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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
10/05/2022 |
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Data da última atualização: |
12/05/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
REZENDE, C. C.; NASCENTE, A. S.; SILVA, M. A.; FRASCA, L. L. de M.; PIRES, R. A. C.; FILIPPI, M. C. C. de; LANNA, A. C.; SILVA, J. F. A. e. |
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Afiliação: |
CÁSSIA CRISTINA REZENDE, UFG; ADRIANO STEPHAN NASCENTE, CNPAF; MARIANA AGUIAR SILVA, UFG; LAYLLA LUANNA DE MELLO FRASCA, UFG; RAFAEL AUGUSTO CORRÊA PIRES, Centro Universitário de Goiás; MARTA CRISTINA CORSI DE FILIPPI, CNPAF; ANNA CRISTINA LANNA, CNPAF; JOSE FRANCISCO ARRUDA E SILVA, CNPAF. |
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Título: |
Physiological and agronomic performance of common bean treated with multifunctional microorganisms. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Revista Brasileira de Ciências Agrárias, v. 16, n. 4, e838, 2021. |
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ISSN: |
1981-0997 |
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DOI: |
https://doi.org/10.5039/agraria.v16i4a838 |
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Idioma: |
Inglês |
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Conteúdo: |
Multifunctional microorganisms (MM) are able of colonizing root system and shoots and then, provide beneficial effects to the plants. Thus, the objective of this study was test whether multifunctional microorganisms affect gas exchange, macronutrient content, yield components and bean grain yield. A completely randomized design with twenty-six treatments and three replications was used under controlled conditions. Treatments consisted of the application of MM and its combinations in pairs, with nine rhizobacteria isolates BRM 32109, BRM 32110 and 1301 (Bacillus sp.), BRM 32111 and BRM 32112 (Pseudomonas sp.), BRM 32113 (Burkholderia sp.), BRM 32114 (Serratia sp.), 1381 (Azospirillum sp.) and Ab-V5 (Azospirillum brasiliense), an edaphic fungal isolate T-26 (Trichoderma koningiopsis), and a control (without MM). The isolates Ab-V5 and BRM 32112, in addition to the combinations BRM 32114 + T-26, 1301 + BRM 32110 and BRM 32114 + BRM 32110 were the highlights treatments, since they provided increases in gas exchange, in the content of macronutrients and in the agronomic performance. |
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Palavras-Chave: |
Bioagentes; Bioagents; Trocas gasosas. |
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Thesagro: |
Feijão; Microrganismo; Nutriente; Phaseolus Vulgaris. |
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Thesaurus Nal: |
Beans; Gas exchange; Grain yield. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1142851/1/rbca-2021.pdf
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Marc: |
LEADER 02104naa a2200349 a 4500
001 2142851
005 2022-05-12
008 2021 bl uuuu u00u1 u #d
022 $a1981-0997
024 7 $ahttps://doi.org/10.5039/agraria.v16i4a838$2DOI
100 1 $aREZENDE, C. C.
245 $aPhysiological and agronomic performance of common bean treated with multifunctional microorganisms.$h[electronic resource]
260 $c2021
520 $aMultifunctional microorganisms (MM) are able of colonizing root system and shoots and then, provide beneficial effects to the plants. Thus, the objective of this study was test whether multifunctional microorganisms affect gas exchange, macronutrient content, yield components and bean grain yield. A completely randomized design with twenty-six treatments and three replications was used under controlled conditions. Treatments consisted of the application of MM and its combinations in pairs, with nine rhizobacteria isolates BRM 32109, BRM 32110 and 1301 (Bacillus sp.), BRM 32111 and BRM 32112 (Pseudomonas sp.), BRM 32113 (Burkholderia sp.), BRM 32114 (Serratia sp.), 1381 (Azospirillum sp.) and Ab-V5 (Azospirillum brasiliense), an edaphic fungal isolate T-26 (Trichoderma koningiopsis), and a control (without MM). The isolates Ab-V5 and BRM 32112, in addition to the combinations BRM 32114 + T-26, 1301 + BRM 32110 and BRM 32114 + BRM 32110 were the highlights treatments, since they provided increases in gas exchange, in the content of macronutrients and in the agronomic performance.
650 $aBeans
650 $aGas exchange
650 $aGrain yield
650 $aFeijão
650 $aMicrorganismo
650 $aNutriente
650 $aPhaseolus Vulgaris
653 $aBioagentes
653 $aBioagents
653 $aTrocas gasosas
700 1 $aNASCENTE, A. S.
700 1 $aSILVA, M. A.
700 1 $aFRASCA, L. L. de M.
700 1 $aPIRES, R. A. C.
700 1 $aFILIPPI, M. C. C. de
700 1 $aLANNA, A. C.
700 1 $aSILVA, J. F. A. e
773 $tRevista Brasileira de Ciências Agrárias$gv. 16, n. 4, e838, 2021.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Café. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
14/07/2020 |
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Data da última atualização: |
14/07/2020 |
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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: |
CARVALHO, H. F.; GALLI, G.; FERRÃO, L. F. V.; NONATO, J. V. A.; PADILHA, L.; MALUF, M. P.; RESENDE JR, M. F. R. de; GUERREIRO FILHO, O.; FRITSCHE-NETO, R. |
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Afiliação: |
HUMBERTO FANELLI CARVALHO, INSTITUTO AGRONÔMICO DE CAMPINAS - IAC; GIOVANNI GALLI, UNIVERSIDADE DE SÃO PAULO; LUÍS FELIPE VENTORIM FERRÃO, UNIVERSITY OF FLORIDA; JULIANA VIEIRA ALMEIDA NONATO, INSTITUTO AGRONÔMICO DE CAMPINAS - IAC; LILIAN PADILHA, CNPCa; MIRIAN PEREZ MALUF, CNPCa; MÁRCIO FERNANDO RIBEIRO DE RESENDE JR, UNIVERSITY OF FLORIDA; OLIVEIRO GUERREIRO FILHO, INSTITUTO AGRONÔMICO DE CAMPINAS - IAC; ROBERTO FRITSCHE-NETO, UNIVERSIDADE DE SÃO PAULO. |
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Título: |
The effect of bienniality on genomic prediction of yield in arabica coffee. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Euphytica, v. 216, n. 101, p. 100-111, 2020. |
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DOI: |
https://doi.org/10.1007/s10681-020-02641-7 |
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Idioma: |
Inglês |
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Conteúdo: |
The most popular beverage worldwide, coffee, is responsible for a billionaire market chain with arabica coffee leading the production. Coffee breeding programs are focusing on high yield, excellent beverage quality, and disease resistance, but the bienniality comes to a challenge to overcome bean production. The bienniality, the seasonal variation between high and low yielding, is a genetically controlled physiological event that affects yield stability in arabica coffee. However, there are no studies on the best strategies to implement genomic selection in coffee, including how to establish training populations and deal with the biennially. Thus, the objective was evaluated the potential of genomic selection applied to arabica coffee, with particular consideration on how to estimate bienniality effect on genomic prediction accuracy for yield. The population (n = 586) high-density genotyped by GBS was measured in the low (2005 and 2007), and high (2006 and 2008) yield years. The genomic prediction models were established considering genotype and genotype × year effects. Different prediction scenarios were proposed, considering single-year training sets and grouping the data according to bienniality. Overall, training genomic models on biennium of successive years, and predicting the following biennium appears to be the most effective strategy between all tested scenarios. The comparison of phenotypic and prediction approaches revealed an increased selection response using genomic selection, mainly due to the reduced time per breeding cycle. These results can shed light on the implementation of a genome-based selection of arabica coffee and lead to more efficient breeding strategies. MenosThe most popular beverage worldwide, coffee, is responsible for a billionaire market chain with arabica coffee leading the production. Coffee breeding programs are focusing on high yield, excellent beverage quality, and disease resistance, but the bienniality comes to a challenge to overcome bean production. The bienniality, the seasonal variation between high and low yielding, is a genetically controlled physiological event that affects yield stability in arabica coffee. However, there are no studies on the best strategies to implement genomic selection in coffee, including how to establish training populations and deal with the biennially. Thus, the objective was evaluated the potential of genomic selection applied to arabica coffee, with particular consideration on how to estimate bienniality effect on genomic prediction accuracy for yield. The population (n = 586) high-density genotyped by GBS was measured in the low (2005 and 2007), and high (2006 and 2008) yield years. The genomic prediction models were established considering genotype and genotype × year effects. Different prediction scenarios were proposed, considering single-year training sets and grouping the data according to bienniality. Overall, training genomic models on biennium of successive years, and predicting the following biennium appears to be the most effective strategy between all tested scenarios. The comparison of phenotypic and prediction approaches revealed an increased sel... Mostrar Tudo |
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Palavras-Chave: |
Sequenciamento genético. |
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Thesagro: |
Coffea Arábica; Genoma; Seleção Genética. |
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Thesaurus NAL: |
Genome; Plant selection guides. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02622naa a2200301 a 4500
001 2123829
005 2020-07-14
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s10681-020-02641-7$2DOI
100 1 $aCARVALHO, H. F.
245 $aThe effect of bienniality on genomic prediction of yield in arabica coffee.$h[electronic resource]
260 $c2020
520 $aThe most popular beverage worldwide, coffee, is responsible for a billionaire market chain with arabica coffee leading the production. Coffee breeding programs are focusing on high yield, excellent beverage quality, and disease resistance, but the bienniality comes to a challenge to overcome bean production. The bienniality, the seasonal variation between high and low yielding, is a genetically controlled physiological event that affects yield stability in arabica coffee. However, there are no studies on the best strategies to implement genomic selection in coffee, including how to establish training populations and deal with the biennially. Thus, the objective was evaluated the potential of genomic selection applied to arabica coffee, with particular consideration on how to estimate bienniality effect on genomic prediction accuracy for yield. The population (n = 586) high-density genotyped by GBS was measured in the low (2005 and 2007), and high (2006 and 2008) yield years. The genomic prediction models were established considering genotype and genotype × year effects. Different prediction scenarios were proposed, considering single-year training sets and grouping the data according to bienniality. Overall, training genomic models on biennium of successive years, and predicting the following biennium appears to be the most effective strategy between all tested scenarios. The comparison of phenotypic and prediction approaches revealed an increased selection response using genomic selection, mainly due to the reduced time per breeding cycle. These results can shed light on the implementation of a genome-based selection of arabica coffee and lead to more efficient breeding strategies.
650 $aGenome
650 $aPlant selection guides
650 $aCoffea Arábica
650 $aGenoma
650 $aSeleção Genética
653 $aSequenciamento genético
700 1 $aGALLI, G.
700 1 $aFERRÃO, L. F. V.
700 1 $aNONATO, J. V. A.
700 1 $aPADILHA, L.
700 1 $aMALUF, M. P.
700 1 $aRESENDE JR, M. F. R. de
700 1 $aGUERREIRO FILHO, O.
700 1 $aFRITSCHE-NETO, R.
773 $tEuphytica$gv. 216, n. 101, p. 100-111, 2020.
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Embrapa Café (CNPCa) |
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