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| Registros recuperados : 16 | |
| 1. |  | LANGE, A.; CARVALHO, J. L. N. de; DAMIN, V.; CRUZ, J. C.; MARQUES, J. J. Alterações em atributos do solo decorrentes da aplicação de nitrogênio e palha em sistema semeadura direta na cultura do milho. Ciencia Rural, Santa Maria, v. 36, n.2, p. 460-467, 2006.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 2. | | CARVALHO, J. L. N.; AVANZI, J. C.; CERRI, C. E. P.; CERRI, C. C. Adequação dos sistemas de produção rumo à sustentabilidade ambiental. In: FALEIRO, F. G.; FARIAS NETO, A. L. de (Ed.). Savanas: desafios e estratégias para o equilíbrio entre sociedade, agronegócio e recursos naturais. Planaltina, DF: Embrapa Cerrados, 2008. cap. 19, p. 673-692.| Biblioteca(s): Embrapa Cerrados. |
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| 3. | | LANGE, A.; CARVALHO, J. L. N.; DAMIN, V.; CRUZ, J. C.; GUILHERME, L. R. G.; MARQUES, J. J. Doses de nitrogênio e de palha em sistema plantio direto de milho no cerrado. Revista Ceres, Viçosa, v. 53, n. 306, p. 171-178, 2006.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 4. | | 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. Classification of soil respiration in areas of sugarcane renewal using decision tree. Scientia Agricola, v. 75, n. 3, p. 216-224, May/June 2018.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 5. | | CARVALHO, J. L. N.; CERRI, C. E. P.; FEIGL, B. P.; PÍCCOLO, M. C.; GODINHO, V. de P. C.; CERRI, C. C. Carbon sequestration in agricultural soils in the Cerrado region of the brazilian Amazon. Soil & Tillage Research, v. 103, n. 2, p. 342-349, May 2009.| Biblioteca(s): Embrapa Rondônia. |
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| 6. | | CARVALHO, J. L. N. CERRI, C. E. P.; CERRI, C. C.; FEIGL, B. J.; PÍCCOLO, M. de C.; GODINHO, V. de P. C.; HERP, U. Changes of chemical properties in an oxisol after clearing of native Cerrado vegetation for agricultural use in Vilhena, Rondonia State, Brazil. Soil & Tillage Research, v. 96, p. 95-102, 2007.| Biblioteca(s): Embrapa Rondônia. |
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| 7. | | BORDONAL, R. O.; MENANDRO, L. M. S.; BARBOSA, L. C.; LAL, R.; MILORI, D. M. B. P.; KOLLN, O. T.; FRANCO, H. C. J.; CARVALHO, J. L. N. Sugarcane yield and soil carbon response to straw removal in south-central Brazil. Geoderma, Amsterdam, v. 328, p. 79-90, 2018.| Biblioteca(s): Embrapa Instrumentação. |
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| 8. | | CARVALHO, J. L. N.; RAUCCI, G. S.; CERRI, C. E. P.; BERNOUX, M.; FEIGL, B. J.; WRUCK, F. J.; CERRI, C. C. Impact of pasture, agriculture and crop-livestock systems on soil C stocks in Brazil. Soil and Tillage Research, v. 110, n. 1, p. 175-186, Sept. 2010.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 9. | | BOSCHIERO, B. N.; CASTRO, S. G. Q. de; CRUZ, L. P. da; CARVALHO, J. L. N.; SILVA, S. R.; BRESSIANI, J. A.; KÖLLN, O. T. Biomass yield, nutrient removal, and chemical composition of energy cane genotypes in Southeast Brazil. Industrial Crops and Products, v. 191, 115993, Jan. 2023.| Biblioteca(s): Embrapa Florestas. |
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| 10. | | CARVALHO, J. L. N.; CERRI, C. E. P.; FEIGL, B. J.; PICCOLO, M. de C.; GODINHO, V. de P.; HERPIN, U.; CERRI, C. C. Conversion of Cerrado into agricultural land in the south-western amazon: carbon stocks and soil fertility. Scientia Agricola, v. 66, n. 2, p. 233-241, mar./abr. 2009.| Biblioteca(s): Embrapa Rondônia. |
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| 12. | | BORDONAL, R. DE O.; LAL, R.; RONQUIM, C. C.; FIGUEIREDO, E. B. DE; CARVALHO, J. L. N.; MALDONADO JUNIOR, W.; MILORI, D. M. B. P.; LA SCALA JUNIOR, N. Changes in quantity and quality of soil carbon due the land-use conversion to sugarcane (Saccharum officinarum) plantation in southern Brazil. Agriculture, Ecosystems and Environment, v. 240, p. 54-65, 2017.| Biblioteca(s): Embrapa Instrumentação; Embrapa Territorial. |
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| 13. | | SEGNINI, A.; CARVALHO, J. L. N.; BOLONHEZI, D.; MILORI, D. M. B. P.; SILVA, W. T. L. da; SIMOES, M. L.; CANTARELLA, H.; MARIA, I. C. de; MARTIN NETO, L. Carbon stock and humification index of organic matter affected by sugarcane straw and soil management. Scientia agrícola, Piracicaba, v. 70, n. 5, p. 321-326, 2013.| Biblioteca(s): Embrapa Instrumentação. |
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| 14. | | CERRI, C. C.; BERNOUX, M.; MAIA, S. M. F.; CERRI, C. E. P.; COSTA JUNIOR, C.; FEIGL, B. J.; FRAZÃO, L. A.; MELLO, F. F. de C.; GALDOS, M. V.; MOREIRA, C. S.; CARVALHO, J. L. N. Greenhouse gas mitigation options in Brazil for land-use change, livestock and agriculture. Scientia Agricola, v. 67, n. 1, p. 102-116, jan./feb., 2010.| Biblioteca(s): Embrapa Algodão. |
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| 15. | | NICOLODELLI, G.; ROMANO, R. A.; SENESI, G. S.; CABRAL, J.; WATANABE, A.; TELLI, S.; BORDONAL, R. de O.; CARVALHO, J. L. N.; MARANGONI, B. S.; MILORI, D. M. B. P. Evaluation of nitrogen fertilization in sugarcane leaves using laser-induced breakdown spectroscopy (LIBS) coupled with principal component analysis (PCA). In: LATIN AMERICA OPTICS AND PHOTONICS CONFERENCE, 2018, Peru. Proceedings... USA: OSA, 2018.| Biblioteca(s): Embrapa Instrumentação. |
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| 16. | | ROSSI NETO, J.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; KÖLLN, O. T.; FERREIRA, D. A.; CARVALHO, J. L. N.; BRAUNBECK, O. A.; FRANCO, H. C. J. Use of the decision tree technique to estimate sugarcane productivity under edaphoclimatic conditions. Sugar Tech, v. 19, n. 6, p. 662-668, Nov./Dec. 2017.| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 16 | |
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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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