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dc.contributor.authorGharaibeh, Mamoun A
dc.contributor.authorAlbalasmeh, Ammar A
dc.contributor.authorPratt, Christopher
dc.contributor.authorEl Hanandeh, Ali
dc.date.accessioned2021-06-11T04:35:16Z
dc.date.available2021-06-11T04:35:16Z
dc.date.issued2021
dc.identifier.issn0341-8162
dc.identifier.doi10.1016/j.catena.2021.105466
dc.identifier.urihttp://hdl.handle.net/10072/405084
dc.description.abstractSoil sodicity is best evaluated by the exchangeable sodium percentage (ESP); however, the determination of this index is laborious and time consuming. Alternatively, the sodium adsorption ratio (SAR) is a simpler index that is commonly used to estimate soil sodicity. The objective of this research is to estimate ESP using four approaches: (1) SAR of saturated paste (SARe), and SAR of 1:5 extracts (SAR1:5), (2) a conversion factor (CF) as a function of saturation percentage (θSP), (3) electrical conductivity of 1:5 extracts (EC1:5), and (4) Generalized Regression Neural Networks (GRNN). Approximately 120 surface soil samples were collected from the Jordan Valley region and ESP, SARe, SAR1:5, (θSP), soil texture, and soil hydraulic conductivity (HC) were determined. The GRNN model (i.e., Approach 4) gave the most accurate estimates for the ESP and was able to handle the heteroscedasticity of the data. Meanwhile the traditional dilution extracts (1) showed that soil ESP was highly related to SARe and to SAR1:5; the CF- θSP approach (2) gave better estimates for prediction of ESP. Moreover, EC1:5 (3) gave reasonably accurate estimation of ESP and could be used as a screening test for assessment of sodicity problems. For the case study site investigations, a reduction of 20% in soil HC was observed when SARe increased from 0 to 3.5 or ESP increased from 0 to 6, indicating that this reduction occurred at ECe < 3 dS m−1 for all soils. While the θSP approach reduced the effect of heteroscedasticity of the data on the predictive model ability, the GRNN models can accurately predict the ESP based on easy-to-obtain soil features. Our models represent a rapid and accurate estimator of soil sodicity, and therefore offer a potentially valuable tool in managing soil landscapes that are vulnerable to degradation.
dc.description.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom105466
dc.relation.ispartofjournalCATENA
dc.relation.ispartofvolume205
dc.subject.fieldofresearchGeology
dc.subject.fieldofresearchPhysical geography and environmental geoscience
dc.subject.fieldofresearchSoil sciences
dc.subject.fieldofresearchHydrology
dc.subject.fieldofresearchcode3705
dc.subject.fieldofresearchcode3709
dc.subject.fieldofresearchcode4106
dc.subject.fieldofresearchcode3707
dc.titleEstimation of exchangeable sodium percentage from sodium adsorption ratio of salt-affected soils using traditional and dilution extracts, saturation percentage, electrical conductivity, and generalized regression neural networks
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationGharaibeh, MA; Albalasmeh, AA; Pratt, C; El Hanandeh, A, Estimation of exchangeable sodium percentage from sodium adsorption ratio of salt-affected soils using traditional and dilution extracts, saturation percentage, electrical conductivity, and generalized regression neural networks, CATENA, 2021, 205, pp. 105466
dc.date.updated2021-06-11T04:34:23Z
gro.hasfulltextNo Full Text
gro.griffith.authorPratt, Chris
gro.griffith.authorEl Hanandeh, Ali


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