Estimation of the electrical conductivity of saturated paste from soil–water extracts of coastal saline paddy soils using random forest and multiple regression models

Purpose Soil salinity is often measured with electrical conductivity (EC) of soil–water extracts (EC w ), and thus, conversion of EC w to saturated paste EC (EC e ) is required for practical uses of EC w . However, conversion factors (CF, the ratio of EC e to EC w ) vary widely depending on soil properties such as soil texture and ionic composition. This study was conducted to develop a novel procedure to obtain soil-specific CF by performing random forest (RF) and multiple regression (MR) modeling using EC and soil properties. Methods Soil samples ( n = 320) were collected from paddy fields in 16 reclaimed coastal areas of South Korea in 2018–2022. The samples were analyzed for EC (EC 1:5 and EC e ) and other soil properties including soil particle size, organic matter content, and exchangeable sodium percentage (ESP). The most important soil properties affecting the CF ratios were explored using RF modeling, and a MR equation was developed to estimate EC e using EC 1:5 and the soil properties as parameters. Results The RF model indicated that ESP and clay content are the key soil factors affecting CF variability, contributing 56.6% and 20.0% of the variability, respectively. Therefore, a multiple regression model was developed to predict CF using ESP and clay content, CF = 7.179 − 0.084 × Clay + 1.096 × ln(ESP). The predicted EC e was linearly correlated with the measured EC e ( r 2 = 0.84) with a marginal error (3.1%). Conclusion Our study provides a novel method to estimate EC e from EC w by performing RF and MR modeling using soil-specific variables such as clay content and ESP.