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Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
23/03/2017 |
Data da última atualização: |
21/01/2020 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
MUDADU, M. de A.; CARVALHO, H. G. de; YOKOO, M. J. I.; CARDOSO, F. F. |
Afiliação: |
MAURICIO DE ALVARENGA MUDADU, CNPTIA; HENRY GOMES DE CARVALHO, CPPSUL; MARCOS JUN ITI YOKOO, CPPSUL; FERNANDO FLORES CARDOSO, CPPSUL. |
Título: |
Genotype imputation of Hereford and Bradford bovine breeds from Brazil. |
Ano de publicação: |
2016 |
Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE OF THE AB3C, 12., 2016, Belo Horizonte. Proceedings... [S.l.]: AB3C, 2016. |
Páginas: |
p. 46. |
Idioma: |
Inglês |
Notas: |
X-meeting 2016. |
Conteúdo: |
The bovine breeding programs in Brazil are trying to adopt the use of genetic markers, a procedure called genomic selection (GS). GS consists in genotyping a given reference population with known phenotype and in the discovery of the associated genetic markers. The effect of the markers are estimated and validated so it is possible to predict the genetic values of the candidates of selection based on their genotypes. |
Palavras-Chave: |
Bioinformática; Marcadores genéticos. |
Thesaurus Nal: |
Bioinformatics; Cattle; Genetic markers; Genotype. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/158028/1/PL-Xmeeting-Genotype-Mudadu.pdf
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Marc: |
LEADER 01156nam a2200241 a 4500 001 2067528 005 2020-01-21 008 2016 bl uuuu u00u1 u #d 100 1 $aMUDADU, M. de A. 245 $aGenotype imputation of Hereford and Bradford bovine breeds from Brazil.$h[electronic resource] 260 $aIn: INTERNATIONAL CONFERENCE OF THE AB3C, 12., 2016, Belo Horizonte. Proceedings... [S.l.]: AB3C$c2016 300 $ap. 46. 500 $aX-meeting 2016. 520 $aThe bovine breeding programs in Brazil are trying to adopt the use of genetic markers, a procedure called genomic selection (GS). GS consists in genotyping a given reference population with known phenotype and in the discovery of the associated genetic markers. The effect of the markers are estimated and validated so it is possible to predict the genetic values of the candidates of selection based on their genotypes. 650 $aBioinformatics 650 $aCattle 650 $aGenetic markers 650 $aGenotype 653 $aBioinformática 653 $aMarcadores genéticos 700 1 $aCARVALHO, H. G. de 700 1 $aYOKOO, M. J. I. 700 1 $aCARDOSO, F. F.
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Embrapa Agricultura Digital (CNPTIA) |
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Registro Completo
Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
20/06/2017 |
Data da última atualização: |
10/11/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
BELLÓN, B.; BEGUÉ, A.; LO SEEN, D.; ALMEIDA, C. A. de; SIMÕES, M. |
Afiliação: |
BEATRIZ BELLÓN, Cirad, UMR TETIS; AGNÈS BEGUÉ, Cirad, UMR TETIS; DANNY LO SEEN, Cirad, UMR TETIS; CLAUDIO APARECIDO DE ALMEIDA, INPE; MARGARETH GONCALVES SIMOES, CNPS. |
Título: |
A remote sensing approach for regional-scale mapping of agricultural land-use systems based on NDVI time series. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
Remote Sensing, v. 9, n. 6, 600, Jun. 2017. |
DOI: |
https://doi.org/10.3390/rs9060600 |
Idioma: |
Inglês |
Conteúdo: |
In response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013-2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting agricultural monitoring and for large-scale land-use systems analysis. MenosIn response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013-2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting a... Mostrar Tudo |
Palavras-Chave: |
Estratificação; GEOBIA; MODIS; PCA. |
Thesagro: |
Sistema de Cultivo. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/160871/1/2017-011.pdf
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Marc: |
LEADER 02273naa a2200241 a 4500 001 2071127 005 2021-11-10 008 2017 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.3390/rs9060600$2DOI 100 1 $aBELLÓN, B. 245 $aA remote sensing approach for regional-scale mapping of agricultural land-use systems based on NDVI time series.$h[electronic resource] 260 $c2017 520 $aIn response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013-2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting agricultural monitoring and for large-scale land-use systems analysis. 650 $aSistema de Cultivo 653 $aEstratificação 653 $aGEOBIA 653 $aMODIS 653 $aPCA 700 1 $aBEGUÉ, A. 700 1 $aLO SEEN, D. 700 1 $aALMEIDA, C. A. de 700 1 $aSIMÕES, M. 773 $tRemote Sensing$gv. 9, n. 6, 600, Jun. 2017.
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