European Journal of Soil Science最新文献

The release of fixed ammonium (NH₄⁺) is likely related to the concentration of exchangeable NH₄⁺ and is regulated by various microbial processes, which are affected by the changes in soil carbon (C) and nitrogen (N) sources that are caused by straw addition. This increases the need to clarify the dose effects of straw on the release of fixed NH₄⁺ and the fate of fixed NH₄⁺-derived N. In this study, the fixed NH₄⁺ pool of an Alfisol was labelled with ¹⁵N under chloroform fumigation, and then the labelled soil (fumigated) was mixed with untreated soil (unfumigated) and incubated for 288 days with four rates of straw addition: S0 (no straw), S4 (4 t ha⁻¹), S8 (8 t ha⁻¹), and S12 (12 t ha⁻¹). The addition of a low amount of straw (4 t ha⁻¹) promoted the release of labelled fixed NH₄⁺, whereas greater amounts of straw (8 and 12 t ha⁻¹) resulted in inhibition. By the end of incubation, 63% of the fixed NH₄⁺-derived N had been transformed into nitrate-N in S0, whereas this percentage significantly decreased in straw treatments. The percentage of the fixed NH₄⁺-derived N that transformed into organic N increased from 23% to 61% with increasing straw addition. The soil total C content was the primary factor influencing the release of fixed NH₄⁺ in S0, and nitrification was responsible for this in S4. For S8 and S12, microbial immobilisation, succeeding nitrification, became the dominant driving factor for fixed NH₄⁺ release. These results indicated that there is a relay effect between soil C source, nitrification, and microbial immobilisation on fixed NH₄⁺ release with increasing straw addition. These results are helpful for improving the understanding of the relay effect of factors that drive fixed NH₄⁺ release in Alfisols with increasing straw addition and provide a basis for optimising the straw management.

Climate change is projected to intensify freeze–thaw cycles (FTCs) in the peatlands of Changbai Mountain, influencing soil biogeochemistry and carbon cycling. In order to elucidate microbial regulation of carbon emissions during FTCs, we performed controlled laboratory simulations using soils from a peatland in the Changbai Mountains, Northeast China. Our findings indicate that after 15 FTCs with small (−5°C to 5°C) and large amplitudes (−10°C to 10°C), the carbon dioxide (CO₂) emission rates from surface soils declined by 63.8% and 64.2%, respectively, compared to the constant-temperature control; in deeper soils, the respective declines were 27.5% and 50.9%. We found that oxidase activities were negatively correlated with CO₂ emissions during FTCs and served as the primary driver of these emissions. Methane (CH₄) was oxidized during FTCs, with oxidation rates inversely related to FTC amplitude and greater under small amplitude than large amplitude conditions. Soil hydrolase activities were negatively correlated with CH₄ oxidation rates, functioning as the primary regulators of methane oxidation. The carbon emissions were subsequently influenced by microbial phospholipid fatty acids, which modulated enzyme activities. This investigation comprehensively explores the interactive effects of soil enzymes, organic carbon fractions, and microbial community composition on carbon emissions. The results underscore the central role of soil enzymes in mediating these processes. Collectively, these findings provide novel insights into the microbial mechanisms governing greenhouse gas emissions from peatlands during FTCs.

Anoxic microsites in soil may lead to oxygen limitation even in well-aerated upland soils, which consequently impedes the rate of soil carbon loss. Nonetheless, the influence of various land use types on the carbon conservation potential within anoxic microsites, which is referred to here as 'anoxic protection', remains poorly understood. This investigation categorizes four land use types on upland soil into two groups based on the level of human influence, with natural shrubland and natural grassland classified as 'natural vegetation land' and farmland and planted forest designated as 'artificially managed land'. The extent of anoxic protection (EAP), which quantifies the contribution of anoxic microsites to soil organic carbon (SOC) preservation, was determined by assessing carbon dioxide (CO₂) efflux rates before and after aeration during soil incubation assays, with gas chromatography serving as the measurement technique. The EAP was 33.5% and 36% of natural shrubland and natural grassland, respectively. Planted forest exhibited a lower protection value at 15.9%, while farmland exhibited the most negligible anoxic protection at −8.9%, which is presumed to be due to agricultural practice-induced soil disruptions. In upland soils, the EAP was positively associated with anoxic bacterial activity, while methanogen DNA abundance was inversely correlated with the oxygen diffusion capacity. The findings indicate that a stable physical soil structure is essential for strong anoxic protection. Even in natural grasslands, where oxygen availability is ample, anoxic microsites enhance the activity of anoxic bacteria and reduce CO₂ emissions by over one-third compared to a fully aerobic environment, thereby offering increased protection for organic matter susceptible to decomposition under aerobic conditions.

Precipitation intensity and nitrogen (N) deposition are projected to increase under global change scenarios, and both are expected to affect greenhouse gas (GHG) fluxes. However, the interactive effects of increasing precipitation intensity and N addition on GHG fluxes are still unknown. To address this gap, a mesocosm simulation experiment was conducted to investigate the individual and combined effects of changing precipitation intensity (with a constant event magnitude of 50 mm) and long-term N addition on GHG fluxes. The results revealed that precipitation application triggered a pulse effect on GHG fluxes, with increases up to 876% compared to pre-precipitation levels. The net changes in water-filled pore spaces (Δ WFPS) affected the temporal dynamics of GHG fluxes. Increasing precipitation intensity suppressed cumulative soil respiration, methane uptake, and nitrous oxide fluxes by directly reducing water availability (WFPS) and indirectly suppressing microbial biomass and substrate availability (dissolved organic carbon (DOC) or nitrate N content (NO₃⁻-N)). Furthermore, precipitation application altered the magnitude or direction of GHG flux responses to N addition. Changes in precipitation intensity and N addition had interactive effects on the Δ cumulative soil respiration and Δ cumulative N₂O fluxes, but not on Δ cumulative CH₄ fluxes. Increasing precipitation intensities decreased the Δ DOC content in the unfertilized treatment and increased Δ DOC content in the N addition treatment, thereby interactively affecting Δ cumulative soil respiration. N addition increased the Δ NO₃⁻-N content, influencing the response of Δ cumulative N₂O fluxes to increasing precipitation intensities. Our findings highlight that precipitation intensity regulates grassland GHG with N interactions, providing mechanistic insights to refine climate feedback predictions in ecosystems.

Risk assessment of per- and polyfluoroalkyl substances (PFASs) requires accurate data on their fate in the environment. Current soil studies are generally based on short-term adsorption tests in soil spiked with PFAS, with limited attention to long-term reactions after that spiking (ageing) or to differences in solid–liquid partitioning between spiked and field-contaminated soils (field to spike). This study addressed both effects with a focus on perfluorooctanoate (PFOA), thereby using carrier-free ¹⁴C-labelled PFOA to discriminate the spiked from the field-originating PFOA. Short-term (48 h) adsorption of trace ¹⁴C-labelled PFOA in soils suspended in 0.01 M CaCl₂ indicated linear sorption; the PFOA distribution (K_D) values ranged from 0.2 to 46 L kg⁻¹ (median 2.2 L kg⁻¹) in 91 soil samples and correlated (p < 0.001) mainly with soil organic carbon (r = +0.65). Three soils were incubated up to 6 months after PFOA spiking. The desorption K_D values were only 1.7–2.8-fold higher than 48 h adsorption K_D values; these factors increased by ageing but plateaued 2–4 months after spiking. Field-contaminated soils were collected (n = 21, 0.5–1100 μg PFOA kg⁻¹). The PFOA desorption K_D was almost zero in field-contaminated soils with continuous fresh deposition and in soils with exceptionally high total PFAS concentrations (21000–53,000 μg kg⁻¹), the latter suggesting the formation of micelles facilitating desorption. In most other soils, PFOA desorption K_D values were similar to or maximally 1.6 times higher than corresponding ¹⁴C-PFOA adsorption K_D values measured in the same soils. Data suggest that PFOA adsorption is generally reversible and that small PFOA ageing effects observed in laboratory conditions at trace PFOA levels do not even occur in field conditions.

Accurate estimation and projection of soil organic carbon (SOC) density is crucial for understanding the terrestrial carbon cycle and formulating carbon neutrality strategies. The increasing availability of SOC and related environmental data, coupled with advanced prediction models, has opened new opportunities for improving the accuracy of SOC (kg C m⁻²) predictions using data-driven methods. In this study, we developed a deep learning model TabTransformer_WT, by coupling the weighted mean squared error loss function with TabTransformer, to optimise estimation of surface (0–20 cm, SOC_0–20) and profile (0–100 cm, SOC_0–100) SOC in China. Using SOC observations and multi-source environmental covariates, we evaluated model performance through time-series-based 10-fold cross-validation across four periods (1979–1984, 2000–2004, 2005–2009 and 2010–2014) and compared it with machine learning and deep learning models (RF, SVR, CNN-1D, LSTM, RNN and TabTransformer). Our results indicate that TabTransformer_WT achieved the best prediction accuracy, with R² improvements of 8%–37% for SOC_0–20 and 6%–38% for SOC_0–100, and RMSE reductions of 0.31–1.07 and 0.99–2.39 kg C m⁻², respectively. We applied the model to evaluate historical and future spatiotemporal evolution of SOC_0–20 and SOC_0–100 in China. Historical analysis (1979–2023) showed China's soil acted as a carbon sink with annual growth rates of 45 Tg C year⁻¹ for surface and 33.37 Tg C year⁻¹ for profile SOC. Future projections using CMIP6 data revealed slow SOC accumulation under SSP1-1.9 but decreasing trends under SSP2-4.5 and SSP5-8.5 scenarios, with the 0–100 cm layer experiencing the greatest loss (−30.64 Tg C year⁻¹) under SSP5-8.5. This study provides a feasible method for large-scale SOC estimation and insights into SOC evolution under climate change.

Visible near-infrared reflectance spectroscopy (Vis–NIR) has been widely used in soil organic carbon (SOC) prediction due to its rapid, cost-effective, and non-destructive characteristics. Numerous soil spectral libraries have been used for SOC prediction. However, the growing volume of Vis–NIR spectral data has amplified its complexity, high dimensionality, and nonlinearity, creating significant challenges for traditional analytical models, particularly in terms of feature extraction, prediction accuracy, and generalisation capacity. To address these limitations, we developed a novel hybrid deep learning model that synergistically combines an enhanced convolutional neural network (CNN), a long short-term memory (LSTM) network, and an efficient channel attention (ECA) mechanism, termed the CNN-LSTM-ECA model. The CNN-LSTM-ECA model was evaluated using the LUCAS spectral library. Additionally, the SOC prediction performance of the CNN-LSTM-ECA model was compared against that of the CNN and CNN-LSTM models. To further assess the predictive performance of the model, spectral data specific to France were extracted from the library for validation. The results show that the CNN-LSTM-ECA model significantly outperforms the CNN and CNN-LSTM models in SOC content prediction. Specifically, the proposed model achieved remarkable prediction accuracy with an R² of 0.92 and an RMSE of 25.07 g kg⁻¹ on the validation, representing significant improvements of 10.72% and 7.15% in RMSE compared to the CNN (RMSE = 28.08 g kg⁻¹) and CNN-LSTM (RMSE = 27.00 g kg⁻¹) models, respectively. The model's generalisation capability was further confirmed through additional testing on the French dataset, where it maintained consistent predictive performance (R² = 0.93, RMSE = 24.83 g kg⁻¹). These findings underscore the model's high prediction accuracy and robust generalisation across diverse datasets. This study illustrates that the CNN-LSTM-ECA model significantly improves both accuracy and generalisation in SOC prediction, thereby providing a promising approach for spectral data analysis.

This study investigates the environmental and geochemical controls on forming and transforming acid sulfate (AS) soils along the southern Baltic Sea coast. Field surveys and laboratory analyses were conducted on a series of coastal soil transects located in hydrologically dynamic environments, including abrasive terraces/beaches, micro-cliffs/beach ridges, and organic-rich depressions. The results revealed a high site-specific variability in AS soil properties driven by topographic position, hydrological regime, and sedimentary history. Hypersulfidic materials, indicative of sulfide accumulation under reducing conditions, were found across all geomorphological settings. Geochemical indicators such as field pH, total organic carbon to total sulfur ratio, chloride, and calcium carbonate content proved effective in assessing the soil variability, including acidification potential. Magnetic susceptibility measurements indicated a predominantly natural origin of potentially toxic elements and the absence of technogenic contamination. However, under changing redox conditions, particularly in carbonate-poor soils, the mobilisation of toxic elements such as chromium, nickel, lead, and zinc cannot be excluded, despite their generally low concentrations. Organic matter, derived from both autochthonous and allochthonous sources, played a key role in sulfidisation processes, although the influence of its humification degree on acidification risk remains unclear. Overall, the study highlights the importance of localised environmental controls in AS soil development and provides a methodological framework for identifying similar systems in other coastal plains of the Baltic Sea.