Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000200144 |
Resumo: | ABSTRACT: The estimation of soil physical and chemical properties at non-sampled areas is valuable information for land management, sustainability and water yield. This work aimed to model and map soil physical-chemical properties by means of knowledge-based digital soil mapping approach as a study case in two watersheds representative of different physiographical regions in Brazil. Two watersheds with contrasting soil-landscape features were studied regarding the spatial modeling and prediction of physical and chemical properties. Since the method uses only one value of soil property for each soil type, the way of choosing typical values as well the role of land use as a covariate in the prediction were tested. Mean prediction error (MPE) and root mean square prediction error (RMSPE) were used to assess the accuracy of the prediction methods. The knowledge-based digital soil mapping by means of fuzzy logics is an accurate option for spatial prediction of soil properties considering: 1) lesser intense sampling scheme; 2) scarce financial resources for intensive sampling in Brazil; 3) adequacy to properties with non-linearity distribution, such as saturated hydraulic conductivity. Land use seems to influence spatial distribution of soil properties thus, it was applied in the soil modeling and prediction. The way of choosing typical values for each condition varied not only according to the prediction method, but also with the nature of spatial distribution of each soil property. |
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oai:scielo:S0103-90162018000200144 |
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USP-18 |
network_name_str |
Scientia Agrícola (Online) |
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spelling |
Knowledge-based digital soil mapping for predicting soil properties in two representative watershedsANOVA testspatial variabilityfuzzy logictypical valuesABSTRACT: The estimation of soil physical and chemical properties at non-sampled areas is valuable information for land management, sustainability and water yield. This work aimed to model and map soil physical-chemical properties by means of knowledge-based digital soil mapping approach as a study case in two watersheds representative of different physiographical regions in Brazil. Two watersheds with contrasting soil-landscape features were studied regarding the spatial modeling and prediction of physical and chemical properties. Since the method uses only one value of soil property for each soil type, the way of choosing typical values as well the role of land use as a covariate in the prediction were tested. Mean prediction error (MPE) and root mean square prediction error (RMSPE) were used to assess the accuracy of the prediction methods. The knowledge-based digital soil mapping by means of fuzzy logics is an accurate option for spatial prediction of soil properties considering: 1) lesser intense sampling scheme; 2) scarce financial resources for intensive sampling in Brazil; 3) adequacy to properties with non-linearity distribution, such as saturated hydraulic conductivity. Land use seems to influence spatial distribution of soil properties thus, it was applied in the soil modeling and prediction. The way of choosing typical values for each condition varied not only according to the prediction method, but also with the nature of spatial distribution of each soil property.Escola Superior de Agricultura "Luiz de Queiroz"2018-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000200144Scientia Agricola v.75 n.2 2018reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/1678-992x-2016-0097info:eu-repo/semantics/openAccessMenezes,Michele Duarte deSilva,Sérgio Henrique GodinhoMello,Carlos Rogério deOwens,Phillip RayCuri,Niltoneng2017-12-01T00:00:00Zoai:scielo:S0103-90162018000200144Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2017-12-01T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds |
title |
Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds |
spellingShingle |
Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds Menezes,Michele Duarte de ANOVA test spatial variability fuzzy logic typical values |
title_short |
Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds |
title_full |
Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds |
title_fullStr |
Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds |
title_full_unstemmed |
Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds |
title_sort |
Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds |
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 |
ANOVA test spatial variability fuzzy logic typical values |
topic |
ANOVA test spatial variability fuzzy logic typical values |
description |
ABSTRACT: The estimation of soil physical and chemical properties at non-sampled areas is valuable information for land management, sustainability and water yield. This work aimed to model and map soil physical-chemical properties by means of knowledge-based digital soil mapping approach as a study case in two watersheds representative of different physiographical regions in Brazil. Two watersheds with contrasting soil-landscape features were studied regarding the spatial modeling and prediction of physical and chemical properties. Since the method uses only one value of soil property for each soil type, the way of choosing typical values as well the role of land use as a covariate in the prediction were tested. Mean prediction error (MPE) and root mean square prediction error (RMSPE) were used to assess the accuracy of the prediction methods. The knowledge-based digital soil mapping by means of fuzzy logics is an accurate option for spatial prediction of soil properties considering: 1) lesser intense sampling scheme; 2) scarce financial resources for intensive sampling in Brazil; 3) adequacy to properties with non-linearity distribution, such as saturated hydraulic conductivity. Land use seems to influence spatial distribution of soil properties thus, it was applied in the soil modeling and prediction. The way of choosing typical values for each condition varied not only according to the prediction method, but also with the nature of spatial distribution of each soil property. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-04-01 |
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=S0103-90162018000200144 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000200144 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-992x-2016-0097 |
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.75 n.2 2018 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_ |
1748936464688742400 |