• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • 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

    Author(s)
    Gharaibeh, Mamoun A
    Albalasmeh, Ammar A
    Pratt, Christopher
    El Hanandeh, Ali
    Griffith University Author(s)
    Pratt, Chris
    El Hanandeh, Ali
    Year published
    2021
    Metadata
    Show full item record
    Abstract
    Soil 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 ...
    View more >
    Soil 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.
    View less >
    Journal Title
    CATENA
    Volume
    205
    DOI
    https://doi.org/10.1016/j.catena.2021.105466
    Subject
    Geology
    Physical geography and environmental geoscience
    Soil sciences
    Publication URI
    http://hdl.handle.net/10072/405084
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander