Abstract

Salt stress severely restricts soybean seedling growth and yield formation, and the redundant indicators and low efficiency of conventional salt tolerance evaluation methods limit the large-scale screening and breeding of salt-tolerant soybean germplasms. In this study, we aimed to establish a simplified and efficient salt tolerance evaluation system for soybean seedlings under saline pond conditions. We determined 16 phenotypic traits of 100 soybean germplasm resources under soil salt stress (0.3% soil salt content, EC 5.0 dS/m), calculated the salt tolerance coefficient (STC) of each trait, and comprehensively analyzed phenotypic variation, correlation, germplasm classification, and core evaluation indices via principal component analysis (PCA), K-means clustering, random forest model, SHAP interpretation, 10-fold nested cross-validation, and correlation network analysis. Biomass-related traits exhibited abundant phenotypic variation, with coefficients of variation ranging from 42.6% to 48.4%. Five principal components explained 82.90% of the total phenotypic variation and divided the accessions into four salt tolerance categories. Total fresh weight (TFW), stem fresh weight (SFW), and leaf fresh weight (LFW) were identified as the core indices, together accounting for over 93% of the total feature importance in the random forest model, whereas the remaining 13 traits each contributed less than 1.2%. The simplified three-index model showed strong consistency with the full 16-trait model (Pearson r > 0.970, AUC = 0.970) and achieved a screening accuracy of 90.0% under 10-fold nested cross-validation. Under the experimental conditions examined, fresh biomass accumulation emerged as the dominant phenotypic characteristic associated with seedling salt tolerance. This simplified evaluation framework may facilitate rapid preliminary screening of salt-tolerant soybean germplasms at the seedling stage, pending further validation across diverse environments and genetic backgrounds.

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