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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
26/06/2020 |
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Data da última atualização: |
26/06/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CORDEIRO, F. R.; CESÁRIO, F. V.; FONTANA, A.; ANJOS, L. H. C. dos; CANTO, A. C. B. do; TEIXEIRA, W. G. |
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Afiliação: |
FERNANDA REIS CORDEIRO, UFRRJ; FERNANDO VIEIRA CESÁRIO, UFF; ADEMIR FONTANA, CNPS; LÚCIA HELENA CUNHA DOS ANJOS, UFRRJ; ANA CAROLINA BARBOSA DO CANTO, UFF; WENCESLAU GERALDES TEIXEIRA, CNPS. |
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Título: |
Pedotransfer functions: the role of soil chemical properties units coversion for soil classification. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Revista Brasileira de Ciência do Solo, v. 44, e0190086, 2020. |
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DOI: |
https://doi.org/10.36783/18069657rbcs20190 |
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Idioma: |
Inglês |
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Conteúdo: |
Chemical soil analysis data can be expressed by weight (i.e., gravimetric basis) or volume (i.e., volumetric basis) of the fine earth (sieved >=2 mm), resulting in different units, cmolc kg-1 and cmolc dm-3, respectively. The research problem is that the difference between methods to express the same soil properties hinders the comparison of results and database or dataset standardization. This paper aims to develop pedotransfer functions (PTF) to obtain the density of fine earth, which will then be used for conversion data expressed in volumetric to gravimetric basis, or vice versa, that will be applied to compare results and to standardize databases with different units. Soils samples, including profiles of the main soil orders in Brazil such as Latossolos (Ferralsols or Oxisols)and Argissolos (Acrisols or Ultisols), from the states of Rondônia, Roraima, and Mato Grosso do Sul (132 horizons) were selected and weighed (in triplicate) to obtain the fine earth mass contained in a volume of 10 cm3. The mass values were used to calculate the fine earth density. Spearman's correlation analysis was used between the density and nine soil properties (coarse sand, fine sand, total sand, silt, clay, clay dispersed in water, clay dispersion, particle density, and organic carbon). The total sand, clay, and organic carbon showed the best correlations, therefore they were selected to construct the pedotransfer functions. Nonlinear regression techniques were used to obtain the models (PTFs) to predict density, which was used for unit conversion. As a result, the residual standard error (RSE) statistics of the models were: 0.0920, 0.1231, and 0.1633 g cm-3, respectively for PTF1 (using total sand as a predictor), PTF2 (using clay), and PTF3 (using organic carbon). Independent data was used to evaluate the accuracy of the models by residue analysis and the RSE. For the validation, the lowest RSE obtained was from the PTF1, so the best performance. Thus, to convert values of the chemical properties from a volumetric to gravimetric basis, the value must be divided by the predicted density. While, the conversion from gravimetric to volumetric basis requires that the value be multiplied by the predicted density. The PTFs using the properties total sand, clay, and organic carbon as predictor variables, allowed conversion of analytical data of soil samples expressed in the volumetric basis to gravimetric and vice versa, which can be used for dataset or database standardization. MenosChemical soil analysis data can be expressed by weight (i.e., gravimetric basis) or volume (i.e., volumetric basis) of the fine earth (sieved >=2 mm), resulting in different units, cmolc kg-1 and cmolc dm-3, respectively. The research problem is that the difference between methods to express the same soil properties hinders the comparison of results and database or dataset standardization. This paper aims to develop pedotransfer functions (PTF) to obtain the density of fine earth, which will then be used for conversion data expressed in volumetric to gravimetric basis, or vice versa, that will be applied to compare results and to standardize databases with different units. Soils samples, including profiles of the main soil orders in Brazil such as Latossolos (Ferralsols or Oxisols)and Argissolos (Acrisols or Ultisols), from the states of Rondônia, Roraima, and Mato Grosso do Sul (132 horizons) were selected and weighed (in triplicate) to obtain the fine earth mass contained in a volume of 10 cm3. The mass values were used to calculate the fine earth density. Spearman's correlation analysis was used between the density and nine soil properties (coarse sand, fine sand, total sand, silt, clay, clay dispersed in water, clay dispersion, particle density, and organic carbon). The total sand, clay, and organic carbon showed the best correlations, therefore they were selected to construct the pedotransfer functions. Nonlinear regression techniques were used to obtain the models (PTF... Mostrar Tudo |
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Palavras-Chave: |
Data standardization; Nonlinear regression; Padronização de dados; Regressão não linear. |
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Thesagro: |
Análise do Solo. |
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Thesaurus Nal: |
Soil analysis. |
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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/214225/1/Pedotransfer-functions-the-role-of-soil-chemical-properties-units-coversion-for-soil-classification-2020.pdf
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Marc: |
LEADER 03358naa a2200265 a 4500
001 2123471
005 2020-06-26
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.36783/18069657rbcs20190$2DOI
100 1 $aCORDEIRO, F. R.
245 $aPedotransfer functions$bthe role of soil chemical properties units coversion for soil classification.$h[electronic resource]
260 $c2020
520 $aChemical soil analysis data can be expressed by weight (i.e., gravimetric basis) or volume (i.e., volumetric basis) of the fine earth (sieved >=2 mm), resulting in different units, cmolc kg-1 and cmolc dm-3, respectively. The research problem is that the difference between methods to express the same soil properties hinders the comparison of results and database or dataset standardization. This paper aims to develop pedotransfer functions (PTF) to obtain the density of fine earth, which will then be used for conversion data expressed in volumetric to gravimetric basis, or vice versa, that will be applied to compare results and to standardize databases with different units. Soils samples, including profiles of the main soil orders in Brazil such as Latossolos (Ferralsols or Oxisols)and Argissolos (Acrisols or Ultisols), from the states of Rondônia, Roraima, and Mato Grosso do Sul (132 horizons) were selected and weighed (in triplicate) to obtain the fine earth mass contained in a volume of 10 cm3. The mass values were used to calculate the fine earth density. Spearman's correlation analysis was used between the density and nine soil properties (coarse sand, fine sand, total sand, silt, clay, clay dispersed in water, clay dispersion, particle density, and organic carbon). The total sand, clay, and organic carbon showed the best correlations, therefore they were selected to construct the pedotransfer functions. Nonlinear regression techniques were used to obtain the models (PTFs) to predict density, which was used for unit conversion. As a result, the residual standard error (RSE) statistics of the models were: 0.0920, 0.1231, and 0.1633 g cm-3, respectively for PTF1 (using total sand as a predictor), PTF2 (using clay), and PTF3 (using organic carbon). Independent data was used to evaluate the accuracy of the models by residue analysis and the RSE. For the validation, the lowest RSE obtained was from the PTF1, so the best performance. Thus, to convert values of the chemical properties from a volumetric to gravimetric basis, the value must be divided by the predicted density. While, the conversion from gravimetric to volumetric basis requires that the value be multiplied by the predicted density. The PTFs using the properties total sand, clay, and organic carbon as predictor variables, allowed conversion of analytical data of soil samples expressed in the volumetric basis to gravimetric and vice versa, which can be used for dataset or database standardization.
650 $aSoil analysis
650 $aAnálise do Solo
653 $aData standardization
653 $aNonlinear regression
653 $aPadronização de dados
653 $aRegressão não linear
700 1 $aCESÁRIO, F. V.
700 1 $aFONTANA, A.
700 1 $aANJOS, L. H. C. dos
700 1 $aCANTO, A. C. B. do
700 1 $aTEIXEIRA, W. G.
773 $tRevista Brasileira de Ciência do Solo$gv. 44, e0190086, 2020.
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Registro original: |
Embrapa Solos (CNPS) |
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| 1. |  | FONTANA, A.; PIRES, A. M. M.; VENTURIERI, A.; JESUS, A. H. de; PAULA, A. J. de; MEDEIROS, A. de S.; VARGAS, A. de G.; BERNDT, A.; FERREIRA, A. L.; LUIZ, A. J. B.; ALVES, A. E.; PEREIRA, A. C.; FREITAS, A. F. de; PACKER, A. P.; CAVALCANTE, A. P. C. M.; FRASCA, A. V. da L.; FERNANDES, A.; ZIMBRES, B.; RUDORFF, B. F. T.; DENARDI, B. M.; BORDRON, B.; ALVES, B. J. R.; KRAHEMBUHL, C. B. B. P.; ARAÚJO, C. E. R.; DUBEUX, C. B. S; MAEQUES, C. L.; SILVA, C. R.; BALDI, C.; JANTALIA, C. P.; GESTEIRA NETO, C. J. M.; BACK, C. D.; ANDRADE, C. A. de; AGUIAR, D. A. de; OBERLING, D. F.; OLIVEIRA, D. M. de; CALÇADO, D. M.; GONÇALVES, D. N. S.; MOLLETA, D. G. S.; GAROFALO, D. F. T.; FERREIRA, D.; GOULART, D.; OLIVEIRA, E. C. de; ROSA, E. R.; ALMEIDA, E. G. de; ALBUQUERQUE, E. R. G. M. de; LA ROVERE, E. L.; RIEGELHAUPT, E. M.; HENRIQUE, F. L.; FARIA, F. F. de; PATERNOST, F. F.; CESÁRIO, F. V.; PEREIRA, F. R. de S.; PAREYN, F. G. C.; RODRIGUES, G. A. H.; BERTANI, G.; LEMOS, G. da S.; RIBEIRO, G. H. P. de M.; CABRAL, G. H.; FROES, G. A. M.; GOES, G. V.; ANDRADE, G. S. de; CARVALHO, G. C. de; NASPOLINI, G. F.; CHRISTO, G. L. de; SANTOS, G. V. dos; BARROS, H. H. D. de; HASENACK, H.; TAVARES, H. C.; MARTINS, I. T.; FRANÇA, I. E. R.; ROITMAN, I.; SOARES, I. M. de M.; SIMIONATO, J.; FREITAS, J.; OMETTO, J. P. H. B.; LINARES, J. A. H.; RISSO, J.; CRUZ FILHO, J. L. V. da; QUINTÃO, J. M. B.; SILVA, J. S. O.; LANA, J. T. de O.; ZANATTA, J. A.; MARACAHIPES, L.; RIBEIRO, L. da S.; VICENTE, L. E.; SCHRAMM, L. F. P.; ROSSI, L. M. B.; SILVA, M. G. da; SARAIVA, M.; D'AGOSTO, M. de A.; VIRTUOSO, M. A.; LIGO, M. A. V.; RACHWAL, M. F. G.; ROSA, M.; PICHARILLO, M. E.; SILVA, M. G. L. da; MATSUURA, M. I. da S. F.; DALLA BETTA, M. M.; PEREIRA, M. J.; CARON, M. L.; ASSIS, M. L. R. de; SANTOS, M. M. de O.; ROCHA, M. B.; BUSTAMANTE, M. M. da C.; WALTER, M. C.; AQUINO, M. F. S. de; SALGADO, M. P. G.; SERRUYA, N. M.; MOREIRA, N. de P.; FERREIRA, N. C.; RAMOS, N. P.; CABRAL, O. M. R.; MOSER, P.; COLTURATO, P. D.; GRÜTZMACHER, P.; AUGUSTO, R. C.; GRISOLI, R. P. S.; SOLARI, R.; PAZIANOTTO, R. A. A.; DUPONT, R. A. B.; CANTINHO, R. Z.; PEIXOTO, R. de A.; PADILHA, R. A.; NICOLOSO, R. da S.; OLIVEIRA, R. R. S. de; HIGA, R. C. V.; GUIDOTTI, R. M. M.; NOGUEIRA, S. F.; MAIA, S. M. F.; ASSIS, T. F. de; IGAWA, T. K.; RODRIGUES, T. da S.; CARMO, T. R. L. do; MIRANDA, V. S.; KONDO, V. Y.; SCIVITTARO, W. B.; WILLS, W.; HOLLER, W. A. Inventário nacional de emissões e remoções antrópicas de gases de efeito estufa. In: BRASIL. Ministério da Ciência, Tecnologia e Inovações. Quarta Comunicação Nacional do Brasil à Convenção-Quadro das Nações Unidas sobre Mudança do Clima. Brasília, DF, 2021. cap. 2, p. 80-181.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Agrobiologia; Embrapa Amazônia Oriental; Embrapa Florestas; Embrapa Pecuária Sudeste; Embrapa Solos. |
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