Journal Paper Digests 2026 #23
- Lipids don’t lie, but methods might: A framework for reproducible soil PLFA analysis
- High-Resolution Prediction of Spatial Distribution of Soil Thickness in Areas With Heterogeneous Soil Parent Materials: Machine Learning Methods and Variable Optimisation
- Bridge health monitoring using existing telecommunication fiber-optic networks
- Trade-Offs in Soil Microbial Carbon Economic Strategies Under Agricultural Management Practices: A Global Meta-Analysis
- Enhancing Soil Health and Cotton Productivity in Sodic Soils Using Treated Alkaline Water and Soil Amendments
- Quantifying soil aggregate hierarchy through energy-based dispersion modelling in contrasting soil types and land uses
Quantifying soil aggregate hierarchy through energy-based dispersion modelling in contrasting soil types and land uses
Aggregate hierarchy, the organization by which microaggregates form progressively larger, structurally distinct macroaggregates, is central to soil stability, governing resistance to erosion and response to disturbance. However, the mechanisms and extent of hierarchical breakdown remain poorly quantified across different soil types and land management. In this study, we addressed this gap by evaluating the stepwise breakdown of soil structure into aggregates, driven by incremental sonication energy, across a range of soils differing in mineral composition and management practices. By applying a quantitative modelling framework, we derived three key parameters: the disruption constant (k₁), reflecting the rate of aggregate breakdown; the dispersion constant (k₂), which describes particle release; and the critical energy threshold (Ecrit), which denotes the transition point between aggregate disruption and full particle dispersion. These parameters were used to evaluate the degree of hierarchy in aggregate breakdown, whereby higher k₁/k₂ ratios signal a pronounced stepwise (hierarchical) disintegration, and ratios near unity indicate direct dispersion into clay-sized particles. Our results indicated that soils enriched in 2:1 phyllosilicate clay minerals, such as Luvisols, exhibited markedly higher k₁/k₂ ratios in larger aggregates, demonstrating a structured, multi-step breakdown process. In contrast, oxide-rich soils like Ferralsols and Andosols typically lacked such hierarchy, dispersing rapidly into smaller fractions, which is consistent with a stronger role of mineral-mineral binding relative to organic-mediated aggregation. In the studied Luvisols, direct seeding was associated with higher stability (higher Ecrit) and a greater degree of hierarchy than conventional tillage, emphasizing the synergistic effects of organic matter input and reduced disturbance on soil structural integrity. These findings highlight the mechanistic roles of distinct pedogenic groups and management practices in controlling aggregate hierarchy and stability. Our study shows that sonication-derived indicators can differentiate not only distinct pedogenic groups but also soil management. These indices can be further developed to provide a quantitative insight into the structural organization that underpins water retention, erosion resistance, and other soil functions critical for conservation.
Enhancing Soil Health and Cotton Productivity in Sodic Soils Using Treated Alkaline Water and Soil Amendments
Trade-Offs in Soil Microbial Carbon Economic Strategies Under Agricultural Management Practices: A Global Meta-Analysis
Agricultural management profoundly influences soil carbon (C) dynamics by regulating microbial processes that drive C cycling. However, a comprehensive and globally integrated understanding of these mechanisms remains limited. In this study, we defined soil microbial carbon economic strategies (MCES) as four linked processes, including acquisition (β glucosidase and cellobiohydrolase activities), conversion (microbial biomass carbon), allocation (microbial carbon use efficiency), and release (soil CO2 emissions). Based on 1957 observations from 140 field experiments, this study evaluated the responses of MCES to agricultural management and identified their underlying drivers at the global scale. Results showed that straw return significantly increased microbial C acquisition, conversion, and release by 27.0%, 55.1%, and 94.4%, respectively. Inorganic fertilization significantly increased microbial C allocation and release by 24.1% and 23.2%, respectively. By contrast, conservation tillage and organic fertilization significantly enhanced microbial C acquisition and conversion by 267.7% and 44.8%, respectively, without significantly increasing microbial C release. Crop diversification (rotation and intercropping) and vegetation restoration mainly increased microbial C allocation, as reflected by microbial carbon use efficiency, by 42.5% and 28.6%, respectively, indicating a shift toward more efficient microbial use of assimilated C rather than a broad stimulation of C acquisition, conversion, or release. Management effects on MCES were strongest in topsoil and neutral soils, crop type and climate further modulated these responses, with C4 crops favoring microbial C conversion and allocation, and arid regions showing stronger increases in microbial C allocation and release. Significant increases in microbial C allocation were mainly detected using stoichiometric modeling and 18O methods rather than 13C methods. Globally, predicted microbial C acquisition increased with latitude, whereas microbial C allocation and release showed localized hotspots. These findings reveal contrasting responses among MCES and highlight organic fertilization, reduced soil disturbance, and diversified cropping systems as promising practices for enhancing microbial C retention while limiting potential C losses in agricultural soils.
Bridge health monitoring using existing telecommunication fiber-optic networks
Aging bridges pose significant risks to public safety. Although existing sensing technologies offer a potential solution for structural health monitoring, the high cost and complexity of installing and maintaining sensing systems have limited their scalability and long-term deployment. Here, we present a scalable and cost-effective approach that turns pre-existing telecommunication (telecom) fiber-optic cables into a dense array of dynamic strain sensors to continuously monitor bridges. Using telecom fibers, we retrieve guided wavefields across bridge superstructures, which was impossible with existing sensing technologies due to their limited scalability and the prohibitive cost of ultra-dense sensor arrays required to capture meter-scale wavelengths. We also estimate quasi-static displacements with millimeter-level accuracy and extract modal parameters. These results demonstrate that our approach is robust to noise from imperfect fiber-bridge coupling, a limitation inherent to the non-dedicated nature of telecom fibers used as sensors. Furthermore, we demonstrated the effectiveness of this approach through field evaluations across six bridges in the United States and South Korea. Because telecom fibers are widely deployed and co-located with civil infrastructure systems, this study introduces a new sensing paradigm that leverages existing fiber-optic networks to enable large-scale, real-time structural monitoring without the cost and logistical burden of dedicated sensors.
High-Resolution Prediction of Spatial Distribution of Soil Thickness in Areas With Heterogeneous Soil Parent Materials: Machine Learning Methods and Variable Optimisation
Soil thickness is an important indicator for evaluating soil production and ecosystem support functions. However, traditional field surveys cannot characterise the spatial distribution of soil thickness with high resolution. Although machine learning models that integrate multiple environmental variables offer a promising approach to predict the high-resolution spatial distribution of soil thickness, their predictive accuracy is often constrained in areas with heterogeneous soil parent materials. In this study, the Zhangbei area with heterogeneous soil parent materials was selected as a case to optimise the model. In detail, soil parent materials were used as one of environmental variables incorporated into the machine-learning model for soil thickness prediction, and their contributions were evaluated. Based on 97 soil thicknesses of surveyed profiles in the Zhangbei area, four machine learning models were developed and compared, including support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme gradient boosting (XGB). Results showed that all models performed better than the simple linear baseline model, indicating the advantage of nonlinear modelling for soil thickness prediction. Especially, the XGB model incorporating soil parent material explained 81% of the spatial variation in soil thickness and outperformed models of SVM, ANN and RF. Based on high-resolution data of soil parent materials, the soil thickness with high-resolution spatial distribution across the study area was precisely predicted. Overall, our study provides a high-precision framework for predicting soil thickness in areas with heterogeneous soil parent materials, and its potential applications are expected for ecological restoration and land management.
Lipids don’t lie, but methods might: A framework for reproducible soil PLFA analysis
Phospholipid fatty acid (PLFA) analysis is a cornerstone of soil microbial ecology, yet reproducibility is often undermined by insufficient reporting on storage, over-reliance on unvalidated solid-phase extraction (SPE) protocols, and single-internal standard quantification. Thus, we aimed to harmonize the PLFA workflow by refining storage, lipid separation, and quantification practices using 13 diverse soils across Europe. Freeze-dried storage at −80 °C outperformed field-moist storage at −20 °C, although both preserved relative treatment differences. Storage at −80 °C is acceptable for short periods of a few weeks, but durations exceeding ∼8 weeks systematically inflated fungi-to-bacteria ratios, mainly driven by increasing relative abundance of the 16:1ω5c marker. Standard SPE workflows using chloroform, acetone, and methanol were insufficient to fully separate lipid fractions, resulting in glycolipid (GLFA) and free fatty acid (FFA) cross-elution that distorted both biomass estimates and overall lipid profiles. We developed and validated a modified SPE protocol where an additional acidic diethyl ether extraction was performed prior to acetone extraction. This protocol ensures complete purification of PLFAs from other lipid classes with all tested soils and includes a novel use of an internal GLFA/FFA standard that monitors SPE lipid separation efficiency and allows for correction in case of minor leakage. Finally, we showed that accurate quantification requires using external standards and calibrating each individual fatty acid separately. By testing, refining, and validating established best practices, we developed a PLFA workflow that is reproducible, transparent, and robust.