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ORIGINAL RESEARCH article

Front. Soil Sci.
Sec. Pedometrics
Volume 4 - 2024 | doi: 10.3389/fsoil.2024.1421661

Modelling the Electrical Conductivity Relationship between Saturated Paste Extract and 1:2.5 Dilution in Different Soil Textural Classes

Provisionally accepted
Moh'D Omar Moh'D Omar 1*Mawazo J Shitindi Mawazo J Shitindi 1Boniface H.J Massawe Boniface H.J Massawe 1Ole Pedersen Ole Pedersen 2Joel L Meliyo Joel L Meliyo 3Kadeghe G Fue Kadeghe G Fue 1
  • 1 Sokoine University of Agriculture, Morogoro, Tanzania
  • 2 University of Copenhagen, Copenhagen, Capital Region of Denmark, Denmark
  • 3 Tanzania Agricultural Research Institute (TARI), Dodoma, Dodoma, Tanzania

The final, formatted version of the article will be published soon.

    Regression models were developed to estimate the electrical conductivity of saturated paste extract (ECe) from the electrical conductivity of soil-water ratio (EC1:2.5) for different soil textural classes. ECe is a crucial parameter used to indicate the presence, type, and distribution of salinity in soils. However, determining ECe is demanding, time-consuming, requires considerable skill to accurately identify the correct soil saturation point, and is not routinely performed by soil testing laboratories. Many laboratories, instead, commonly measure the electrical conductivity of soil-water extracts at various dilutions, such as EC1:1, EC1:2.5, or EC1:5. In this study, 706 soil samples were collected from depths of 0 -30 cm across three rice irrigation schemes to determine EC1:2.5, with 50% analyzed for ECe. ECe values were grouped based on soil textural classes. The results showed a strong linear relationship between EC1:2.5 and ECe values, with a high coefficient of determination (R² > 0.95). The Root Mean Square Error values were low (1.4 < RMSE), and the Mean Absolute Error values were similarly low (0.85 < MAE). Therefore, the regression models developed provide a practical means of estimating ECe for various soil textural classes, thereby enhancing soil salinity assessment and management strategies.

    Keywords: Soil health, Soil salinity, ECe prediction models, Salinity management, Regression model, agricultural productivity

    Received: 22 Apr 2024; Accepted: 18 Nov 2024.

    Copyright: © 2024 Omar, Shitindi, Massawe, Pedersen, Meliyo and Fue. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Moh'D Omar, Sokoine University of Agriculture, Morogoro, Tanzania

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.