Spatial prediction of soil penetration resistance using functional geostatistics
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | https://www.revistas.usp.br/sa/article/view/119300 |
Resumo: | Knowledge of agricultural soils is a relevant factor for the sustainable development of farming activities. Studies on agricultural soils usually begin with the analysis of data obtained from sampling a finite number of sites in a particular region of interest. The variables measured at each site can be scalar (chemical properties) or functional (infiltration water or penetration resistance). The use of functional geostatistics (FG) allows to perform spatial curve interpolation to generate prediction curves (instead of single variables) at sites that lack information. This study analyzed soil penetration resistance (PR) data measured between 0 and 35 cm depth at 75 sites within a 37 ha plot dedicated to livestock. The data from each site were converted to curves using non-parametric smoothing techniques. In this study, a B-splines basis of 18 functions was used to estimate PR curves for each of the 75 sites. The applicability of FG as a spatial prediction tool for PR curves was then evaluated using cross-validation, and the results were compared with classical spatial prediction methods (univariate geostatistics) that are generally used for studying this type of information. We concluded that FG is a reliable tool for analyzing PR because a high correlation was obtained between the observed and predicted curves (R2 = 94 %). In addition, the results from descriptive analyses calculated from field data and FG models were similar for the observed and predicted values. |
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oai:revistas.usp.br:article/119300 |
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Scientia Agrícola (Online) |
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Spatial prediction of soil penetration resistance using functional geostatistics Knowledge of agricultural soils is a relevant factor for the sustainable development of farming activities. Studies on agricultural soils usually begin with the analysis of data obtained from sampling a finite number of sites in a particular region of interest. The variables measured at each site can be scalar (chemical properties) or functional (infiltration water or penetration resistance). The use of functional geostatistics (FG) allows to perform spatial curve interpolation to generate prediction curves (instead of single variables) at sites that lack information. This study analyzed soil penetration resistance (PR) data measured between 0 and 35 cm depth at 75 sites within a 37 ha plot dedicated to livestock. The data from each site were converted to curves using non-parametric smoothing techniques. In this study, a B-splines basis of 18 functions was used to estimate PR curves for each of the 75 sites. The applicability of FG as a spatial prediction tool for PR curves was then evaluated using cross-validation, and the results were compared with classical spatial prediction methods (univariate geostatistics) that are generally used for studying this type of information. We concluded that FG is a reliable tool for analyzing PR because a high correlation was obtained between the observed and predicted curves (R2 = 94 %). In addition, the results from descriptive analyses calculated from field data and FG models were similar for the observed and predicted values. Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2016-10-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/11930010.1590/0103-9016-2015-0113Scientia Agricola; v. 73 n. 5 (2016); 455-461Scientia Agricola; Vol. 73 Núm. 5 (2016); 455-461Scientia Agricola; Vol. 73 No. 5 (2016); 455-4611678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/119300/116676Copyright (c) 2016 Scientia Agricolainfo:eu-repo/semantics/openAccessCortés-D, Diego LeonardoCamacho-Tamayo, Jesús HernánGiraldo, Ramón2016-08-18T15:46:21Zoai:revistas.usp.br:article/119300Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2016-08-18T15:46:21Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Spatial prediction of soil penetration resistance using functional geostatistics |
title |
Spatial prediction of soil penetration resistance using functional geostatistics |
spellingShingle |
Spatial prediction of soil penetration resistance using functional geostatistics Cortés-D, Diego Leonardo |
title_short |
Spatial prediction of soil penetration resistance using functional geostatistics |
title_full |
Spatial prediction of soil penetration resistance using functional geostatistics |
title_fullStr |
Spatial prediction of soil penetration resistance using functional geostatistics |
title_full_unstemmed |
Spatial prediction of soil penetration resistance using functional geostatistics |
title_sort |
Spatial prediction of soil penetration resistance using functional geostatistics |
author |
Cortés-D, Diego Leonardo |
author_facet |
Cortés-D, Diego Leonardo Camacho-Tamayo, Jesús Hernán Giraldo, Ramón |
author_role |
author |
author2 |
Camacho-Tamayo, Jesús Hernán Giraldo, Ramón |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Cortés-D, Diego Leonardo Camacho-Tamayo, Jesús Hernán Giraldo, Ramón |
description |
Knowledge of agricultural soils is a relevant factor for the sustainable development of farming activities. Studies on agricultural soils usually begin with the analysis of data obtained from sampling a finite number of sites in a particular region of interest. The variables measured at each site can be scalar (chemical properties) or functional (infiltration water or penetration resistance). The use of functional geostatistics (FG) allows to perform spatial curve interpolation to generate prediction curves (instead of single variables) at sites that lack information. This study analyzed soil penetration resistance (PR) data measured between 0 and 35 cm depth at 75 sites within a 37 ha plot dedicated to livestock. The data from each site were converted to curves using non-parametric smoothing techniques. In this study, a B-splines basis of 18 functions was used to estimate PR curves for each of the 75 sites. The applicability of FG as a spatial prediction tool for PR curves was then evaluated using cross-validation, and the results were compared with classical spatial prediction methods (univariate geostatistics) that are generally used for studying this type of information. We concluded that FG is a reliable tool for analyzing PR because a high correlation was obtained between the observed and predicted curves (R2 = 94 %). In addition, the results from descriptive analyses calculated from field data and FG models were similar for the observed and predicted values. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-10-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.revistas.usp.br/sa/article/view/119300 10.1590/0103-9016-2015-0113 |
url |
https://www.revistas.usp.br/sa/article/view/119300 |
identifier_str_mv |
10.1590/0103-9016-2015-0113 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://www.revistas.usp.br/sa/article/view/119300/116676 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 Scientia Agricola info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 Scientia Agricola |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz |
publisher.none.fl_str_mv |
Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz |
dc.source.none.fl_str_mv |
Scientia Agricola; v. 73 n. 5 (2016); 455-461 Scientia Agricola; Vol. 73 Núm. 5 (2016); 455-461 Scientia Agricola; Vol. 73 No. 5 (2016); 455-461 1678-992X 0103-9016 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_ |
1800222792884420608 |