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dc.contributor.authorOLIVEIRA, R. P. deeng
dc.contributor.authorRODRIGUES, H. M.eng
dc.contributor.authorVASQUES, G. de M.eng
dc.contributor.authorTAVARES, S. R. de L.eng
dc.contributor.authorHERNANI, L. C.eng
dc.contributor.authorBACA, J. F. M.eng
dc.contributor.authorCOELHO, M. R.eng
dc.date.accessioned2019-10-04T18:06:28Z-
dc.date.available2019-10-04T18:06:28Z-
dc.date.created2019-10-04
dc.date.issued2019
dc.identifier.citationIn: GLOBAL WORKSHOP ON PROXIMAL SOIL SENSING, 5., 2019, Columbia, MO. Program and proceedings. Columbia, MO: University of Missouri, 2019. p. 273-278.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1112779-
dc.descriptionSustainable management of agricultural lands requires detailed information on soil properties. Although the literature has shown the potential of PSS data integration to predict spatial variations of soil properties, most of these studies were done in temperate soils considering up to three sensors. Study cases here introduced to contribute in applying PSS to: (i) assess the spatial variation of tropical soil chemical and physical attributes; (ii) understand processes controlling spatial soil variations; and (iii) compare spatial dependence and patterns among proximally-sensed and laboratory-measured soil attributes. In three preliminary study cases PSS was applied for digital soil mapping, soil salinity mapping, and within-field crop variations. Hand held and "on-the-go" sensors, respectively, for point-based and continuous monitoring readings, include apparent electrical conductivity and magnetic susceptibility meters; gamma ray, X-ray fluorescence and near infrared spectrometers; and mechanical resistance meters among others. Variables were significantly correlated (p < 0.05), and their spatial dependence structure (i.e: variogram analysis) and the spatial distribution patterns (i.e.: kriging) were all-similar. In addition, combined PSS datasets have shown improved predictions of soil properties (i.e.: R2adj. from 0.21 to 0.94). Results have indicated the potential of PSS to assess the spatial variation of soil attributes that are more difficult to collect and analyze, supporting detailed soil mapping for precision agriculture and related activities.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectSensoriamento Proximaleng
dc.subjectAtributos do Soloeng
dc.subjectMapeamento Digital do Soloeng
dc.titleProximal soil sensing platform for effective mapping of soil attributes in Brazil.eng
dc.typeArtigo em anais e proceedingseng
dc.date.updated2019-10-04T18:06:28Z
dc.subject.thesagroSensoriamento Remotoeng
dc.subject.thesagroSolo Tropicaleng
dc.subject.thesagroMapaeng
dc.subject.nalthesaurusRemote sensingeng
dc.subject.nalthesaurusTropical soilseng
dc.subject.nalthesaurusDigital imageseng
dc.subject.nalthesaurusSoil mapeng
riaa.ainfo.id1112779eng
riaa.ainfo.lastupdate2019-10-04
dc.contributor.institutionRONALDO PEREIRA DE OLIVEIRA, CNPS; HUGO M. RODRIGUES, UFRRJ; GUSTAVO DE MATTOS VASQUES, CNPS; SILVIO ROBERTO DE LUCENA TAVARES, CNPS; LUIS CARLOS HERNANI, CNPS; JESUS FERNANDO MANSILLA BACA, CNPS; MAURICIO RIZZATO COELHO, CNPS.eng
Aparece en las colecciones:Artigo em anais de congresso (CNPS)

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