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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
04/12/2023 |
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Data da última atualização: |
06/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SILVA, M. A.; NASCENTE, A. S.; CRUZ, D. R. C.; FRASCA, L. L. de M.; SILVA, J. F. A. e; FERREIRA, A. L.; FERREIRA, E. P. de B.; LANNA, A. C.; BEZERRA, G. de A.; FILIPPI, M. C. C. de. |
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Afiliação: |
MARIANA AGUIAR SILVA, UNIVERSIDADE FEDERAL DE GOIÁS; ADRIANO STEPHAN NASCENTE, CNPAF; DENNIS RICARDO CABRAL CRUZ, UNIVERSIDADE FEDERAL DE GOIÁS; LAYLLA LUANNA DE MELLO FRASCA, UNIVERSIDADE FEDERAL DE GOIÁS; JOSE FRANCISCO ARRUDA E SILVA, CNPAF; AMANDA LOPES FERREIRA, bolsista CNPAF; ENDERSON PETRONIO DE BRITO FERREIRA, CNPAF; ANNA CRISTINA LANNA, CNPAF; GUSTAVO DE ANDRADE BEZERRA, UNIVERSIDADE FEDERAL DE GOIÁS; MARTA CRISTINA CORSI DE FILIPPI, CNPAF. |
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Título: |
Initial development of upland rice inoculated and co-inoculated with multifunctional rhizobacteria. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Semina: Ciências Agrárias, v. 44, n. 1, p. 273-283, jan./fev. 2023. |
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ISSN: |
1676-546X |
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Idioma: |
Inglês |
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Conteúdo: |
Inoculation and co-inoculation of upland rice with multifunctional rhizobacteria can promote plant growth, especially the root system. Thus, this study aimed to evaluate the effect of inoculation and co-inoculation with Azospirillum sp. and Bacillus sp. in the early development of upland rice. The experiment was conducted using a completely randomized design with 4 treatments and 10 replications, totaling 40 plots. The treatments were: 1) Ab-V5 (Azospirillum brasilense), 2) BRM 63573 (Bacillus sp.), 3) co-inoculation of Ab-V5 + BRM 63573, and 4) control (without rhizobacteria). Inoculation and co-inoculation with the multifunctional rhizobacteria Ab-V5 and BRM 63573 provided positive effects on the initial development of upland rice. Inoculation with isolate BRM 63573 had significant effects on root length, shoot, and total biomass, while inoculation with isolate Ab-V5 had significant effects on root length and production of root and total biomass. Co-inoculation treatment had significant effects on variables such as diameter, volume, total surface, root biomass, and total biomass. The control treatment (without multifunctional rhizobacteria) had the worst results for most of the analyzed variables. |
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Palavras-Chave: |
Rizobactérias. |
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Thesagro: |
Arroz; Desenvolvimento Sustentável; Fator de Crescimento; Inoculação; Oryza Sativa. |
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Thesaurus Nal: |
Azospirillum brasilense; Bacillus (bacteria); Environmental sustainability; Plant growth-promoting rhizobacteria; Rice. |
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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/1159093/1/semina-2023-silva.pdf
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Marc: |
LEADER 02349naa a2200373 a 4500
001 2159093
005 2023-12-06
008 2023 bl uuuu u00u1 u #d
022 $a1676-546X
100 1 $aSILVA, M. A.
245 $aInitial development of upland rice inoculated and co-inoculated with multifunctional rhizobacteria.$h[electronic resource]
260 $c2023
520 $aInoculation and co-inoculation of upland rice with multifunctional rhizobacteria can promote plant growth, especially the root system. Thus, this study aimed to evaluate the effect of inoculation and co-inoculation with Azospirillum sp. and Bacillus sp. in the early development of upland rice. The experiment was conducted using a completely randomized design with 4 treatments and 10 replications, totaling 40 plots. The treatments were: 1) Ab-V5 (Azospirillum brasilense), 2) BRM 63573 (Bacillus sp.), 3) co-inoculation of Ab-V5 + BRM 63573, and 4) control (without rhizobacteria). Inoculation and co-inoculation with the multifunctional rhizobacteria Ab-V5 and BRM 63573 provided positive effects on the initial development of upland rice. Inoculation with isolate BRM 63573 had significant effects on root length, shoot, and total biomass, while inoculation with isolate Ab-V5 had significant effects on root length and production of root and total biomass. Co-inoculation treatment had significant effects on variables such as diameter, volume, total surface, root biomass, and total biomass. The control treatment (without multifunctional rhizobacteria) had the worst results for most of the analyzed variables.
650 $aAzospirillum brasilense
650 $aBacillus (bacteria)
650 $aEnvironmental sustainability
650 $aPlant growth-promoting rhizobacteria
650 $aRice
650 $aArroz
650 $aDesenvolvimento Sustentável
650 $aFator de Crescimento
650 $aInoculação
650 $aOryza Sativa
653 $aRizobactérias
700 1 $aNASCENTE, A. S.
700 1 $aCRUZ, D. R. C.
700 1 $aFRASCA, L. L. de M.
700 1 $aSILVA, J. F. A. e
700 1 $aFERREIRA, A. L.
700 1 $aFERREIRA, E. P. de B.
700 1 $aLANNA, A. C.
700 1 $aBEZERRA, G. de A.
700 1 $aFILIPPI, M. C. C. de
773 $tSemina: Ciências Agrárias$gv. 44, n. 1, p. 273-283, jan./fev. 2023.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
07/07/2015 |
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Data da última atualização: |
19/05/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
GRUNWALD, S.; VASQUES, G. M.; RIVERO, R. G. |
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Afiliação: |
SABINE GRUNWALD, UNIVERSITY OF FLORIDA; GUSTAVO DE MATTOS VASQUES, CNPS; ROSANNA G. RIVERO, UNIVERSITY OF GEORGIA. |
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Título: |
Fusion of soil and remote sensing data to model soil properties. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Advances in Agronomy, v. 131, p. 1-109, 2015. |
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DOI: |
10.1016/bs.agron.2014.12.004 |
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Idioma: |
Inglês |
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Conteúdo: |
Grand global challenges of our time, such as food security and soil security, cannot be met without up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize soil ecosystems. At local and regional scales, accurate and precise soil assessment is critical for management, soil health, and sustainability. This article presents integration pathways fusing lab and field-based soil measurements, proximal and remote sensor data, environmental covariates, and/or methods within the framework of the Meta Soil Model which is poised to extend contemporary soil applications. The STEP-AWBH model allows to quantify soil-environmental covariates (S: soil, T: topography, E: ecology, P: parent material, A: atmosphere, W: water, B: biota, H: human factors) of which numerous can be sensed. We present an in-depth overview of proximal and remote sensor technologies that are used in the realm of digital soil assessment. Specific attention is given to the fusion process of (1) proximal, (2) proximal/remote, and (3) remote sensors to directly sense or predict soil properties. We highlight the promises and perils of sensor-derived proxies that allow inferences on soil properties and their change. From our review it is evident that there is no such single sensor or method that fits all soil applications. In many studies the fusion/integration of data and methods enhance the capabilities to assess specific soil properties. We critically contrast the benefits and constraints of proximal and remote sensing, fusion of soil-environmental data, and integration pathways to mash data and methods into complex soil assessments. MenosGrand global challenges of our time, such as food security and soil security, cannot be met without up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize soil ecosystems. At local and regional scales, accurate and precise soil assessment is critical for management, soil health, and sustainability. This article presents integration pathways fusing lab and field-based soil measurements, proximal and remote sensor data, environmental covariates, and/or methods within the framework of the Meta Soil Model which is poised to extend contemporary soil applications. The STEP-AWBH model allows to quantify soil-environmental covariates (S: soil, T: topography, E: ecology, P: parent material, A: atmosphere, W: water, B: biota, H: human factors) of which numerous can be sensed. We present an in-depth overview of proximal and remote sensor technologies that are used in the realm of digital soil assessment. Specific attention is given to the fusion process of (1) proximal, (2) proximal/remote, and (3) remote sensors to directly sense or predict soil properties. We highlight the promises and perils of sensor-derived proxies that allow inferences on soil properties and their change. From our review it is evident that there is no such single sensor or method that fits all soil applications. In many studies the fusion/integration of data and methods enhance the capabilities to assess specific soil properties. We critically contr... Mostrar Tudo |
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Palavras-Chave: |
Covariáveis ambientais; Detecção proximal; Fusão de dados; Mapeamento digital do solo; Modelagem digital do solo; Modelo meta do solo; Pedometria; Propriedades do solo; Sensores; Vias de integração. |
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Thesagro: |
Mapa; Sensoriamento remoto. |
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Thesaurus NAL: |
Remote sensing; Soil surveys. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02618naa a2200325 a 4500
001 2019476
005 2020-05-19
008 2015 bl uuuu u00u1 u #d
024 7 $a10.1016/bs.agron.2014.12.004$2DOI
100 1 $aGRUNWALD, S.
245 $aFusion of soil and remote sensing data to model soil properties.$h[electronic resource]
260 $c2015
520 $aGrand global challenges of our time, such as food security and soil security, cannot be met without up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize soil ecosystems. At local and regional scales, accurate and precise soil assessment is critical for management, soil health, and sustainability. This article presents integration pathways fusing lab and field-based soil measurements, proximal and remote sensor data, environmental covariates, and/or methods within the framework of the Meta Soil Model which is poised to extend contemporary soil applications. The STEP-AWBH model allows to quantify soil-environmental covariates (S: soil, T: topography, E: ecology, P: parent material, A: atmosphere, W: water, B: biota, H: human factors) of which numerous can be sensed. We present an in-depth overview of proximal and remote sensor technologies that are used in the realm of digital soil assessment. Specific attention is given to the fusion process of (1) proximal, (2) proximal/remote, and (3) remote sensors to directly sense or predict soil properties. We highlight the promises and perils of sensor-derived proxies that allow inferences on soil properties and their change. From our review it is evident that there is no such single sensor or method that fits all soil applications. In many studies the fusion/integration of data and methods enhance the capabilities to assess specific soil properties. We critically contrast the benefits and constraints of proximal and remote sensing, fusion of soil-environmental data, and integration pathways to mash data and methods into complex soil assessments.
650 $aRemote sensing
650 $aSoil surveys
650 $aMapa
650 $aSensoriamento remoto
653 $aCovariáveis ambientais
653 $aDetecção proximal
653 $aFusão de dados
653 $aMapeamento digital do solo
653 $aModelagem digital do solo
653 $aModelo meta do solo
653 $aPedometria
653 $aPropriedades do solo
653 $aSensores
653 $aVias de integração
700 1 $aVASQUES, G. M.
700 1 $aRIVERO, R. G.
773 $tAdvances in Agronomy$gv. 131, p. 1-109, 2015.
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