Data/hora: 05/06/2024 09:17:58

Biblioteca(s): | Embrapa Agricultura Digital.
Tipo da produção científica: | Artigo em Periódico Indexado
Circulação/Nível: | A - 1
Data corrente: | 23/09/2020
Data da última atualização: | 12/11/2020
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).
Palavras-Chave: | Carbon stock; Classificação de solo; Estoque de carbono; Modelo preditivo.
Thesaurus Nal: | Soil classification.
Categoria do assunto: | --
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (20501-1) - UPC - AP - DD -  -  -  - DIS