Estimating soil loss by laminar erosion using precision agriculture computational tools
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Brasileira de Engenharia Agrícola e Ambiental (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662022001200907 |
Resumo: | ABSTRACT The study aimed to identify and evaluate the spatial variability in laminar erosion in areas using precision agriculture tools. Soil data from three properties in the western region of Paraná state, Brazil, were used: one in the municipality of Céu Azul (area A) and two in Serranópolis do Iguaçu (areas B and C). To identify discrepant data (outliers), analysis of the dispersion of quartiles was performed using a box-plot graph. Data normality was verified using the Kolmogorov-Smirnov test. A spatial analysis was performed using AgDataBox-Map software. The parameters of the universal soil loss equation were estimated and used with map algebra to produce a model to identify areas susceptible to erosion. Area A (soil loss estimate = 0-200 t ha-1 per year) presented greater susceptibility to erosion than areas B and C (soil loss estimate = 0-150 t ha-1 per year); however, all areas had a low susceptibility to erosion. |
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Estimating soil loss by laminar erosion using precision agriculture computational toolsprecision agriculturethematic mapsgeostatisticsuniversal soil loss equationkrigingABSTRACT The study aimed to identify and evaluate the spatial variability in laminar erosion in areas using precision agriculture tools. Soil data from three properties in the western region of Paraná state, Brazil, were used: one in the municipality of Céu Azul (area A) and two in Serranópolis do Iguaçu (areas B and C). To identify discrepant data (outliers), analysis of the dispersion of quartiles was performed using a box-plot graph. Data normality was verified using the Kolmogorov-Smirnov test. A spatial analysis was performed using AgDataBox-Map software. The parameters of the universal soil loss equation were estimated and used with map algebra to produce a model to identify areas susceptible to erosion. Area A (soil loss estimate = 0-200 t ha-1 per year) presented greater susceptibility to erosion than areas B and C (soil loss estimate = 0-150 t ha-1 per year); however, all areas had a low susceptibility to erosion.Departamento de Engenharia Agrícola - UFCG2022-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662022001200907Revista Brasileira de Engenharia Agrícola e Ambiental v.26 n.12 2022reponame:Revista Brasileira de Engenharia Agrícola e Ambiental (Online)instname:Universidade Federal de Campina Grande (UFCG)instacron:UFCG10.1590/1807-1929/agriambi.v26n12p907-914info:eu-repo/semantics/openAccessKrug,Evelin T. S.Gomes,Glaucio J.Souza,Eduardo G. deGebler,LucianoSobjak,RicardoBazzi,Claudio L.eng2022-08-03T00:00:00Zoai:scielo:S1415-43662022001200907Revistahttp://www.scielo.br/rbeaaPUBhttps://old.scielo.br/oai/scielo-oai.php||agriambi@agriambi.com.br1807-19291415-4366opendoar:2022-08-03T00:00Revista Brasileira de Engenharia Agrícola e Ambiental (Online) - Universidade Federal de Campina Grande (UFCG)false |
dc.title.none.fl_str_mv |
Estimating soil loss by laminar erosion using precision agriculture computational tools |
title |
Estimating soil loss by laminar erosion using precision agriculture computational tools |
spellingShingle |
Estimating soil loss by laminar erosion using precision agriculture computational tools Krug,Evelin T. S. precision agriculture thematic maps geostatistics universal soil loss equation kriging |
title_short |
Estimating soil loss by laminar erosion using precision agriculture computational tools |
title_full |
Estimating soil loss by laminar erosion using precision agriculture computational tools |
title_fullStr |
Estimating soil loss by laminar erosion using precision agriculture computational tools |
title_full_unstemmed |
Estimating soil loss by laminar erosion using precision agriculture computational tools |
title_sort |
Estimating soil loss by laminar erosion using precision agriculture computational tools |
author |
Krug,Evelin T. S. |
author_facet |
Krug,Evelin T. S. Gomes,Glaucio J. Souza,Eduardo G. de Gebler,Luciano Sobjak,Ricardo Bazzi,Claudio L. |
author_role |
author |
author2 |
Gomes,Glaucio J. Souza,Eduardo G. de Gebler,Luciano Sobjak,Ricardo Bazzi,Claudio L. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Krug,Evelin T. S. Gomes,Glaucio J. Souza,Eduardo G. de Gebler,Luciano Sobjak,Ricardo Bazzi,Claudio L. |
dc.subject.por.fl_str_mv |
precision agriculture thematic maps geostatistics universal soil loss equation kriging |
topic |
precision agriculture thematic maps geostatistics universal soil loss equation kriging |
description |
ABSTRACT The study aimed to identify and evaluate the spatial variability in laminar erosion in areas using precision agriculture tools. Soil data from three properties in the western region of Paraná state, Brazil, were used: one in the municipality of Céu Azul (area A) and two in Serranópolis do Iguaçu (areas B and C). To identify discrepant data (outliers), analysis of the dispersion of quartiles was performed using a box-plot graph. Data normality was verified using the Kolmogorov-Smirnov test. A spatial analysis was performed using AgDataBox-Map software. The parameters of the universal soil loss equation were estimated and used with map algebra to produce a model to identify areas susceptible to erosion. Area A (soil loss estimate = 0-200 t ha-1 per year) presented greater susceptibility to erosion than areas B and C (soil loss estimate = 0-150 t ha-1 per year); however, all areas had a low susceptibility to erosion. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-01 |
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=S1415-43662022001200907 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662022001200907 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1807-1929/agriambi.v26n12p907-914 |
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 |
Departamento de Engenharia Agrícola - UFCG |
publisher.none.fl_str_mv |
Departamento de Engenharia Agrícola - UFCG |
dc.source.none.fl_str_mv |
Revista Brasileira de Engenharia Agrícola e Ambiental v.26 n.12 2022 reponame:Revista Brasileira de Engenharia Agrícola e Ambiental (Online) instname:Universidade Federal de Campina Grande (UFCG) instacron:UFCG |
instname_str |
Universidade Federal de Campina Grande (UFCG) |
instacron_str |
UFCG |
institution |
UFCG |
reponame_str |
Revista Brasileira de Engenharia Agrícola e Ambiental (Online) |
collection |
Revista Brasileira de Engenharia Agrícola e Ambiental (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Engenharia Agrícola e Ambiental (Online) - Universidade Federal de Campina Grande (UFCG) |
repository.mail.fl_str_mv |
||agriambi@agriambi.com.br |
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1750297688686985216 |