| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
|
Data corrente: |
23/09/2020 |
|
Data da última atualização: |
12/11/2020 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
LÓPEZ-NORONHA, R.; SOUZA, Z. M. de; SOARES, M. D. R.; CAMPOS, M. C. C.; FARHATE, C. V. V.; OLIVEIRA, S. R. de M. |
|
Afiliação: |
RENATO LÓPEZ-NORONHA, Feagri/Unicamp; ZIGOMAR MENEZES DE SOUZA, Feagri/Unicamp; MARCELO DAYRON RODRIGUES SOARES, Univ. of Amazonas; MILTON CÉSAR COSTA CAMPOS, Univ. of Amazonas; CAMILA VIANA VIEIRA FARHATE, Feagri/Unicamp; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA, Feagri/Unicamp. |
|
Título: |
Soil carbon stock in archaeological black earth under different land use systems in the Brazilian Amazon. |
|
Ano de publicação: |
2020 |
|
Fonte/Imprenta: |
Agronomy Journal, v. 112, n. 5, p. 4437-4450, Sept./Oct. 2020. |
|
DOI: |
https://doi.org/10.1002/agj2.20345 |
|
Idioma: |
Inglês |
|
Conteúdo: |
In the Amazon, there are soils associated with continued human occupation known as "archeological black earth" (ABE). Due to its physical and chemical properties, ABE is more productive than other typical soils in the same region. Therefore, its carbon (C) sequestration mechanism has been a major topic of discussion by the scientific community, aiming to replicate similar characteristics in other soils. Thus, the objective of this study was to develop a predictive model using feature selection and decision tree induction methods for predicting soil C stock in ABE under different land use scenarios. The experiment was carried out in agricultural (coffee, cacao, and beans), pasture, and forest areas. Four feature selection approaches were used to identify the most relevant variables for the proposed model: (i) correlation-based feature selection, (ii) the x test, (iii) the Wrapper method, and (iv) no feature selection. The decision tree induction technique available in the Weka software was selected for data classification. Soils under cacao and coffee cultivation tend to accumulate more C when compared with soils located at bean crops, pasture, or forest land use systems. Land use and sand content were among the most important variables for the prediction of soil C stock in ABE. Furthermore, the use of a decision tree was effective at predicting soil C stocks for these soils because it enables the creation of models with high accuracy rates of 83, 74, and 81% (using seven, seven, and four rules at depths of 0.00-0.05, 0.05-0.10, and 0.10-0.20 m, respectively). MenosIn the Amazon, there are soils associated with continued human occupation known as "archeological black earth" (ABE). Due to its physical and chemical properties, ABE is more productive than other typical soils in the same region. Therefore, its carbon (C) sequestration mechanism has been a major topic of discussion by the scientific community, aiming to replicate similar characteristics in other soils. Thus, the objective of this study was to develop a predictive model using feature selection and decision tree induction methods for predicting soil C stock in ABE under different land use scenarios. The experiment was carried out in agricultural (coffee, cacao, and beans), pasture, and forest areas. Four feature selection approaches were used to identify the most relevant variables for the proposed model: (i) correlation-based feature selection, (ii) the x test, (iii) the Wrapper method, and (iv) no feature selection. The decision tree induction technique available in the Weka software was selected for data classification. Soils under cacao and coffee cultivation tend to accumulate more C when compared with soils located at bean crops, pasture, or forest land use systems. Land use and sand content were among the most important variables for the prediction of soil C stock in ABE. Furthermore, the use of a decision tree was effective at predicting soil C stocks for these soils because it enables the creation of models with high accuracy rates of 83, 74, and 81% (using seven, se... Mostrar Tudo |
|
Palavras-Chave: |
Carbon stock; Classificação de solo; Estoque de carbono; Modelo preditivo. |
|
Thesaurus Nal: |
Soil classification. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 02403naa a2200253 a 4500
001 2125072
005 2020-11-12
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1002/agj2.20345$2DOI
100 1 $aLÓPEZ-NORONHA, R.
245 $aSoil carbon stock in archaeological black earth under different land use systems in the Brazilian Amazon.$h[electronic resource]
260 $c2020
520 $aIn the Amazon, there are soils associated with continued human occupation known as "archeological black earth" (ABE). Due to its physical and chemical properties, ABE is more productive than other typical soils in the same region. Therefore, its carbon (C) sequestration mechanism has been a major topic of discussion by the scientific community, aiming to replicate similar characteristics in other soils. Thus, the objective of this study was to develop a predictive model using feature selection and decision tree induction methods for predicting soil C stock in ABE under different land use scenarios. The experiment was carried out in agricultural (coffee, cacao, and beans), pasture, and forest areas. Four feature selection approaches were used to identify the most relevant variables for the proposed model: (i) correlation-based feature selection, (ii) the x test, (iii) the Wrapper method, and (iv) no feature selection. The decision tree induction technique available in the Weka software was selected for data classification. Soils under cacao and coffee cultivation tend to accumulate more C when compared with soils located at bean crops, pasture, or forest land use systems. Land use and sand content were among the most important variables for the prediction of soil C stock in ABE. Furthermore, the use of a decision tree was effective at predicting soil C stocks for these soils because it enables the creation of models with high accuracy rates of 83, 74, and 81% (using seven, seven, and four rules at depths of 0.00-0.05, 0.05-0.10, and 0.10-0.20 m, respectively).
650 $aSoil classification
653 $aCarbon stock
653 $aClassificação de solo
653 $aEstoque de carbono
653 $aModelo preditivo
700 1 $aSOUZA, Z. M. de
700 1 $aSOARES, M. D. R.
700 1 $aCAMPOS, M. C. C.
700 1 $aFARHATE, C. V. V.
700 1 $aOLIVEIRA, S. R. de M.
773 $tAgronomy Journal$gv. 112, n. 5, p. 4437-4450, Sept./Oct. 2020.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Registros recuperados : 2 | |
| 1. |  | PAULETTO, H.; JUNGES, A. H.; HOFF, R.; VALENTI, E. da S. Avaliação de imagens de diferentes resoluções espaciais para caracterização do índice de vegetação NDVI em vinhedo da região da Campanha, Brasil. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA, 17., ENCONTRO DE PÓS-GRADUANDOS DA EMBRAPA UVA E VINHO, 13., 2019, Bento Gonçalves. Anais... Bento Gonçalves: Embrapa Uva e Vinho, 18 e 19 de julho de 2019. (Embrapa Uva e Vinho. Documentos, 115). Resumo 07.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Uva e Vinho. |
|    |
| 2. | | PAULETTO, H.; HOFF, R.; JUNGES, A. H.; VALENTI, E. da S.; ALBERTI, R. Orbital images for spatial and temporal characterization of vineyard by vegetation index in the "campanha gaúcha" wine region, Brazil. Brazilian Journal of Agriculture, v. 97, n. 1, p. 86-116, 2022.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 2 |
| Biblioteca(s): Embrapa Uva e Vinho. |
| |
| Registros recuperados : 2 | |
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
