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Registro Completo |
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Biblioteca(s): |
Embrapa Pecuária Sul; Embrapa Trigo. |
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Data corrente: |
07/01/1991 |
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Data da última atualização: |
07/05/1996 |
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Autoria: |
INTERNATIONAL SOIL CLASSIFICATION WORKSHOP, 1., 1977, Rio de Janeiro. |
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Título: |
Proceedings... |
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Ano de publicação: |
1978 |
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Fonte/Imprenta: |
Rio de Janeiro: EMBRAPA-SNLCS, 1978. |
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Páginas: |
376 p. |
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Idioma: |
Inglês |
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Conteúdo: |
PART - I -- PAPERS, REPORTS AN RECOMMENDATIONS: Opening adress; Chemistry of soils withmixtures of pH-depent and permanent charge minerals; Comparison of analytical data from four soil laboratories on three soils of kindaruma in Kenya; The chemistry and phisics of low activity clays; Importance of mineral constituents in pedology; Characteristics and processes of ferralitic soils; Report on the Brazil meeting of the commitee on the classification of Alfisols with Low Activity Clays; Report on a "satate-of-art" (SOTA)study on soil Taxonomy in the tropics; workshop; PART II - SOILS STUDIED IN THE FIELD TOUR - PEDOLOGIC, ANALYTICAL AND MICROMORPHOLOGIC DATA AND GENERAL INFORMATION: Route map and location of pedons; Laboratory methods of analyses; Profile and sitedescriptions, analytical data, and sumary of discussions; ominant mineralogy of the clay and silt fractions some soils of Brazil - summary data; Report on the micromorphology of selected Brazilian pedons; Correlation of some Brazil and SCS-USDA laboratoties; Calculated cation excharge capacities for some Brazilian soils. |
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Palavras-Chave: |
Classificação; Congresso. |
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Thesagro: |
Solo. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01502nam a2200157 a 4500
001 1816453
005 1996-05-07
008 1978 bl uuuu 00u1 u #d
100 1 $aINTERNATIONAL SOIL CLASSIFICATION WORKSHOP, 1., 1977, Rio de Janeiro.
245 $aProceedings...
260 $aRio de Janeiro: EMBRAPA-SNLCS$c1978
300 $a376 p.
520 $aPART - I -- PAPERS, REPORTS AN RECOMMENDATIONS: Opening adress; Chemistry of soils withmixtures of pH-depent and permanent charge minerals; Comparison of analytical data from four soil laboratories on three soils of kindaruma in Kenya; The chemistry and phisics of low activity clays; Importance of mineral constituents in pedology; Characteristics and processes of ferralitic soils; Report on the Brazil meeting of the commitee on the classification of Alfisols with Low Activity Clays; Report on a "satate-of-art" (SOTA)study on soil Taxonomy in the tropics; workshop; PART II - SOILS STUDIED IN THE FIELD TOUR - PEDOLOGIC, ANALYTICAL AND MICROMORPHOLOGIC DATA AND GENERAL INFORMATION: Route map and location of pedons; Laboratory methods of analyses; Profile and sitedescriptions, analytical data, and sumary of discussions; ominant mineralogy of the clay and silt fractions some soils of Brazil - summary data; Report on the micromorphology of selected Brazilian pedons; Correlation of some Brazil and SCS-USDA laboratoties; Calculated cation excharge capacities for some Brazilian soils.
650 $aSolo
653 $aClassificação
653 $aCongresso
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Registro original: |
Embrapa Trigo (CNPT) |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
03/01/2024 |
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Data da última atualização: |
03/01/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
AZEVEDO, C. F.; FERRÃO, L. F. V.; BENEVENUTO, J.; RESENDE, M. D. V. de; NASCIMENTO, M.; NASCIMENTO, A. C. C.; MUNOZ, P. R. |
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Afiliação: |
CAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; LUIS FELIPE VENTORIM FERRÃO, UNIVERSITY OF FLORID; JULIANA BENEVENUTO, UNIVERSITY OF FLORID; MARCOS DEON VILELA DE RESENDE, CNPCa; MOYSES NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; ANA CAROLINA CAMPANA NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; PATRICIO R. MUNOZ, UNIVERSITY OF FLORID. |
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Título: |
Using visual scores for genomic prediction of complex traits in breeding programs. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Theoretical and Applied Genetics, v. 137, n. 1, 2024. |
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Páginas: |
16 p. |
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DOI: |
https://doi.org/10.1007/s00122-023-04512-w |
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Idioma: |
Inglês |
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Conteúdo: |
An approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and categorical phenotypes found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using visual scores traits to explore genetic information in breeding programs and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping. MenosAn approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and ... Mostrar Tudo |
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Thesaurus NAL: |
Animal breeding; Bayesian theory; Genome; Inheritance (genetics); Phenotype; Plant breeding. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1160409/1/Using-visual-scores-for-genomic-prediction.pdf
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Marc: |
LEADER 02817naa a2200289 a 4500
001 2160409
005 2024-01-03
008 2024 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s00122-023-04512-w$2DOI
100 1 $aAZEVEDO, C. F.
245 $aUsing visual scores for genomic prediction of complex traits in breeding programs.$h[electronic resource]
260 $c2024
300 $a16 p.
520 $aAn approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and categorical phenotypes found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using visual scores traits to explore genetic information in breeding programs and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping.
650 $aAnimal breeding
650 $aBayesian theory
650 $aGenome
650 $aInheritance (genetics)
650 $aPhenotype
650 $aPlant breeding
700 1 $aFERRÃO, L. F. V.
700 1 $aBENEVENUTO, J.
700 1 $aRESENDE, M. D. V. de
700 1 $aNASCIMENTO, M.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aMUNOZ, P. R.
773 $tTheoretical and Applied Genetics$gv. 137, n. 1, 2024.
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