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| Registros recuperados : 76 | |
| 21. |  | SILVA, D. T. da; MOURA, E. F.; FARIAS NETO, J. T. de; RAMALHO, G. F. Divergência genética entre acessos de macaxeira com mesma sinonímia no banco de germoplasma de mandioca da Embrapa Amazônia Oriental. In: SEMINÁRIO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA, 14., 2010, Belém, PA. Bolsista de iniciação científica: um aporte ao desenvolvimento da pesquisa agropecuária: anais. Belém, PA: Embrapa Amazônia Oriental, 2010. 1 CD-ROM. PIBIC 2010.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 22. | | PONTES, L. C. G; MOURA, E. F.; THERRIER, J.; CARVALHO, J. E. U. de. Divergência genética entre e dentro de progênies de bacurizeiro do BAG da Embrapa Amazônia Oriental. In: SEMINÁRIO DE INICIAÇÃO CIENTÍFICA, 18.; SEMINÁRIO DE PÓS-GRADUAÇÃO DA EMBRAPA AMAZÔNIA ORIENTAL, 2., 2014, Belém, PA. Anais. Belém, PA: Embrapa Amazônia Oriental, 2014. 1 CD-ROM.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 23. | | OLIVEIRA, N. P. de; OLIVEIRA, M. do S. P. de; MOURA, E. F. Divergência genética entre genótipos de tucumã-do-pará (Astrocaryum vulgare Mart.) desejáveis para frutos por meio de marcadores moleculares. In: CONGRESSO DA REDE BRASILEIRA DE TECNOLOGIA DE BIODIESEL, 4.; CONGRESSO BRASILEIRO DE PLANTAS OLEAGINOSAS, ÓLEOS, GORDURAS E BIODIESEL, 7., 2010, Belo Horizonte. Biodiesel: inovação tecnológica e qualidade: anais: trabalhos científicos. Lavras: UFLA, 2010. v. 1, p. 91-92.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 25. | | RAMALHO, G. F.; MOURA, E. F.; FARIAS NETO, J. T. de; SILVA, D. T. da. Diferenciação genética entre possíveis duplicatas no banco de germoplasma de mandioca da Embrapa Amazônia Oriental por meio de marcadores microssatélites. In: SEMINÁRIO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA, 14., 2010, Belém, PA. Bolsista de iniciação científica: um aporte ao desenvolvimento da pesquisa agropecuária: anais. Belém, PA: Embrapa Amazônia Oriental, 2010. 1 CD-ROM. PIBIC 2010. PIBIC 2010.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 26. | | LEDO, A. da S.; MOURA, E. F.; MENEZES, I. C. de; LAMEIRA, O. A. Calogênese em cotiledones de cupuaçu (Theobroma grandiflorum). Genetics and Molecular Biology, v. 23, n. 3, p. 398-399, 2000. Suplements. Trabalho apresentado no Congresso Nacional de Genética, 46., 2000, Aguas de Lindoia.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 31. | | GAMA, C. M. P.; MOURA, E. F.; GURGEL, F. de L.; SAMPAIO, J. E. Floração em acessos de mandioca do banco de germoplasma da Embrapa Amazônia Oriental. In: SEMINÁRIO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA AMAZÔNIA ORIENTAL, 15., 2011, Belém, PA. A ciência de fazer ciência: anais. Belém, PA: Embrapa Amazônia Oriental, 2011. 1 CD-ROM. PIBIC-2011.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 32. | | NOGUEIRA, R. C.; LAMEIRA, O. A.; LOPES, S. C.; MENEZES, I. C. de; MOURA, E. F. Efeito de reguladores de crescimento na micropropagação de mogno (Swietenia macrophyla). In: SEMINÁRIO DE INICIAÇÃO CIENTIFICA DA FCAP, 9.; SEMINÁRIO DE INICIAÇÃO CIENTIFICA DA EMBRAPA AMAZÔNIA ORIENTAL, 3., 1999, Belém, PA. Resumos. Belém, PA: FCAP: Embrapa Amazônia Oriental, 1999. p. 288-289.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 36. | | SILVA, R. de S.; MOURA, E. F.; FARIAS NETO, J. T. de; SAMPAIO, J. E. Genetic parameters and agronomic evaluation of cassava genotypes. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 51, n. 7, p. 834-841, jul. 2016.| Biblioteca(s): Embrapa Amazônia Oriental; Embrapa Unidades Centrais. |
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| 37. | | NOGUEIRA, R. C.; LAMEIRA, O. A.; MENEZES, I. C. de; MOURA, E. F. Micropropagação de cedro (Cedrela odorata). Genetics and Molecular Biology, v. 23, n. 3, p. 463, 2000. Suplements. Trabalho apresentado no Congresso Nacional de Genética, 46., 2000. Aguas de Lindoia.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 38. | | FORTES, A. C. R.; OLIVEIRA, M. do S. P. de; MOURA, E. F. Transferibilidade de locos SSR de Astrocaryum aculeatum Mart. para Astrocaryum vulgare Mart. In: SEMINÁRIO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA AMAZÔNIA ORIENTAL, 17.; SEMINÁRIO DE PÓS-GRADUAÇÃO DA EMBRAPA AMAZÔNIA ORIENTAL, 1., 2013, Belém, PA. Anais. Belém, PA: Embrapa Amazônia Oriental, 2013. 1 CD-ROM. PIBIC 2013.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| Registros recuperados : 76 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
14/03/2018 |
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Data da última atualização: |
30/12/2020 |
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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.; TAVARES, R. L. M.; CARVALHO, J. L. N. |
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Afiliação: |
CAMILA VIANA VIEIRA FARHATE, Unicamp; ZIGOMAR MENEZES DE SOUZA, Unicamp; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; ROSE LUIZA MORAES TAVARES, Rio Verde University; JOÃO LUÍS NUNES CARVALHO, Brazilian Center for Research in Energy and Materials. |
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Título: |
Use of data mining techniques to classify soil CO2 emission induced by crop management in sugarcane field. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Plos One, v. 13, n. 3, p. 1-18, 2018. |
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DOI: |
https://doi.org/ 10.1371/journal.pone.0193537 |
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Idioma: |
Inglês |
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Notas: |
Artigo e0193537. |
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Conteúdo: |
Soil CO2 emissions are regarded as one of the largest flows of the global carbon cycle and small changes in their magnitude can have a large effect on the CO2 concentration in the atmosphere. Thus, a better understanding of this attribute would enable the identification of promoters and the development of strategies to mitigate the risks of climate change. Therefore, our study aimed at using data mining techniques to predict the soil CO2 emission induced by crop management in sugarcane areas in Brazil. To do so, we used different variable selection methods (correlation, chi-square, wrapper) and classification (Decision tree, Bayesian models, neural networks, support vector machine, bagging with logistic regression), and finally we tested the efficiency of different approaches through the Receiver Operating Characteristic (ROC) curve. The original dataset consisted of 19 variables (18 independent variables and one dependent (or response) variable). The association between cover crop and minimum tillage are effective strategies to promote the mitigation of soil CO2 emissions, in which the average CO2 emissions are 63 kg ha-1 day-1. The variables soil moisture, soil temperature (Ts), rainfall, pH, and organic carbon were most frequently selected for soil CO2 emission classification using different methods for attribute selection. According to the results of the ROC curve, the best approaches for soil CO2 emission classification were the following: (I)-the Multilayer Perceptron classifier with attribute selection through the wrapper method, that presented rate of false positive of 13,50%, true positive of 94,20% area under the curve (AUC) of 89,90% (II)-the Bagging classifier with logistic regression with attribute selection through the Chi-square method, that presented rate of false positive of 13,50%, true positive of 94,20% AUC of 89,90%. However, the (I) approach stands out in relation to (II) for its higher positive class accuracy (high CO2 emission) and lower computational cost. MenosSoil CO2 emissions are regarded as one of the largest flows of the global carbon cycle and small changes in their magnitude can have a large effect on the CO2 concentration in the atmosphere. Thus, a better understanding of this attribute would enable the identification of promoters and the development of strategies to mitigate the risks of climate change. Therefore, our study aimed at using data mining techniques to predict the soil CO2 emission induced by crop management in sugarcane areas in Brazil. To do so, we used different variable selection methods (correlation, chi-square, wrapper) and classification (Decision tree, Bayesian models, neural networks, support vector machine, bagging with logistic regression), and finally we tested the efficiency of different approaches through the Receiver Operating Characteristic (ROC) curve. The original dataset consisted of 19 variables (18 independent variables and one dependent (or response) variable). The association between cover crop and minimum tillage are effective strategies to promote the mitigation of soil CO2 emissions, in which the average CO2 emissions are 63 kg ha-1 day-1. The variables soil moisture, soil temperature (Ts), rainfall, pH, and organic carbon were most frequently selected for soil CO2 emission classification using different methods for attribute selection. According to the results of the ROC curve, the best approaches for soil CO2 emission classification were the following: (I)-the Multilayer Perceptron ... Mostrar Tudo |
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Palavras-Chave: |
Carbon dioxide emission; Data mining; Emissão de dióxido de carbono; Manejo de cultivos; Mineração de dados. |
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Thesagro: |
Cana de açúcar; Dióxido de carbono. |
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Thesaurus NAL: |
Carbon dioxide; Crop management; Sugarcane. |
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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/173937/1/AP-Useofdata-Ferhate-etal.pdf
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Marc: |
LEADER 02994naa a2200313 a 4500
001 2089160
005 2020-12-30
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/ 10.1371/journal.pone.0193537$2DOI
100 1 $aFARHATE, C. V. V.
245 $aUse of data mining techniques to classify soil CO2 emission induced by crop management in sugarcane field.$h[electronic resource]
260 $c2018
500 $aArtigo e0193537.
520 $aSoil CO2 emissions are regarded as one of the largest flows of the global carbon cycle and small changes in their magnitude can have a large effect on the CO2 concentration in the atmosphere. Thus, a better understanding of this attribute would enable the identification of promoters and the development of strategies to mitigate the risks of climate change. Therefore, our study aimed at using data mining techniques to predict the soil CO2 emission induced by crop management in sugarcane areas in Brazil. To do so, we used different variable selection methods (correlation, chi-square, wrapper) and classification (Decision tree, Bayesian models, neural networks, support vector machine, bagging with logistic regression), and finally we tested the efficiency of different approaches through the Receiver Operating Characteristic (ROC) curve. The original dataset consisted of 19 variables (18 independent variables and one dependent (or response) variable). The association between cover crop and minimum tillage are effective strategies to promote the mitigation of soil CO2 emissions, in which the average CO2 emissions are 63 kg ha-1 day-1. The variables soil moisture, soil temperature (Ts), rainfall, pH, and organic carbon were most frequently selected for soil CO2 emission classification using different methods for attribute selection. According to the results of the ROC curve, the best approaches for soil CO2 emission classification were the following: (I)-the Multilayer Perceptron classifier with attribute selection through the wrapper method, that presented rate of false positive of 13,50%, true positive of 94,20% area under the curve (AUC) of 89,90% (II)-the Bagging classifier with logistic regression with attribute selection through the Chi-square method, that presented rate of false positive of 13,50%, true positive of 94,20% AUC of 89,90%. However, the (I) approach stands out in relation to (II) for its higher positive class accuracy (high CO2 emission) and lower computational cost.
650 $aCarbon dioxide
650 $aCrop management
650 $aSugarcane
650 $aCana de açúcar
650 $aDióxido de carbono
653 $aCarbon dioxide emission
653 $aData mining
653 $aEmissão de dióxido de carbono
653 $aManejo de cultivos
653 $aMineração de dados
700 1 $aSOUZA, Z. M. de
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aTAVARES, R. L. M.
700 1 $aCARVALHO, J. L. N.
773 $tPlos One$gv. 13, n. 3, p. 1-18, 2018.
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