Journal Paper Digests 2026 #7
- A Mineral Protection Paradigm for Soil Organic Carbon Fractionation: Iron and Calcium as a Geochemical Bridge in Arid and Semi-Arid Grasslands
- Soil Carbon Modeling at Crossroads: Building Reliable Methods for Policy and Practice
- An innovative mapping framework for soil erodibility integrating spatial association dimensions and machine learning
- Uncertainties of enhanced rock weathering for climate-change mitigation
Uncertainties of enhanced rock weathering for climate-change mitigation
Enhanced rock weathering (ERW) on agricultural soils is under consideration as a long-term carbon dioxide removal (CDR) strategy. In this Perspective, we evaluate uncertainties related to ERW around feedstock availability, plant–soil system impacts, CDR efficiency along the land–ocean continuum and socio-economic considerations. The composition of (ultra)mafic rocks places constraints on the availability of suitable feedstock when considering their potential for CDR and toxic element contents. For ERW application at scale, dedicated mining for suitable feedstock seems unavoidable. ERW can positively and negatively affect soil structure, hydrology, and overall carbon and nutrient cycles, and so optimal ERW will require site-specific assessment of effective CDR and mitigation of potential negative impacts. Additionally, the fate of weathering products along the land–ocean continuum in rivers remains poorly constrained, which is a challenge for verifying successful CDR. The socio-economic effects and constraints of ERW regarding financing and risk responsibility are also uncertain. Ultimately, large-scale ERW deployment seems limited by substantial challenges throughout its application, from its initial set-up to final CDR. Future research prioritizing site-specific assessments, long-term monitoring along the land–ocean continuum, and system modelling to constrain uncertainties and address socio-economic factors is needed to ensure that ERW deployment is effective, equitable, and sustainable.
An innovative mapping framework for soil erodibility integrating spatial association dimensions and machine learning
Accurate spatial mapping of soil erodibility (K) is essential for assessing erosion risks and formulating conservation strategies. However, existing empirical models and spatial prediction face challenges, including underestimating spatial variability, static local environmental associations, and limited regional adaptability. This study proposed an innovative framework integrating empirical models, the second dimension of spatial association (SDA, incorporating multi-scale neighborhood features), and machine learning to select the optimal K values mapping. First, based on soil surveys and laboratory analyses in the Northeast China Black Soil (Mollisols) Region, three empirical models: the erosion-productivity impact calculator (K_EPIC), the Shirazi (K_Shirazi), and the Torri (K_Torri), were used to calculate K values. Second, SDA reconstructed environmental covariates by extracting quantile features (0−1) within radius-defined neighborhoods (100–3000 m), capturing multi-scale spatial dynamics. Third, Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) were employed for digital mapping, while per configuration generated 40 ensemble models (10 random seeds × 4-fold cross-validation) to enhance model robustness. Results demonstrated that SDA-based models improved R2 by 12–89 % compared to conventional static local association models. Considering data distribution, model accuracy, and spatial prediction, K_Shirazi demonstrated optimal regional representation (R2=0.4562, in SDA-GBDT). Moreover, climate and landscape are important driving factors for K_EPIC and K_Shirazi, while topography additionally influences K_Torri. The proposed framework offers a scientific and effective method to create the optimal pathway for soil erodibility mapping through multi-scale environmental feature extraction and integrated machine learning modeling, which could be transferred to other regions or fields.
Soil Carbon Modeling at Crossroads: Building Reliable Methods for Policy and Practice
Soil carbon mapping (SCM) is rapidly becoming a cornerstone of soil science and environmental decision-making, from precisionagriculture to national carbon inventories. Yet SCM is at a crossroads: the methods that often promise high-accuracy metrics canmask structural weaknesses that limit generalization and undermine policy relevance. Similar problems apply to larger DigitalSoil Mapping (DSM). In this article, using soil organic carbon (SOC) as an illustrative example, we highlight three systematicsources of error that consistently inflate SCM performance: depth, bulk density, and spatial autocorrelation. Soil profile depth isoften mishandled when profile increments are split between training and test sets, leading to inflated accuracy estimates. Bulkdensity (BD), essential for converting concentrations to stocks, is inconsistently applied and rarely accompanied by uncertaintyestimates. SOC stocks at sampling locations are often derived using BD, and when BD is reintroduced as a predictor in machinelearning models, it inflates reported accuracy and the model’s predictive skill. Spatial autocorrelation further exaggerates accu-racy when conventional random splits are used, while spatial blocking reveals much lower and more realistic predictive skill.Drawing on recent literature and our own analysis, we argue that SCM must adopt more rigorous practices, including profile-level validation, spatially aware blocking, standardized reporting of assumptions, and alignment with policy-relevant depth in-tervals. These steps will enhance comparability across studies and ensure that SCM outputs are credible for carbon accounting,climate mitigation, and land management purposes. The future of SCM and DSM depends on both new algorithms and method-ological rigor and transparency
A Mineral Protection Paradigm for Soil Organic Carbon Fractionation: Iron and Calcium as a Geochemical Bridge in Arid and Semi-Arid Grasslands
Mineral association is widely recognized as a fundamental mechanism of soil organic carbon (SOC) stabilization; however, its relative importance versus climatic and vegetation drivers, and the key controlling geochemical factors remain poorly quantified in arid and semi-arid grasslands (mean annual precipitation, MAP < 400 mm). Combining a regional survey across the Mongolian Plateau (n = 260) with a global data synthesis (n = 2,097), we quantified the overwhelming dominance of mineral-associated organic carbon (MAOC), which constituted 79.8 ± 0.6% of SOC and established a benchmark for Eurasian drylands. More critically, we establish a hierarchical framework for MAOC accumulation: macro-scale environmental parameters (C:P ratio, pH) set the stabilization capacity, whereas localized geochemical actors (Fe, Ca) actuate this capacity via direct physiochemical interactions. In contrast, POC (particulate organic carbon) and CPOC (coarse particulate organic carbon) fractions were predominantly regulated by the C:P ratio, mean annual precipitation minus potential evapotranspiration (MAP-PET), and aboveground plus belowground biomass (AGB + BGB), suggesting a stronger dependence on recent carbon inputs and decomposition. Effective moisture (MAP-PET) served as the principal indirect control modulating both carbon inputs and mineral weathering. We thus propose a “mineral protection paradigm” for these ecosystems, wherein Fe and Ca directly enhance SOC sequestration through adsorption and cation bridging, forming a geochemically driven core process that is indirectly amplified by climate (MAP-PET) through its influence on vegetation drivers (AGB + BGB). This study establishes a synergistic Climate-Geochemistry-Vegetation framework that provides a scientific basis for SOC management in arid grassland ecosystems.