Assessment of salt‐affected soil in Indo‐Gangetic Plain of Uttar Pradesh and prediction using regression model

Abstract

AbstractIn the present study, multiple sites in the Indo‐Gangetic Plain (IGP) were examined, and most of the sites were found to be non‐saline, with electrical conductivity (EC) values below 4.0 dSm−1. The highest value of EC 8.5 dSm−1 was reported for samples collected from JAN‐2, which, followed by site RBL‐3, also shows a high value of EC 6.5 dSm−1. The highest CEC (cmolkg−1) was reported in samples collected from site JAN‐1 (8.4), JAN‐2 (8.2), and JAN‐3 (8.21), and the lowest at RBL‐2 (3.99). The highest exchangeable sodium percentage (ESP) value of 34.07% was observed at RBL‐3, indicating a potential salinity risk associated with elevated sodium contents. The analysis showed that salt was present at all study sites, and there were strong correlations between the variables examined. The relationship between measured ESP and predicted ESP shows a strong correlation with an r2 value of 0.91, indicating the applicability of ESP–sodium adsorption ratio (SAR). Paired samples t‐test results p‐value = 0.27 indicated no significant difference (p > 0.05) between the soil ESP values predicted by the soil ESP–SAR model and those obtained through laboratory testing. The statistical results of the study suggested that the soil ESP–SAR model was reliable for predicting ESP values. The soil ESP differences between the two methods were normally distributed, and the 95% limits of agreement were calculated as −7.79 and 5.55. The mean difference between the ESP–SAR model and the “Measured” method was calculated as 2.24, representing the average deviation between the two. The study presents the ESP–SAR model as a cost‐effective and efficient method for estimating soil ESP and assessing soil salinity. By analyzing soil properties and validating the model, it provides valuable insights for policymakers and stakeholders in combating agricultural land degradation in the IGP. The novelty lies in its comprehensive evaluation of soil salinity indices, reinforcing the applicability of the ESP–SAR model for future research and agricultural management.