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 | Acesso ao texto completo restrito à biblioteca da Embrapa Uva e Vinho. Para informações adicionais entre em contato com cnpuv.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Uva e Vinho. |
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Data corrente: |
03/03/2022 |
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Data da última atualização: |
24/08/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SILVA-SANGO, D. V. da; HORST, T. Z.; MOURA-BUENO, J. M.; DALMOLIN, R. S. D.; SEBEM, E.; GEBLER, L.; SANTOS, M. da S. |
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Afiliação: |
DANIELY VAZ DA SILVA-SANGO, University Federal of Santa Maria (UFSM); TACIARA ZBOROWSKI HORST, University Federal of Santa Maria (UFSM); JEAN MICHEL MOURA-BUENO, University Federal of Santa Maria (UFSM); RICARDO SIMÃO DINIZ DALMOLIN, University Federal of Santa Maria (UFSM); ELÓDIO SEBEM, University Federal of Santa Maria (UFSM); LUCIANO GEBLER, CNPUV; MÁRCIO DA SILVA SANTOS, CNPUV. |
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Título: |
Soil organic matter and clay predictions by laboratory spectroscopy: Data spatial correlation. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Geoderma Regional, v. 28, e00486, mar. 2022. |
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Páginas: |
11 |
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Idioma: |
Inglês |
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Conteúdo: |
Soil spectroscopy (Vis-NIR-SWIR 350?2500 nm) has known potential to predict clay content and soil organic matter (SOM), which are properties that determine soil quality. It is well known that data predicted from Vis-NIR-SWIR carry prediction errors resulting from modeling process. However, these errors are not spatially correlated and, even being numerically small, they can alter the spatial correlation of the data. Thus, the aim of the study was to evaluate wether clay and SOM data predicted by spectroscopy models preserve the spatial structure of data obtained by traditional chemical analyses. This was done considering a soil-spectral dataset from a small viticultural area (3 ha) in Southern Brazil. Soil samples were collected in 74 sites, in two depths (0.00?0.20 m and 0.20?0.40 m depth). Collected in 74 sites, totaling 148 soil samples. The Vis-NIR-SWIR data were used to train spectral models in raw form, and subjected to three pre-processing techniques (smoothing - SMO; Savitzky-Golay with first derivative - SGD; Binning - BIN). In the calibration step, three machine learning techniques were tested (Cubist ?CUB; Random Forest ?RF; Partial Least Squares Regression - PLSR). The spatial correlation of the data measured by traditional wet chemistry, as well the data predicted by spectroscopy, was analyzed clay and SOM maps were then generated by ordinary kriging for both data. Among the spectral models, the RF + SGD model presented the best cross-validation performance for clay, with R2cv = 0.95 and RMSEcv = 1.06%, and the PLSR + SGD for SOM, with R2cv = 0.98 and RMSEcv = 0.07%. The data predicted by Vis-NIRSWIR spectroscopy preserved the spatial structure of the data obtained by the traditional wet chemical analysis. However, our results suggest that spectral modeling was more effective in predicting SOM while spatial interpolation was more effective in predicting clay. Data predicted by Vis-NIR-SWIR spectroscopy with lower accuracy increased the residuals of the spatial predictions. The clay and SOM maps prodiced with spectroscopy predictions show similarities and feasible accuracy for the use and management of agricultural soils in vineyard areas. MenosSoil spectroscopy (Vis-NIR-SWIR 350?2500 nm) has known potential to predict clay content and soil organic matter (SOM), which are properties that determine soil quality. It is well known that data predicted from Vis-NIR-SWIR carry prediction errors resulting from modeling process. However, these errors are not spatially correlated and, even being numerically small, they can alter the spatial correlation of the data. Thus, the aim of the study was to evaluate wether clay and SOM data predicted by spectroscopy models preserve the spatial structure of data obtained by traditional chemical analyses. This was done considering a soil-spectral dataset from a small viticultural area (3 ha) in Southern Brazil. Soil samples were collected in 74 sites, in two depths (0.00?0.20 m and 0.20?0.40 m depth). Collected in 74 sites, totaling 148 soil samples. The Vis-NIR-SWIR data were used to train spectral models in raw form, and subjected to three pre-processing techniques (smoothing - SMO; Savitzky-Golay with first derivative - SGD; Binning - BIN). In the calibration step, three machine learning techniques were tested (Cubist ?CUB; Random Forest ?RF; Partial Least Squares Regression - PLSR). The spatial correlation of the data measured by traditional wet chemistry, as well the data predicted by spectroscopy, was analyzed clay and SOM maps were then generated by ordinary kriging for both data. Among the spectral models, the RF + SGD model presented the best cross-validation performance for ... Mostrar Tudo |
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Palavras-Chave: |
Clay content; Digital soil mapping; Ferralsol; Spatial structure; Vis-NIR-SWIR spectroscopy. |
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Thesaurus Nal: |
Kriging; Soil organic matter. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 03014naa a2200289 a 4500
001 2140489
005 2023-08-24
008 2022 bl uuuu u00u1 u #d
100 1 $aSILVA-SANGO, D. V. da
245 $aSoil organic matter and clay predictions by laboratory spectroscopy$bData spatial correlation.$h[electronic resource]
260 $c2022
300 $a11
520 $aSoil spectroscopy (Vis-NIR-SWIR 350?2500 nm) has known potential to predict clay content and soil organic matter (SOM), which are properties that determine soil quality. It is well known that data predicted from Vis-NIR-SWIR carry prediction errors resulting from modeling process. However, these errors are not spatially correlated and, even being numerically small, they can alter the spatial correlation of the data. Thus, the aim of the study was to evaluate wether clay and SOM data predicted by spectroscopy models preserve the spatial structure of data obtained by traditional chemical analyses. This was done considering a soil-spectral dataset from a small viticultural area (3 ha) in Southern Brazil. Soil samples were collected in 74 sites, in two depths (0.00?0.20 m and 0.20?0.40 m depth). Collected in 74 sites, totaling 148 soil samples. The Vis-NIR-SWIR data were used to train spectral models in raw form, and subjected to three pre-processing techniques (smoothing - SMO; Savitzky-Golay with first derivative - SGD; Binning - BIN). In the calibration step, three machine learning techniques were tested (Cubist ?CUB; Random Forest ?RF; Partial Least Squares Regression - PLSR). The spatial correlation of the data measured by traditional wet chemistry, as well the data predicted by spectroscopy, was analyzed clay and SOM maps were then generated by ordinary kriging for both data. Among the spectral models, the RF + SGD model presented the best cross-validation performance for clay, with R2cv = 0.95 and RMSEcv = 1.06%, and the PLSR + SGD for SOM, with R2cv = 0.98 and RMSEcv = 0.07%. The data predicted by Vis-NIRSWIR spectroscopy preserved the spatial structure of the data obtained by the traditional wet chemical analysis. However, our results suggest that spectral modeling was more effective in predicting SOM while spatial interpolation was more effective in predicting clay. Data predicted by Vis-NIR-SWIR spectroscopy with lower accuracy increased the residuals of the spatial predictions. The clay and SOM maps prodiced with spectroscopy predictions show similarities and feasible accuracy for the use and management of agricultural soils in vineyard areas.
650 $aKriging
650 $aSoil organic matter
653 $aClay content
653 $aDigital soil mapping
653 $aFerralsol
653 $aSpatial structure
653 $aVis-NIR-SWIR spectroscopy
700 1 $aHORST, T. Z.
700 1 $aMOURA-BUENO, J. M.
700 1 $aDALMOLIN, R. S. D.
700 1 $aSEBEM, E.
700 1 $aGEBLER, L.
700 1 $aSANTOS, M. da S.
773 $tGeoderma Regional$gv. 28, e00486, mar. 2022.
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Registro original: |
Embrapa Uva e Vinho (CNPUV) |
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| Registros recuperados : 12 | |
| 4. |  | SANTOS, M. da S.; SEBEM, E.; GEBLER, L. Correlação entre parâmetros produtivos e enológicos com a condutividade elétrica aparente do solo e índices de vegetação. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA, 17., ENCONTRO DE PÓS-GRADUANDOS DA EMBRAPA UVA E VINHO, 13., 2019, Bento Gonçalves. Anais... Bento Gonçalves: Embrapa Uva e Vinho, 18 e 19 de julho de 2019. (Embrapa Uva e Vinho. Documentos, 115). Resumo 08.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Uva e Vinho. |
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| 5. | | SANTOS, M. da S.; GEBLER, L.; RUFATO, A. de R. Correlação do Índice de Vegetação por Diferença Normalizada (NDVI) com a com a fenologia da Malus domestica Borkh. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA, 18., ENCONTRO DE PÓS-GRADUAÇÃO DA EMBRAPA UVA E VINHO, 14., 2020, Bento Gonçalves. Anais... Bento Gonçalves: Embrapa Uva e Vinho, p. 23, 2020. (Embrapa Uva e Vinho. Documentos, 124).| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Uva e Vinho. |
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| 6. | | MONTANARI, M. F.; GEBLER, L.; FORTUNA, J. A.; SANTOS, M. da S. Correlação entre a umidade do solo e condutividade elétrica em um latossolo bruno da região de Vacaria: resultados parciais. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA, 17., ENCONTRO DE PÓS-GRADUANDOS DA EMBRAPA UVA E VINHO, 13., 2019, Bento Gonçalves. Anais... Bento Gonçalves: Embrapa Uva e Vinho, 18 e 19 de julho de 2019. (Embrapa Uva e Vinho. Documentos, 115). Resumo 12.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Uva e Vinho. |
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| 8. | | GOLINELLI, D. C.; SANTOS, M. da S.; MARCONCINI, L. V.; COLNAGO, L. A. Uso da RMN-DT no desenvolvimento e validação de método para detecção de adulteração em azeites de oliva comerciais lacrados. In: JORNADA CIENTÍFICA - EMBRAPA SÃO CARLOS, 5., 2013, São Carlos, SP. Anais... São Carlos, SP: Embrapa Pecuária Sudeste: Embrapa Instrumentação , 2013. p. 47 Editado por Ana Rita de Araújo Nogueira, Simone Cristina Méo Niciura. (Embrapa Pecuária Sudeste. Documentos, 110).| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Instrumentação. |
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| 9. | | GOLINELLI, D. C.; SANTOS, M. da S.; MARCONCINI, L. V.; COLNAGO, L. A. Uso da RMN-DT para detecção de adulteração em azeites de oliva comerciais lacrados. In: JORNADA CIENTÍFICA - EMBRAPA SÃO CARLOS, 4., 2012, São Carlos, SP. Anais... São Carlos: Embrapa Instrumentação: Embrapa Pecuária Sudeste, 2012. p. 91. Editores técnicos: João de Mendonça Naime, Lucimara Aparecida Forato, Maria Alice Martins, Ladislau Marcelino Rabello, Rubens Bernardes Filho. (Embrapa Instrumentação. Documentos, 56)| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Instrumentação. |
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| 10. | | GEBLER, L.; RUFATO, A. de R.; MAGRO, R. B.; SPERANZA, E. A.; SANTOS, M. da S.; ABREU, J. T. de. Geração de zonas de manejo em pomares de macieiras com base em NDVI e produtividade. In: BASSOI, L. H.; BERNARDI, A. C. de C.; VAZ, C. M. P.; PIRES, J. L. F.; GEBLER, L.; JORGE, L. A. de C.; INAMASU, R. Y. (ed.). Agricultura de precisão: um novo olhar na era digital. São Carlos, SP: Cubo, 2024. seção 3, cap. 57, p. 509-517.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Uva e Vinho. |
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| 11. | | SILVA-SANGO, D. V. da; HORST, T. Z.; MOURA-BUENO, J. M.; DALMOLIN, R. S. D.; SEBEM, E.; GEBLER, L.; SANTOS, M. da S. Soil organic matter and clay predictions by laboratory spectroscopy: Data spatial correlation. Geoderma Regional, v. 28, e00486, mar. 2022. 11| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Uva e Vinho. |
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| 12. | | SANTOS, M. da S.; BARROS, H. M. M.; LIMA, V. L. A. de; BELTRÃO, N. E. de M.; NASCIMENTO, F. A. do; TRAVASSOS, K. D.; BARACUHY M. NETO, G. Estimate of the area to foliate of you cultivate of sesame in function of the manuring and of the quality of the water of irrigation. In: CIGR - INTERNATIONAL CONFERENCE OF AGRICULTURAL ENGINEERING, 2.; CONGRESSO BRASILEIRO DE ENGENHARIA AGRÍCOLA, 37., 2008, Foz do Iguaçu. Technology for all: sharing the knowledge for development: proceedings... Foz do Iguaçu: SBEA, 2008. 1 CD-ROM| Tipo: Artigo em Anais de Congresso / Nota Técnica |
| Biblioteca(s): Embrapa Algodão. |
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| Registros recuperados : 12 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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