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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Corte. |
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Data corrente: |
10/01/2020 |
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Data da última atualização: |
13/01/2020 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
FONSECA, D.; PEREIRA, S.; LAURA, V. A.; MIRANDA, C.; MASTELARO, A. P.; ALVES, F. V.; ALMEIDA, R. G. de. |
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Afiliação: |
Diego Fonseca, Universidade Federal do Mato Grosso do Sul, Campo Grande, MS; Silvia Pereira, Universidade Federal do Mato Grosso do Sul, Campo Grande, MS; VALDEMIR ANTONIO LAURA, CNPGC; Camila Miranda, Universidade Federal do Mato Grosso do Sul, Campo Grande, MS; Ariadne Pegoraro Mastelaro, Universidade Federal do Paraná; FABIANA VILLA ALVES, CNPGC; ROBERTO GIOLO DE ALMEIDA, CNPGC. |
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Título: |
Crescimento de árvores nativas de cerrado com potencial de uso na arborização de pastagens. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Pesquisa Florestal Brasileira, Colombo, v. 39, (nesp), e201902043, 2019. |
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ISSN: |
1983-2605 (online) |
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Idioma: |
Português |
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Notas: |
Edição especial dos resumos do IUFRO World Congress, 25., 2019, Curitiba. |
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Palavras-Chave: |
Sistema silvipastoril. |
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Thesagro: |
Arborização; Árvore Florestal; Cerrado; Dipteryx Alata; Espécie Nativa; Guazuma Ulmifolia; Pastagem; Peltophorum Dubium. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/208587/1/Crescimento-de-arvores-nativas.pdf
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Marc: |
LEADER 00963nam a2200301 a 4500
001 2118533
005 2020-01-13
008 2019 bl uuuu u00u1 u #d
022 $a1983-2605 (online)
100 1 $aFONSECA, D.
245 $aCrescimento de árvores nativas de cerrado com potencial de uso na arborização de pastagens.$h[electronic resource]
260 $aPesquisa Florestal Brasileira, Colombo, v. 39, (nesp), e201902043$c2019
500 $aEdição especial dos resumos do IUFRO World Congress, 25., 2019, Curitiba.
650 $aArborização
650 $aÁrvore Florestal
650 $aCerrado
650 $aDipteryx Alata
650 $aEspécie Nativa
650 $aGuazuma Ulmifolia
650 $aPastagem
650 $aPeltophorum Dubium
653 $aSistema silvipastoril
700 1 $aPEREIRA, S.
700 1 $aLAURA, V. A.
700 1 $aMIRANDA, C.
700 1 $aMASTELARO, A. P.
700 1 $aALVES, F. V.
700 1 $aALMEIDA, R. G. de
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Registro original: |
Embrapa Gado de Corte (CNPGC) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
01/06/2018 |
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Data da última atualização: |
06/06/2018 |
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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: |
TAVARES, R. L. M.; OLIVEIRA, S. R. de M.; BARROS, F. M. M. de; FARHATE, C. V. V.; SOUZA, Z. M. de; LA SCALA JUNIOR, N. |
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Afiliação: |
ROSE LUIZA MORAES TAVARES, Rio Verde University; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; FLÁVIO MARGARITO MARTINS DE BARROS, Feagri/Unicamp; CAMILA VIANA VIEIRA FARHATE, Feagri/Unicamp; ZIGOMAR MENEZES DE SOUZA, Feagri/Unicamp; NEWTON LA SCALA JUNIOR, FCAV/Unesp. |
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Título: |
Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Scientia Agricola, Piracicaba, v. 74, n. 4, p. 281-287, July/Aug. 2018. |
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DOI: |
http://dx.doi.org/10.1590/1678-992X-2017-0095 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT: The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical, chemical, and microbiological attributes. For data interpretation, we used the Random Forest algorithm, based on the combination of predicted decision trees (machine learning algorithms) in which every tree depends on the values of a random vector sampled independently with the same distribution to all the trees of the forest. The results indicated that clay content in the soil was the most important attribute to explain the CO2 flux in the areas studied during the evaluated period. The use of the Random Forest algorithm originated a model with a good fit (R2 = 0.80) for predicted and observed values. |
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Palavras-Chave: |
Data mining; Green sugarcane; Mineração de dados; Random Forest algorithm. |
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Thesagro: |
Argila; Cana de Açúcar; Saccharum Officinarum. |
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Thesaurus NAL: |
Clay; Soil organic carbon; Soil respiration; 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/177973/1/AP-Prediction-Tavares-etal.pdf
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Marc: |
LEADER 02375naa a2200325 a 4500
001 2092118
005 2018-06-06
008 2018 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1590/1678-992X-2017-0095$2DOI
100 1 $aTAVARES, R. L. M.
245 $aPrediction of soil CO2 flux in sugarcane management systems using the Random Forest approach.$h[electronic resource]
260 $c2018
520 $aABSTRACT: The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical, chemical, and microbiological attributes. For data interpretation, we used the Random Forest algorithm, based on the combination of predicted decision trees (machine learning algorithms) in which every tree depends on the values of a random vector sampled independently with the same distribution to all the trees of the forest. The results indicated that clay content in the soil was the most important attribute to explain the CO2 flux in the areas studied during the evaluated period. The use of the Random Forest algorithm originated a model with a good fit (R2 = 0.80) for predicted and observed values.
650 $aClay
650 $aSoil organic carbon
650 $aSoil respiration
650 $aSugarcane
650 $aArgila
650 $aCana de Açúcar
650 $aSaccharum Officinarum
653 $aData mining
653 $aGreen sugarcane
653 $aMineração de dados
653 $aRandom Forest algorithm
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aBARROS, F. M. M. de
700 1 $aFARHATE, C. V. V.
700 1 $aSOUZA, Z. M. de
700 1 $aLA SCALA JUNIOR, N.
773 $tScientia Agricola, Piracicaba$gv. 74, n. 4, p. 281-287, July/Aug. 2018.
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Embrapa Agricultura Digital (CNPTIA) |
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