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Soil carbon stock in archaeological black earth under different land use systems in the Brazilian Amazon
Author(s) -
LópezNoronha Renato,
Souza Zigomar Menezes,
Soares Marcelo Dayron Rodrigues,
Campos Milton César Costa,
Farhate Camila Viana Vieira,
Oliveira Stanley Robson de Medeiros
Publication year - 2020
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.1002/agj2.20345
Subject(s) - soil water , decision tree , amazon rainforest , replicate , feature selection , stock (firearms) , pasture , land use , agroforestry , environmental science , decision tree learning , soil science , agricultural engineering , forestry , geography , mathematics , ecology , machine learning , computer science , statistics , engineering , biology , archaeology
Abstract 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 χ 2 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).