土壤学报2026，Vol.63Issue(3)：730-741,12.DOI:10.11766/trxb202503020095
河北滨海盐碱地土壤钠吸附比特征及预测研究
Characteristics and Prediction of Soil-sodium Adsorption Ratio in Hebei Coastal Saline-alkali Soil
摘要
Abstract
The Sodium Adsorption Ratio(SAR)is a critical indicator for characterizing the hazard of sodium ions and the degree of soil sodification in saline-alkali soils.However,in the coastal region of Hebei,the characteristics and key influencing factors of soil SAR remain unclear due to unique processes of salt formation and accumulation,as well as complex physicochemical interactions,which hinders its accurate prediction.[Objective]This study aims to elucidate the spatial distribution and profile variation patterns of SAR in representative coastal saline-alkali soils of Cangzhou,Hebei;to identify and quantify the key soil physicochemical factors influencing SAR dynamics.Also,the study seeks to develop and select an optimal machine learning model for accurately predicting SAR based on easily measurable parameters.[Method]Taking typical coastal saline-alkali land in Cangzhou City,Hebei Province as the research area,soil samples were collected from two layers(0-20 cm and 20-40 cm).A comprehensive set of properties was measured,including ionic composition,bulk density(BD),soil water content(SWC),soil organic matter(SOM),total porosity(STP),capillary porosity(SCP),saturated hydraulic conductivity(Ks),electrical conductivity(EC),and pH.The characteristics of SAR were analyzed,its main influencing factors were explored,and four machine learning models:Linear Regression(LR),Decision Tree(DT),Random Forest(RF),and K-Nearest Neighbors(KNN),were used to predict SAR.Model performance was evaluated using the coefficient of determination(R2)and Root Mean Square Error(RMSE).[Result]The mean SAR values in the upper(0-20 cm)and lower(20-40 cm)layers were 22.23 and 28.02,respectively,with no significant difference(P=0.126).The soil in the study area was classified as moderately saline-sodic soil.Correlation analysis revealed that SAR was significantly correlated with K+,Cl-,SO2-4,EC,pH,BD,HC O3-,SOM,SWC,STP,SCP,and Ks.Among these,the correlations with Cl-,SO2-4,and EC were the strongest,identifying them as the primary influencing factors.In the comparison of SAR prediction models,a model using both EC and pH as predictors achieved higher accuracy,and the RF model demonstrated the best predictive performance,with soil EC being the most significant feature.[Conclusion]The RF model can achieve robust prediction of SAR in the coastal saline-alkali soils of Hebei based on easily measurable indicators such as EC and pH.This study identified the key driving factors of SAR in the region and developed an effective predictive framework,providing a scientific basis and practical tools for the precise reclamation and sustainable utilization of local saline-alkali lands.关键词
土壤盐渍化/钠吸附比/盐分离子/滨海盐碱地/机器学习Key words
Soil salinization/Sodium adsorption ratio/Salt ion/Coastal saline-alkali land/Machine learning分类
农业科技引用本文复制引用
陈天明,张冲,高会,王丰,尚白军,付宇航,陈惠泽,刘金铜,付同刚..河北滨海盐碱地土壤钠吸附比特征及预测研究[J].土壤学报,2026,63(3):730-741,12.基金项目
中国科学院青年创新促进会项目(2020102)和河北省"三三三"人才工程项目(C20231028)资助 Supported by the Project of the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2020102)and the Hebei Province Three-Three-Three Talent Project(No.C20231028) （2020102）
