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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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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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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca(s): |
Embrapa Suínos e Aves. |
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Data corrente: |
29/09/2021 |
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Data da última atualização: |
29/09/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
CELLA, A.; LUDKE, J. V.; COLDEBELLA, A.; LUDKE, M. do C. M. M.; SIQUEIRA, H. A.; OLIVEIRA JÚNIOR. J. M. de; PERIPOLLI, V.; BIANCHI, I. |
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Afiliação: |
IFC/Araquari; JORGE VITOR LUDKE, CNPSA; ARLEI COLDEBELLA, CNPSA; MARIA DO CARMO MOHAUPT MARQUES LUDKE, UFRPE; HELLOA ALAIDE SIQUEIRA, IFC/Araquari; IFC/Araquari; VANESSA PERIPOLLI, IFC/Araquari; IVAN BIANCHI, IFC/Araquari. |
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Título: |
Nutritional and quality changes in piglet concentrate affected by formulation and storage. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Arquivo Brasileiro de Medicina Veterinária e Zootecnia, v. 73, n. 5, p. 1194-1208, 2021. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: The aim of this study was to evaluate the variations in the stability and nutrient concentration in concentrates for piglet feeding. Five treatments were established: T1 - Control, standard concentrate formulation (SCF); T2 - PXMore5, SCF with more 5% vitamin-mineral premix (VMP); T3 - PXLess5, SCF with less 5% VMP. All these three treatments used 400kg batches in an INTECNIAL mixer; T4 - FeedMixer, SCF using a 4,000kg batch in an IMOTO mixer; T5 - PremixMixer, SCF using a 1,200kg batch in an MUYANG mixer. For each treatment, bags of 20kg were stored in three storage places for four months where room temperature and relative humidity was recorded daily. The concentration of nutrients was evaluated through centesimal and mineral analysis. The water activity of concentrate was affected by temperature and relative air humidity in different storage places. The greatest variation in concentration of crude protein, mineral residue, copper, zinc, and selenium was due to the PremixMixer treatment. Regarding the guaranteed levels, the critical value was verified only for the chrome concentration. Resumo: O objetivo deste estudo foi avaliar as variações na estabilidade e na concentração de nutrientes em concentrados para alimentação de leitões. Foram estabelecidos cinco tratamentos: T1 - controle, concentrado com formulação padrão (CFP); T2 - PXMais5, CFP com 5% a mais de vitaminas e minerais da pré-mistura (PVM); T3 - PXMenos5, CFP com 5% a menos de PVM (todos os três tratamentos utilizaram lotes de 400kg em um misturador INTECNIAL); T4 - FeedMixer, CFP usando um lote de 4.000kg em um misturador IMOTO; T5 - PremixMixer, CFP usando um lote de 1.200kg em um misturador MUYANG. Para cada tratamento, sacos de 20kg foram armazenados em três ambientes distintos por quatro meses, onde a temperatura ambiente e a umidade relativa do ar foram registradas diariamente. A concentração de nutrientes foi avaliada por meio de análises centesimal e mineral. A atividade de água do concentrado foi afetada pela temperatura e a umidade relativa do ar nos diferentes locais de armazenamento. A maior variação na concentração de proteína bruta, resíduo mineral, cobre, zinco e selênio foi devido ao tratamento ?MistPremix?. Em relação aos níveis de garantia, foi verificado valor crítico apenas para a concentração de cromo. MenosAbstract: The aim of this study was to evaluate the variations in the stability and nutrient concentration in concentrates for piglet feeding. Five treatments were established: T1 - Control, standard concentrate formulation (SCF); T2 - PXMore5, SCF with more 5% vitamin-mineral premix (VMP); T3 - PXLess5, SCF with less 5% VMP. All these three treatments used 400kg batches in an INTECNIAL mixer; T4 - FeedMixer, SCF using a 4,000kg batch in an IMOTO mixer; T5 - PremixMixer, SCF using a 1,200kg batch in an MUYANG mixer. For each treatment, bags of 20kg were stored in three storage places for four months where room temperature and relative humidity was recorded daily. The concentration of nutrients was evaluated through centesimal and mineral analysis. The water activity of concentrate was affected by temperature and relative air humidity in different storage places. The greatest variation in concentration of crude protein, mineral residue, copper, zinc, and selenium was due to the PremixMixer treatment. Regarding the guaranteed levels, the critical value was verified only for the chrome concentration. Resumo: O objetivo deste estudo foi avaliar as variações na estabilidade e na concentração de nutrientes em concentrados para alimentação de leitões. Foram estabelecidos cinco tratamentos: T1 - controle, concentrado com formulação padrão (CFP); T2 - PXMais5, CFP com 5% a mais de vitaminas e minerais da pré-mistura (PVM); T3 - PXMenos5, CFP com 5% a menos de PVM (todos os três trata... Mostrar Tudo |
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Palavras-Chave: |
Acidity index; Análises microbiológicas; Atividade de água; Evolução dos compostos lipídicos; Evolution of lipid compounds; Índice de acidez; Microbiological analysis; Tempo de prateleira. |
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Thesaurus NAL: |
Shelf life; Water activity. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/226475/1/final9781.pdf
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Marc: |
LEADER 03394naa a2200325 a 4500
001 2134872
005 2021-09-29
008 2021 bl uuuu u00u1 u #d
100 1 $aCELLA, A.
245 $aNutritional and quality changes in piglet concentrate affected by formulation and storage.$h[electronic resource]
260 $c2021
520 $aAbstract: The aim of this study was to evaluate the variations in the stability and nutrient concentration in concentrates for piglet feeding. Five treatments were established: T1 - Control, standard concentrate formulation (SCF); T2 - PXMore5, SCF with more 5% vitamin-mineral premix (VMP); T3 - PXLess5, SCF with less 5% VMP. All these three treatments used 400kg batches in an INTECNIAL mixer; T4 - FeedMixer, SCF using a 4,000kg batch in an IMOTO mixer; T5 - PremixMixer, SCF using a 1,200kg batch in an MUYANG mixer. For each treatment, bags of 20kg were stored in three storage places for four months where room temperature and relative humidity was recorded daily. The concentration of nutrients was evaluated through centesimal and mineral analysis. The water activity of concentrate was affected by temperature and relative air humidity in different storage places. The greatest variation in concentration of crude protein, mineral residue, copper, zinc, and selenium was due to the PremixMixer treatment. Regarding the guaranteed levels, the critical value was verified only for the chrome concentration. Resumo: O objetivo deste estudo foi avaliar as variações na estabilidade e na concentração de nutrientes em concentrados para alimentação de leitões. Foram estabelecidos cinco tratamentos: T1 - controle, concentrado com formulação padrão (CFP); T2 - PXMais5, CFP com 5% a mais de vitaminas e minerais da pré-mistura (PVM); T3 - PXMenos5, CFP com 5% a menos de PVM (todos os três tratamentos utilizaram lotes de 400kg em um misturador INTECNIAL); T4 - FeedMixer, CFP usando um lote de 4.000kg em um misturador IMOTO; T5 - PremixMixer, CFP usando um lote de 1.200kg em um misturador MUYANG. Para cada tratamento, sacos de 20kg foram armazenados em três ambientes distintos por quatro meses, onde a temperatura ambiente e a umidade relativa do ar foram registradas diariamente. A concentração de nutrientes foi avaliada por meio de análises centesimal e mineral. A atividade de água do concentrado foi afetada pela temperatura e a umidade relativa do ar nos diferentes locais de armazenamento. A maior variação na concentração de proteína bruta, resíduo mineral, cobre, zinco e selênio foi devido ao tratamento ?MistPremix?. Em relação aos níveis de garantia, foi verificado valor crítico apenas para a concentração de cromo.
650 $aShelf life
650 $aWater activity
653 $aAcidity index
653 $aAnálises microbiológicas
653 $aAtividade de água
653 $aEvolução dos compostos lipídicos
653 $aEvolution of lipid compounds
653 $aÍndice de acidez
653 $aMicrobiological analysis
653 $aTempo de prateleira
700 1 $aLUDKE, J. V.
700 1 $aCOLDEBELLA, A.
700 1 $aLUDKE, M. do C. M. M.
700 1 $aSIQUEIRA, H. A.
700 1 $aOLIVEIRA JÚNIOR. J. M. de
700 1 $aPERIPOLLI, V.
700 1 $aBIANCHI, I.
773 $tArquivo Brasileiro de Medicina Veterinária e Zootecnia$gv. 73, n. 5, p. 1194-1208, 2021.
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