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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
04/12/2017 |
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Data da última atualização: |
04/12/2017 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
KUCHLER, P. C.; SIMÕES, M.; BÉGUÉ, A.; MACHADO, P. L. O. de A.; FERRAZ, R. P. D.; MADARI, B. E.; FREITAS, P. L. de; MANZATTO, C. V. |
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Afiliação: |
PATRICK C. KUCHLER, UERJ; MARGARETH GONCALVES SIMOES, CNPS; AGNÈS BÉGUÉ, CIRAD; PEDRO LUIZ OLIVEIRA DE A MACHADO, SRI; RODRIGO PECANHA DEMONTE FERRAZ, CNPS; BEATA EMOKE MADARI, CNPAF; PEDRO LUIZ DE FREITAS, CNPS; CELSO VAINER MANZATTO, CNPMA. |
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Título: |
Monitoring Brazilian low-carbon agriculture plan: the potential of remote sensing to detect adoption of selected agricultural practices. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: EFITA WCCA CONGRESS, 2017, Montpellier. Conference proceedings. Montpellier: Efita, 2017. |
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Páginas: |
p. 169-170. |
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Idioma: |
Inglês |
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Conteúdo: |
This paper aims at analyzing the spectral behavior of integrated croplivestock systems (ICLS) compared to a neighboring native Cerrado forest and a continuous degraded pasture. The study was conducted in the "Capivara Research Farm " of the National Rice and Bean Research Center of the Brazilian Research Corporation - Embrapa, in Santo Antônio de Goiás, Brazil. |
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Palavras-Chave: |
Agricultura de baixa emissão de carbono; Mapeamento de uso do solo; NDVI; Séries temporais; Sistema integrado lavoura-pecuária. |
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Thesaurus Nal: |
Landsat. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/167874/1/2017-069.pdf
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Marc: |
LEADER 01325naa a2200289 a 4500
001 2081308
005 2017-12-04
008 2017 bl uuuu u00u1 u #d
100 1 $aKUCHLER, P. C.
245 $aMonitoring Brazilian low-carbon agriculture plan$bthe potential of remote sensing to detect adoption of selected agricultural practices.$h[electronic resource]
260 $c2017
300 $ap. 169-170.
520 $aThis paper aims at analyzing the spectral behavior of integrated croplivestock systems (ICLS) compared to a neighboring native Cerrado forest and a continuous degraded pasture. The study was conducted in the "Capivara Research Farm " of the National Rice and Bean Research Center of the Brazilian Research Corporation - Embrapa, in Santo Antônio de Goiás, Brazil.
650 $aLandsat
653 $aAgricultura de baixa emissão de carbono
653 $aMapeamento de uso do solo
653 $aNDVI
653 $aSéries temporais
653 $aSistema integrado lavoura-pecuária
700 1 $aSIMÕES, M.
700 1 $aBÉGUÉ, A.
700 1 $aMACHADO, P. L. O. de A.
700 1 $aFERRAZ, R. P. D.
700 1 $aMADARI, B. E.
700 1 $aFREITAS, P. L. de
700 1 $aMANZATTO, C. V.
773 $tIn: EFITA WCCA CONGRESS, 2017, Montpellier. Conference proceedings. Montpellier: Efita, 2017.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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Registro Completo
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
03/05/2005 |
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Data da última atualização: |
03/08/2018 |
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Autoria: |
DUARTE, J. B.; VENCOVSKY, R. |
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Afiliação: |
João Batista Duarte, Universidade Federal de Goiás - UFG/Escola de Agronomia e Engenharia de Alimentos; Roland Vencovsky, Universidade de São Paulo - Usp/Escola Superior de Agricultura “Luiz de Queiroz” - Esalq/Departamento de Genética. |
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Título: |
Spatial statistical analysis and selection of genotypes in plant breeding. |
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Ano de publicação: |
2005 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF, v. 40, n. 2, p. 107-114, fev. 2005 |
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Idioma: |
Inglês |
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Notas: |
Título em português: Seleção de genótipos e análise estatística espacial no melhoramento de plantas. |
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Conteúdo: |
The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained. |
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Palavras-Chave: |
augmented design; autocorrelação; correlated data; dados correlacionados; delineamento aumentado; geoestatística; information recovery; mixed model; modelo misto; recuperação de informação. |
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Thesaurus NAL: |
autocorrelation; geostatistics. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/107908/1/Spatial.pdf
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Marc: |
LEADER 02107naa a2200289 a 4500
001 1113848
005 2018-08-03
008 2005 bl uuuu u00u1 u #d
100 1 $aDUARTE, J. B.
245 $aSpatial statistical analysis and selection of genotypes in plant breeding.
260 $c2005
500 $aTítulo em português: Seleção de genótipos e análise estatística espacial no melhoramento de plantas.
520 $aThe objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.
650 $aautocorrelation
650 $ageostatistics
653 $aaugmented design
653 $aautocorrelação
653 $acorrelated data
653 $adados correlacionados
653 $adelineamento aumentado
653 $ageoestatística
653 $ainformation recovery
653 $amixed model
653 $amodelo misto
653 $arecuperação de informação
700 1 $aVENCOVSKY, R.
773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 40, n. 2, p. 107-114, fev. 2005
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Registro original: |
Embrapa Unidades Centrais (AI-SEDE) |
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