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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
14/12/2016 |
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Data da última atualização: |
20/02/2017 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
FREITAS, M. do S. C. de; GONÇALVES, M.; JESUS, T. S. de; SOUTO, J. S; GIONGO, V. |
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Afiliação: |
MARIA DO SOCORRO CONCEIÇÃO DE FREITAS, UFPB; MARIANA GONÇALVES, UPE; TAMIRES SANTOS DE JESUS, UPE; JACOB SILVA SOUTO, Professor da UFCG; VANDERLISE GIONGO, CPATSA. |
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Título: |
Carbono do solo em um agroecossistema de manga cultivado com coquetéis vegetais no Semiárido brasileiro. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: JORNADA DE INTEGRAÇÃO DA PÓS-GRADUAÇÃO DA EMBRAPA SEMIÁRIDO, 1., 2016, Petrolina. Anais... Petrolina: Embrapa Semiárido, 2016. |
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Páginas: |
p. 41-42. |
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Série: |
(Embrapa Semiárido. Documentos, 274). |
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ISSN: |
1808-9992 |
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Idioma: |
Português |
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Conteúdo: |
O objetivo deste estudo foi avaliar o efeito de dois sistemas de preparo do solo e do uso de coquetéis vegetais na entrelinha de cultivo de mangueira sobre o teor de carbono total do solo (CT). |
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Palavras-Chave: |
Adubação orgânica; Agroecossistema; Coquetel vegetal; Green manure; Mangiferla indica; Mango; No tillage. |
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Thesagro: |
Adubação Verde; Manga; Matéria Orgânica; Plantio Direto; Solo. |
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Thesaurus Nal: |
Organic matter. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/151802/1/Artigo-14..pdf
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Marc: |
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001 2058670
005 2017-02-20
008 2016 bl uuuu u00u1 u #d
022 $a1808-9992
100 1 $aFREITAS, M. do S. C. de
245 $aCarbono do solo em um agroecossistema de manga cultivado com coquetéis vegetais no Semiárido brasileiro.$h[electronic resource]
260 $aIn: JORNADA DE INTEGRAÇÃO DA PÓS-GRADUAÇÃO DA EMBRAPA SEMIÁRIDO, 1., 2016, Petrolina. Anais... Petrolina: Embrapa Semiárido$c2016
300 $ap. 41-42.
490 $a(Embrapa Semiárido. Documentos, 274).
520 $aO objetivo deste estudo foi avaliar o efeito de dois sistemas de preparo do solo e do uso de coquetéis vegetais na entrelinha de cultivo de mangueira sobre o teor de carbono total do solo (CT).
650 $aOrganic matter
650 $aAdubação Verde
650 $aManga
650 $aMatéria Orgânica
650 $aPlantio Direto
650 $aSolo
653 $aAdubação orgânica
653 $aAgroecossistema
653 $aCoquetel vegetal
653 $aGreen manure
653 $aMangiferla indica
653 $aMango
653 $aNo tillage
700 1 $aGONÇALVES, M.
700 1 $aJESUS, T. S. de
700 1 $aSOUTO, J. S
700 1 $aGIONGO, V.
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Registro original: |
Embrapa Semiárido (CPATSA) |
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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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Embrapa Agricultura Digital (CNPTIA) |
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