Modeling the electrical conductivity relationship between saturated paste extract and 1:2.5 dilution in different soil textural classes

Regression models were developed to estimate the electrical conductivity of saturated paste extract (ECe) from the electrical conductivity of soil-water ratio (EC 1:2.5 ) 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 EC 1:1 , EC 1:2.5 , or EC 1:5 . In this study, 706 soil samples were collected from depths of 0 - 30 cm across three rice irrigation schemes to determine EC 1:2.5 , with 50% analyzed for ECe. ECe values were grouped based on soil textural classes. The results showed a strong linear relationship between EC 1:2.5 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.

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Publication Details

Journal
Frontiers in Soil Science
Published
2024-12-04
DOI
https://doi.org/10.3389/fsoil.2024.1421661
Citations
9
Primary Topic
Soil and Unsaturated Flow
Type
article
Field-Weighted Citation Impact
2.35

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article

Modeling the electrical conductivity relationship between saturated paste extract and 1:2.5 dilution in different soil textural classes

Kadeghe G. Fue, Ole Pedersen, Joel Meliyo, Boniface H. J. Massawe et al.
9 citations
Frontiers in Soil Science
Soil and Unsaturated Flow
2.35
article

Modeling the electrical conductivity relationship between saturated paste extract and 1:2.5 dilution in different soil textural classes

Kadeghe G. Fue, Ole Pedersen, Joel Meliyo, Boniface H. J. Massawe, Mawazo Jamson Shitindi, Moh’d M. Omar
article en
9 citations

Abstract

Regression models were developed to estimate the electrical conductivity of saturated paste extract (ECe) from the electrical conductivity of soil-water ratio (EC 1:2.5 ) 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 EC 1:1 , EC 1:2.5 , or EC 1:5 . In this study, 706 soil samples were collected from depths of 0 - 30 cm across three rice irrigation schemes to determine EC 1:2.5 , with 50% analyzed for ECe. ECe values were grouped based on soil textural classes. The results showed a strong linear relationship between EC 1:2.5 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.

Frontiers in Soil ScienceVol. 4
University of Copenhagen (DK), Mikocheni Agricultural Research Institute (TZ), Sokoine University of Agriculture (TZ)
Danish International Development Agency
Life in Land
Openalex Percentile: Top 12%
Soil and Unsaturated Flow
2.35
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Modeling the electrical conductivity relationship between saturated paste extract and 1:2.5 dilution in different soil textural classes — Kadeghe G. Fue, Ole Pedersen, et al. · Frontiers in Soil Science (2024) | TGRS Research Map | TGRS