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Registro Completo |
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Biblioteca(s): |
Embrapa Agrobiologia; Embrapa Solos. |
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Data corrente: |
21/05/2018 |
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Data da última atualização: |
11/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
VERGARA, C.; ARAUJO, K. E. C.; URQUIAGA, S.; SANTA-CATARINA, C.; SCHULTZ, N.; ARAUJO, E. da S.; BALIEIRO, F. de C.; XAVIER, G. R.; ZILLI, J. E. |
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Afiliação: |
CARLOS VERGARA, UFRRJ; KARLA E. C. ARAUJO, UFRRJ; SEGUNDO SACRAMENTO U CABALLERO, CNPAB; CLAUDETE SANTA-CATARINA, UENF; NIVALDO SCHULTZ, UFRRJ; EDNALDO DA SILVA ARAUJO, CNPAB; FABIANO DE CARVALHO BALIEIRO, CNPS; GUSTAVO RIBEIRO XAVIER, CNPAB; JERRI EDSON ZILLI, CNPAB. |
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Título: |
Dark septate endophytic fungi increase green manure-15N recovery efficiency, N contents, and micronutrients in rice grains. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Frontiers in Plant Science, v. 9, article 613, May 2018. |
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DOI: |
https://doi.org/10.3389/fpls.2018.00613 |
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Idioma: |
Inglês |
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Conteúdo: |
An understanding of the interaction between rice and dark septate endophytic (DSE) fungi, under green fertilization, may lead to sustainable agricultural practices. Nevertheless, this interaction is still poorly understood. Therefore, in this study, we aimed to evaluate the accumulation of macro- and micronutrients, dry matter, and protein and N recovery efficiency from Canavalia ensiformis (L.)-15N in rice inoculated with DSE fungi. An experiment under greenhouse conditions was conducted in a randomized complete block design comprising split-plots, with five replicates of rice plants potted in non-sterilized soil. Rice (Piauí variety) seedlings were inoculated with DSE fungi, A101 and A103, or left uninoculated (control) and transplanted into pots containing 12 kg of soil, which had previously been supplemented with dry, finely ground shoot biomass of C. ensiformis enriched with 2.15 atom % 15N. Two collections were performed in the experiment: one at 54 days after transplanting (DAT) and one at 130 DAT (at maturation). Growth indicators (at 54 DAT), grain yield, nutrient content, recovery efficiency, and the amount of N derived from C. ensiformis were quantified. At 54 DAT, the N content, chlorophyll content, and plant height of inoculated plants had increased significantly compared with the control, and these plants were more proficient in the use of N derived from C. ensiformis. At maturation, plants inoculated with A103 were distinguished by the recovery efficiency and amount of N derived from C. ensiformis and N content in the grain and shoot being equal to that in A101 inoculation and higher than that in the control, resulting in a higher accumulation of crude protein and dry matter in the full grain and panicle of DSE-rice interaction. In addition, Fe and Ni contents in the grains of rice inoculated with these fungi doubled with respect to the control, and in A103 inoculation, we observed Mn accumulation that was three times higher than in the other treatments. Our results suggest that the inoculation of rice with DSE fungi represents a strategy to improve green manure-N recovery, grain yield per plant, and grain quality in terms of micronutrients contents in cropping systems with a low N input. MenosAn understanding of the interaction between rice and dark septate endophytic (DSE) fungi, under green fertilization, may lead to sustainable agricultural practices. Nevertheless, this interaction is still poorly understood. Therefore, in this study, we aimed to evaluate the accumulation of macro- and micronutrients, dry matter, and protein and N recovery efficiency from Canavalia ensiformis (L.)-15N in rice inoculated with DSE fungi. An experiment under greenhouse conditions was conducted in a randomized complete block design comprising split-plots, with five replicates of rice plants potted in non-sterilized soil. Rice (Piauí variety) seedlings were inoculated with DSE fungi, A101 and A103, or left uninoculated (control) and transplanted into pots containing 12 kg of soil, which had previously been supplemented with dry, finely ground shoot biomass of C. ensiformis enriched with 2.15 atom % 15N. Two collections were performed in the experiment: one at 54 days after transplanting (DAT) and one at 130 DAT (at maturation). Growth indicators (at 54 DAT), grain yield, nutrient content, recovery efficiency, and the amount of N derived from C. ensiformis were quantified. At 54 DAT, the N content, chlorophyll content, and plant height of inoculated plants had increased significantly compared with the control, and these plants were more proficient in the use of N derived from C. ensiformis. At maturation, plants inoculated with A103 were distinguished by the recovery efficiency and ... Mostrar Tudo |
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Palavras-Chave: |
Dark septate endophytic fungi; DSE fungi. |
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Thesagro: |
Arroz; Canavalia Ensiformis; Ferro; Fungo; Grão; Manganês; Níquel; Oryza Sativa. |
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Thesaurus Nal: |
fungi; grains; iron; manganese; nickel; rice. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/189028/1/2018-065.pdf
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Marc: |
LEADER 03392naa a2200421 a 4500
001 2091716
005 2021-11-11
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3389/fpls.2018.00613$2DOI
100 1 $aVERGARA, C.
245 $aDark septate endophytic fungi increase green manure-15N recovery efficiency, N contents, and micronutrients in rice grains.$h[electronic resource]
260 $c2018
520 $aAn understanding of the interaction between rice and dark septate endophytic (DSE) fungi, under green fertilization, may lead to sustainable agricultural practices. Nevertheless, this interaction is still poorly understood. Therefore, in this study, we aimed to evaluate the accumulation of macro- and micronutrients, dry matter, and protein and N recovery efficiency from Canavalia ensiformis (L.)-15N in rice inoculated with DSE fungi. An experiment under greenhouse conditions was conducted in a randomized complete block design comprising split-plots, with five replicates of rice plants potted in non-sterilized soil. Rice (Piauí variety) seedlings were inoculated with DSE fungi, A101 and A103, or left uninoculated (control) and transplanted into pots containing 12 kg of soil, which had previously been supplemented with dry, finely ground shoot biomass of C. ensiformis enriched with 2.15 atom % 15N. Two collections were performed in the experiment: one at 54 days after transplanting (DAT) and one at 130 DAT (at maturation). Growth indicators (at 54 DAT), grain yield, nutrient content, recovery efficiency, and the amount of N derived from C. ensiformis were quantified. At 54 DAT, the N content, chlorophyll content, and plant height of inoculated plants had increased significantly compared with the control, and these plants were more proficient in the use of N derived from C. ensiformis. At maturation, plants inoculated with A103 were distinguished by the recovery efficiency and amount of N derived from C. ensiformis and N content in the grain and shoot being equal to that in A101 inoculation and higher than that in the control, resulting in a higher accumulation of crude protein and dry matter in the full grain and panicle of DSE-rice interaction. In addition, Fe and Ni contents in the grains of rice inoculated with these fungi doubled with respect to the control, and in A103 inoculation, we observed Mn accumulation that was three times higher than in the other treatments. Our results suggest that the inoculation of rice with DSE fungi represents a strategy to improve green manure-N recovery, grain yield per plant, and grain quality in terms of micronutrients contents in cropping systems with a low N input.
650 $afungi
650 $agrains
650 $airon
650 $amanganese
650 $anickel
650 $arice
650 $aArroz
650 $aCanavalia Ensiformis
650 $aFerro
650 $aFungo
650 $aGrão
650 $aManganês
650 $aNíquel
650 $aOryza Sativa
653 $aDark septate endophytic fungi
653 $aDSE fungi
700 1 $aARAUJO, K. E. C.
700 1 $aURQUIAGA, S.
700 1 $aSANTA-CATARINA, C.
700 1 $aSCHULTZ, N.
700 1 $aARAUJO, E. da S.
700 1 $aBALIEIRO, F. de C.
700 1 $aXAVIER, G. R.
700 1 $aZILLI, J. E.
773 $tFrontiers in Plant Science$gv. 9, article 613, May 2018.
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Registro original: |
Embrapa Solos (CNPS) |
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
05/12/2014 |
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Data da última atualização: |
09/12/2014 |
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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: |
LU, D.; LI, G.; MORAN, E.; DUTRA, L.; BATISTELLA, M. |
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Afiliação: |
DENGSHENG LU, Zhejiang A&F University/Michigan State University; GUIYING LI, Michigan State University; EMILIO MORAN, Michigan State University; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM. |
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Título: |
The roles of textural images in improving land-cover classification in the Brazilian Amazon. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
International Journal of Remote Sensing, v. 35, n. 24, p. 8188-8207, 2014. |
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ISBN: |
0143-1161 |
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DOI: |
10.1080/01431161.2014.980920 |
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Idioma: |
Inglês |
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Conteúdo: |
Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l?Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving landcover classification. The classification accuracy can be improved by 5.2?13.4% as the pixel size changes from 30 to 0.6 m. MenosTexture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l?Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover ty... Mostrar Tudo |
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Palavras-Chave: |
Advanced Land Observing Satellite; Land-cover classification; Landsat Thematic Mapper; Phased Array type L-band Synthetic Aperture Radar. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/113201/1/4208.pdf
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Marc: |
LEADER 02554naa a2200241 a 4500
001 2001846
005 2014-12-09
008 2014 bl uuuu u00u1 u #d
022 $a0143-1161
024 7 $a10.1080/01431161.2014.980920$2DOI
100 1 $aLU, D.
245 $aThe roles of textural images in improving land-cover classification in the Brazilian Amazon.$h[electronic resource]
260 $c2014
520 $aTexture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l?Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving landcover classification. The classification accuracy can be improved by 5.2?13.4% as the pixel size changes from 30 to 0.6 m.
653 $aAdvanced Land Observing Satellite
653 $aLand-cover classification
653 $aLandsat Thematic Mapper
653 $aPhased Array type L-band Synthetic Aperture Radar
700 1 $aLI, G.
700 1 $aMORAN, E.
700 1 $aDUTRA, L.
700 1 $aBATISTELLA, M.
773 $tInternational Journal of Remote Sensing$gv. 35, n. 24, p. 8188-8207, 2014.
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