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 | Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
24/07/2018 |
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Data da última atualização: |
05/02/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
DIAS, K. O. das G.; GEZAN, S. A.; GUIMARÃES, C. T.; NAZARIAN, A.; SILVA, L. da C. e; PARENTONI, S. N.; GUIMARAES, P. E. de O.; ANONI, C. de O.; PÁDUA, J. M. V.; PINTO, M. de O.; NODA, R. W.; RIBEIRO, C. A. G.; MAGALHAES, J. V. de; GARCIA, A. A. F.; SOUZA, J. C. de; GUIMARAES, L. J. M.; PASTINA, M. M. |
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Afiliação: |
Kaio Olímpio das Graças Dias, Universidade Federal de Lavras; Salvador Alejandro Gezan, School of Forest Resources & Conservation, University of Florida, Gainesville.; CLAUDIA TEIXEIRA GUIMARAES, CNPMS; Alireza Nazarian, School of Forest Resources & Conservation, University of Florida, Gainesville.; Luciano da Costa e Silva, JMP Division, SAS Institute Inc., Cary.; SIDNEY NETTO PARENTONI, CNPMS; PAULO EVARISTO DE O GUIMARAES, CNPMS; Carina de Oliveira Anoni, Escola Superior de Agricultura “Luiz de Queiroz”; José Maria Villela Pádua, Universidade Federal de Lavras; MARCOS DE OLIVEIRA PINTO, CNPMS; ROBERTO WILLIANS NODA, CNPMS; Carlos Alexandre Gomes Ribeiro, Universidade Federal de Viçosa; JURANDIR VIEIRA DE MAGALHAES, CNPMS; Antonio Augusto Franco Garcia, Escola Superior de Agricultura “Luiz de Queiroz”; João Cândido de Souza, Universidade Federal de Lavras; LAURO JOSE MOREIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS. |
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Título: |
Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Heredity, London, v. 121, n. 1, p. 24-37, 2018. |
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DOI: |
10.1038/s41437-018-0053-6 |
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Idioma: |
Inglês |
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Conteúdo: |
Breeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids. MenosBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS a... Mostrar Tudo |
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Thesagro: |
Milho; Resistência a Seca. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 03081naa a2200349 a 4500
001 2093500
005 2019-02-05
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1038/s41437-018-0053-6$2DOI
100 1 $aDIAS, K. O. das G.
245 $aImproving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials.$h[electronic resource]
260 $c2018
520 $aBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids.
650 $aMilho
650 $aResistência a Seca
700 1 $aGEZAN, S. A.
700 1 $aGUIMARÃES, C. T.
700 1 $aNAZARIAN, A.
700 1 $aSILVA, L. da C. e
700 1 $aPARENTONI, S. N.
700 1 $aGUIMARAES, P. E. de O.
700 1 $aANONI, C. de O.
700 1 $aPÁDUA, J. M. V.
700 1 $aPINTO, M. de O.
700 1 $aNODA, R. W.
700 1 $aRIBEIRO, C. A. G.
700 1 $aMAGALHAES, J. V. de
700 1 $aGARCIA, A. A. F.
700 1 $aSOUZA, J. C. de
700 1 $aGUIMARAES, L. J. M.
700 1 $aPASTINA, M. M.
773 $tHeredity, London$gv. 121, n. 1, p. 24-37, 2018.
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Embrapa Milho e Sorgo (CNPMS) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agrobiologia. |
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Data corrente: |
20/06/2022 |
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Data da última atualização: |
20/06/2022 |
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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: |
MATOS, P. S.; SILVA, C. F. da; PEREIRA, M. G.; SILVA, E. M. R. da; TARRÉ, R. M.; FRANCO, A. L. C.; ZONTA, E. |
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Afiliação: |
PRISCILA SILVA MATOS, UFRRJ; CRISTIANE FIGUEIRA DA SILVA, UFRRJ; MARCOS GERVÁSIO PEREIRA, UFRRJ; ELIANE MARIA RIBEIRO DA SILVA, CNPAB; RICARDO MARTINEZ TARRÉ, UFRJ; ANDRÉ LUIZ CUSTÓDIO FRANCO, Colorado State University, USA; EVERALDO ZONTA, UFRRJ. |
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Título: |
Short-term modifications of mycorrhizal fungi, glomalin and soil attributes in a tropical agroforestry. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Acta Oecologica, v. 114, 103815, May 2022. |
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ISSN: |
1146-609X |
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DOI: |
https://doi.org/10.1016/j.actao.2022.103815 |
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Idioma: |
Inglês |
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Conteúdo: |
Arbuscular mycorrhizal fungi (AMF) community and the Glomalin-related soil protein (GRSP) they produce plays important roles in maintaining soil ecosystem functions, promoting ecological restoration, and are important for monitoring changes in soil health from land use change. This research addressed the subtle modifications of AMF and GRSP in different land use types and their association with soil properties. Our objectives were: 1) to assess land use effects on AMF spore density, diversity, and composition besides glomalin fractions (EEG, easily extracted GRSP; TG, total GRSP), and 2) to quantify the relationships between glomalin, AMF community metrics, soil organic carbon (SOC), and key soil quality parameters. Soils were collected in the dry and rainy seasons of 2018 under five land uses, including: three types of agroforestry systems (AS1, AS2 and AS3), an unmanaged pasture and a secondary forest in the state of Rio de Janeiro, Brazil. Linear mixed-effects model and multivariate analyses showed that land uses had influenced the AMF community, meanly at the family level. On the other hand, seasonality has not proved to be an essential factor that modulates the changes of the AMF community and glomalin production. The management practices had influenced AMF sporulation and the number of total species in agroforestry systems. Glomalin is a potential contributor for SOC, mainly in agroforestry systems and pasture plots. Moreover, we found a correlation between the AMF community and key soil parameters. For example, most of the AMF families and spore density were positively correlated with the stability of soil aggregates and SOC. Our findings shed light on that land-use change can shift the AMF community, glomalin and their relationship to key soil quality parameters. Moreover, the adoption of agroforestry systems indicates maintenance of biodiversity and other soil quality parameters with future implications for their use to recover degraded areas MenosArbuscular mycorrhizal fungi (AMF) community and the Glomalin-related soil protein (GRSP) they produce plays important roles in maintaining soil ecosystem functions, promoting ecological restoration, and are important for monitoring changes in soil health from land use change. This research addressed the subtle modifications of AMF and GRSP in different land use types and their association with soil properties. Our objectives were: 1) to assess land use effects on AMF spore density, diversity, and composition besides glomalin fractions (EEG, easily extracted GRSP; TG, total GRSP), and 2) to quantify the relationships between glomalin, AMF community metrics, soil organic carbon (SOC), and key soil quality parameters. Soils were collected in the dry and rainy seasons of 2018 under five land uses, including: three types of agroforestry systems (AS1, AS2 and AS3), an unmanaged pasture and a secondary forest in the state of Rio de Janeiro, Brazil. Linear mixed-effects model and multivariate analyses showed that land uses had influenced the AMF community, meanly at the family level. On the other hand, seasonality has not proved to be an essential factor that modulates the changes of the AMF community and glomalin production. The management practices had influenced AMF sporulation and the number of total species in agroforestry systems. Glomalin is a potential contributor for SOC, mainly in agroforestry systems and pasture plots. Moreover, we found a correlation between the AMF com... Mostrar Tudo |
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Palavras-Chave: |
Biological indicators. |
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Thesaurus NAL: |
Soil quality. |
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Categoria do assunto: |
S Ciências Biológicas |
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Marc: |
LEADER 02721naa a2200241 a 4500
001 2144172
005 2022-06-20
008 2022 bl uuuu u00u1 u #d
022 $a1146-609X
024 7 $ahttps://doi.org/10.1016/j.actao.2022.103815$2DOI
100 1 $aMATOS, P. S.
245 $aShort-term modifications of mycorrhizal fungi, glomalin and soil attributes in a tropical agroforestry.$h[electronic resource]
260 $c2022
520 $aArbuscular mycorrhizal fungi (AMF) community and the Glomalin-related soil protein (GRSP) they produce plays important roles in maintaining soil ecosystem functions, promoting ecological restoration, and are important for monitoring changes in soil health from land use change. This research addressed the subtle modifications of AMF and GRSP in different land use types and their association with soil properties. Our objectives were: 1) to assess land use effects on AMF spore density, diversity, and composition besides glomalin fractions (EEG, easily extracted GRSP; TG, total GRSP), and 2) to quantify the relationships between glomalin, AMF community metrics, soil organic carbon (SOC), and key soil quality parameters. Soils were collected in the dry and rainy seasons of 2018 under five land uses, including: three types of agroforestry systems (AS1, AS2 and AS3), an unmanaged pasture and a secondary forest in the state of Rio de Janeiro, Brazil. Linear mixed-effects model and multivariate analyses showed that land uses had influenced the AMF community, meanly at the family level. On the other hand, seasonality has not proved to be an essential factor that modulates the changes of the AMF community and glomalin production. The management practices had influenced AMF sporulation and the number of total species in agroforestry systems. Glomalin is a potential contributor for SOC, mainly in agroforestry systems and pasture plots. Moreover, we found a correlation between the AMF community and key soil parameters. For example, most of the AMF families and spore density were positively correlated with the stability of soil aggregates and SOC. Our findings shed light on that land-use change can shift the AMF community, glomalin and their relationship to key soil quality parameters. Moreover, the adoption of agroforestry systems indicates maintenance of biodiversity and other soil quality parameters with future implications for their use to recover degraded areas
650 $aSoil quality
653 $aBiological indicators
700 1 $aSILVA, C. F. da
700 1 $aPEREIRA, M. G.
700 1 $aSILVA, E. M. R. da
700 1 $aTARRÉ, R. M.
700 1 $aFRANCO, A. L. C.
700 1 $aZONTA, E.
773 $tActa Oecologica$gv. 114, 103815, May 2022.
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