Continuous planting of euhalophyte Suaeda salsa enhances microbial diversity and multifunctionality of saline soil.

Halophyte-based remediation emerges as a novel strategy for ameliorating saline soils, offering a sustainable alternative to conventional leaching methods. While bioremediation is recognized for its ability to energize soil fertility and structure, the complex interplays among plant traits, soil functions, and soil microbial diversity remain greatly unknown. Here, we conducted a 5-year field experiment involving the continuous cultivation of the annual halophyte Suaeda salsa in saline soils to explore soil microbial diversity and their relationships with plant traits and soil functions. Our findings demonstrate that a decline in soil salinity corresponded with increases in the biomass and seed yield of S. salsa, which sustained a consistent seed oil content of approximately 22% across various salinity levels. Significantly, prolonged cultivation of halophytes substantially augmented soil microbial diversity, particularly from the third year of cultivation. Moreover, we identified positive associations between soil multifunctionality, seed yield, and taxonomic richness within a pivotal microbial network module. Soils enriched with taxa from this module showed enhanced multifunctionality and greater seed yields, correlating with the presence of functional genes implicated in nitrogen fixation and nitrification. Genomic analysis suggests that these taxa have elevated gene copy numbers of crucial functional genes related to nutrient cycling. Overall, our study emphasizes that the continuous cultivation of S. salsa enhances soil microbial diversity and recovers soil multifunctionality, expanding the understanding of plant-soil-microbe feedback in bioremediation.IMPORTANCEThe restoration of saline soils utilizing euhalophytes offers a viable alternative to conventional irrigation techniques for salt abatement and soil quality enhancement. The ongoing cultivation of the annual Suaeda salsa and its associated plant traits, soil microbial diversity, and functionalities are, however, largely underexplored. Our investigation sheds light on these dynamics, revealing that cultivation of S. salsa sustains robust plant productivity while fostering soil microbial diversity and multifunctionality. Notably, the links between enhanced soil multifunctionality, increased seed yield, and network-dependent taxa were found, emphasizing the importance of key microbial taxa linked with functional genes vital to nitrogen fixation and nitrification. These findings introduce a novel understanding of the role of soil microbes in bioremediation and advance our knowledge of the ecological processes that are vital for the rehabilitation of saline environments.

Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China.

Soil salinization and sodification, the primary causes of land degradation and desertification in arid and semi-arid regions, demand effective monitoring for sustainable land management. This study explores the utility of partial least square (PLS) latent variables (LVs) derived from visible and near-infrared (Vis-NIR) spectroscopy, combined with remote sensing (RS) and auxiliary variables, to predict electrical conductivity (EC) and sodium absorption ratio (SAR) in northern Xinjiang, China. Using 90 soil samples from the Karamay district, machine learning models (Random Forest, Support Vector Regression, Cubist) were tested in four scenarios. Modeling results showed that RS and Land use alone were unreliable predictors, but the addition of topographic attributes significantly improved the prediction accuracy for both EC and SAR. The incorporation of PLS LVs derived from Vis-NIR spectroscopy led to the highest performance by the Random Forest model for EC (CCC = 0.83, R2 = 0.80, nRMSE = 0.48, RPD = 2.12) and SAR (CCC = 0.78, R2 = 0.74, nRMSE = 0.58, RPD = 2.25). The variable importance analysis identified PLS LVs, certain topographic attributes (e.g., valley depth, elevation, channel network base level, diffuse insolation), and specific RS data (i.e., polarization index of VV + VH) as the most influential predictors in the study area. This study affirms the efficiency of Vis-NIR data for digital soil mapping, offering a cost-effective solution. In conclusion, the integration of proximal soil sensing techniques and highly relevant topographic attributes with the RF model has the potential to yield a reliable spatial model for mapping soil EC and SAR. This integrated approach allows for the delineation of hazardous zones, which in turn enables the consideration of best management practices and contributes to the reduction of the risk of degradation in salt-affected and sodicity-affected soils.