Pedofunctions applied to the least limiting water range to estimate soil water content at specific potentials

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dc.contributor.authorTavanti, Renan F.R. [UNESP]
dc.contributor.authorFreddi, Onã da S.
dc.contributor.authorTavanti, Tauan R. [UNESP]
dc.contributor.authorRigotti, Adriel
dc.contributor.authorMagalhães, Wellington de A.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de Mato Grosso - UFMT
dc.date.accessioned2019-10-06T15:56:01Z
dc.date.available2019-10-06T15:56:01Z
dc.date.issued2019-01-01
dc.description.abstractThe least limiting water range (LLWR) is a soil physical quality indicator that receives much attention. It has been criticized and put to the test regarding mathematical models that compose it since they describe the behavior of soil physical attributes in a simplified way. This study aimed to assess the efficiency of some pedofunctions proposed in the literature and artificial neural networks on the accuracy in predicting soil water retention at potentials equivalent to field capacity (θFC) and permanent wilting point (θPWP). In other words, to apply the best models to LLWR of two soil types (Oxisol and Ultisol) and verify changes in their structure. The results indicated that pedofunctions using sand, silt, clay, bulk density, and soil organic matter contents are more efficient in estimating θFC and θPWP. However, the use of multiple linear regression models to predict θFC values below 0.20 m3 m-3 may present a slight tendency to overestimate it, which is not observed in the neural networks. As in R2, equations from neural networks were more efficient in estimating θFC and θPWP. Pedofunctions used to calculate LLWR differ in the establishment of the critical soil bulk density, exposing the limitations of the model.en
dc.description.affiliationUniversidade Estadual Paulista 'Júlio de Mesquita Filho' - UNESP
dc.description.affiliationUniversidade Federal de Mato Grosso - UFMT
dc.description.affiliationUnespUniversidade Estadual Paulista 'Júlio de Mesquita Filho' - UNESP
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.format.extent444-456
dc.identifierhttp://dx.doi.org/10.1590/1809-4430-Eng.Agric.v39n4p444-456/2019
dc.identifier.citationEngenharia Agricola, v. 39, n. 4, p. 444-456, 2019.
dc.identifier.doi10.1590/1809-4430-Eng.Agric.v39n4p444-456/2019
dc.identifier.fileS0100-69162019000400444.pdf
dc.identifier.issn1808-4389
dc.identifier.issn0100-6916
dc.identifier.scieloS0100-69162019000400444
dc.identifier.scopus2-s2.0-85072024853
dc.identifier.urihttp://hdl.handle.net/11449/188061
dc.language.isoeng
dc.relation.ispartofEngenharia Agricola
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectArtificial neural networks
dc.subjectAvailable water
dc.subjectPedotransfer functions
dc.subjectSoil physical quality indicator
dc.subjectSoil physics
dc.titlePedofunctions applied to the least limiting water range to estimate soil water content at specific potentialsen
dc.typeArtigo
unesp.author.orcid0000-0002-4496-9660[1]

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