Soil organic matter (SOM) is a complex mixture of organic compounds derived from the decomposition of plant and animal residues. SOM that has undergone microbial transformation and formed stable associations with minerals represents the stabilized fraction of soil organic carbon, which differs from the simple physical accumulation of external organic materials. Current understanding suggests that particulate organic matter (POM) includes both undecomposed and partially decomposed residues. Conventional analytical methods cannot clearly distinguish undecomposed exogenous organic residues from indigenous SOM. Consequently, increases in operationally defined POM are often misinterpreted as evidence of SOM stabilization or microbially transformed organic carbon formation. In this study, straw and biochar were magnetized through chemical coprecipitation and applied to the soil. Magnetic separation was performed at successive incubation times to isolate undegraded magnetic residues, thereby enabling more accurate tracking of SOM dynamics. Five treatments were established: blank control (CK), untreated straw (CS), untreated biochar with carbon input equivalent to straw (Bc), magnetized straw (MCS), and magnetized biochar (MBc). The recovery of magnetized straw residues declined continuously and reached 54.55 % after 360 d, whereas biochar residues remained highly persistent at 92.48 %. In the CS and Bc treatments, the organic carbon content of POM fractions and their proportion in total SOM were consistently higher than in CK, particularly during early incubation. However, after removing undegraded residues by magnetic separation, values were close to those of CK. This result indicates that the observed POM increases mainly originated from undecomposed external residues rather than microbially stabilized SOM. On day 30, the apparent increase in particulate organic carbon (POC) was 63.48 % in CS and 58.99 % in Bc. Over time, the apparent POC increase in CS declined to 15.34 % by day 360, whereas that in Bc remained high (53.71 %). These findings suggest that interpreting total POM as stabilized or microbially transformed SOM may lead to misleading conclusions about SOM stability, particularly in short-term incubations or agroecosystems receiving fresh organic amendments. This study provides a basis for a more accurate evaluation of soil organic matter transformation dynamics and content.

Accurate mapping of soil properties is vital for many applications, yet existing models for digital soil maps often underestimate their spatial variability or prediction uncertainties, which introduces risk for applications such as irrigation and drainage management. This study introduces an approach, iterative residual correction (IRC), to update existing probabilistic soil maps when new soil observations become available. We demonstrated its application for enhanced soil mapping performance using a Californian case study. To implement this, we first generate prior probabilistic soil property maps using a pruned hierarchical Random Forest (pHRF) method. These prior estimates are then refined by integrating additional soil profile data and iteratively adjusting residuals of distribution of soil properties (reducing differences between observations and prior predictions) pixel by pixel. For this purpose, we employed Random Forest regressors to gradually adjust the soil property distributions and incrementally correct prior bias. Updated soil maps were evaluated over California and at 1 km resolution to test the methodology, using additional soil observations from the World Soil Information Service, the Soil Characterization Database, the University of California Riverside, and the United States Department of Agriculture Agricultural Research Service. Posterior soil texture predictions achieved an RMSE below 10, a 7 % relative reduction in errors (mass fraction of the fine-earth fraction) over priors. RMSE and spatial representation for soil organic matter and bulk density also improved. Furthermore, the method reduced prediction uncertainties (narrower prediction intervals compared to the priors) and enforced physical constraints on soil property bounds. Looking forward, this IRC method offers a scalable pathway to improve existing probabilistic soil maps, providing a strategy for the evolution of digital soil products as new soil observations emerge.

Arctic landscapes could add 55–230 PgCO₂CO₂CH₄ emissions, by the end of this century. These estimates could be quantified more accurately by constraining the contribution of rapid thawing processes such as thermokarst landscapes to permafrost carbon loss, and by investigating the exposed organic carbon (OC) interacting with mineral surfaces or metallic cations, i.e., the nature of these interactions and what controls their relative abundance. Here, we investigate two contrasted types of hillslope thermokarst landscapes: an Active Layer Detachment (ALD) which is a one-time event, and a Retrogressive Thaw Slump (RTS) which repeats annually during summer months in the Cape Bounty Arctic Watershed Observatory (Melville Island, Canada). We analyzed mineralogy, total and soluble element concentrations, total OC and mineral–OC interactions within the headwalls of both disturbances, and within corresponding undisturbed profiles. Our results show that small fragments of biopolymers stabilized by chemical bonds account for 13 ± 5 % of total OC in the form of organo–metallic complexes and up to 6 ± 2 % associated with poorly crystalline iron oxides. If we add the mechanisms of physical protection of particulate organic matter in aggregates and larger molecules stabilized by chemical bonds, we reach 64 ± 10 % of the total OC being stabilized. Importantly, we observe a decrease in the proportion of mineral-bound OC in the deeper layers exposed by the retrogressive thaw slump: the proportion of organo–metallic complexes drops from ∼ 18 % in surface samples (2–22 cm) to ∼ 1 % in the deepest samples (50–70 cm). These results therefore suggest that the OC exposed by thermokarst disturbances at Cape Bounty is protected by interactions with minerals to a certain extent, but that deep thaw features could expose OC more readily accessible to degradation.

Soil carbon sequestration refers to the process of capturing atmospheric carbon through plant photosynthesis and storing it in soil as organic carbon. The primary mechanism for carbon sequestration is the adsorption of organic carbon molecules onto the mineral surfaces of the soil's fine fraction (clay + silt ≤ 20 µm), forming mineral-associated organic carbon (MAOC). Soil has a finite capacity to stabilise and sequester organic carbon, known as carbon saturation capacity, which depends on the proportion of reactive minerals in the soil. The difference between the current MAOC content and the carbon saturation capacity is referred to as the organic carbon saturation deficit (C_def) or sequestration potential. Fourier-transformed (FTIR) mid-infrared (mid-IR) spectroscopy can simultaneously measure soil properties relevant to carbon stabilisation: organic carbon functional groups, clay and iron-oxide mineralogy and particle size. Therefore, we hypothesise that mid-IR spectroscopy can effectively and accurately estimate C_def. Here, we aim to (i) develop spectroscopic models to estimate the MAOC and C_defC_defC_def+ silt relationship to derive C_def. We recorded mid-IR spectra of the samples and used the regression trees method CUBIST to model MAOC content and C_def. We interpreted these models by examining the regression trees and using SHAP. The models were unbiased and estimated MAOC content with R^2−1), and C_defR^2−1). Model interpretation showed that C_defC_def, providing a rapid, cost-effective method for assessing and monitoring this critical soil function.

Alpine and subalpine soils are significant reservoirs of labile carbon (C) and are highly sensitive to warming, yet the mechanistic interactions between temperature and organic inputs are poorly understood. A one-year laboratory incubation was conducted with mineral surface soils from a subalpine pasture and an adjacent coniferous forest site. Soil samples were incubated in closed jars at three different temperatures: current growing season temperature (12.5 °C), and two increased temperature treatments (16.5 and 20.5 °C). To assess decomposition differences between litter and native soil organic matter (SOM), ¹³C-labelled plant litter was added to a subset of the jars. CO₂δ¹³C partitioning, and phospholipid fatty acid (PLFA) profiles were used to quantify soil organic matter (SOM) and litter decomposition, and to assess microbial dynamics. Warming increased total CO₂+77 % over controls) and declined to near zero after one month. Cumulative litter-induced respiration (LIR) was highest at 16.5 °C (+6 %–10 % vs. 12.5 °C) in both soils, coinciding with maximum microbial biomass; 20.5 °C reduced microbial biomass by up to 25 % and accelerated ¹³C label loss. The response of pasture soils was more rapid and pronounced compared to forest soils, which exhibited slower, more sustained responses. PLFA profiles revealed warming-induced declines in Gram^+− Copernicus Electronic Production Support Office 2026-05-12T06:26:44+02:00 2026-05-12T06:26:44+02:00 https://doi.org/10.5194/soil-12-583-2026

Paleosols formed by the burial of topsoil during landscape evolution can sequester substantial amounts of soil organic carbon (SOC) over millennia due to protection from surface disturbances. We investigated the moisture sensitivity of buried SOC storage in the Brady paleosol, a loess-derived soil in Nebraska, USA, where historical aeolian deposition during the Pleistocene–Holocene transition buried soils up to 6 m deep. Topsoils from erosional (up to 1.8 m depth) and burial (up to 5.8 m depth) transects were incubated under two moisture regimes – continuous wetting (60 % water-holding capacity) and repeated drying–rewetting – to assess soil organic matter (SOM) vulnerability to changing hydrologic conditions.SOC decomposition rates modeled from CO₂>96 % of SOC was stored in a slow-cycling pool, particularly in deeply buried soils where stabilization was linked to mineral association, fine particles, and Ca-mediated flocculation. However, this pool decomposed more rapidly in shallower Brady soils (higher turnover rate relative to buried soil), reflecting increased microbial responsiveness to surface-driven processes.Drying–rewetting cycles caused greater C losses from Brady soils than continuous wetting, despite the dominance of the slow pool and depletion of labile C. These cycles also accelerated fast pool decay in modern soils and erosional transects, whereas burial dampened variability in Brady soils. Although continuous wetting increased overall decay in burial transects during the incubation period, wet–dry cycles destabilized the slow pool, which may result in greater long-term C loss. Together, these results underscore the importance of burial depth, geomorphic context, and moisture regime in shaping the long-term vulnerability of ancient SOC under climate change.

Mineral-associated organic matter, the dominant form of relatively stable carbon (C) in soil, often co-occurs with reactive iron (Fe) and aluminum (Al) phases across soils. Yet, how organo-metallic associations at the molecular scale give rise to emergent soil properties such as aggregate formation and the persistence of organic matter (OM) in soil remains unclear. The organo-metallic glue hypothesis proposes that dissolved metal released from weathering and microbially processed OM form cohesive organo-metallic phases that bind other particles into stable assemblages. We tested this concept using an artificial soil system comprising crushed rocks (fine basalt: 20&#8211;38&#8201;&#181;m, coarse basalt and granite: 38&#8211;75&#8201;&#181;m, and river sand), mixed with leaf compost and microbial inoculum, subjected to eight wet-dry cycles using artificial rainwater (pH&#160;4.7) over 55&#8201;d. Sequential density fractionation after the incubation revealed the formation of meso-density, organo-mineral assemblages (1.8&#8211;2.4&#8201;g&#8201;cm^&#8722;3: MF) in the following order: fine basalt&#8201;>&#8201;coarse basalt&#8201;>&#8201;granite&#8201;>&#8201;sand. The accretion of C and oxalate-extractable Fe, Al, and Si in MF generally followed the same pattern. Fine basalt showed the strongest increase in extractable metals, especially Fe, in MF and the highest leaching of Fe and base cations (esp. Na and Ca). Enrichment of extractable Fe, Al, and Si in MF and their slight depletion in the high-density fraction (>&#8201;2.4&#8201;g&#8201;cm^&#8722;3) suggest that weathering-derived metals first associated with OM, forming organo-metal-rich phases that subsequently bound other particles to form organo-mineral assemblages. MF formed in fine basalt treatment had the C&#8201;:&#8201;(Fe+Al) molar ratio of 0.6, consistent with organo-metal coprecipitates. Preferential incorporation of microbially-processed, N-rich OM into MF in the two basalt treatments was indicated by lower C:N&#948;¹³C and &#948;¹⁵N by 0.9&#8201;&#8240;&#8211;1.2&#8201;&#8240; and 0.6&#8201;&#8240;, respectively, relative to low-density fraction (<&#8201;1.8&#8201;g&#8201;cm^&#8722;3). SEM and STXM/NEXAFS analyses of limited MF materials confirmed the presence of shaking-resistant microaggregates and the co-localization of microbially altered C with Fe and Al. Collectively, these results provide experimental evidence supporting the organo-metallic glue hypothesis and demonstrate that basaltic rock weathering can promote organo-mineral assemblage formation. This mechanism links microbial processing, mineral weathering, and reactive metal dynamics, offering insights into early pedogenesis and soil OM formation under rock amendment conditions.

Soil health assessment depends on the appropriate selection of indicators and robust, sensitive methods for its determination. In this study, four integrative approaches were evaluated to assess the impacts of no-till systems with and without agricultural terraces on soil health in Southern Brazil. The different methods used were: (1)&#160;Principal Component Analysis&#160;(PCA); (2)&#160;expert opinion&#160;(EO); (3)&#160;Soil Fertility and Biology approach&#160;(FERTBIO), based on the Soil Bioanalysis framework; and (4)&#160;Soil Management Assessment Framework&#160;(SMAF). All approaches followed four steps: (i)&#160;selection of indicators; (ii)&#160;interpretation of indicators; (iii)&#160;integration of indicators; and (iv)&#160;calculation of soil health indices. The methods varied in the steps of indicator selection, interpretation, and the approach to indicator integration. The indicators used included physical (bulk density, total porosity, soil penetration resistance, and water retention capacity), chemical&#160;(pH, calcium, phosphorus, potassium, organic matter, CEC, and base saturation), and biological indicators (microbial biomass carbon, &#946;-glucosidase, and arylsulfatase). Crop yield was evaluated for maize (2019/2020 and 2021/2022 harvests), wheat (2021&#160;harvest), and soybean (2020/2021 harvest). Descriptive statistics, median comparisons, principal component analysis, and Spearman correlation analysis were applied to analyze the results. The results showed that only the&#160;EO and FERTBIO approaches were sensitive enough to detect differences in soil health between management systems, indicating that no-till with terraces resulted in better soil health. Biological indicators were more sensitive in differentiating treatments, showing a rapid response in the short term. Maize (2019/2020 harvest) and wheat (2021&#160;harvest) yields were higher under the no-till with terraces treatment. Over time, crop yield showed a stronger relationship with soil health. The results highlight the importance of selecting appropriate indicators for soil health assessment and reinforce the benefits of agricultural terracing for the sustainability of production systems.

A better understanding of peatland dynamics requires more data on more peat properties than provided by existing databases. These data needs may be addressed with resource-efficient measurement tools, such as models that predict peat properties from mid-infrared spectra (MIRS). High-quality spectral prediction models are already used for mineral soils, but similar developments for peatland-focused research lag behind. Here, we present transmission-MIRS prediction models for peat that are openly available, easy to use, include quality checks to assess prediction quality, and propagate prediction errors. The models target element contents (C, N, H, O, P, S, K, Ca, Si, Ti), element ratios ($\mathrm{C}/\mathrm{N}$, $\mathrm{H}/\mathrm{C}$, $\mathrm{O}/\mathrm{C}$), isotope values (&#948;¹³C, &#948;¹⁵N), physical properties (bulk density, loss on ignition (LOI), macroporosity, non-macroporosity, volume fraction of solids, hydraulic conductivity, specific heat capacity, dry thermal conductivity), thermodynamic properties (Gibbs free energy of formation ($\mathrm{\&\#916;}{\text{G}}_{\text{f}}^{\mathrm{0}}$)), and nominal oxidation state of carbon (NOSC). They are representative for a more diverse set of peat samples than currently existing peat-only models while having a competitive predictive accuracy. Relatively accurate predictions can be made, for example, for many element contents (C, N, O, S, Si, Ca, $\mathrm{\&\#916;}{\text{G}}_{\text{f}}^{\mathrm{0}}$, $\mathrm{O}/\mathrm{C}$, $\mathrm{H}/\mathrm{C}$, bulk density, and LOI). Many of these properties are not predicted by existing high-quality prediction models focusing on mineral soils. For some of the target variables, high-quality prediction models focused on mineral soils exist. These models may be more accurate, but reported predictive accuracies are not directly comparable because the training data is imbalanced in the number of organic versus mineral soil samples. We suggest that some soil properties are easier to predict for peat, whereas others are easier to predict for mineral soils, emphasizing that we need new approaches to meaningfully compare prediction errors of spectral models computed on datasets with variable amounts of organic soil samples. Our tests also indicate that replacing &#948;¹³C and &#948;¹⁵N measurements with MIRS models probably is unlikely to be feasible due to large prediction errors. Future studies should address the lack of open training and validation data for some peat properties (O, H, NOSC, $\mathrm{\&\#916;}{\text{G}}_{\text{f}}^{\mathrm{0}}$, LOI, $\mathrm{H}/\mathrm{C}$, $\mathrm{O}/\mathrm{C}$), the lack of mineral-rich peat samples, and improve and standardize model validation and comparison for models trained on data with very different proportions of peat soils. This study is a step to catch up with high quality standards set by models for mineral soils and provides models for several peat properties for which we could not find descriptions of previous models in the literature. By filling data gaps in the Peatland Mid-Infrared Database, we make a step towards providing the data required to better understand peatland dynamics.