Unveiling the role of soil microorganisms in indicating paddy soil health via metagenomics combined with machine learning

The soil microbiome performs various ecological functions, making it a potentially vital component of soil health assessment; however, the indicator taxa of soil health remain unidentified. This study explored these taxa in paddy soils of the black soil region in Northeast China. First, the soil health index (SHI) was evaluated using representative physicochemical and biological parameters, revealing that approximately one-third of the soils had a low health level. A Random Forest model was then developed based on microbial species’ relative abundance to predict the SHI, achieving an R2 value greater than 0.6. Based on the SHapley Additive exPlanations (SHAP) values of this model, 40 microbial species were identified as potential indicator taxa of soil health, with 39 of these taxa occurring in more than 50% of the samples. Specifically, paddy soils with more abundant carbon- and nitrogen-fixing bacteria exhibited higher soil organic matter and total nitrogen contents, along with higher health levels. Conversely, soils rich in denitrifying bacteria exhibited lower SHI values because of increased nitrogen loss. Furthermore, carbon-fixing, nitrogen-fixing, and denitrifying genes showed functional relationships with the corresponding soil properties and SHI. In addition, halophilic, halotolerant, and eutrophic bacteria indicated soil health by reflecting salinity and nutrient status. The potential of these indicator taxa was validated at multidecadal and regional spatial scales. These results highlight the practical value of such indicator taxa, which elucidate the ecological processes associated with soil health and respond predictably to changes in soil health, thereby serving as rapid diagnostic tools for assessing soil health.