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| Registros recuperados : 643 | |
| 145. |  | OLIVEIRA, M. do S. P. de; MOCHIUTTI, S.; FARIAS NETO, J. T. de. Domestication and breeding of assai palm. In: BORÉM, A.; LOPES, M. T. G.; CLEMENT, C. R.; NODA, H. (Ed.). Domestication and breeding: amazonian species. Viçosa, MG: Universidade Federal de Viçosa, 2012. Cap. 11, p. 209-235.| Biblioteca(s): Embrapa Amapá; Embrapa Amazônia Oriental. |
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| 154. | | SOUZA, P. I. M.; KIIHL, R. A. S.; ALMEIDA, L. A.; FARIAS NETO, A. L. Cultivar de soja BRS Nova Savana. In: REUNIAO DE PESQUISA DE SOJA DA REGIAO CENTRAL DO BRASIL, 22., 2000, Cuiaba, MT. Resumos. Cuiaba: Embrapa Soja, 2000. p.121-122. (Embrapa Soja. Documentos, 144).| Biblioteca(s): Embrapa Cerrados. |
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| Registros recuperados : 643 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
11/12/2018 |
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Data da última atualização: |
08/07/2019 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
SAMUEL-ROSA, A.; DALMOLIN, R. S. D.; GUBIANI, P. I.; OLIVEIRA, S. R. de M.; TEIXEIRA, W. G.; VIANA, J. H. M.; RIBEIRO, E.; TORNQUIST, C. G.; ANJOS, L. H. C. dos; SOUZA, J. J. E. L. de; OTTONI, M. V.; MEDEIROS, P. S. C. de; GRIS, D. J.; ROSIN, N. A.; BUENO, J. M. M.; SANTOS, H. G. dos; WEBER, E. J.; FLORES, C. A.; COSTA, E. M.; OLIVEIRA, R. P. de; FILIPPINI ALBA, J. M.; PEDROSO NETO, J. C.; PEDRON, F. de A.; CAVIGLIONE, J. H.; VALLADARES, G. S.; MIRANDA, C. S. S.; DEMATTÊ, J. A. M.; MARQUES JÚNIOR, J.; SIQUEIRA, D. S.; AQUINO, R. E. de; SILVERO, N. E. Q.; GENÚ, A. M.; BROETTO, T.; CANCIAN, L. C.; MIGUEL, P.; ZALAMENA, J.; DOTTO, A. C.; ALMEIDA, J. A. de; REICHERT.; CURCIO, G. R.; COLLIER, L. S.; CARVALHO JUNIOR, W. de; FONTANA, A.; OLIVEIRA, A. P. de; VOGELMANN, E. S.; MALLMANN, F. J. K.; VASQUES, G. de M.; LEPSCH, I. F.; FINK, J. R.; KER, J. C.; SILVA, L. S. da; FREITAS, P. L. de; BIELUCZYK, B.; TIECHER, T. |
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Afiliação: |
ALESSANDRO SAMUEL-ROSA, UFSM; RICARDO SIMÃO DINIZ DALMOLIN, UFSM; PAULO IVONIR GUBIANI, UFSM; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; WENCESLAU GERALDES TEIXEIRA, CNPS; JOAO HERBERT MOREIRA VIANA, CNPMS; ELOI RIBEIRO, ISRIC World Soil Information; CARLOS GUSTAVO TORNQUIST, UFRGS; LÚCIA HELENA CUNHA DOS ANJOS, UFRRJ; JOSÉ JOÃO LELIS LEAL DE SOUZA, UFRGN; MARTA VASCONCELOS OTTONI, Serviço Geológico do Brasil; PAULA SUÉLEN CORRÊA DE MEDEIROS, IBGE; DIEGO JOSÉ GRIS, UFSM; NÍCOLAS AUGUSTO ROSIN, UFSM; JEAN MICHEL MOURA BUENO, UFSM; HUMBERTO GONCALVES DOS SANTOS, CNPS; ELISEU JOSÉ WEBER, UFRGS; CARLOS ALBERTO FLORES, CPACT; ELIAS MENDES COSTA, UFRRJ; RONALDO PEREIRA DE OLIVEIRA, CNPS; JOSE MARIA FILIPPINI ALBA, CPACT; JOÃO CHRISÓSTOMO PEDROSO NETO, Epamig; FABRÍCIO DE ARAÚJO PEDRON, UFSM; JOÃO HENRIQUE CAVIGLIONE, Iapar; GUSTAVO SOUZA VALLADARES, UFPI; CARMEM SUEZE SILVA MIRANDA, Univasf; JOSÉ ALEXANDRE MELO DEMATTÊ, USP; JOSÉ MARQUES JÚNIOR, Unesp; DIEGO SILVA SIQUEIRA, Unesp; RENATO ELEOTERIO DE AQUINO, Unesp; NELIDA ELIZABET QUIÑONEZ SILVERO, Unesp; ALINE MARQUES GENÚ, UNICENTRO; TIAGO BROETTO, Catena Planejamento Territorial; LUCIANO CAMPOS CANCIAN, UFSM; PABLO MIGUEL, UFPel; JOVANI ZALAMENA, UFSC; ANDRÉ CARNIELETTO DOTTO, USP; JAIME ANTONIO DE ALMEIDA, Udesc; JOSÉ MIGUEL REICHERT, UFSM; GUSTAVO RIBAS CURCIO, CNPF; LEONARDO SANTOS COLLIER, UFG; WALDIR DE CARVALHO JUNIOR, CNPS; ADEMIR FONTANA, CNPS; ALINE PACOBAHYBA DE OLIVEIRA, CNPS; EDUARDO SALDANHA VOGELMANN, FURG; FÁBIO JOEL KOCHEM MALLMANN, Universidade Regional Integrada do Alto Uruguai e das Missões; GUSTAVO DE MATTOS VASQUES, CNPS; IGO FERNANDO LEPSCH, USP; JESSÉ RODRIGO FINK, IFPR; JOÃO CARLOS KER, UFV; LEANDRO SOUZA DA SILVA, UFSM; PEDRO LUIZ DE FREITAS, CNPS; WANDERLEI BIELUCZYK, USP; TALES TIECHER, UFRGS. |
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Título: |
Bringing together Brazilian soil scientists to share soil data. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: proceedings... Viçosa, MG: SBCS, 2018. v. 1, p. 63-64. WCSS 2018. |
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Idioma: |
Inglês |
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Conteúdo: |
In the Amazon region, types of soil known as Archaeological Black Lands (ABL) present anthropic horizon A and are associated with prolonged human occupation by indigenous societies from the pre-Columbian period, where chemical and physical attributes have better quality than other types of soil in the Amazon, setting a large organic carbon reservoir. However, the conversion of these natural ecosystems into cultivated environments make emerge changes in soil carbon dynamics, often leading to a decline in soil organic carbon content. Therefore, our aim was to use data mining techniques to generate predictive models for the effect of soil use on carbon stock in natural and transformed areas of Archaeological Black Lands. We carried out our experiment in Manicoré and Apuí, Amazonas State, Brazil. After field data collection and laboratory analysis, we obtained a set of data consisting of 21 attributes, 20 predictive attributes consisting of 13 soil physical attributes, 6 soil chemical attributes, 1 soil use related attribute, and 1 response variable, referring to soil carbon stock (SCS), which is the classification target. Due to the large number of attributes, we performed a selection procedure to eliminate attributes of low correlation to the response variable. For data classification, we used the binary induction technique of the decision tree through software Weka 3.8. The results obtained showed that for the depth of 0.00-0.05 and 0.05-0.10 m, the best selected subset was determined using the Wrapper method for attribute selection. In the depth of 0.00-0.05 m, we generated a model of 79% accuracy containing only six rules, including, as the most important classification attribute was soil use. For the depth of 0.05-0.10 m, we generated an eight-rule decision tree of 74% accuracy including sand as the most important attribute. In this context, we highlight the Wrapper method efficiency to select subsets of predictive attributes, capable to generate more understandable decision trees, using a smaller number of attributes in the classification process, making it faster and with a lower computational cost. In addition, data mining techniques were efficient at providing predictive models capable to assist the decision-making process on possible management practices with the potential to conserve or increase soil carbon stock in archeological black lands. MenosIn the Amazon region, types of soil known as Archaeological Black Lands (ABL) present anthropic horizon A and are associated with prolonged human occupation by indigenous societies from the pre-Columbian period, where chemical and physical attributes have better quality than other types of soil in the Amazon, setting a large organic carbon reservoir. However, the conversion of these natural ecosystems into cultivated environments make emerge changes in soil carbon dynamics, often leading to a decline in soil organic carbon content. Therefore, our aim was to use data mining techniques to generate predictive models for the effect of soil use on carbon stock in natural and transformed areas of Archaeological Black Lands. We carried out our experiment in Manicoré and Apuí, Amazonas State, Brazil. After field data collection and laboratory analysis, we obtained a set of data consisting of 21 attributes, 20 predictive attributes consisting of 13 soil physical attributes, 6 soil chemical attributes, 1 soil use related attribute, and 1 response variable, referring to soil carbon stock (SCS), which is the classification target. Due to the large number of attributes, we performed a selection procedure to eliminate attributes of low correlation to the response variable. For data classification, we used the binary induction technique of the decision tree through software Weka 3.8. The results obtained showed that for the depth of 0.00-0.05 and 0.05-0.10 m, the best selected subset was d... Mostrar Tudo |
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Palavras-Chave: |
Anthropic soils; Árvore de decisão; Data mining techniques; Decision tree; Estoque de carbono; Mineração de dados; Soil management system. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 04807nam a2200829 a 4500
001 2101195
005 2019-07-08
008 2018 bl uuuu u01u1 u #d
100 1 $aSAMUEL-ROSA, A.
245 $aBringing together Brazilian soil scientists to share soil data.$h[electronic resource]
260 $aIn: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: proceedings... Viçosa, MG: SBCS, 2018. v. 1, p. 63-64. WCSS 2018.$c2018
520 $aIn the Amazon region, types of soil known as Archaeological Black Lands (ABL) present anthropic horizon A and are associated with prolonged human occupation by indigenous societies from the pre-Columbian period, where chemical and physical attributes have better quality than other types of soil in the Amazon, setting a large organic carbon reservoir. However, the conversion of these natural ecosystems into cultivated environments make emerge changes in soil carbon dynamics, often leading to a decline in soil organic carbon content. Therefore, our aim was to use data mining techniques to generate predictive models for the effect of soil use on carbon stock in natural and transformed areas of Archaeological Black Lands. We carried out our experiment in Manicoré and Apuí, Amazonas State, Brazil. After field data collection and laboratory analysis, we obtained a set of data consisting of 21 attributes, 20 predictive attributes consisting of 13 soil physical attributes, 6 soil chemical attributes, 1 soil use related attribute, and 1 response variable, referring to soil carbon stock (SCS), which is the classification target. Due to the large number of attributes, we performed a selection procedure to eliminate attributes of low correlation to the response variable. For data classification, we used the binary induction technique of the decision tree through software Weka 3.8. The results obtained showed that for the depth of 0.00-0.05 and 0.05-0.10 m, the best selected subset was determined using the Wrapper method for attribute selection. In the depth of 0.00-0.05 m, we generated a model of 79% accuracy containing only six rules, including, as the most important classification attribute was soil use. For the depth of 0.05-0.10 m, we generated an eight-rule decision tree of 74% accuracy including sand as the most important attribute. In this context, we highlight the Wrapper method efficiency to select subsets of predictive attributes, capable to generate more understandable decision trees, using a smaller number of attributes in the classification process, making it faster and with a lower computational cost. In addition, data mining techniques were efficient at providing predictive models capable to assist the decision-making process on possible management practices with the potential to conserve or increase soil carbon stock in archeological black lands.
653 $aAnthropic soils
653 $aÁrvore de decisão
653 $aData mining techniques
653 $aDecision tree
653 $aEstoque de carbono
653 $aMineração de dados
653 $aSoil management system
700 1 $aDALMOLIN, R. S. D.
700 1 $aGUBIANI, P. I.
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aTEIXEIRA, W. G.
700 1 $aVIANA, J. H. M.
700 1 $aRIBEIRO, E.
700 1 $aTORNQUIST, C. G.
700 1 $aANJOS, L. H. C. dos
700 1 $aSOUZA, J. J. E. L. de
700 1 $aOTTONI, M. V.
700 1 $aMEDEIROS, P. S. C. de
700 1 $aGRIS, D. J.
700 1 $aROSIN, N. A.
700 1 $aBUENO, J. M. M.
700 1 $aSANTOS, H. G. dos
700 1 $aWEBER, E. J.
700 1 $aFLORES, C. A.
700 1 $aCOSTA, E. M.
700 1 $aOLIVEIRA, R. P. de
700 1 $aFILIPPINI ALBA, J. M.
700 1 $aPEDROSO NETO, J. C.
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700 1 $aCAVIGLIONE, J. H.
700 1 $aVALLADARES, G. S.
700 1 $aMIRANDA, C. S. S.
700 1 $aDEMATTÊ, J. A. M.
700 1 $aMARQUES JÚNIOR, J.
700 1 $aSIQUEIRA, D. S.
700 1 $aAQUINO, R. E. de
700 1 $aSILVERO, N. E. Q.
700 1 $aGENÚ, A. M.
700 1 $aBROETTO, T.
700 1 $aCANCIAN, L. C.
700 1 $aMIGUEL, P.
700 1 $aZALAMENA, J.
700 1 $aDOTTO, A. C.
700 1 $aALMEIDA, J. A. de
700 1 $aREICHERT.
700 1 $aCURCIO, G. R.
700 1 $aCOLLIER, L. S.
700 1 $aCARVALHO JUNIOR, W. de
700 1 $aFONTANA, A.
700 1 $aOLIVEIRA, A. P. de
700 1 $aVOGELMANN, E. S.
700 1 $aMALLMANN, F. J. K.
700 1 $aVASQUES, G. de M.
700 1 $aLEPSCH, I. F.
700 1 $aFINK, J. R.
700 1 $aKER, J. C.
700 1 $aSILVA, L. S. da
700 1 $aFREITAS, P. L. de
700 1 $aBIELUCZYK, B.
700 1 $aTIECHER, T.
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