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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
12/12/2022 |
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Data da última atualização: |
12/12/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SILVA, M. A.; CRUZ, D. R. C.; FRASCA, L. L. de M.; FILIPPI, M. C. C. de; FERREIRA, A. L.; NASCENTE, A. S. |
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Afiliação: |
MARIANA AGUIAR SILVA, UNIVERSIDADE FEDERAL DE GOIÁS; DENNIS RICARDO CABRAL CRUZ, UNIVERSIDADE FEDERAL DE GOIÁS; LAYLLA LUANNA DE MELLO FRASCA, UNIVERSIDADE FEDERAL DE GOIÁS; MARTA CRISTINA CORSI DE FILIPPI, CNPAF; AMANDA LOPES FERREIRA, UNIVERSIDADE FEDERAL DE GOIÁS; ADRIANO STEPHAN NASCENTE, CNPAF. |
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Título: |
Inoculation and co-inoculation with multifunctional rhizobacteria for the initial development of soybean. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Tropical, v. 52, e73558, 2022. |
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ISSN: |
1983-4063 |
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DOI: |
https://doi.org/10.1590/1983-40632022v5273558 |
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Idioma: |
Inglês |
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Conteúdo: |
Soybean inoculation and co-inoculation with multifunctional rhizobacteria is a sustainable alternative that may contribute to plant growth and increased agricultural production, making the product more competitive, as well as reducing costs for the producer. This study aimed to evaluate the effect of inoculation and co-inoculation with multifunctional Serratia sp. and Bacillus sp. rhizobacteria on the early development of soybean. A completely randomized experimental design was used, with four treatments and ten replicates, totaling 40 experimental plots. The treatments consisted of the microbiolization of soybean seeds and a control treatment: BRM 32114 (Serratia sp.) isolate; BRM 63573 (Bacillus sp.) isolate (formerly named 1301); co-inoculation with BRM 32114 + BRM 63573; and control (without microbiolization). BRM 32114 and BRM 63753, both isolated and combined, were eficient to improve the initial development of soybean seedlings, providing significant effects for most of the analyzed variables (length, total surface, root volume and root, shoot and total biomass), when compared to the control treatment. |
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Palavras-Chave: |
Bacillus sp; Rhizobacteria. |
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Thesagro: |
Glycine Max; Inoculação; Soja. |
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Thesaurus Nal: |
Inoculation methods; Serratia; Soybeans. |
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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/1149542/1/pat-2022.pdf
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Marc: |
LEADER 02013naa a2200301 a 4500
001 2149542
005 2022-12-12
008 2022 bl uuuu u00u1 u #d
022 $a1983-4063
024 7 $ahttps://doi.org/10.1590/1983-40632022v5273558$2DOI
100 1 $aSILVA, M. A.
245 $aInoculation and co-inoculation with multifunctional rhizobacteria for the initial development of soybean.$h[electronic resource]
260 $c2022
520 $aSoybean inoculation and co-inoculation with multifunctional rhizobacteria is a sustainable alternative that may contribute to plant growth and increased agricultural production, making the product more competitive, as well as reducing costs for the producer. This study aimed to evaluate the effect of inoculation and co-inoculation with multifunctional Serratia sp. and Bacillus sp. rhizobacteria on the early development of soybean. A completely randomized experimental design was used, with four treatments and ten replicates, totaling 40 experimental plots. The treatments consisted of the microbiolization of soybean seeds and a control treatment: BRM 32114 (Serratia sp.) isolate; BRM 63573 (Bacillus sp.) isolate (formerly named 1301); co-inoculation with BRM 32114 + BRM 63573; and control (without microbiolization). BRM 32114 and BRM 63753, both isolated and combined, were eficient to improve the initial development of soybean seedlings, providing significant effects for most of the analyzed variables (length, total surface, root volume and root, shoot and total biomass), when compared to the control treatment.
650 $aInoculation methods
650 $aSerratia
650 $aSoybeans
650 $aGlycine Max
650 $aInoculação
650 $aSoja
653 $aBacillus sp
653 $aRhizobacteria
700 1 $aCRUZ, D. R. C.
700 1 $aFRASCA, L. L. de M.
700 1 $aFILIPPI, M. C. C. de
700 1 $aFERREIRA, A. L.
700 1 $aNASCENTE, A. S.
773 $tPesquisa Agropecuária Tropical$gv. 52, e73558, 2022.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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Biblioteca |
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Origem |
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Cutter |
Registro |
Volume |
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URL |
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Registro Completo
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Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
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Data corrente: |
08/12/2022 |
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Data da última atualização: |
08/12/2022 |
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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: |
CARVALHO, R. R. B. de; CORTES, D. F. M.; SOUSA, M. B. e; OLIVEIRA, L. A. de; OLIVEIRA, E. J. de. |
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Afiliação: |
RAVENA ROCHA BESSA DE CARVALHO, UNIVERSIDADE FEDERAL DO RECÔNCAVO DA BAHIA; DIEGO FERNANDO MARMOLEJO CORTES; MASSAINE BANDEIRA E SOUSA; LUCIANA ALVES DE OLIVEIRA, CNPMF; EDER JORGE DE OLIVEIRA, CNPMF. |
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Título: |
Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
PLoS One, v.17, n.1, e0263326, January, 2022. |
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ISSN: |
1932-6203 |
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DOI: |
https://doi.org/10.1371/journal.pone.0263326 |
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Idioma: |
Inglês |
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Conteúdo: |
Phenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability. MenosPhenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The st... Mostrar Tudo |
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Thesagro: |
Mandioca. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1149383/1/journal.pone.0263326.pdf
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Marc: |
LEADER 02501naa a2200205 a 4500
001 2149383
005 2022-12-08
008 2022 bl uuuu u00u1 u #d
022 $a1932-6203
024 7 $ahttps://doi.org/10.1371/journal.pone.0263326$2DOI
100 1 $aCARVALHO, R. R. B. de
245 $aImage-based phenotyping of cassava roots for diversity studies and carotenoids prediction.$h[electronic resource]
260 $c2022
520 $aPhenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability.
650 $aMandioca
700 1 $aCORTES, D. F. M.
700 1 $aSOUSA, M. B. e
700 1 $aOLIVEIRA, L. A. de
700 1 $aOLIVEIRA, E. J. de
773 $tPLoS One$gv.17, n.1, e0263326, January, 2022.
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Registro original: |
Embrapa Mandioca e Fruticultura (CNPMF) |
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| Registros recuperados : 909 | |
| 161. |  | 5153958, ANNALS OF MATHEMATICAL STATISTICS, Institute of Mathematical Statistics, Ann Arbor, Mich., US
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Unidades Centrais. |   |
| 162. | | 0905093, ANUARIO ESTATISTICO DO AMAPA, Secretaria de Planejamento e Coordenacao, Macapa, AP
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Amapá; Embrapa Amazônia Oriental. | |
| 163. | | 0350129, ARCHIV FUR TIERZUCHT, Akademi Verlag, Dummerstof-Alemanha
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Suínos e Aves. | |
| 164. | | 0600633, AUSTRALIAN SEPTORIA NEWSLETTER, Wheat Industry Research Council of Australia, Australia
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Trigo. | |
| 165. | | 0900360, BAHIA RURAL, Cooperativa Central Instituto de Pecuaria da Bahia, Salvador, BA
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Semiárido. | |
| 167. | | 2450182, BIOTECHNOLOGY AND DEVELOPMENT MONITOR, Dept. of International Relations and Public International Law of University Amsterdam, Amsterdam-Holanda
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Amapá; Embrapa Meio Ambiente; Embrapa Soja; Embrapa Trigo. | |
| 169. | | 0900808, BRASIL MOAGEIRO, Acao Moageira, Porto Alegre-RS
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Trigo. | |
| 171. | | 2000160, CAHIERS DE MEDECINE VETERINAIRE, Societe Parisiense d'Expansion Chimique, Paris-Franca
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Amazônia Oriental. | |
| 172. | | 5151638, CALIFORNIA TOMATO GROWER, California Tomato Growers Association, California-CA
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Hortaliças. | |
| 174. | | 0901626, SUÍNOS & CIA, Campinas - SP
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Suínos e Aves. |  |
| 175. | | 2000090, CHIMIE ANALYTIQUE, Societe de Productions Documentaires, Malmaison-Franca
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Agroindústria de Alimentos. | |
| 176. | | 5150982, COLEOPTERISTS BULLETIN, Coleopterists Society, Chicago-IL
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Acre; Embrapa Algodão; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Cerrados; Embrapa Gado de Corte; Embrapa Meio Norte / UEP-Parnaíba; Embrapa Semiárido; Embrapa Soja; Embrapa Suínos e Aves; Embrapa Trigo. MenosCatálogo Coletivo de Periódicos Embrapa; Embrapa Acre; Embrapa Algodão; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Cerrados; Embrapa Gado de Corte; Embrapa Meio Norte / UEP-Parnaíba; Embrapa Semiárido; Embrapa Soja... Mostrar Todas | |
| 177. | | 3600041, COMERCIO EXTERIOR. MEXICO, Banco Nacional de Comercio Exterior, Mexico-DF
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Semiárido. | |
| 179. | | 0600031, CSIRO FOOD RESEARCH QUARTERLY, CSIRO, Melbourne-Australia
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Agroindústria de Alimentos; Embrapa Cerrados. | |
| 180. | | 2650380, DAIRY SCIENCE ABSTRACTS, Commonwealth of Dairy Science Technology, Farnham Royal-Inglaterra
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Pantanal; Embrapa Semiárido. | |
| Registros recuperados : 909 | |
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