORTEZ BEZERRA, CNPA; GILVAN BARBOSA FERREIRA, CNPA; JOSÉ C. MEDEIROS, CNPA. |
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Título: |
Spectral reflectance for growth and yield assessment of irrigated cotton. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Australian Journal of Crop Science, v. 9, n. 1, p. 75-84, jan. 2015. |
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ISSN: |
1835-2693 |
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Idioma: |
Português |
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Conteúdo: |
The canopy reflectance using ground-based sensors has the potential to provide information on crop nitrogen content. The objective of this study was to determine relationships between canopy spectral reflectance and leaf N content, leaf area index (LAI), aboveground biomass (Biom) and yield of irrigated cotton under four nitrogen rates (0, 90, 180 and 270kg ha-1). Measurements of canopy reflectance were made throughout the growing seasons (2009-2010) using a hand-held spectroradiometer. Samples for LAI and Biom were obtained three times from squaring until fruiting. The normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI) and the modified transformed vegetation index (MTVI2) were calculated from the hyperspectral reflectance data. All vegetation indexes (VIs) and also SPAD-502 readings allowed to figure out mathematical models for N content prediction in cotton leaves with great precision (r > 0.74). It also showed good correlations (r from 0.55 to 0.96) with the LAI since the first flowering. The vegetation indexes obtained through the canopy reflectance, explained more than 64% of the variation in cotton biomass. The MTVI2 was the index that provided the best LAI prediction in advanced stages. The peak flowering stage was the best time to estimate the cotton biomass, where the MSAVI and MTVI2 showed to be excellent predictors. The results indicate that the cotton yield can be estimated through the hyperspectral reflectance since the squaring until fruiting in irrigated conditions. MenosThe canopy reflectance using ground-based sensors has the potential to provide information on crop nitrogen content. The objective of this study was to determine relationships between canopy spectral reflectance and leaf N content, leaf area index (LAI), aboveground biomass (Biom) and yield of irrigated cotton under four nitrogen rates (0, 90, 180 and 270kg ha-1). Measurements of canopy reflectance were made throughout the growing seasons (2009-2010) using a hand-held spectroradiometer. Samples for LAI and Biom were obtained three times from squaring until fruiting. The normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI) and the modified transformed vegetation index (MTVI2) were calculated from the hyperspectral reflectance data. All vegetation indexes (VIs) and also SPAD-502 readings allowed to figure out mathematical models for N content prediction in cotton leaves with great precision (r > 0.74). It also showed good correlations (r from 0.55 to 0.96) with the LAI since the first flowering. The vegetation indexes obtained through the canopy reflectance, explained more than 64% of the variation in cotton biomass. The MTVI2 was the index that provided the best LAI prediction in advanced stages. The peak flowering stage was the best time to estimate the cotton biomass, where the MSAVI and MTVI2 showed to be excellent predictors. The results indicate that the cotton yield can be estimated through... Mostrar Tudo |
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Palavras-Chave: |
Irrigated cotton; Nitrogen fertilization. |
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Thesagro: |
Algodão; Área foliar; Gossypium hirsutum. |
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Thesaurus NAL: |
leaf area index; remote sensing. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/141569/1/Spectral-reflectance-for-growth-....pdf
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Marc: |
LEADER 02356naa a2200265 a 4500
001 2041649
005 2016-03-22
008 2015 bl uuuu u00u1 u #d
022 $a1835-2693
100 1 $aBRANDAO, Z. N.
245 $aSpectral reflectance for growth and yield assessment of irrigated cotton.$h[electronic resource]
260 $c2015
520 $aThe canopy reflectance using ground-based sensors has the potential to provide information on crop nitrogen content. The objective of this study was to determine relationships between canopy spectral reflectance and leaf N content, leaf area index (LAI), aboveground biomass (Biom) and yield of irrigated cotton under four nitrogen rates (0, 90, 180 and 270kg ha-1). Measurements of canopy reflectance were made throughout the growing seasons (2009-2010) using a hand-held spectroradiometer. Samples for LAI and Biom were obtained three times from squaring until fruiting. The normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI) and the modified transformed vegetation index (MTVI2) were calculated from the hyperspectral reflectance data. All vegetation indexes (VIs) and also SPAD-502 readings allowed to figure out mathematical models for N content prediction in cotton leaves with great precision (r > 0.74). It also showed good correlations (r from 0.55 to 0.96) with the LAI since the first flowering. The vegetation indexes obtained through the canopy reflectance, explained more than 64% of the variation in cotton biomass. The MTVI2 was the index that provided the best LAI prediction in advanced stages. The peak flowering stage was the best time to estimate the cotton biomass, where the MSAVI and MTVI2 showed to be excellent predictors. The results indicate that the cotton yield can be estimated through the hyperspectral reflectance since the squaring until fruiting in irrigated conditions.
650 $aleaf area index
650 $aremote sensing
650 $aAlgodão
650 $aÁrea foliar
650 $aGossypium hirsutum
653 $aIrrigated cotton
653 $aNitrogen fertilization
700 1 $aSOFIATTI, V.
700 1 $aBEZERRA, J. R. C.
700 1 $aFERREIRA, G. B.
700 1 $aMEDEIROS, J. C.
773 $tAustralian Journal of Crop Science$gv. 9, n. 1, p. 75-84, jan. 2015.
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