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 | Acesso ao texto completo restrito à biblioteca da Embrapa Semiárido. Para informações adicionais entre em contato com cpatsa.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
30/01/2018 |
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Data da última atualização: |
06/09/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
GAVA, C. A. T.; CASTRO, A. P. C.; PEREIRA, C. A.; FERNANDES JUNIOR, P. I. |
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Afiliação: |
CARLOS ALBERTO TUAO GAVA, CPATSA; ANA PAULA CARVALHO CASTRO, UPE; CARLIANA ARAÚJO PEREIRA, UPE; PAULO IVAN FERNANDES JUNIOR, CPATSA. |
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Título: |
Isolation of fruit colonizer yeasts and screening against mango decay caused by multiple pathogens. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Biological Control, v. 117, p. 137-146, 2018. |
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DOI: |
10.1016/j.biocontrol.2017.11.005 |
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Idioma: |
Inglês |
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Conteúdo: |
Most studies selecting antagonistic microorganisms have focused on one pathosystem and then extended to others, but this approach was not a viable alternative for mango decay which is caused by a pool of pathogens in the tropics. In this study, 182 yeast isolates were isolated from ripe fruits of native wild plant species and other fruit crops from the Brazilian semi-arid region and selected against four prevalent agents of mango decay in tropical regions. The largest number of isolates was obtained from table and wine-grapes, followed by the native Spondias tuberosa. In dual culture assays, 8.8% of the yeast isolates showed growth inhibition halos of at least one of the pathogens. However, only six yeasts reduced the development of lesions in vivo to less than 50% to all the pathogens. Yeast/pathogens co-inoculation in healthy mango fruits in semi-commercial conditions showed that S. cerevisiae ESA45, Saccharomyces sp. ESA47 and Pichia kudriavzevii CMIAT171 had the larger spectrum of control efficiency, significantly reducing rot incidence and severity of all pathogens. The isolates also showed a variable tolerance to abiotic stress and biomass production. These results show the potential of these epiphytic yeasts as biocontrol agents of multiple pathogens infection in mango fruits. |
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Palavras-Chave: |
Biocontrole multi-patógeno; Microorganismo; Multi-pathogen biocontrol; Plant disease. |
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Thesagro: |
Controle biológico; Doença; Fruta; Manga; Pós-colheita. |
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Thesaurus Nal: |
Biological control; Colletotrichum; Fusicoccum; Lasiodiplodia; Neofusicoccum. |
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Categoria do assunto: |
H Saúde e Patologia |
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Marc: |
LEADER 02288naa a2200337 a 4500
001 2086725
005 2022-09-06
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1016/j.biocontrol.2017.11.005$2DOI
100 1 $aGAVA, C. A. T.
245 $aIsolation of fruit colonizer yeasts and screening against mango decay caused by multiple pathogens.$h[electronic resource]
260 $c2018
520 $aMost studies selecting antagonistic microorganisms have focused on one pathosystem and then extended to others, but this approach was not a viable alternative for mango decay which is caused by a pool of pathogens in the tropics. In this study, 182 yeast isolates were isolated from ripe fruits of native wild plant species and other fruit crops from the Brazilian semi-arid region and selected against four prevalent agents of mango decay in tropical regions. The largest number of isolates was obtained from table and wine-grapes, followed by the native Spondias tuberosa. In dual culture assays, 8.8% of the yeast isolates showed growth inhibition halos of at least one of the pathogens. However, only six yeasts reduced the development of lesions in vivo to less than 50% to all the pathogens. Yeast/pathogens co-inoculation in healthy mango fruits in semi-commercial conditions showed that S. cerevisiae ESA45, Saccharomyces sp. ESA47 and Pichia kudriavzevii CMIAT171 had the larger spectrum of control efficiency, significantly reducing rot incidence and severity of all pathogens. The isolates also showed a variable tolerance to abiotic stress and biomass production. These results show the potential of these epiphytic yeasts as biocontrol agents of multiple pathogens infection in mango fruits.
650 $aBiological control
650 $aColletotrichum
650 $aFusicoccum
650 $aLasiodiplodia
650 $aNeofusicoccum
650 $aControle biológico
650 $aDoença
650 $aFruta
650 $aManga
650 $aPós-colheita
653 $aBiocontrole multi-patógeno
653 $aMicroorganismo
653 $aMulti-pathogen biocontrol
653 $aPlant disease
700 1 $aCASTRO, A. P. C.
700 1 $aPEREIRA, C. A.
700 1 $aFERNANDES JUNIOR, P. I.
773 $tBiological Control$gv. 117, p. 137-146, 2018.
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Registro original: |
Embrapa Semiárido (CPATSA) |
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Origem |
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Registro |
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URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
20/05/2022 |
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Data da última atualização: |
20/01/2023 |
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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: |
OSCO, L. P.; FURUYA, D. E. G.; FURUYA, M. T. G.; CORRÊA, D. V.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; BORGES, M.; MORAES, M. C. B.; MICHEREFF, M. F. F.; AQUINO, M. F. S.; LAUMANN, R. A.; LISENBERG, V.; RAMOS, A. P. M.; JORGE, L. A. de C. |
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Afiliação: |
LUCAS PRADO OSCO, Unoeste; DANIELLE ELIS GARCIA FURUYA, Unoeste; MICHELLE TAÍS GARCIA FURUYA, Unoeste; DANIEL VERAS CORRÊA, Unoeste; WESLEY NUNES GONÇALVEZ, UFMS; JOSÉ MARCATO JUNIOR, UFMS; MIGUEL BORGES, Cenargen; MARIA CAROLINA BLASSIOLI MORAES, Cenargen; MIRIAN FERNANDES FURTADO MICHEREFF; MICHELY FERREIRA SANTOS AQUINO; RAUL ALBERTO LAUMANN, Cenargen; VERALDO LISENBERG, UDESC; ANA PAULA MARQUES RAMOS, Unoeste; LUCIO ANDRE DE CASTRO JORGE, CNPDIA. |
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Título: |
An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Infrared Physics & Technology, v. 123, 2022. 104203. |
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DOI: |
https://doi.org/10.1016/j.infrared.2022.104203 |
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Idioma: |
Português |
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Notas: |
Na publicação: Maria Carolina Blassioli-Moraes. |
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Palavras-Chave: |
Field spectroscopy. |
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Thesaurus NAL: |
Artificial intelligence; Precision agriculture; Remote sensing. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1143293/1/1-s2.0-S1350449522001840-main.pdf
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Marc: |
LEADER 01134naa a2200337 a 4500
001 2143293
005 2023-01-20
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.infrared.2022.104203$2DOI
100 1 $aOSCO, L. P.
245 $aAn impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods.$h[electronic resource]
260 $c2022
500 $aNa publicação: Maria Carolina Blassioli-Moraes.
650 $aArtificial intelligence
650 $aPrecision agriculture
650 $aRemote sensing
653 $aField spectroscopy
700 1 $aFURUYA, D. E. G.
700 1 $aFURUYA, M. T. G.
700 1 $aCORRÊA, D. V.
700 1 $aGONÇALVEZ, W. N.
700 1 $aMARCATO JUNIOR, J.
700 1 $aBORGES, M.
700 1 $aMORAES, M. C. B.
700 1 $aMICHEREFF, M. F. F.
700 1 $aAQUINO, M. F. S.
700 1 $aLAUMANN, R. A.
700 1 $aLISENBERG, V.
700 1 $aRAMOS, A. P. M.
700 1 $aJORGE, L. A. de C.
773 $tInfrared Physics & Technology$gv. 123, 2022. 104203.
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Embrapa Recursos Genéticos e Biotecnologia (CENARGEN) |
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