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Registro Completo |
Biblioteca(s): |
Embrapa Hortaliças. |
Data corrente: |
30/12/2019 |
Data da última atualização: |
19/11/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
MELO, R. A. de C. e; VENDRAME, L. P. de C.; MADEIRA, N. R.; BLIND, A. D.; VILELA, N. J. |
Afiliação: |
RAPHAEL AUGUSTO DE CASTRO E MELO, CNPH; LARISSA PEREIRA DE CASTRO VENDRAME, CNPH; NUNO RODRIGO MADEIRA, CNPH; ARIEL D. BLIND, INSTITUTO NACIONAL DE PESQUISAS DA AMAZÔNIA; NIRLENE JUNQUEIRA VILELA, CNPH. |
Título: |
Characterization of the Brazilian vegetable brassicas production chain. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
Horticultura Brasileira, v. 37, n. 4, p. 366-372, Oct./Dec. 2019. |
Idioma: |
Inglês |
Conteúdo: |
To meet the challenges and list the trends within the activities of different agents, a multiple case study was held in the states of Amazon, Minas Gerais, Paraná and Rio de Janeiro. |
Thesagro: |
Brassica Oleracea. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/207964/1/1806-9991-hb-37-04-366.pdf
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Marc: |
LEADER 00734naa a2200181 a 4500 001 2117906 005 2020-11-19 008 2019 bl uuuu u00u1 u #d 100 1 $aMELO, R. A. de C. e 245 $aCharacterization of the Brazilian vegetable brassicas production chain.$h[electronic resource] 260 $c2019 520 $aTo meet the challenges and list the trends within the activities of different agents, a multiple case study was held in the states of Amazon, Minas Gerais, Paraná and Rio de Janeiro. 650 $aBrassica Oleracea 700 1 $aVENDRAME, L. P. de C. 700 1 $aMADEIRA, N. R. 700 1 $aBLIND, A. D. 700 1 $aVILELA, N. J. 773 $tHorticultura Brasileira$gv. 37, n. 4, p. 366-372, Oct./Dec. 2019.
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Embrapa Hortaliças (CNPH) |
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Registro Completo
Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
29/06/2020 |
Data da última atualização: |
02/07/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
MOURA-BUENO, J. M.; DALMOLIN, R. S. D.; HORST-HEINEN, T. Z.; CATEN, A. ten; VASQUES, G. de M.; DOTTO, A. C.; GRUNWALD, S. |
Afiliação: |
JEAN MICHEL MOURA-BUENO, UFSM; RICARDO SIMÃO DINIZ DALMOLIN, UFSM; TACIARA ZBOROWSKI HORST-HEINEN, UFSM; ALEXANDRE TEN CATEN, UFSC; GUSTAVO DE MATTOS VASQUES, CNPS; ANDRÉ CARNIELETTO DOTTO, ESALQ; SABINE GRUNWALD, UNIVERSITY OF FLORIDA. |
Título: |
When does stratification of a subtropical soil spectral library improve predictions of soil organic carbon content? |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Science of The Total Environment, v. 737, 139895, Oct. 2020. |
DOI: |
https://doi.org/10.1016/j.scitotenv.2020.139895 |
Idioma: |
Inglês |
Conteúdo: |
More accurate models for the prediction of soil organic carbon (SOC) by visible-near-infrared (Vis-NIR) spectroscopy remains a challenging task, especially when the soil spectral libraries (SSL) is composed of soils with a high pedological variation. One proposition to increase the models accuracy is to reduce the SSL variance, which can be achieved by stratifying the library into sub-libraries. Thus, the main objective of this study was to evaluate whether the stratification of a SSL by environmental, pedological and Vis-NIR spectral criteria results in greater accuracy of spectroscopic models than to general models for prediction of SOC content. The performance of the models was evaluated considering the variance of soil components and sample number. In addition, we tested the effect of two spectral preprocessing techniques and two multivariate calibration methods on spectroscopic modeling. For these purposes, a SSL composed of 2471 samples from Southern Brazil was stratified based on i) physiographic region; ii) land-use/land-cover; iii) soil texture, and iv) spectral class. Two spectral processing techniques: Savitzky-Golay - 1st derivative (SGD) and continuum removed reflectance (CRR) and two multivariate methods (partial least squares regression - PLSR and Cubist) were used to fit the models. The best performances for the global and local models were achieved with the CRR spectral processing associated with the Cubist method. The stratification of the SSL in more homogeneous sample groups by environmental criteria (physiographic regions and land-use/land-cover) improved the accuracy of SOC predictions compared to pedological (soil texture) and Vis-NIR spectral (spectral classes) criteria. The reduction in the number of samples negatively affected the performance of models for sub-libraries with high pedological and spectral variation. Stratification criteria were proposed in a flowchart to assist in decision making in future studies. Our findings suggest the importance of sample balance across environmental, pedological and spectral strata, in order to optimize SOC predictions. MenosMore accurate models for the prediction of soil organic carbon (SOC) by visible-near-infrared (Vis-NIR) spectroscopy remains a challenging task, especially when the soil spectral libraries (SSL) is composed of soils with a high pedological variation. One proposition to increase the models accuracy is to reduce the SSL variance, which can be achieved by stratifying the library into sub-libraries. Thus, the main objective of this study was to evaluate whether the stratification of a SSL by environmental, pedological and Vis-NIR spectral criteria results in greater accuracy of spectroscopic models than to general models for prediction of SOC content. The performance of the models was evaluated considering the variance of soil components and sample number. In addition, we tested the effect of two spectral preprocessing techniques and two multivariate calibration methods on spectroscopic modeling. For these purposes, a SSL composed of 2471 samples from Southern Brazil was stratified based on i) physiographic region; ii) land-use/land-cover; iii) soil texture, and iv) spectral class. Two spectral processing techniques: Savitzky-Golay - 1st derivative (SGD) and continuum removed reflectance (CRR) and two multivariate methods (partial least squares regression - PLSR and Cubist) were used to fit the models. The best performances for the global and local models were achieved with the CRR spectral processing associated with the Cubist method. The stratification of the SSL in more homog... Mostrar Tudo |
Palavras-Chave: |
Aprendizagem ambiental; Carbono orgânico do solo; Environmental-based learning; Modelos espectrais; SOC variance; Spectral models; Spectral variation; Variação espectral; Vis-NIR data mining. |
Thesaurus NAL: |
Soil organic carbon. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
Marc: |
LEADER 03175naa a2200325 a 4500 001 2123501 005 2020-07-02 008 2020 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1016/j.scitotenv.2020.139895$2DOI 100 1 $aMOURA-BUENO, J. M. 245 $aWhen does stratification of a subtropical soil spectral library improve predictions of soil organic carbon content?$h[electronic resource] 260 $c2020 520 $aMore accurate models for the prediction of soil organic carbon (SOC) by visible-near-infrared (Vis-NIR) spectroscopy remains a challenging task, especially when the soil spectral libraries (SSL) is composed of soils with a high pedological variation. One proposition to increase the models accuracy is to reduce the SSL variance, which can be achieved by stratifying the library into sub-libraries. Thus, the main objective of this study was to evaluate whether the stratification of a SSL by environmental, pedological and Vis-NIR spectral criteria results in greater accuracy of spectroscopic models than to general models for prediction of SOC content. The performance of the models was evaluated considering the variance of soil components and sample number. In addition, we tested the effect of two spectral preprocessing techniques and two multivariate calibration methods on spectroscopic modeling. For these purposes, a SSL composed of 2471 samples from Southern Brazil was stratified based on i) physiographic region; ii) land-use/land-cover; iii) soil texture, and iv) spectral class. Two spectral processing techniques: Savitzky-Golay - 1st derivative (SGD) and continuum removed reflectance (CRR) and two multivariate methods (partial least squares regression - PLSR and Cubist) were used to fit the models. The best performances for the global and local models were achieved with the CRR spectral processing associated with the Cubist method. The stratification of the SSL in more homogeneous sample groups by environmental criteria (physiographic regions and land-use/land-cover) improved the accuracy of SOC predictions compared to pedological (soil texture) and Vis-NIR spectral (spectral classes) criteria. The reduction in the number of samples negatively affected the performance of models for sub-libraries with high pedological and spectral variation. Stratification criteria were proposed in a flowchart to assist in decision making in future studies. Our findings suggest the importance of sample balance across environmental, pedological and spectral strata, in order to optimize SOC predictions. 650 $aSoil organic carbon 653 $aAprendizagem ambiental 653 $aCarbono orgânico do solo 653 $aEnvironmental-based learning 653 $aModelos espectrais 653 $aSOC variance 653 $aSpectral models 653 $aSpectral variation 653 $aVariação espectral 653 $aVis-NIR data mining 700 1 $aDALMOLIN, R. S. D. 700 1 $aHORST-HEINEN, T. Z. 700 1 $aCATEN, A. ten 700 1 $aVASQUES, G. de M. 700 1 $aDOTTO, A. C. 700 1 $aGRUNWALD, S. 773 $tScience of The Total Environment$gv. 737, 139895, Oct. 2020.
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