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Biblioteca(s): |
Embrapa Unidades Centrais. |
Data corrente: |
17/08/2021 |
Data da última atualização: |
17/08/2021 |
Autoria: |
BARROS, G. S. de C. |
Afiliação: |
GERALDO SANT’ANA DE CAMARGO BARROS, Cepea/Esalq-USP. |
Título: |
Entendendo a inflação de 2020: gatilhos e repercussões. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Revista de Política Agrícola, ano 30, n. 2, p. 133-135, abr./maio/jun. 2021. |
Idioma: |
Português |
Conteúdo: |
O que mais surpreendeu em relação à inflação (medida pelo IPCA) em 2020 foi o fato de que ela acabou sendo menor do que a esperada, a despeito da sua aceleração no último trimestre. A inflação esperada era a prevista com base em dados até dezembro de 2019. Naquele mês, o Cepea projetava uma inflação de 6,11% para 2020; a inflação observada, no entanto, foi de 4,42% (Barros et al., 2021). |
Thesagro: |
Inflação. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/225283/1/Entendedo-a-inflacao.pdf
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Marc: |
LEADER 00822naa a2200133 a 4500 001 2133663 005 2021-08-17 008 2021 bl uuuu u00u1 u #d 100 1 $aBARROS, G. S. de C. 245 $aEntendendo a inflação de 2020$bgatilhos e repercussões.$h[electronic resource] 260 $c2021 520 $aO que mais surpreendeu em relação à inflação (medida pelo IPCA) em 2020 foi o fato de que ela acabou sendo menor do que a esperada, a despeito da sua aceleração no último trimestre. A inflação esperada era a prevista com base em dados até dezembro de 2019. Naquele mês, o Cepea projetava uma inflação de 6,11% para 2020; a inflação observada, no entanto, foi de 4,42% (Barros et al., 2021). 650 $aInflação 773 $tRevista de Política Agrícola, ano 30$gn. 2, p. 133-135, abr./maio/jun. 2021.
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Embrapa Unidades Centrais (AI-SEDE) |
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Registro Completo
Biblioteca(s): |
Embrapa Café. |
Data corrente: |
07/12/2017 |
Data da última atualização: |
07/12/2017 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
FERRÃO, L. F. V.; FERRÃO, R. G.; FERRAO, M. A. G.; FONSECA, A. F. A. da; GARCIA, A. A. F. |
Afiliação: |
LUIS FELIPE VENTORIM FERRÃO, DG/ESALQ; ROMÁRIO GAVA FERRÃO, INCAPER; MARIA AMELIA GAVA FERRAO, SAPC; AYMBIRE FRANCISCO A DA FONSECA, SAPC; ANTONIO AUGUSTO FRANCO GARCIA, DG/ESALQ. |
Título: |
A mixed model to multiple harvest-location trials applied to genomic prediction in Coffea canephora. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
Tree Genetics & Genomes, v. 13, n. 95, 2017. |
Idioma: |
Inglês |
Conteúdo: |
Genomic selection (GS) has been studied in several crops to increase the rates of genetic gain and reduce the length of breeding cycles. Despite its relevance, there are only a modest number of reports applied to the genus Coffea. Effective implementation depends on the ability to consider genomic models, which correctly represent breeding scenario in which the species are inserted. Coffee experimentation, in general, is represented by evaluations in multiple locations and harvests to understand the interaction and predict the performance of untested genotypes. Therefore, the main objective of this study was to investigate GS models suitable for use in Coffea canephora. An expansion of traditional GBLUP was considered and genomic analysis was performed using a genotyping-by-sequencing (GBS) approach, showed good potential to be used in coffee breeding programs. Interactions were modeled using the multiplicative mixed model theory, which is commonly used in multi-environment trials (MET) analysis in perennial crops. The effectiveness of the method used was compared with other genetic models in terms of goodness-of-fit statistics and prediction accuracy. Different scenarios that mimic coffee breeding were used in the cross-validation process. The method used had the lowest AIC and BIC values and, consequently, the best fit. In terms of predictive ability, the incorporation of the MET modeling showed higher accuracy (on average 10–17% higher) and lower prediction errors than traditional GBLUP. The results may be used as basis for additional studies into the genus Coffea and can be expanded for similar perennial crops. MenosGenomic selection (GS) has been studied in several crops to increase the rates of genetic gain and reduce the length of breeding cycles. Despite its relevance, there are only a modest number of reports applied to the genus Coffea. Effective implementation depends on the ability to consider genomic models, which correctly represent breeding scenario in which the species are inserted. Coffee experimentation, in general, is represented by evaluations in multiple locations and harvests to understand the interaction and predict the performance of untested genotypes. Therefore, the main objective of this study was to investigate GS models suitable for use in Coffea canephora. An expansion of traditional GBLUP was considered and genomic analysis was performed using a genotyping-by-sequencing (GBS) approach, showed good potential to be used in coffee breeding programs. Interactions were modeled using the multiplicative mixed model theory, which is commonly used in multi-environment trials (MET) analysis in perennial crops. The effectiveness of the method used was compared with other genetic models in terms of goodness-of-fit statistics and prediction accuracy. Different scenarios that mimic coffee breeding were used in the cross-validation process. The method used had the lowest AIC and BIC values and, consequently, the best fit. In terms of predictive ability, the incorporation of the MET modeling showed higher accuracy (on average 10–17% higher) and lower prediction errors than tr... Mostrar Tudo |
Palavras-Chave: |
GBLUP; Genotyping-by-sequencing; Multi-environment trials; Perennial crops. |
Thesaurus NAL: |
Marker-assisted selection. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/168435/1/A-mixed-model-to-multiple-harvest-location.pdf
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Marc: |
LEADER 02351naa a2200229 a 4500 001 2081803 005 2017-12-07 008 2017 bl uuuu u00u1 u #d 100 1 $aFERRÃO, L. F. V. 245 $aA mixed model to multiple harvest-location trials applied to genomic prediction in Coffea canephora.$h[electronic resource] 260 $c2017 520 $aGenomic selection (GS) has been studied in several crops to increase the rates of genetic gain and reduce the length of breeding cycles. Despite its relevance, there are only a modest number of reports applied to the genus Coffea. Effective implementation depends on the ability to consider genomic models, which correctly represent breeding scenario in which the species are inserted. Coffee experimentation, in general, is represented by evaluations in multiple locations and harvests to understand the interaction and predict the performance of untested genotypes. Therefore, the main objective of this study was to investigate GS models suitable for use in Coffea canephora. An expansion of traditional GBLUP was considered and genomic analysis was performed using a genotyping-by-sequencing (GBS) approach, showed good potential to be used in coffee breeding programs. Interactions were modeled using the multiplicative mixed model theory, which is commonly used in multi-environment trials (MET) analysis in perennial crops. The effectiveness of the method used was compared with other genetic models in terms of goodness-of-fit statistics and prediction accuracy. Different scenarios that mimic coffee breeding were used in the cross-validation process. The method used had the lowest AIC and BIC values and, consequently, the best fit. In terms of predictive ability, the incorporation of the MET modeling showed higher accuracy (on average 10–17% higher) and lower prediction errors than traditional GBLUP. The results may be used as basis for additional studies into the genus Coffea and can be expanded for similar perennial crops. 650 $aMarker-assisted selection 653 $aGBLUP 653 $aGenotyping-by-sequencing 653 $aMulti-environment trials 653 $aPerennial crops 700 1 $aFERRÃO, R. G. 700 1 $aFERRAO, M. A. G. 700 1 $aFONSECA, A. F. A. da 700 1 $aGARCIA, A. A. F. 773 $tTree Genetics & Genomes$gv. 13, n. 95, 2017.
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Embrapa Café (CNPCa) |
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