PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | |
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=S0100-06832015000200397 |
Resumo: | Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland) forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2) and Akaike information criterion (AIC) and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region. |
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PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIAAmazon forestterra firme (upland)ManausBrazilsoil carbon stocksUnder field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland) forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2) and Akaike information criterion (AIC) and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region.Sociedade Brasileira de Ciência do Solo2015-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832015000200397Revista Brasileira de Ciência do Solo v.39 n.2 2015reponame:Revista Brasileira de Ciência do Solo (Online)instname:Sociedade Brasileira de Ciência do Solo (SBCS)instacron:SBCS10.1590/01000683rbcs20140358info:eu-repo/semantics/openAccessBarros,Henrique SeixasFearnside,Philip Martineng2015-07-15T00:00:00Zoai:scielo:S0100-06832015000200397Revistahttp://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:2015-07-15T00:00Revista Brasileira de Ciência do Solo (Online) - Sociedade Brasileira de Ciência do Solo (SBCS)false |
dc.title.none.fl_str_mv |
PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA |
title |
PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA |
spellingShingle |
PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA Barros,Henrique Seixas Amazon forest terra firme (upland) Manaus Brazil soil carbon stocks |
title_short |
PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA |
title_full |
PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA |
title_fullStr |
PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA |
title_full_unstemmed |
PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA |
title_sort |
PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA |
author |
Barros,Henrique Seixas |
author_facet |
Barros,Henrique Seixas Fearnside,Philip Martin |
author_role |
author |
author2 |
Fearnside,Philip Martin |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Barros,Henrique Seixas Fearnside,Philip Martin |
dc.subject.por.fl_str_mv |
Amazon forest terra firme (upland) Manaus Brazil soil carbon stocks |
topic |
Amazon forest terra firme (upland) Manaus Brazil soil carbon stocks |
description |
Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland) forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2) and Akaike information criterion (AIC) and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-04-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832015000200397 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832015000200397 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/01000683rbcs20140358 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência do Solo |
publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência do Solo |
dc.source.none.fl_str_mv |
Revista Brasileira de Ciência do Solo v.39 n.2 2015 reponame:Revista Brasileira de Ciência do Solo (Online) instname:Sociedade Brasileira de Ciência do Solo (SBCS) instacron:SBCS |
instname_str |
Sociedade Brasileira de Ciência do Solo (SBCS) |
instacron_str |
SBCS |
institution |
SBCS |
reponame_str |
Revista Brasileira de Ciência do Solo (Online) |
collection |
Revista Brasileira de Ciência do Solo (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Ciência do Solo (Online) - Sociedade Brasileira de Ciência do Solo (SBCS) |
repository.mail.fl_str_mv |
||sbcs@ufv.br |
_version_ |
1752126520240898048 |