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Registro Completo |
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Biblioteca(s): |
Embrapa Agrobiologia. |
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Data corrente: |
28/01/2000 |
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Data da última atualização: |
08/08/2013 |
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Autoria: |
ELMERICH, C.; KONDOROSI, A.; NEWTON, W. E. |
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Título: |
Biological nitrogen fixation for the 21st century. |
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Ano de publicação: |
1998 |
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Fonte/Imprenta: |
Dordrecht: Kluwer, 1998. |
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Páginas: |
707p. |
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Série: |
(Current Plant Science and Biotechnology in Agriculture, v.31). |
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ISBN: |
0-7923-4834-6 |
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Idioma: |
Inglês |
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Notas: |
Proceedings of the 11th International Congress on Nitrogen Fixation, Institut Pasteur, Paris, France, July 20-25, 1997. |
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Conteúdo: |
Chemistry and Biochemistry. Genetics and Regulation. Genetics and regulation of nitrogen fixation. Cyanobacteria and photosynthetic bacteria. Symbioses and associations. Rhizobium-Legume Symbiosis. Genetic and symbiotic genes. Actinorhizal Symbiosis. Associations with Grasses. Environmental and Physiological Factors Controlling Nitrogen Fixation. Carbon Metabolism. Environmental Stresses. Genome Structure, Taxonomy and Ecology. Sustainable Agriculture and Forestry. Sustainable Agriculture. Forestry. Prospects for Agriculture. Nitrogenase. Azotobacter vinelandii. Iron. Sulfur. Klebsiella pneumoniae. Prokaryotes. Cyanobacteria. Acetobacter diazotrophicus. Herbaspirillum seropedicae. Sugarcane. Bradyrhizobium japonicum. Rhizobium meliloti. Rhizobium leguminosarum. Pisum sativum. Lotus japonicum. Medicago sativa. Medicago truncatula. Pisum sativum. Lupinus luteus. Characterization of genes involved in regulation of nitrogen fixation and ammonium sensing in Acetobacter diazotrophicus, an endophyte of sugarcane. Nitrogen-fixing endophytes: Recent advances in the association with graminaceous plants grown in the tropics. Use of molecular methods for identification and in situ studies of diazotrophic plant colonizing bacteria. Maize colonization by Acetobacter diazotrophicus. Studies on Acetobacter diazotrophicus: Analysis of nif and related genes and contributions to sugarcane nutrition. Convener comments, importance of biological nitrogen fixation in sustainable agriculture. Contribution of biological nitrogen fixation to tropical agriculture: Actual and potential. The use of nodulated and mycorrhizal legume trees for land reclamation in mining sites.
Carbono C MenosChemistry and Biochemistry. Genetics and Regulation. Genetics and regulation of nitrogen fixation. Cyanobacteria and photosynthetic bacteria. Symbioses and associations. Rhizobium-Legume Symbiosis. Genetic and symbiotic genes. Actinorhizal Symbiosis. Associations with Grasses. Environmental and Physiological Factors Controlling Nitrogen Fixation. Carbon Metabolism. Environmental Stresses. Genome Structure, Taxonomy and Ecology. Sustainable Agriculture and Forestry. Sustainable Agriculture. Forestry. Prospects for Agriculture. Nitrogenase. Azotobacter vinelandii. Iron. Sulfur. Klebsiella pneumoniae. Prokaryotes. Cyanobacteria. Acetobacter diazotrophicus. Herbaspirillum seropedicae. Sugarcane. Bradyrhizobium japonicum. Rhizobium meliloti. Rhizobium leguminosarum. Pisum sativum. Lotus japonicum. Medicago sativa. Medicago truncatula. Pisum sativum. Lupinus luteus. Characterization of genes involved in regulation of nitrogen fixation and ammonium sensing in Acetobacter diazotrophicus, an endophyte of sugarcane. Nitrogen-fixing endophytes: Recent advances in the association with graminaceous plants grown in the tropics. Use of molecular methods for identification and in situ studies of diazotrophic plant colonizing bacteria. Maize colonization by Acetobacter diazotrophicus. Studies on Acetobacter diazotrophicus: Analysis of nif and related genes and contributions to sugarcane nutrition. Convener comments, importance of biological nitrogen fixation in sustainable agriculture. Contr... Mostrar Tudo |
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Palavras-Chave: |
BNF; Cianobacteria; Cyanophyta; FBN; Fixacao biologica de nitrogenio; Nitrogen fixing bacteria; Sustainability. |
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Thesagro: |
Agricultura Sustentável; Bactéria; Bioquímica; Carbono; Ecologia; Floresta; Fotossíntese; Genética; Gramínea; Metabolismo; Química; Simbiose; Taxonomia. |
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Thesaurus Nal: |
biochemistry; carbon; chemistry; ecology; forestry; genetics; grasses; metabolism; photosynthesis; symbiosis; taxonomy. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 03143nam a2200553 a 4500
001 1596858
005 2013-08-08
008 1998 bl uuuu 00u1 u #d
020 $a0-7923-4834-6
100 1 $aELMERICH, C.
245 $aBiological nitrogen fixation for the 21st century.
260 $aDordrecht: Kluwer$c1998
300 $a707p.
490 $a(Current Plant Science and Biotechnology in Agriculture, v.31).
500 $aProceedings of the 11th International Congress on Nitrogen Fixation, Institut Pasteur, Paris, France, July 20-25, 1997.
520 $aChemistry and Biochemistry. Genetics and Regulation. Genetics and regulation of nitrogen fixation. Cyanobacteria and photosynthetic bacteria. Symbioses and associations. Rhizobium-Legume Symbiosis. Genetic and symbiotic genes. Actinorhizal Symbiosis. Associations with Grasses. Environmental and Physiological Factors Controlling Nitrogen Fixation. Carbon Metabolism. Environmental Stresses. Genome Structure, Taxonomy and Ecology. Sustainable Agriculture and Forestry. Sustainable Agriculture. Forestry. Prospects for Agriculture. Nitrogenase. Azotobacter vinelandii. Iron. Sulfur. Klebsiella pneumoniae. Prokaryotes. Cyanobacteria. Acetobacter diazotrophicus. Herbaspirillum seropedicae. Sugarcane. Bradyrhizobium japonicum. Rhizobium meliloti. Rhizobium leguminosarum. Pisum sativum. Lotus japonicum. Medicago sativa. Medicago truncatula. Pisum sativum. Lupinus luteus. Characterization of genes involved in regulation of nitrogen fixation and ammonium sensing in Acetobacter diazotrophicus, an endophyte of sugarcane. Nitrogen-fixing endophytes: Recent advances in the association with graminaceous plants grown in the tropics. Use of molecular methods for identification and in situ studies of diazotrophic plant colonizing bacteria. Maize colonization by Acetobacter diazotrophicus. Studies on Acetobacter diazotrophicus: Analysis of nif and related genes and contributions to sugarcane nutrition. Convener comments, importance of biological nitrogen fixation in sustainable agriculture. Contribution of biological nitrogen fixation to tropical agriculture: Actual and potential. The use of nodulated and mycorrhizal legume trees for land reclamation in mining sites. Carbono C
650 $abiochemistry
650 $acarbon
650 $achemistry
650 $aecology
650 $aforestry
650 $agenetics
650 $agrasses
650 $ametabolism
650 $aphotosynthesis
650 $asymbiosis
650 $ataxonomy
650 $aAgricultura Sustentável
650 $aBactéria
650 $aBioquímica
650 $aCarbono
650 $aEcologia
650 $aFloresta
650 $aFotossíntese
650 $aGenética
650 $aGramínea
650 $aMetabolismo
650 $aQuímica
650 $aSimbiose
650 $aTaxonomia
653 $aBNF
653 $aCianobacteria
653 $aCyanophyta
653 $aFBN
653 $aFixacao biologica de nitrogenio
653 $aNitrogen fixing bacteria
653 $aSustainability
700 1 $aKONDOROSI, A.
700 1 $aNEWTON, W. E.
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Registro original: |
Embrapa Agrobiologia (CNPAB) |
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Biblioteca |
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Registro |
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Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
16/11/2022 |
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Data da última atualização: |
22/11/2022 |
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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: |
FERREIRA, A. C. de S.; CEDDIA, M. B.; COSTA, E. M.; PINHEIRO, E. F. M.; NASCIMENTO, M. M. do; VASQUES, G. M. |
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Afiliação: |
ANA CAROLINA DE S. FERREIRA, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; MARCOS B. CEDDIA, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; ELIAS M. COSTA, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; ÉRIKA F. M. PINHEIRO, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; MARIANA MELO DO NASCIMENTO, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; GUSTAVO DE MATTOS VASQUES, CNPS. |
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Título: |
Use of airborne radar images and machine learning algorithms to map soil clay, silt, and sand contents in remote areas under the Amazon rainforest. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Remote Sensing, v. 14, n. 22, 5711, 2022. |
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DOI: |
https://doi.org/10.3390/rs14225711 |
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Idioma: |
Inglês |
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Conteúdo: |
Soil texture has a great influence on the physical-hydric and chemical behavior of soils. In the Amazon regions, due to the presence of dense forest cover and limited access to roads, carrying out surveys and mapping of soils is challenging. When data exist, they are relatively sparse and the distribution is quite uneven. In this context, machine learning algorithms (ML) associated with remote sensor covariates offer a framework to derive digital maps of soil attributes. The objective of this study was to produce maps of surface and subsurface soil clay, silt, and sand contents in a 13.440 km2 area in the Amazon. The specific objectives were to a) evaluate the gain in prediction accuracy when using the P-band of airborne radar as a covariate; b) evaluate two sampling approaches (Reference Area-RA and Total Area-TA); and c) evaluate the transferability and performance of three ML algorithms: regression tree (RT), random forest (RF), and support vector machine (SVM). The study site was divided into three blocks, called Urucu, Araracanga, and Juruá, respectively. The soil dataset consisted of 151 surface and subsurface sand, silt, and clay observations and 21 covariates (20 relief variables and the backscattering coefficient from the P-band). Both the RA and TA sampling approach used 114 observations for training the prediction models (75%) and 37 for validation (25%). The RA approach was better for the development of sand and silt models. Overall, RF derived the most accurate predictions for all variables. The effect of introducing the P-band backscattering coefficient improved the sand prediction accuracy at the surface and subsurface in Araracanga, which had the highest sand content, with relative improvements (RI) of the R2, root mean square error (RMSE), and mean absolute error (MAE) of 46%, 3%, and 4% at the surface, respectively, and 66.7%, 4.4%, and 5.2% at the subsurface, respectively. For silt, the P-band improved the predictions at the surface in Araracanga, which had the lowest silt contents among the blocks. For clay, adding the P-band improved the RF predictions at the subsurface, with RI of the R2, RMSE, and MAE of 29%, 5%, and 5%, respectively. Despite the low observation density, inherently hindered by the low accessibility of the area and high costs of sampling thereof, the results showed the potential of ML algorithms boosted by airborne radar P-band to map soil clay, silt, and sand contents in the Amazon. MenosSoil texture has a great influence on the physical-hydric and chemical behavior of soils. In the Amazon regions, due to the presence of dense forest cover and limited access to roads, carrying out surveys and mapping of soils is challenging. When data exist, they are relatively sparse and the distribution is quite uneven. In this context, machine learning algorithms (ML) associated with remote sensor covariates offer a framework to derive digital maps of soil attributes. The objective of this study was to produce maps of surface and subsurface soil clay, silt, and sand contents in a 13.440 km2 area in the Amazon. The specific objectives were to a) evaluate the gain in prediction accuracy when using the P-band of airborne radar as a covariate; b) evaluate two sampling approaches (Reference Area-RA and Total Area-TA); and c) evaluate the transferability and performance of three ML algorithms: regression tree (RT), random forest (RF), and support vector machine (SVM). The study site was divided into three blocks, called Urucu, Araracanga, and Juruá, respectively. The soil dataset consisted of 151 surface and subsurface sand, silt, and clay observations and 21 covariates (20 relief variables and the backscattering coefficient from the P-band). Both the RA and TA sampling approach used 114 observations for training the prediction models (75%) and 37 for validation (25%). The RA approach was better for the development of sand and silt models. Overall, RF derived the most accurate ... Mostrar Tudo |
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Palavras-Chave: |
Digital soil mapping; Radar P-band; Reference area. |
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Thesagro: |
Mapa; Reconhecimento do Solo; Textura do Solo. |
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Thesaurus NAL: |
Soil surveys; Soil texture. |
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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/doc/1148295/1/Use-of-airborne-radar-images-and-machine-learning-algorithms-2022.pdf
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Marc: |
LEADER 03374naa a2200289 a 4500
001 2148295
005 2022-11-22
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/rs14225711$2DOI
100 1 $aFERREIRA, A. C. de S.
245 $aUse of airborne radar images and machine learning algorithms to map soil clay, silt, and sand contents in remote areas under the Amazon rainforest.$h[electronic resource]
260 $c2022
520 $aSoil texture has a great influence on the physical-hydric and chemical behavior of soils. In the Amazon regions, due to the presence of dense forest cover and limited access to roads, carrying out surveys and mapping of soils is challenging. When data exist, they are relatively sparse and the distribution is quite uneven. In this context, machine learning algorithms (ML) associated with remote sensor covariates offer a framework to derive digital maps of soil attributes. The objective of this study was to produce maps of surface and subsurface soil clay, silt, and sand contents in a 13.440 km2 area in the Amazon. The specific objectives were to a) evaluate the gain in prediction accuracy when using the P-band of airborne radar as a covariate; b) evaluate two sampling approaches (Reference Area-RA and Total Area-TA); and c) evaluate the transferability and performance of three ML algorithms: regression tree (RT), random forest (RF), and support vector machine (SVM). The study site was divided into three blocks, called Urucu, Araracanga, and Juruá, respectively. The soil dataset consisted of 151 surface and subsurface sand, silt, and clay observations and 21 covariates (20 relief variables and the backscattering coefficient from the P-band). Both the RA and TA sampling approach used 114 observations for training the prediction models (75%) and 37 for validation (25%). The RA approach was better for the development of sand and silt models. Overall, RF derived the most accurate predictions for all variables. The effect of introducing the P-band backscattering coefficient improved the sand prediction accuracy at the surface and subsurface in Araracanga, which had the highest sand content, with relative improvements (RI) of the R2, root mean square error (RMSE), and mean absolute error (MAE) of 46%, 3%, and 4% at the surface, respectively, and 66.7%, 4.4%, and 5.2% at the subsurface, respectively. For silt, the P-band improved the predictions at the surface in Araracanga, which had the lowest silt contents among the blocks. For clay, adding the P-band improved the RF predictions at the subsurface, with RI of the R2, RMSE, and MAE of 29%, 5%, and 5%, respectively. Despite the low observation density, inherently hindered by the low accessibility of the area and high costs of sampling thereof, the results showed the potential of ML algorithms boosted by airborne radar P-band to map soil clay, silt, and sand contents in the Amazon.
650 $aSoil surveys
650 $aSoil texture
650 $aMapa
650 $aReconhecimento do Solo
650 $aTextura do Solo
653 $aDigital soil mapping
653 $aRadar P-band
653 $aReference area
700 1 $aCEDDIA, M. B.
700 1 $aCOSTA, E. M.
700 1 $aPINHEIRO, E. F. M.
700 1 $aNASCIMENTO, M. M. do
700 1 $aVASQUES, G. M.
773 $tRemote Sensing$gv. 14, n. 22, 5711, 2022.
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