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Registro Completo |
Biblioteca(s): |
Embrapa Hortaliças. |
Data corrente: |
23/03/2000 |
Data da última atualização: |
23/03/2000 |
Autoria: |
SILVA, E. T. da; SCHWONKA, F. |
Título: |
Viabilidade economica da producao hidroponica de alface crespa em estufa tipo arco na regiao de Colombo (PR). |
Ano de publicação: |
1999 |
Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE ENGENHARIA AGRICOLA, 28., 1999, Pelotas, RS. A engenharia agricola: tendencias e inovacoes - [anais]. Pelotas: SBEA, 1999. |
Páginas: |
nao paginado. |
Descrição Física: |
CD-ROM. |
Idioma: |
Português |
Notas: |
Trabalho 179. |
Palavras-Chave: |
Brasil; Colombo; Cost benefit; Curitiba; Hidroponia; Hidroponics; Parana; Production. |
Thesagro: |
Alface; Análise Econômica; Custo-Benefício; Estufa; Lactuca Sativa; Produção. |
Thesaurus Nal: |
Brazil; economic analysis; greenhouses; lettuce. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01054naa a2200361 a 4500 001 1766925 005 2000-03-23 008 1999 bl uuuu u00u1 u #d 100 1 $aSILVA, E. T. da 245 $aViabilidade economica da producao hidroponica de alface crespa em estufa tipo arco na regiao de Colombo (PR). 260 $c1999 300 $anao paginado.$cCD-ROM. 500 $aTrabalho 179. 650 $aBrazil 650 $aeconomic analysis 650 $agreenhouses 650 $alettuce 650 $aAlface 650 $aAnálise Econômica 650 $aCusto-Benefício 650 $aEstufa 650 $aLactuca Sativa 650 $aProdução 653 $aBrasil 653 $aColombo 653 $aCost benefit 653 $aCuritiba 653 $aHidroponia 653 $aHidroponics 653 $aParana 653 $aProduction 700 1 $aSCHWONKA, F. 773 $tIn: CONGRESSO BRASILEIRO DE ENGENHARIA AGRICOLA, 28., 1999, Pelotas, RS. A engenharia agricola: tendencias e inovacoes - [anais]. Pelotas: SBEA, 1999.
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Embrapa Hortaliças (CNPH) |
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Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
01/06/2018 |
Data da última atualização: |
06/06/2018 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
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. |
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. |
Título: |
Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
Scientia Agricola, Piracicaba, v. 74, n. 4, p. 281-287, July/Aug. 2018. |
DOI: |
http://dx.doi.org/10.1590/1678-992X-2017-0095 |
Idioma: |
Inglês |
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. |
Palavras-Chave: |
Data mining; Green sugarcane; Mineração de dados; Random Forest algorithm. |
Thesagro: |
Argila; Cana de Açúcar; Saccharum Officinarum. |
Thesaurus NAL: |
Clay; Soil organic carbon; Soil respiration; Sugarcane. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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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