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Registro Completo |
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Biblioteca(s): |
Embrapa Agrossilvipastoril. |
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Data corrente: |
19/11/2018 |
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Data da última atualização: |
19/11/2018 |
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Tipo da produção científica: |
Comunicado Técnico/Recomendações Técnicas |
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Autoria: |
LULU, J.; ZOLIN, C. A. |
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Afiliação: |
JORGE LULU, CPAMT; CORNELIO ALBERTO ZOLIN, CPAMT. |
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Título: |
Boletim Agrometeorológico: Início da 1ª safra 2018/2019 em Mato Grosso. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Sinop, MT: Embrapa Agrossilvipatoril, 2018. |
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Série: |
(Embrapa Agrossilvipastoril. Boletim Agrometeorológico, 7) |
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Idioma: |
Português |
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Conteúdo: |
O presente boletim agrometeorológico tem por objetivo fornecer informações relevantes e consolidadas para dar suporte ao setor produtivo de Mato Grosso no sentido do acompanhamento das condições de chuva e déficit hídrico nas regiões produtoras do estado. Importante destacar que, considerando a escala de análise e dado o fator de variabilidade das precipitações e condições de seca, é necessário cautela na interpretação das informações aqui apresentadas. As informações deste boletim são fundamentadas em fontes de dados da Embrapa (Agritempo), CPTEC/INPE (Centro de Previsão de Tempo e Estudos Climáticos) e do INMET (Instituto Nacional de Meteorologia). |
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Palavras-Chave: |
Agritempo; Dados climaticos. |
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Thesagro: |
Meteorologia. |
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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/186285/1/2018-cpamt-jorge-lulu-boletim-agrometeorologico-7.pdf
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Marc: |
LEADER 01212nam a2200169 a 4500
001 2099585
005 2018-11-19
008 2018 bl uuuu u0uu1 u #d
100 1 $aLULU, J.
245 $aBoletim Agrometeorológico$bInício da 1ª safra 2018/2019 em Mato Grosso.$h[electronic resource]
260 $aSinop, MT: Embrapa Agrossilvipatoril$c2018
490 $a(Embrapa Agrossilvipastoril. Boletim Agrometeorológico, 7)
520 $aO presente boletim agrometeorológico tem por objetivo fornecer informações relevantes e consolidadas para dar suporte ao setor produtivo de Mato Grosso no sentido do acompanhamento das condições de chuva e déficit hídrico nas regiões produtoras do estado. Importante destacar que, considerando a escala de análise e dado o fator de variabilidade das precipitações e condições de seca, é necessário cautela na interpretação das informações aqui apresentadas. As informações deste boletim são fundamentadas em fontes de dados da Embrapa (Agritempo), CPTEC/INPE (Centro de Previsão de Tempo e Estudos Climáticos) e do INMET (Instituto Nacional de Meteorologia).
650 $aMeteorologia
653 $aAgritempo
653 $aDados climaticos
700 1 $aZOLIN, C. A.
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Registro original: |
Embrapa Agrossilvipastoril (CPAMT) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
13/02/2019 |
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Data da última atualização: |
13/02/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
FARHATE, C. V. V.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; CARVALHO, J. L. N.; LA SCALA JÚNIOR, N.; SANTOS, A. P. G. |
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Afiliação: |
CAMILA VIANA VIEIRA FARHATE, Feagri/Unicamp; ZIGOMAR MENEZES DE SOUZA, Feagri/Unicamp; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA, Feagri/Unicamp; JOÃO LUÍS NUNES CARVALHO, CNPEM; NEWTON LA SCALA JÚNIOR, Unesp; ANA PAULA GUIMARÃES SANTOS, Feagri/Unicamp. |
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Título: |
Classification of soil respiration in areas of sugarcane renewal using decision tree. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Scientia Agricola, v. 75, n. 3, p. 216-224, May/June 2018. |
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DOI: |
http://dx.doi.org/10.1590/1678-992X-2016-0473 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT: The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of variable selection were evaluated: no variable selection, correlation-based feature selection (CFS), chisquare method (χ2) and Wrapper. To classify soil respiration, we used the decision tree induction technique available in the Weka software package. Our results showed that data mining techniques allow the development of a model for soil respiration classification with accuracy of 81 %, resulting in a knowledge base composed of 27 rules for prediction of soil respiration. In particular, the wrapper method for variable selection identified a subset of only five variables out of 18 available in the original dataset, and they had the following order of influence in determining soil respiration: soil temperature > precipitation > macroporosity > soil moisture > potential acidity. MenosABSTRACT: The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of variable selection were evaluated: no variable selection, correlation-based feature selection (CFS), chisquare method (χ2) and Wrapper. To classify soil respiration, we used the decision tree induction technique available in the Weka software package. Our results showed that data mining techniques allow the development of a model for soil respiration classification with accuracy of 81 %, resulting in a knowledge base composed of 27 rules for prediction of soil respiration. In particular, the wrapper method for variable selection identified a subset of only five variables out of 18 available in the original dataset, and they had the following order of influence in determining soil respiration: soil temperature > precipi... Mostrar Tudo |
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Palavras-Chave: |
Árvore de decisão; Data mining; Decision tree; Emissão de gás carbônico no solo; Matéria orgânica no solo; Mineração de dados; Seleção de variável; Temperatura no solo; Variable selection. |
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Thesagro: |
Respiração do Solo. |
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Thesaurus NAL: |
Carbon dioxide; Soil organic matter; Soil temperature. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/192660/1/AP-Classification-soil-Farhate.pdf
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Marc: |
LEADER 02675naa a2200349 a 4500
001 2105884
005 2019-02-13
008 2018 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1590/1678-992X-2016-0473$2DOI
100 1 $aFARHATE, C. V. V.
245 $aClassification of soil respiration in areas of sugarcane renewal using decision tree.$h[electronic resource]
260 $c2018
520 $aABSTRACT: The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of variable selection were evaluated: no variable selection, correlation-based feature selection (CFS), chisquare method (χ2) and Wrapper. To classify soil respiration, we used the decision tree induction technique available in the Weka software package. Our results showed that data mining techniques allow the development of a model for soil respiration classification with accuracy of 81 %, resulting in a knowledge base composed of 27 rules for prediction of soil respiration. In particular, the wrapper method for variable selection identified a subset of only five variables out of 18 available in the original dataset, and they had the following order of influence in determining soil respiration: soil temperature > precipitation > macroporosity > soil moisture > potential acidity.
650 $aCarbon dioxide
650 $aSoil organic matter
650 $aSoil temperature
650 $aRespiração do Solo
653 $aÁrvore de decisão
653 $aData mining
653 $aDecision tree
653 $aEmissão de gás carbônico no solo
653 $aMatéria orgânica no solo
653 $aMineração de dados
653 $aSeleção de variável
653 $aTemperatura no solo
653 $aVariable selection
700 1 $aSOUZA, Z. M. de
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aCARVALHO, J. L. N.
700 1 $aLA SCALA JÚNIOR, N.
700 1 $aSANTOS, A. P. G.
773 $tScientia Agricola$gv. 75, n. 3, p. 216-224, May/June 2018.
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