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Registros recuperados : 17 | |
1. | | NAVA, G.; SETE, P. B.; NACHTIGALL, G. R.; MOURA-BUENO, J. M.; BRUNETTO, G.; MULAZZANI, R. P.; CIOTTA, M. N. Balanço de nutrientes em pomares. In: BRUNETTO, G.; ROZANE, D. E.; LOSS, A.; NATALE, W. (ed.). Estratégias de manejo para melhorar o aproveitamento de nutrientes em frutíferas. Santa Maria: Palotti, 2023. p. 53-75. Biblioteca(s): Embrapa Clima Temperado; Embrapa Uva e Vinho. |
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3. | | WOLSKI, M. S.; DALMOLIN, R. S. D.; FLORES, C. A.; MOURA-BUENO, J. M.; CATEN, A. ten; KAISER, D. R. Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 52, n. 8, p. 633-642, fev. 2017. Título em português: Mapeamento digital do solo e suas implicações na extrapolação das relações solo-paisagem em escala de detalhe. Biblioteca(s): Embrapa Unidades Centrais. |
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5. | | NAVA, G.; REISSER JUNIOR, C.; PARENT, LÉON-ÉTIENNE; BRUNETTO, G.; MOURA-BUENO, J. M.; NOVRASKI, R.; BENATI, J. A.; BARRETO, C. F. Esmeralda Peach (Prunus persica) Fruit Yield and Quality Response to Nitrogen Fertilization. Plants, v. 11, n. 352. p. 1-17, 2022. Biblioteca(s): Embrapa Clima Temperado. |
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6. | | MOURA-BUENO, J. M.; HORST, T. Z.; DALMOLIN, R. S. D.; SAMUEL-ROSA, A.; DEMATTÊ, J. A. M.; MENDONÇA-SANTOS, M. de L.; MIGUEL, P.; ROSIN, N. A. Pedometria: histórico, princípios e aplicações. In: SOUZA-FILHO, L. F.; SILVA, R. C. da; CÉSAR, F. R. C. F.; SOUZA, C. M. M. (ed.). Tópicos em ciência do solo. Viçosa, MG: Sociedade Brasileira de Ciência do Solo, 2021. v. 11, p. 354-415. Biblioteca(s): Embrapa Solos. |
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7. | | 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 Biblioteca(s): Embrapa Uva e Vinho. |
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8. | | ARGENTA, L. C.; THEWES, F. R.; ANESE, R. de O.; FREITAS, S. T. de; MOURA-BUENO, J. M.; OGOSHI, C.; BASEGGIO. Storability of 'SCS417 Monalisa' apple as affected by harvest maturity, 1-methylcyclopropene treatment, and storage atmosphere. Pesquisa Agropecuária Brasileira, v. 58, e03121, 2023. Biblioteca(s): Embrapa Semiárido; Embrapa Unidades Centrais. |
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9. | | PANZIERA, W.; LIMA, C. L. R. de; MOURA-BUENO, J. M.; PAULETTO, E. A.; SILVA, S. D. dos A. e; TIMM, L. C.; STUMPF, L. Spatial variability of soil physical attributes in sugarcane using different row spacings. Australian Journal of Crop Science, v. 14, n. 09, p. 1399-1404, 2020. Biblioteca(s): Embrapa Clima Temperado. |
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11. | | MOURA-BUENO, J. M.; DALMOLIN, R. S. D.; HORST-HEINEN, T. Z.; CATEN, A. ten; VASQUES, G. de M.; DOTTO, A. C.; GRUNWALD, S. When does stratification of a subtropical soil spectral library improve predictions of soil organic carbon content? Science of The Total Environment, v. 737, 139895, Oct. 2020. Biblioteca(s): Embrapa Solos. |
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12. | | PANZIERA, W.; LIMA, C. L. R. de; TIMM, L. C.; AQUINO, L. S.; BARROS, W. S.; STUMPF, L.; SILVA, S. D. dos A. e; MOURA-BUENO, J. M.; DUTRA JUNIOR, L. A.; PAULETTO, E. A. Investigating the relationships between soil and sugarcane attributes under different row spacing configurations and crop cycles using the state-space approach. Soil & Tillage Research, v. 217, 105270, 2022. Biblioteca(s): Embrapa Clima Temperado. |
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13. | | AGUILAR, M. V. M.; WERTONGE, G. S.; BIRCK, T. P.; LOVATO, L. da R.; ROSA, F. C. R. da; HINDERSMANN, J.; MAYER, N. A.; MOURA-BUENO, J. M.; BRUNETTO, G.; TABALDI, L. A. Oxidative stress as markers in identification of aluminum-tolerant peach tree rootstock cultivars and clonal selections. Revista Brasileira de Ciência do Solo, v. 48, e0220112, 2024. Biblioteca(s): Embrapa Clima Temperado. |
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14. | | OLIVEIRA, V. A. de; LUMBRERAS, J. F.; SILVA, M. B. e; COELHO, M. R.; ALMEIDA, J. A. de; ARAUJO FILHO, J. C. de; MENDONÇA-SANTOS, M. de L.; MOURA-BUENO, J. M.; SANTIAGO, C. M. Solos da XIII Reunião Brasileira de Classificação e Correlação de Solos (RCC do Maranhão). In: SILVA, M. B. e; LUMBRERAS, J. F.; COELHO, M. R.; OLIVEIRA, V. A. de (ed.). Guia de campo da XIII Reunião Brasileira de Classificação e Correlação de Solos: RCC do Maranhão. Brasília, DF: Embrapa, 2020. E-book. cap. 6. Biblioteca(s): Embrapa Cocais; Embrapa Solos. |
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15. | | TRAPP, T.; INACIO, C. de T.; BRUNETTO, G.; CIOTTA, M. N.; LOURENZI, C. R.; ROZANE, D. E.; LOSS, A.; COMIN, J. J.; MOURA-BUENO, J. M.; MARCHEZAN, C.; PALERMO, M. N.; DE CONTI, L. Proposição da análise da abundância natural de 15N para diagnosticar alimentos orgânicos. Florianópolis: UFSC, 2022. 17 p. (UFSC. Boletim técnico). Biblioteca(s): Embrapa Solos. |
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16. | | SAMUEL-ROSA, A.; DALMOLIN, R. S. D.; GUBIANI, P. I.; TEIXEIRA, W. G.; OLIVEIRA, S. R. de M.; VIANA, J. H. M.; TORNQUIST, C. G.; ANJOS, L.; SOUZA, J. J. L. L. de; RIBEIRO, E.; OTTONI, M.; MEDEIROS, P. S. C. de; REICHERT, J. M.; SIQUEIRA, D. S.; MARQUES JÚNIOR; DEMATTÊ, J. A. M.; DOTTO, A. C.; COLLIER, L.; VASQUES, G. de M.; VALLADARES, G.; PEDRON, F. A.; PEDROSO NETO, J. C.; FILIPPINI ALBA, J. M.; OLIVEIRA, R. P. de; CAVIGLIONE, J. H.; MIGUEL, P.; SANTOS, H. G. dos; FLORES, C. A.; LEPSCH, I.; GRIS, D. J.; ROSIN, N. A.; MOURA-BUENO, J. M. Bringing together Brazilian soil scientists to share soil data. In: REUNIÃO SUL BRASILEIRA DE CIÊNCIA DO SOLO, 12., 2018, Xanxerê. Solo, água, ar e biodiversidade: componentes essenciais para a vida: anais. Chapecó: Argos, 2018. Na publicação: Wenceslau Teixeira. Biblioteca(s): Embrapa Solos. |
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17. | | SAMUEL-ROSA, A.; DALMOLIN, R. S. D.; GUBIANI, P. I.; TEIXEIRA, W. G.; OLIVEIRA, S. R. de M.; VIANA, J. H. M.; TORNQUIST, C. G.; ANJOS, L.; SOUZA, J. J. L. L. de; RIBEIRO, E.; OTTONI, M.; MEDEIROS, P. S. C. de; REICHERT, J. M.; SIQUEIRA, D. S.; MARQUES JÚNIOR, J.; DEMATTÊ, J. A. M.; DOTTO, A. C.; COLLIER, L.; VASQUES, G. de M.; VALLADARES, G.; PEDRON, F. A.; PEDROSO NETO, J. C.; FILIPPINI ALBA, J. M.; OLIVEIRA, R. P. de; CAVIGLIONE, J. H.; MIGUEL, P.; SANTOS, H. G. dos; FLORES, C. A.; LEPSCH, I.; GRIS, D. J.; ROSIN, N. A.; MOURA-BUENO, J. M. Bringing together Brazilian soil scientists to share soil data. In: REUNIÃO SUL BRASILEIRA DE CIÊNCIA DO SOLO, 12., 2018, Xanxerê. Solo, água, ar e biodiversidade: componentes essenciais para a vida: anais. Chapecó: Argos, 2018. 4 p. Na publicação: Wenceslau Teixeira. Biblioteca(s): Embrapa Agricultura Digital. |
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Registros recuperados : 17 | |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
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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