Soil Systems

Soil salinity imposes a critical constraint on plant productivity, highlighting the need for sustainable biological strategies to enhance stress tolerance. This study assessed the effects of volatile organic compounds (VOCs) emitted by ten plant-growth-promoting rhizobacteria (PGPR) isolated from the rhizosphere of Euphorbia antisyphilitica on the growth of Arabidopsis thaliana seedlings exposed to 0, 50, and 100 mM NaCl. A divided Petri dish system was used to quantify biomass, root architecture, proline accumulation, sodium content, and chlorophyll concentration. Three strains—Siccibacter colletis CASEcto12, Enterobacter quasihormaechei NFbEcto18, and Bacillus wiedmannii NFbEndo12—significantly enhanced seedling development under saline and non-saline conditions (p ≤ 0.05). At 50 mM NaCl, S. colletis CASEcto12 increased primary root length from 40.25 to 64.81 mm and fresh weight from 45.05 to 133.33 mg, while E. quasihormaechei NFbEcto18 elevated lateral root number from 10 to 24, compared to the uninoculated control. Under 100 mM NaCl, E. quasihormaechei NFbEcto18 increased proline accumulation (0.564–1.378 mmol g⁻¹ FW) and reduced Na⁺ content (0.146–0.084 mmol g⁻¹ FW), indicating improved osmotic and ionic regulation. VOC profiling using SPME-GC-MS revealed aldehydes, ketones, and alcohols as predominant classes. Overall, these findings demonstrate the potential of candelilla-associated PGPR VOCs as promising biostimulants for enhancing plant performance in salt-affected soils.

Soil biodiversity is crucial for maintaining biological soil resilience, understood as a temporal property and as the ability of soils to uphold or recover their ecological functions under stress thanks to the diversity and complementarity of their biological communities. To evaluate this property, we developed the Biological Soil Resilience Index (BSR), conceived as an evolution of the QBS-ar approach by integrating additional key bioindicators—entomopathogenic nematodes, entomopathogenic fungi, and earthworms—together with microarthropod eco-morphological adaptation scores. This multi-taxon framework provides a more comprehensive assessment of soil biological conditions than single-group indices and is specifically designed to be applied repeatedly over time to detect resilience trajectories. The Biodiversity Soil Resilience (BSR) Index was applied across nine sites subject to low, medium, and high anthropogenic disturbance, spanning urban, industrial, and airport environments. Results revealed not a resilience gradient but a clear disturbance gradient: low-impact sites achieved the highest BSR values (52–59), reflecting diverse and functionally complementary assemblages; medium-impact sites maintained moderate BSR value (27–42), but displayed imbalances among faunal groups; and high-impact sites showed the lowest values, including a critically low score at C_HI (17.86), where entomopathogens were absent and earthworm populations reduced. Entomopathogenic organisms proved particularly sensitive, disappearing entirely under severe disturbance. The BSR was sensitive to environmental gradients and effective in distinguishing ecologically meaningful differences among soil communities. Because it can be repeatedly applied over time, BSR provides the basis for monitoring long-term resilience dynamics, detecting early warning signals, and support timely mitigation or restoration measures. Overall, the study highlights the pivotal role of biodiversity in sustaining soil resilience and supports the BSR Index as a simple yet integrative tool for soil health assessment and for future resilience monitoring in disturbed landscapes.

Soil maps are essential for managing Earth’s resources, but the accuracy of widely used global and pan-European digital soil maps in heterogeneous landscapes remains a critical concern. This study provides a comprehensive evaluation of two prominent datasets, ISRIC-SoilGrids and the European Soil Data Centre (ESDAC), by comparing their soil texture predictions against the detailed Greek National Soil Map, which is based on over 10,000 field samples. The results from statistical and spatial analyses reveal significant discrepancies and weak correlations, with a very low overall accuracy for soil texture class prediction (19–21%) and high Root Mean Square Error (RMSE) values ranging from 13% to 19%. The global models failed to capture local variability, showing very low explanatory power (R² < 0.2) and systematically underrepresenting soils with extreme textures. Furthermore, these prediction errors are not entirely random but are significantly clustered in hot spots linked to distinct parent materials and geomorphological features. Our findings demonstrate that while invaluable for large-scale assessments, the direct application of global soil databases for regional policy or precision agriculture in a geologically complex country like Greece is subject to considerable uncertainty, highlighting the critical need for local calibration and the integration of national datasets to improve the reliability of soil information.