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Registro Completo |
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Biblioteca(s): |
Embrapa Acre; Embrapa Agricultura Digital; Embrapa Agrobiologia; Embrapa Agropecuária Oeste; Embrapa Amazônia Ocidental; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Cerrados; Embrapa Clima Temperado; Embrapa Florestas; Embrapa Instrumentação; Embrapa Meio Ambiente; Embrapa Meio-Norte; Embrapa Pantanal; Embrapa Semiárido; Embrapa Solos; Embrapa Tabuleiros Costeiros; Embrapa Territorial; Embrapa Unidades Centrais; Embrapa Uva e Vinho. MenosEmbrapa Acre; Embrapa Agricultura Digital; Embrapa Agrobiologia; Embrapa Agropecuária Oeste; Embrapa Amazônia Ocidental; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Cerrados; Embrapa Clima Temperado; Embrapa Florestas... Mostrar Todas |
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Data corrente: |
24/09/2008 |
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Data da última atualização: |
18/11/2022 |
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Tipo da produção científica: |
Autoria/Organização/Edição de Livros |
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Autoria: |
HARTEMINK, A. E.; McBRATNEY, A.; MENDONÇA-SANTOS, M. de L. (ed.). |
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Afiliação: |
A. E. Harrtemink, World Soil Information - ISRIC; A. McBratney Faculty of Agriculture Food & Natural Resouces - University of Sydney; Maria de Lourdes Mendonça Santos Brefin, Embrapa Solos. |
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Título: |
Digital soil mapping with limited data. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
New York: Springer, 2008. |
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Páginas: |
445 p. |
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ISBN: |
978-1-4020-8591-8 |
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Idioma: |
Inglês
Português |
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Notas: |
Indicador de Meta também como Metodologia Científica |
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Conteúdo: |
Contents: I. Introduction.- II. Dealing with limited spatial data infrastructures.- III. Digital Soil Mapping - Methodologies.- IV. Digital Soil Mapping - Examples.- V. Priorities in Digital Soil Mapping.
There has been considerable expansion in the use of digital soil mapping technologies and development of methodologies that improve digital soil mapping at all scales and levels of resolution. These developments have occurred in all parts of the world in the past few years and also in countries where it was previously absent. There is almost always a shortage of data in soil research and its applications and this may lead to unsupported statements, poor statistics, misrepresentations and ultimately bad resource management. In digital soil mapping, maximum use is made of sparse data and this book contains useful examples of how this can be done.
This book focuses on digital soil mapping methodologies and applications for areas where data are limited, and has the following sections (i) introductory papers, (ii) dealing with limited spatial data infrastructures, (iii) methodology development, and (iv) examples of digital soil mapping in various parts of the globe (including USA, Brazil, UK, France, Czech Republic, Honduras, Kenya, Australia). The final chapter summarises priorities for digital soil mapping. |
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Palavras-Chave: |
Brasil; Ciência dos solos; Dado espacial; Digital Soil Mapping; Digitalização; Informação digital; Map; Mapeamento digital; Monitoramento; Pedology; Software; Soil Map Density; Solos; Sparse Data. |
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Thesagro: |
Cartografia; Mapa; Pedologia; Satélite; Solo; Topografia. |
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Thesaurus Nal: |
soil. |
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Categoria do assunto: |
--
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02380nam a2200421 a 4500
001 1339704
005 2022-11-18
008 2008 bl uuuu 00u1 u #d
020 $a978-1-4020-8591-8
100 1 $aHARTEMINK, A. E.
245 $aDigital soil mapping with limited data.
260 $aNew York: Springer$c2008
300 $a445 p.
500 $aIndicador de Meta também como Metodologia Científica
520 $aContents: I. Introduction.- II. Dealing with limited spatial data infrastructures.- III. Digital Soil Mapping - Methodologies.- IV. Digital Soil Mapping - Examples.- V. Priorities in Digital Soil Mapping. There has been considerable expansion in the use of digital soil mapping technologies and development of methodologies that improve digital soil mapping at all scales and levels of resolution. These developments have occurred in all parts of the world in the past few years and also in countries where it was previously absent. There is almost always a shortage of data in soil research and its applications and this may lead to unsupported statements, poor statistics, misrepresentations and ultimately bad resource management. In digital soil mapping, maximum use is made of sparse data and this book contains useful examples of how this can be done. This book focuses on digital soil mapping methodologies and applications for areas where data are limited, and has the following sections (i) introductory papers, (ii) dealing with limited spatial data infrastructures, (iii) methodology development, and (iv) examples of digital soil mapping in various parts of the globe (including USA, Brazil, UK, France, Czech Republic, Honduras, Kenya, Australia). The final chapter summarises priorities for digital soil mapping.
650 $asoil
650 $aCartografia
650 $aMapa
650 $aPedologia
650 $aSatélite
650 $aSolo
650 $aTopografia
653 $aBrasil
653 $aCiência dos solos
653 $aDado espacial
653 $aDigital Soil Mapping
653 $aDigitalização
653 $aInformação digital
653 $aMap
653 $aMapeamento digital
653 $aMonitoramento
653 $aPedology
653 $aSoftware
653 $aSoil Map Density
653 $aSolos
653 $aSparse Data
700 1 $aMcBRATNEY, A.
700 1 $aMENDONÇA-SANTOS, M. de L.
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Registro original: |
Embrapa Solos (CNPS) |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
03/01/2018 |
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Data da última atualização: |
11/01/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; CRUZ, C. D.; NASCIMENTO, A. C. C.; VIANA, J. M. S.; AZEVEDO, C. F.; BARROSO, L. M. A. |
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Afiliação: |
M. Nascimento, UFV; F. F. e Silva, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; C. D. Cruz, UFV; A. C. C. Nascimento, UFV; J. M. S. Viana, UFV; C. F. Azevedo, UFV; L. M. A. Barroso, UFV. |
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Título: |
Regularized quantile regression applied to genome-enabled prediction of quantitative traits. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 16, n. 1, gmr16019538, 2017. |
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Páginas: |
12 p. |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively. MenosGenomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved be... Mostrar Tudo |
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Palavras-Chave: |
Genomic selection; Regularized regression; Seleção genômica; SNP effects. |
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Thesagro: |
Estatística. |
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Thesaurus NAL: |
Marker-assisted selection; Simulation models; Statistics. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/170209/1/2017-M.Deon-GMR-Regularized.pdf
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Marc: |
LEADER 02634naa a2200313 a 4500
001 2084109
005 2018-01-11
008 2017 bl uuuu u00u1 u #d
100 1 $aNASCIMENTO, M.
245 $aRegularized quantile regression applied to genome-enabled prediction of quantitative traits.$h[electronic resource]
260 $c2017
300 $a12 p.
520 $aGenomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qt(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that ?best? represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively.
650 $aMarker-assisted selection
650 $aSimulation models
650 $aStatistics
650 $aEstatística
653 $aGenomic selection
653 $aRegularized regression
653 $aSeleção genômica
653 $aSNP effects
700 1 $aSILVA, F. F. e
700 1 $aRESENDE, M. D. V. de
700 1 $aCRUZ, C. D.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aVIANA, J. M. S.
700 1 $aAZEVEDO, C. F.
700 1 $aBARROSO, L. M. A.
773 $tGenetics and Molecular Research$gv. 16, n. 1, gmr16019538, 2017.
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