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dc.contributor.authorWeindorf, David C.es_ES
dc.contributor.authorChakraborty, S.es_ES
dc.contributor.authorHerrero Isern, Juanes_ES
dc.contributor.authorHerrero Isern, Juanes_ES
dc.contributor.authorCastañeda del Álamo, Carmenes_ES
dc.contributor.authorChoudhury, A.es_ES
dc.date.issued2016-03-
dc.identifier.citationSimultaneous assessment of key properties of arid soil by combined PXRF and Vis–NIR data. European Journal of Soil Science 67 (2): 173–183 (2016)es_ES
dc.identifier.issn1351-0754-
dc.identifier.urihttp://hdl.handle.net/10261/130215-
dc.description11 Pags.- 3 Tabls.- 5 Figs.es_ES
dc.description.abstractArid soil is common worldwide and has unique properties that often limit agronomic productivity, specifically, salinity expressed as soluble salts and large amounts of calcium carbonate and gypsum. Currently, the most common methods for evaluating these properties in soil are laboratory-based techniques such as titration, gasometry and electrical conductivity. In this research, we used two proximal sensors (portable X-ray fluorescence (PXRF) and visible near-infrared diffuse reflectance spectroscopy (Vis–NIR DRS)) to scan a diverse set (n = 116) of samples from arid soil in Spain. Then, samples were processed by standard laboratory procedures and the two datasets were compared with advanced statistical techniques. The latter included penalized spline regression (PSR), support vector regression (SVR) and random forest (RF) analysis, which were applied to Vis–NIR DRS data, PXRF data and PXRF and Vis–NIR DRS data, respectively. Independent validation (30% of the data) of the calibration equations showed that PSR + RF predicted gypsum with a ratio of performance to interquartile distance (RPIQ) of 5.90 and residual prediction deviation (RPD) of 4.60, electrical conductivity (1:5 soil : water) with RPIQ of 3.14 and RPD of 2.10, Ca content with RPIQ of 2.92 and RPD of 2.07 and calcium carbonate equivalent with RPIQ of 2.13 and RPD of 1.74. The combined PXRF and Vis–NIR DRS approach was superior to those that use data from a single proximal sensor, enabling the prediction of several properties from two simple, rapid, non-destructive scans.es_ES
dc.description.sponsorshipThe authors are grateful for financial support from the BL Allen Endowment in Pedology at Texas Tech University. Field study and laboratory work in Spain were part of the research projects AGL2012-40100 and PCIN-2014-106 funded by the Spanish Ministry of Economy and Competitiveness.es_ES
dc.language.isoenges_ES
dc.publisherJohn Wiley & Sonses_ES
dc.rightsclosedAccesses_ES
dc.titleSimultaneous assessment of key properties of arid soil by combined PXRF and Vis–NIR dataes_ES
dc.typeartículoes_ES
dc.identifier.doi10.1111/ejss.12320-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.1111/ejss.12320es_ES
dc.identifier.e-issn1365-2389-
dc.contributor.funderTexas Tech Universityes_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España)es_ES
dc.relation.csices_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/100007131es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
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