Spatial prediction of soil properties in two contrasting physiographic regions in Brazil
Main Author: | |
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Publication Date: | 2016 |
Other Authors: | , , , |
Format: | Article |
Language: | eng |
Source: | Scientia Agrícola (Online) |
Download full: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162016000300274 |
Summary: | ABSTRACT This study compared the performance of ordinary kriging (OK) and regression kriging (RK) to predict soil physical-chemical properties in topsoil (0-15 cm). Mean prediction of error and root mean square of prediction error were used to assess the prediction methods. Two watersheds with contrasting soil-landscape features were studied, for which the prediction methods were performed differently. A multiple linear stepwise regression model was performed with RK using digital terrain models (DTMs) and remote sensing images in order to choose the best auxiliary covariates. Different pedogenic factors and land uses control soil property distributions in each watershed, and soil properties often display contrasting scales of variability. Environmental covariables and predictive methods can be useful in one site study, but inappropriate in another one. A better linear correlation was found at Lavrinha Creek Watershed, suggesting a relationship between contemporaneous landforms and soil properties, and RK outperformed OK. In most cases, RK did not outperform OK at the Marcela Creek Watershed due to lack of linear correlation between covariates and soil properties. Since alternatives of simple OK have been sought, other prediction methods should also be tested, considering not only the linear relationships between covariate and soil properties, but also the systematic pattern of soil property distributions over that landscape. |
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Spatial prediction of soil properties in two contrasting physiographic regions in Brazilordinary krigingmultiple linear regressionregression krigingABSTRACT This study compared the performance of ordinary kriging (OK) and regression kriging (RK) to predict soil physical-chemical properties in topsoil (0-15 cm). Mean prediction of error and root mean square of prediction error were used to assess the prediction methods. Two watersheds with contrasting soil-landscape features were studied, for which the prediction methods were performed differently. A multiple linear stepwise regression model was performed with RK using digital terrain models (DTMs) and remote sensing images in order to choose the best auxiliary covariates. Different pedogenic factors and land uses control soil property distributions in each watershed, and soil properties often display contrasting scales of variability. Environmental covariables and predictive methods can be useful in one site study, but inappropriate in another one. A better linear correlation was found at Lavrinha Creek Watershed, suggesting a relationship between contemporaneous landforms and soil properties, and RK outperformed OK. In most cases, RK did not outperform OK at the Marcela Creek Watershed due to lack of linear correlation between covariates and soil properties. Since alternatives of simple OK have been sought, other prediction methods should also be tested, considering not only the linear relationships between covariate and soil properties, but also the systematic pattern of soil property distributions over that landscape.Escola Superior de Agricultura "Luiz de Queiroz"2016-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162016000300274Scientia Agricola v.73 n.3 2016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/0103-9016-2015-0071info:eu-repo/semantics/openAccessMenezes,Michele Duarte deSilva,Sérgio Henrique GodinhoMello,Carlos Rogério deOwens,Phillip RayCuri,Niltoneng2016-05-16T00:00:00Zoai:scielo:S0103-90162016000300274Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2016-05-16T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Spatial prediction of soil properties in two contrasting physiographic regions in Brazil |
title |
Spatial prediction of soil properties in two contrasting physiographic regions in Brazil |
spellingShingle |
Spatial prediction of soil properties in two contrasting physiographic regions in Brazil Menezes,Michele Duarte de ordinary kriging multiple linear regression regression kriging |
title_short |
Spatial prediction of soil properties in two contrasting physiographic regions in Brazil |
title_full |
Spatial prediction of soil properties in two contrasting physiographic regions in Brazil |
title_fullStr |
Spatial prediction of soil properties in two contrasting physiographic regions in Brazil |
title_full_unstemmed |
Spatial prediction of soil properties in two contrasting physiographic regions in Brazil |
title_sort |
Spatial prediction of soil properties in two contrasting physiographic regions in Brazil |
author |
Menezes,Michele Duarte de |
author_facet |
Menezes,Michele Duarte de Silva,Sérgio Henrique Godinho Mello,Carlos Rogério de Owens,Phillip Ray Curi,Nilton |
author_role |
author |
author2 |
Silva,Sérgio Henrique Godinho Mello,Carlos Rogério de Owens,Phillip Ray Curi,Nilton |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Menezes,Michele Duarte de Silva,Sérgio Henrique Godinho Mello,Carlos Rogério de Owens,Phillip Ray Curi,Nilton |
dc.subject.por.fl_str_mv |
ordinary kriging multiple linear regression regression kriging |
topic |
ordinary kriging multiple linear regression regression kriging |
description |
ABSTRACT This study compared the performance of ordinary kriging (OK) and regression kriging (RK) to predict soil physical-chemical properties in topsoil (0-15 cm). Mean prediction of error and root mean square of prediction error were used to assess the prediction methods. Two watersheds with contrasting soil-landscape features were studied, for which the prediction methods were performed differently. A multiple linear stepwise regression model was performed with RK using digital terrain models (DTMs) and remote sensing images in order to choose the best auxiliary covariates. Different pedogenic factors and land uses control soil property distributions in each watershed, and soil properties often display contrasting scales of variability. Environmental covariables and predictive methods can be useful in one site study, but inappropriate in another one. A better linear correlation was found at Lavrinha Creek Watershed, suggesting a relationship between contemporaneous landforms and soil properties, and RK outperformed OK. In most cases, RK did not outperform OK at the Marcela Creek Watershed due to lack of linear correlation between covariates and soil properties. Since alternatives of simple OK have been sought, other prediction methods should also be tested, considering not only the linear relationships between covariate and soil properties, but also the systematic pattern of soil property distributions over that landscape. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-06-01 |
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=S0103-90162016000300274 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162016000300274 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-9016-2015-0071 |
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 |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.73 n.3 2016 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1748936463897067520 |