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Biblioteca(s): |
Embrapa Milho e Sorgo. |
Data corrente: |
19/05/2022 |
Data da última atualização: |
10/04/2024 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
MAGALHAES, E. P.; SOUZA, M. R. de; HILGENBERG, D. M.; SILVA, F. R. da; COSTA, R. V. da. |
Afiliação: |
EDUARDA PEREIRA MAGALHÃES; MICAELE RODRIGUES DE SOUZA; DALYSSA MARIA HILGENBERG; FERNANDA RODRIGUES DA SILVA; RODRIGO VERAS DA COSTA, CNPMS. |
Título: |
Avaliação de métodos de inoculação de Macrophomina phaseolina em plântulas de soja. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
In: SEMINÁRIO DE INICIAÇÃO CIENTÍFICA PIBIC/CNPq, 18., 2020, Sete Lagoas. [Trabalhos apresentados]. Sete Lagoas: Embrapa Milho e Sorgo, 2020. |
Idioma: |
Português |
Thesagro: |
Doença Fúngica; Fungo; Podridão; Zea Mays. |
Categoria do assunto: |
H Saúde e Patologia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/241282/1/Avaliacao-de-metodos-de-inoculacao-de-Macrophomina-phaseolina-em-plantulas-de-soja.pdf
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Marc: |
LEADER 00681nam a2200193 a 4500 001 2143247 005 2024-04-10 008 2020 bl uuuu u00u1 u #d 100 1 $aMAGALHAES, E. P. 245 $aAvaliação de métodos de inoculação de Macrophomina phaseolina em plântulas de soja.$h[electronic resource] 260 $aIn: SEMINÁRIO DE INICIAÇÃO CIENTÍFICA PIBIC/CNPq, 18., 2020, Sete Lagoas. [Trabalhos apresentados]. Sete Lagoas: Embrapa Milho e Sorgo$c2020 650 $aDoença Fúngica 650 $aFungo 650 $aPodridão 650 $aZea Mays 700 1 $aSOUZA, M. R. de 700 1 $aHILGENBERG, D. M. 700 1 $aSILVA, F. R. da 700 1 $aCOSTA, R. V. da
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Embrapa Milho e Sorgo (CNPMS) |
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Registro Completo
Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
01/03/2007 |
Data da última atualização: |
04/03/2020 |
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
Autoria: |
MENDONÇA-SANTOS, M. de L.; MCBRATNEY, A. B.; MINASNY, B. |
Afiliação: |
MARIA DE LOURDES M SANTOS BREFIN, CNPS. |
Título: |
Soil prediction with spatially decomposed environmental factors. |
Ano de publicação: |
2007 |
Fonte/Imprenta: |
In: LAGACHERIE, P.; MCBRATNEY, A. B.; VOLTZ, M. (Ed.). Digital soil mapping: an introductory perspective. Amsterdam: Elsevier, 2007. cap. 21, 269-278. |
Idioma: |
Inglês |
Conteúdo: |
Prediction of soil attributes and soil classes in digital soil mapping relies on finding relationships between soil and the predictor variables of soil-forming factors and processes. The predictor variables can be remotely or proximally sensed images of soil, landscape, parent material or climatic factors. Till date, most prediction methods are based on performing regression on the predictor variables directly to predict soil attributes or classes. There are problems using data layers from different sources, particularly, multicollinearity, and the fact that the relationships between soil and environmental variables can change with spatial scale. To overcome the problem of correlation between variables, principal component analysis can be performed on the predictor variables. With respect to the spatial dependency, each of these variables can be decomposed into separate spatial components and mapped separately. One of the methods of achieving this is wavelet analysis, which decomposes the variables into separate hierarchical spatial components of decreasing spatial resolution. These components could all be derived and subsequently used as separate layers in predicting soil classes or soil attributes. In this chapter, data are decomposed using the wavelet method and examples of predictions of soil classes and surface-clay content are shown, in order to evaluate the effect of using the decomposed layers in comparison with the original data. |
Palavras-Chave: |
Atributos do solo. |
Thesagro: |
Sensoriamento Remoto. |
Thesaurus NAL: |
Remote sensing. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
Marc: |
LEADER 02099naa a2200181 a 4500 001 1338904 005 2020-03-04 008 2007 bl uuuu u00u1 u #d 100 1 $aMENDONÇA-SANTOS, M. de L. 245 $aSoil prediction with spatially decomposed environmental factors.$h[electronic resource] 260 $c2007 520 $aPrediction of soil attributes and soil classes in digital soil mapping relies on finding relationships between soil and the predictor variables of soil-forming factors and processes. The predictor variables can be remotely or proximally sensed images of soil, landscape, parent material or climatic factors. Till date, most prediction methods are based on performing regression on the predictor variables directly to predict soil attributes or classes. There are problems using data layers from different sources, particularly, multicollinearity, and the fact that the relationships between soil and environmental variables can change with spatial scale. To overcome the problem of correlation between variables, principal component analysis can be performed on the predictor variables. With respect to the spatial dependency, each of these variables can be decomposed into separate spatial components and mapped separately. One of the methods of achieving this is wavelet analysis, which decomposes the variables into separate hierarchical spatial components of decreasing spatial resolution. These components could all be derived and subsequently used as separate layers in predicting soil classes or soil attributes. In this chapter, data are decomposed using the wavelet method and examples of predictions of soil classes and surface-clay content are shown, in order to evaluate the effect of using the decomposed layers in comparison with the original data. 650 $aRemote sensing 650 $aSensoriamento Remoto 653 $aAtributos do solo 700 1 $aMCBRATNEY, A. B. 700 1 $aMINASNY, B. 773 $tIn: LAGACHERIE, P.; MCBRATNEY, A. B.; VOLTZ, M. (Ed.). Digital soil mapping: an introductory perspective. Amsterdam: Elsevier, 2007. cap. 21, 269-278.
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