Omar, Moh’d M.Shitindi, Mawazo J.Massawe, Boniface H. J.Pedersen, OleMeliyo, Joel L.Fue, Kadeghe G.2025-05-072025-05-072024-12Front. Soil Sci. 4:1421661.https://www.suaire.sua.ac.tz/handle/123456789/6714doi: 10.3389/fsoil.2024.1-10Regression 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 strategiesensoil healthsoil salinityECe prediction modelssalinity managementregression modelagricultural productivitArticle