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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
18/09/2018 |
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Data da última atualização: |
18/09/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MORAIS JÚNIOR, O. P.; DUARTE, J. B.; BRESEGHELLO, F.; COELHO, A. S. G.; MORAIS, O. P.; MAGALHÃES JÚNIOR, A. M. |
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Afiliação: |
ODILON PEIXOTO MORAIS JUNIOR, UFG; JOAO BATISTA DUARTE, UFG; FLAVIO BRESEGHELLO, CNPAF; ALEXANDRE S. G. COELHO, UFG; ORLANDO PEIXOTO DE MORAIS, CNPAF; ARIANO MARTINS DE MAGALHAES JUNIOR, CPACT. |
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Título: |
Single-step reaction norm models for genomic prediction in multienvironment recurrent selection trials. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Crop Science, v. 58, n. 2, p. 592-607, Mar./Apr. 2018. |
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ISSN: |
0011-183X |
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DOI: |
10.2135/cropsci2017.06.0366 |
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Idioma: |
Inglês |
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Conteúdo: |
In recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bias of phenotypic prediction. Within-cycle data were sufficient for accurate prediction of untested progenies, even in untested environments. We concluded that the RN-HBLUP model, with the comprehensive structure, could be useful in improving the prediction accuracy of quantitative traits in RGS programs. MenosIn recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bi... Mostrar Tudo |
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Palavras-Chave: |
Multienvironment prediction. |
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Thesagro: |
Arroz; Melhoramento Genético Vegetal; Oryza Sativa; Progênie; Seleção Recorrente. |
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Thesaurus Nal: |
Genomics; Plant breeding; Recurrent selection; Rice; Variety trials. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02811naa a2200337 a 4500
001 2095887
005 2018-09-18
008 2018 bl uuuu u00u1 u #d
022 $a0011-183X
024 7 $a10.2135/cropsci2017.06.0366$2DOI
100 1 $aMORAIS JÚNIOR, O. P.
245 $aSingle-step reaction norm models for genomic prediction in multienvironment recurrent selection trials.$h[electronic resource]
260 $c2018
520 $aIn recurrent selection programs, progeny testing is done in multienvironment trials, which generates genotype × environment interaction (G × E). Therefore, modeling G × E is essential for genomic prediction in the context of recurrent genomic selection (RGS). Developing single-step, best linear unbiased prediction-based reaction norm models (termed RN-HBLUP) using data from nongenotyped and genotyped progenies, can enhance predictive accuracy. Our objectives were to evaluate: (i) a class of RN-HBLUP models accommodating combined relationship of pedigree and genomic data, environmental covariates, and their interactions for prediction of phenotypic responses; (ii) the predictive accuracy of these models and the relative importance of main effects and interaction components; and (iii) the influence of different grouping strategies of genetic?environmental data (within selection cycles or across cycles) on prediction accuracy of the merit for untested progenies. The genetic material comprised 667 S1:3 progenies of irrigated rice (Oryza sativa L.) and six check cultivars. These materials were evaluated in yield trials conducted in 10 environments during three selection cycles. Genomic information was derived from single-nucleotide polymorphism markers genotyped on 174 progenies in the third cycle. We evaluated six predictive models. Environmental covariates and G × E interaction explained a significant portion of the phenotypic variance, increasing accuracy and decreasing the bias of phenotypic prediction. Within-cycle data were sufficient for accurate prediction of untested progenies, even in untested environments. We concluded that the RN-HBLUP model, with the comprehensive structure, could be useful in improving the prediction accuracy of quantitative traits in RGS programs.
650 $aGenomics
650 $aPlant breeding
650 $aRecurrent selection
650 $aRice
650 $aVariety trials
650 $aArroz
650 $aMelhoramento Genético Vegetal
650 $aOryza Sativa
650 $aProgênie
650 $aSeleção Recorrente
653 $aMultienvironment prediction
700 1 $aDUARTE, J. B.
700 1 $aBRESEGHELLO, F.
700 1 $aCOELHO, A. S. G.
700 1 $aMORAIS, O. P.
700 1 $aMAGALHÃES JÚNIOR, A. M.
773 $tCrop Science$gv. 58, n. 2, p. 592-607, Mar./Apr. 2018.
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Embrapa Arroz e Feijão (CNPAF) |
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Registro Completo
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
05/10/2022 |
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Data da última atualização: |
13/01/2023 |
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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: |
SANTOS, D, dos; ARAUJO, G. G. L. de; SANTOS, E. M.; OLIVEIRA, J. de; LAMBAIS, E.; LAMBAIS, G.; NAGAHAMA, H.; ZANINE, A.; SANTOS, F. N.; SOARES, R.; SOBRAL, G.; JUSTINO, E.; LEMOS, M.; OLIVEIRA, C. J. de. |
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Afiliação: |
DAIANE DOS SANTOS, Federal University of Paraíba, Areia, PB; GHERMAN GARCIA LEAL DE ARAUJO, CPATSA; EDSON MAURO SANTOS, Federal University of Paraíba, Areia, PB; JULIANA DE OLIVEIRA, Federal University of Paraíba, Areia, PB; ÉRICA LAMBAIS, INSA; GEORGE LAMBAIS, INSA; HIDEO NAGAHAMA, UNIVASF; ANDERSON ZANINE, Federal University of Maranhão, Chapadinha, MA; FRANCISCO NAYSSON SANTOS, Federal University of Maranhão, Chapadinha, MA; RAFAEL SOARES, Federal University of Paraíba, Areia, PB; GILBERTO SOBRAL, Federal University of Paraíba, Areia, PB; EVANDRA JUSTINO, Federal University of Paraíba, Areia, PB; MATEUS LEMOS, Federal University of Paraíba, Areia, PB; CELSO JOSÉ DE OLIVEIRA, Federal University of Paraíba, Areia, PB. |
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Título: |
Bacterial community and chemical composition of mixed fresh cactus forage and buffel grass hay during aerobic exposure. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Agronomy, v. 12, n. 8, 1927, Ago. 2022. |
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DOI: |
https://doi.org/10.3390/agronomy12081927 |
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Idioma: |
Inglês |
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Conteúdo: |
The chemical composition of cactus forage becomes a favorable culture medium for accelerated microbial activity when exposed to air, as it contains high content of non-fiber carbohydratesand water. Thus, the aim of this study was to evaluate the bacterial community dynamics of different mixtures, using fresh forage of cactus and buffel grass hay as a function of the period of exposure to air. The experimental design used was a 5 × 5 factorial completely randomized (five levels of cactus forage × five times of exposure to air), with five replications. The peak of Escherichia coli population growth was after 16.06 h of exposure to air, observed in treatments of 90% and 100% cactus forage. There was an increase in microbial richness and uniformity of all treatments after six hours. The most abundant genera were Weissella, Lactobacillus, Bacteroides, Pseudomonas, Sphingobacterium, and Sphingomonas. The diet with 100% cactus forage showed a predominance of Weissella, Lactobacillus, and Leuconostoc. With 20% cactus forage, there was a greater apparent abundance of Pseudomonas, Sphingomonas, and Sphingobacterium. Aerobic exposure of mixtures of cactus forage with buffel grass hay increases the proliferation of microorganisms with pathogenic potential in the diet. Aerobic exposure of mixtures of cactus forage with buffel grass hay increases the proliferation of microorganisms with pathogenic potential in the diet. Therefore, an exposure period of fewer than six hours with 20% cactus forage is recommended to minimize levels of E. coli. Avoiding negative effects of the multiplication of pathogenic microorganisms on animal and human health MenosThe chemical composition of cactus forage becomes a favorable culture medium for accelerated microbial activity when exposed to air, as it contains high content of non-fiber carbohydratesand water. Thus, the aim of this study was to evaluate the bacterial community dynamics of different mixtures, using fresh forage of cactus and buffel grass hay as a function of the period of exposure to air. The experimental design used was a 5 × 5 factorial completely randomized (five levels of cactus forage × five times of exposure to air), with five replications. The peak of Escherichia coli population growth was after 16.06 h of exposure to air, observed in treatments of 90% and 100% cactus forage. There was an increase in microbial richness and uniformity of all treatments after six hours. The most abundant genera were Weissella, Lactobacillus, Bacteroides, Pseudomonas, Sphingobacterium, and Sphingomonas. The diet with 100% cactus forage showed a predominance of Weissella, Lactobacillus, and Leuconostoc. With 20% cactus forage, there was a greater apparent abundance of Pseudomonas, Sphingomonas, and Sphingobacterium. Aerobic exposure of mixtures of cactus forage with buffel grass hay increases the proliferation of microorganisms with pathogenic potential in the diet. Aerobic exposure of mixtures of cactus forage with buffel grass hay increases the proliferation of microorganisms with pathogenic potential in the diet. Therefore, an exposure period of fewer than six hours with 20% cactus... Mostrar Tudo |
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Palavras-Chave: |
Comunidade bacteriana. |
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Thesagro: |
Aerobiose; Cacto; Capim Buffel; Composição Química; Escherichia Coli; Forragem; Nopalea Cochenillifera; Palma Forrageira. |
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Thesaurus NAL: |
Aerobiosis; Escherichia; Forage; Forage grasses. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1147143/1/Bacterial-Community-and-Chemical-Composition-of-Mixed-2022.pdf
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Marc: |
LEADER 02901naa a2200445 a 4500
001 2147143
005 2023-01-13
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/agronomy12081927$2DOI
100 1 $aSANTOS, D, dos
245 $aBacterial community and chemical composition of mixed fresh cactus forage and buffel grass hay during aerobic exposure.$h[electronic resource]
260 $c2022
520 $aThe chemical composition of cactus forage becomes a favorable culture medium for accelerated microbial activity when exposed to air, as it contains high content of non-fiber carbohydratesand water. Thus, the aim of this study was to evaluate the bacterial community dynamics of different mixtures, using fresh forage of cactus and buffel grass hay as a function of the period of exposure to air. The experimental design used was a 5 × 5 factorial completely randomized (five levels of cactus forage × five times of exposure to air), with five replications. The peak of Escherichia coli population growth was after 16.06 h of exposure to air, observed in treatments of 90% and 100% cactus forage. There was an increase in microbial richness and uniformity of all treatments after six hours. The most abundant genera were Weissella, Lactobacillus, Bacteroides, Pseudomonas, Sphingobacterium, and Sphingomonas. The diet with 100% cactus forage showed a predominance of Weissella, Lactobacillus, and Leuconostoc. With 20% cactus forage, there was a greater apparent abundance of Pseudomonas, Sphingomonas, and Sphingobacterium. Aerobic exposure of mixtures of cactus forage with buffel grass hay increases the proliferation of microorganisms with pathogenic potential in the diet. Aerobic exposure of mixtures of cactus forage with buffel grass hay increases the proliferation of microorganisms with pathogenic potential in the diet. Therefore, an exposure period of fewer than six hours with 20% cactus forage is recommended to minimize levels of E. coli. Avoiding negative effects of the multiplication of pathogenic microorganisms on animal and human health
650 $aAerobiosis
650 $aEscherichia
650 $aForage
650 $aForage grasses
650 $aAerobiose
650 $aCacto
650 $aCapim Buffel
650 $aComposição Química
650 $aEscherichia Coli
650 $aForragem
650 $aNopalea Cochenillifera
650 $aPalma Forrageira
653 $aComunidade bacteriana
700 1 $aARAUJO, G. G. L. de
700 1 $aSANTOS, E. M.
700 1 $aOLIVEIRA, J. de
700 1 $aLAMBAIS, E.
700 1 $aLAMBAIS, G.
700 1 $aNAGAHAMA, H.
700 1 $aZANINE, A.
700 1 $aSANTOS, F. N.
700 1 $aSOARES, R.
700 1 $aSOBRAL, G.
700 1 $aJUSTINO, E.
700 1 $aLEMOS, M.
700 1 $aOLIVEIRA, C. J. de
773 $tAgronomy$gv. 12, n. 8, 1927, Ago. 2022.
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