| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Agrobiologia. |
|
Data corrente: |
30/11/2022 |
|
Data da última atualização: |
28/08/2023 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
ARAUJO, K. E. C.; VERGARA. C.; SANTOS, R. C. dos; SANTOS, W. de M.; SOUZA, R. de F.; SILVA, C. de F.; GUIMARÃES, A. P.; JANTALIA, C. P.; URQUIAGA, S.; ARAUJO, E. da S.; ALVES, B. J. R.; BODDEY, R. M. |
|
Afiliação: |
KARLA E. C. ARAUJO, Faculdade de Ciências Agrárias de Araripina; CARLOS VERGARA, Universidade Púnguè; RICARDO C. DOS SANTOS, UFRRJ; WADSON DE M. SANTOS, UFRRJ; ROBERTA DE FREITAS SOUZA, Instituto Federal do Tocantins; CAROLINE DE FARIAS SILVA, UFRRJ; ANA PAULA GUIMARÃES, UFRRJ; CLAUDIA POZZI JANTALIA, CNPAB; SEGUNDO SACRAMENTO U CABALLERO, CNPAB; EDNALDO DA SILVA ARAUJO, CNPAB; BRUNO JOSE RODRIGUES ALVES, CNPAB; ROBERT MICHAEL BODDEY, CNPAB. |
|
Título: |
Can 15N leaf-labelling reliably quantify rhizodeposited nitrogen remaining after a nodulated legume crop? |
|
Ano de publicação: |
2022 |
|
Fonte/Imprenta: |
Nutrient Cycling Agroecosystem, v. 125, n. 2, p. 235-260, 2023. |
|
ISSN: |
1385-1314 |
|
DOI: |
https://doi.org/10.1007/s10705-022-10238-w |
|
Idioma: |
Inglês |
|
Palavras-Chave: |
15N mass balance; Belowground N; Rhizodeposits. |
|
Thesaurus Nal: |
Soybeans. |
|
Categoria do assunto: |
S Ciências Biológicas |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1149034/1/Can-15N-leaf-labelling-reliably-quantify.pdf
|
|
Marc: |
LEADER 00958naa a2200313 a 4500
001 2149034
005 2023-08-28
008 2022 bl uuuu u00u1 u #d
022 $a1385-1314
024 7 $ahttps://doi.org/10.1007/s10705-022-10238-w$2DOI
100 1 $aARAUJO, K. E. C.
245 $aCan 15N leaf-labelling reliably quantify rhizodeposited nitrogen remaining after a nodulated legume crop?$h[electronic resource]
260 $c2022
650 $aSoybeans
653 $a15N mass balance
653 $aBelowground N
653 $aRhizodeposits
700 1 $aVERGARA. C.
700 1 $aSANTOS, R. C. dos
700 1 $aSANTOS, W. de M.
700 1 $aSOUZA, R. de F.
700 1 $aSILVA, C. de F.
700 1 $aGUIMARÃES, A. P.
700 1 $aJANTALIA, C. P.
700 1 $aURQUIAGA, S.
700 1 $aARAUJO, E. da S.
700 1 $aALVES, B. J. R.
700 1 $aBODDEY, R. M.
773 $tNutrient Cycling Agroecosystem$gv. 125, n. 2, p. 235-260, 2023.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agrobiologia (CNPAB) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
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
|
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Expressão de busca inválida. Verifique!!! |
|
|
