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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
21/09/2020 |
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Data da última atualização: |
14/12/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
REIS, A. A. dos; SILVA, B. C.; WERNER, J. P. S.; SILVA, Y. F.; ROCHA, J. V.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C; MAGALHÃES, P. S. G. |
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Afiliação: |
Feagri, Nipe/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; Nipe/Unicamp; Nipe/Unicamp. |
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Título: |
Exploring the potential of high-resolution PlanetScope imagery for pasture biomass estimation in an integrated crop-livestock system. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42-3, W12, p. 419-424, 2020. |
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DOI: |
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020 |
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Idioma: |
Inglês |
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Notas: |
Publicado também em: IEEE LATIN AMERICAN GRSS; ISPRS REMOTE SENSING CONFERENCE, Santiago, 2020. Proceedings... [Piscataway]: IEEE, 2020. p. 675-680. LAGIRS 2020. |
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Conteúdo: |
ABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring. MenosABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promisi... Mostrar Tudo |
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Palavras-Chave: |
Aprendizado de máquina; Dove satellites; Floresta aleatória; Índice de vegetação; Integração lavoura-pecuária; Integrated crop-livestock system; Machine Learning; Nano-Satellites; Pastureland; Random Forest; Vegetation Indices. |
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Thesagro: |
Biomassa; Pastagem. |
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Thesaurus Nal: |
Biomass; Pasture management; Vegetation index. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 03286naa a2200457 a 4500
001 2125045
005 2021-12-14
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020$2DOI
100 1 $aREIS, A. A. dos
245 $aExploring the potential of high-resolution PlanetScope imagery for pasture biomass estimation in an integrated crop-livestock system.$h[electronic resource]
260 $c2020
500 $aPublicado também em: IEEE LATIN AMERICAN GRSS; ISPRS REMOTE SENSING CONFERENCE, Santiago, 2020. Proceedings... [Piscataway]: IEEE, 2020. p. 675-680. LAGIRS 2020.
520 $aABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring.
650 $aBiomass
650 $aPasture management
650 $aVegetation index
650 $aBiomassa
650 $aPastagem
653 $aAprendizado de máquina
653 $aDove satellites
653 $aFloresta aleatória
653 $aÍndice de vegetação
653 $aIntegração lavoura-pecuária
653 $aIntegrated crop-livestock system
653 $aMachine Learning
653 $aNano-Satellites
653 $aPastureland
653 $aRandom Forest
653 $aVegetation Indices
700 1 $aSILVA, B. C.
700 1 $aWERNER, J. P. S.
700 1 $aSILVA, Y. F.
700 1 $aROCHA, J. V.
700 1 $aFIGUEIREDO, G. K. D. A.
700 1 $aANTUNES, J. F. G.
700 1 $aESQUERDO, J. C. D. M.
700 1 $aCOUTINHO, A. C.
700 1 $aLAMPARELLI, R. A. C
700 1 $aMAGALHÃES, P. S. G.
773 $tThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences$gv. 42-3, W12, p. 419-424, 2020.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
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Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
06/09/2021 |
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Data da última atualização: |
06/09/2021 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
QUEIROZ, L. C. R.; MAGNABOSCO, C. de U.; BRUNES, L. C.; PEREIRA, L. S.; ALVES, M. G. O.; COSTA, M. F. O. e. |
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Afiliação: |
CLAUDIO DE ULHOA MAGNABOSCO, CPAC; MARCOS FERNANDO OLIVEIRA E COSTA, CNPAF. |
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Título: |
ASSOCIAÇÃO ENTRE MACIEZ DA CARNE E CARACTERÍSTICAS DE CRESCIMENTO EM BOVINOS DA RAÇA NELORE. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE ZOOTECNIA, 29., 2019, Uberaba. Tecnologias que alimentam o mundo: anais... Uberaba: ABZ: Fazu: ABCZ, 2019. Zootec. 5 p. |
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Páginas: |
5 p. |
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Idioma: |
Português |
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Conteúdo: |
Abstract: This study was carried out to analyze the association between meat tenderness (WBSF) and growth traits, which were birth weight (BW), weight at 120 (W120), 210 (W210), 365 (W365) and 450 (W450) days of age, and average daily gain (ADG) in 654 Nellore cattle. The data were from Guaporé Agropecuaria?s OB Choice Program. Pearson?s correlations and analysis of variance were used to analyze the phenotypic relationships, using software R. A low and no significant correlation between WBSF and growth traits (-0.15, -0.11, -0.06, 0.04, 0.02 and 0.08 for BW, W120, W210, W365 and W450, respectively) was estimated in this study, inferring that the selection for growth traits did not influences the meat tenderness. Segregating groups that presented higher WBSF also presented higher average daily gains and higher body weight. |
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Palavras-Chave: |
Anova; Desempenho produtivo; Zebuínos. |
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Thesagro: |
Gado de Corte. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/225717/1/Magnabosco-Associacao-entre-maciez-da-carne-e-caracteristicas-de-crescimento-em-bovinos.pdf
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Marc: |
LEADER 01613nam a2200229 a 4500
001 2134074
005 2021-09-06
008 2019 bl uuuu u00u1 u #d
100 1 $aQUEIROZ, L. C. R.
245 $aASSOCIAÇÃO ENTRE MACIEZ DA CARNE E CARACTERÍSTICAS DE CRESCIMENTO EM BOVINOS DA RAÇA NELORE.$h[electronic resource]
260 $aIn: CONGRESSO BRASILEIRO DE ZOOTECNIA, 29., 2019, Uberaba. Tecnologias que alimentam o mundo: anais... Uberaba: ABZ: Fazu: ABCZ, 2019. Zootec. 5 p.$c2019
300 $a5 p.
520 $aAbstract: This study was carried out to analyze the association between meat tenderness (WBSF) and growth traits, which were birth weight (BW), weight at 120 (W120), 210 (W210), 365 (W365) and 450 (W450) days of age, and average daily gain (ADG) in 654 Nellore cattle. The data were from Guaporé Agropecuaria?s OB Choice Program. Pearson?s correlations and analysis of variance were used to analyze the phenotypic relationships, using software R. A low and no significant correlation between WBSF and growth traits (-0.15, -0.11, -0.06, 0.04, 0.02 and 0.08 for BW, W120, W210, W365 and W450, respectively) was estimated in this study, inferring that the selection for growth traits did not influences the meat tenderness. Segregating groups that presented higher WBSF also presented higher average daily gains and higher body weight.
650 $aGado de Corte
653 $aAnova
653 $aDesempenho produtivo
653 $aZebuínos
700 1 $aMAGNABOSCO, C. de U.
700 1 $aBRUNES, L. C.
700 1 $aPEREIRA, L. S.
700 1 $aALVES, M. G. O.
700 1 $aCOSTA, M. F. O. e
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