Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon

Detalhes bibliográficos
Autor(a) principal: Souza,Cristiano Marcelo Pereira de
Data de Publicação: 2018
Outros Autores: Thomazini,André, Schaefer,Carlos Ernesto Gonçalves Reynaud, Veloso,Gustavo Vieira, Moreira,Guilherme Musse, Fernandes Filho,Elpídio Inácio
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Ciência do Solo (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100317
Resumo: ABSTRACT: Ultisols are the most common soil order in the Brazilian Amazon. The Legal Amazon (LA) has an area of 5 × 106 km2, with few accessible areas, which restricts studies of soils at a detailed level. The pedological properties can be estimated more efficiently using statistical procedures and machine learning techniques, tools which are capable of recognizing patterns in a large soil database. We analyzed the main chemical and physical properties of the B horizons of the Ultisols of the Brazilian Amazon, as well as the spatial variability of the most explanatory properties of these horizons. Physical and chemical data of 1,068 profiles of the RadamBrasil Project were used. A principal component analysis (PCA) was applied and the most explanatory variables were separated by morphostructural units and climate zones. The technique of machine learning was used for spatialization of the explanatory variables based on predictive covariates. In general, the horizons are thick, clay, with a predominance of negative charges, and low levels of exchangeable cations. The variables retained in the PCA were: sum of bases (SB), Al3+, degree of flocculation (Floc), ∆pH, and organic carbon content (C). Areas of greater precipitation have low SB, with higher values in the basement complex (BC) and in areas under the Andean influence. Higher levels of Al3+ and degrees of flocculation were also associated with greater precipitation. However, the soils are predominantly electronegative, showing a kaolinitic mineralogy. The C contents in general were low, with an increase in more humid zones due to the process of mineralization and illuviation (podzolization), and in the BC due to the protection of C by the aggregation of clay. The use of multivariate analysis allowed a better understanding of the Ultisols’ main properties in different morphostructural and climatic domains, and its spatialization facilitated the interpretation of properties and their relationships with environmental characteristics in the Legal Amazon.
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spelling Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian AmazonLegal Amazonprincipal component analysismorphostructural domainsclimatic zonesABSTRACT: Ultisols are the most common soil order in the Brazilian Amazon. The Legal Amazon (LA) has an area of 5 × 106 km2, with few accessible areas, which restricts studies of soils at a detailed level. The pedological properties can be estimated more efficiently using statistical procedures and machine learning techniques, tools which are capable of recognizing patterns in a large soil database. We analyzed the main chemical and physical properties of the B horizons of the Ultisols of the Brazilian Amazon, as well as the spatial variability of the most explanatory properties of these horizons. Physical and chemical data of 1,068 profiles of the RadamBrasil Project were used. A principal component analysis (PCA) was applied and the most explanatory variables were separated by morphostructural units and climate zones. The technique of machine learning was used for spatialization of the explanatory variables based on predictive covariates. In general, the horizons are thick, clay, with a predominance of negative charges, and low levels of exchangeable cations. The variables retained in the PCA were: sum of bases (SB), Al3+, degree of flocculation (Floc), ∆pH, and organic carbon content (C). Areas of greater precipitation have low SB, with higher values in the basement complex (BC) and in areas under the Andean influence. Higher levels of Al3+ and degrees of flocculation were also associated with greater precipitation. However, the soils are predominantly electronegative, showing a kaolinitic mineralogy. The C contents in general were low, with an increase in more humid zones due to the process of mineralization and illuviation (podzolization), and in the BC due to the protection of C by the aggregation of clay. The use of multivariate analysis allowed a better understanding of the Ultisols’ main properties in different morphostructural and climatic domains, and its spatialization facilitated the interpretation of properties and their relationships with environmental characteristics in the Legal Amazon.Sociedade Brasileira de Ciência do Solo2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100317Revista Brasileira de Ciência do Solo v.42 2018reponame:Revista Brasileira de Ciência do Solo (Online)instname:Sociedade Brasileira de Ciência do Solo (SBCS)instacron:SBCS10.1590/18069657rbcs20170419info:eu-repo/semantics/openAccessSouza,Cristiano Marcelo Pereira deThomazini,AndréSchaefer,Carlos Ernesto Gonçalves ReynaudVeloso,Gustavo VieiraMoreira,Guilherme MusseFernandes Filho,Elpídio Inácioeng2018-12-03T00:00:00Zoai:scielo:S0100-06832018000100317Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=0100-0683&lng=es&nrm=isohttps://old.scielo.br/oai/scielo-oai.php||sbcs@ufv.br1806-96570100-0683opendoar:2018-12-03T00:00Revista Brasileira de Ciência do Solo (Online) - Sociedade Brasileira de Ciência do Solo (SBCS)false
dc.title.none.fl_str_mv Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon
title Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon
spellingShingle Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon
Souza,Cristiano Marcelo Pereira de
Legal Amazon
principal component analysis
morphostructural domains
climatic zones
title_short Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon
title_full Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon
title_fullStr Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon
title_full_unstemmed Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon
title_sort Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon
author Souza,Cristiano Marcelo Pereira de
author_facet Souza,Cristiano Marcelo Pereira de
Thomazini,André
Schaefer,Carlos Ernesto Gonçalves Reynaud
Veloso,Gustavo Vieira
Moreira,Guilherme Musse
Fernandes Filho,Elpídio Inácio
author_role author
author2 Thomazini,André
Schaefer,Carlos Ernesto Gonçalves Reynaud
Veloso,Gustavo Vieira
Moreira,Guilherme Musse
Fernandes Filho,Elpídio Inácio
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Souza,Cristiano Marcelo Pereira de
Thomazini,André
Schaefer,Carlos Ernesto Gonçalves Reynaud
Veloso,Gustavo Vieira
Moreira,Guilherme Musse
Fernandes Filho,Elpídio Inácio
dc.subject.por.fl_str_mv Legal Amazon
principal component analysis
morphostructural domains
climatic zones
topic Legal Amazon
principal component analysis
morphostructural domains
climatic zones
description ABSTRACT: Ultisols are the most common soil order in the Brazilian Amazon. The Legal Amazon (LA) has an area of 5 × 106 km2, with few accessible areas, which restricts studies of soils at a detailed level. The pedological properties can be estimated more efficiently using statistical procedures and machine learning techniques, tools which are capable of recognizing patterns in a large soil database. We analyzed the main chemical and physical properties of the B horizons of the Ultisols of the Brazilian Amazon, as well as the spatial variability of the most explanatory properties of these horizons. Physical and chemical data of 1,068 profiles of the RadamBrasil Project were used. A principal component analysis (PCA) was applied and the most explanatory variables were separated by morphostructural units and climate zones. The technique of machine learning was used for spatialization of the explanatory variables based on predictive covariates. In general, the horizons are thick, clay, with a predominance of negative charges, and low levels of exchangeable cations. The variables retained in the PCA were: sum of bases (SB), Al3+, degree of flocculation (Floc), ∆pH, and organic carbon content (C). Areas of greater precipitation have low SB, with higher values in the basement complex (BC) and in areas under the Andean influence. Higher levels of Al3+ and degrees of flocculation were also associated with greater precipitation. However, the soils are predominantly electronegative, showing a kaolinitic mineralogy. The C contents in general were low, with an increase in more humid zones due to the process of mineralization and illuviation (podzolization), and in the BC due to the protection of C by the aggregation of clay. The use of multivariate analysis allowed a better understanding of the Ultisols’ main properties in different morphostructural and climatic domains, and its spatialization facilitated the interpretation of properties and their relationships with environmental characteristics in the Legal Amazon.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
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publisher.none.fl_str_mv Sociedade Brasileira de Ciência do Solo
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