European Journal of Soil Science最新文献

Soil aggregates are fundamental structural units that host spatially distinct microbial communities, which drive carbon (C) and nitrogen (N) stabilization through niche-specific biotic and abiotic processes. Yet, the mechanisms underlying microbial-mediated C/N stabilization across aggregate size fractions under different organic inputs remain poorly understood. Here, we conducted a long-term field experiment in a rice–oilseed rape rotation system with three treatments: chemical fertiliser alone (NPK), straw incorporation (NPKS), and biochar application (NPKB). Soil aggregates were fractionated into > 2 mm (large macroaggregates, LA), 0.25–2 mm (macroaggregates, MA), 0.053–0.25 mm (microaggregates, SA), and < 0.053 mm (silt + clay, XSA). Results showed that biochar preferentially enhanced C and N concentrations in finer fractions (< 0.053 mm), indicating enhanced long-term stabilization due to chemical recalcitrance and physical protection. In contrast, straw incorporation promoted complex microbial co-occurrence networks, enriched nitrogen fixation functions, and higher microbial diversity in microaggregates (0.053–0.25 mm), identifying this fraction as a microbial hotspot for dynamic C/N turnover. Moreover, the reduced abundance of chitin-degrading taxa and upregulation of nitrogen-conserving functions under NPKS implied a shift in microbial functional strategies under labile carbon input. These findings reveal contrasting pathways of aggregate-scale C/N stabilization driven by straw and biochar, underscoring the critical role of microaggregates as ecological interfaces linking microbial structure, function, and soil chemistry under organic amendments. These insights offer guidance for optimising organic input strategies to balance short-term nutrient activation and long-term carbon sequestration in sustainable agriculture.

In the contemporary discourse on sustainable development, reducing nitrogen (N) pollution is as critical as mitigating carbon dioxide (CO₂) emissions. Nitrogen is a vital macronutrient for plant growth, and its application in fertilizers has significantly enhanced crop yields. However, intensive and inefficient N fertilization has led to serious environmental consequences, including water eutrophication, biodiversity loss, and increased emissions of nitrous oxide (N₂O), a potent greenhouse gas for which agriculture is the main anthropogenic source. Stabilizing fertilizers with nitrification inhibitors (NIs) presents a promising strategy to improve N use efficiency and mitigate N losses. Despite their demonstrated benefits, NI-stabilized fertilizers still represent only a minor share of the global fertilizer market. This opinion article explores the key uncertainties that may be limiting their broader adoption from an economic and ecological perspective. We examine current knowledge gaps regarding the effects of NIs on soil health and microbial communities, the potential for resistance development among nitrifiers, and the context-dependent variability in their field performance. We also emphasize the need for full life cycle assessments to evaluate whether their environmental benefits outweigh the costs associated with production and application. Finally, we propose strategies to optimize both the use and design of NIs, such as soil-specific application approaches, decoupling NI dosage from N rates, and the discovery of more potent and selective inhibitors. By addressing these uncertainties and proposing strategies for further improvement of current and novel NIs, NI-stabilized fertilizers could become a central tool for sustainable N management in agriculture.

Soil organic carbon (SOC) is essential for maintaining soil functioning as well as for mitigating climate change. SOC stabilisation in mineral-associated organic matter (MAOM) fraction plays a key role, and MAOM distribution should be quantified in divergent environments to improve predictions of SOC dynamics. However, quantitative information on the OC storage capacity of MAOM based on MAOM distribution is still limited, especially in tropical alkaline soils, which might have a lower OC storage capacity because of weaker interactions between C and clay via cation bridges such as Ca and Mg, compared to common Al-, Fe-organo-metal complexes in tropical acidic soils. This study aimed to evaluate the OC storage capacity of MAOM based on the relationship between the mass proportion (%) and OC content in MAOM (g C kg⁻¹ soil) in 46 tropical alkaline surface soils of southern India with variable soil types (Alfisols, Inceptisols, and Vertisols) and land uses (paddy fields, croplands, and bushes). Boundary line analysis was used to estimate the upper distribution of MAOM-C for our dataset, and the slope of the regression line (0.16, R¹ = 0.16, p < 0.001) was substantially lower compared to that in a previous review of tropic soils (0.53, R¹ = 0.46, p < 0.005) mainly conducted on acidic to neutral soils (Fujisaki et al., 2018). We found that MAOM-C content was positively correlated with Ca_ex, Mg_ex, and clay, and was well predicted by Mg_ex both in a simple and multiple linear regression model. Considering the alkaline soil pH of dataset (8.0 ± 0.1), these results indicate that a lower OC storage capacity in this study might be attributed to the weaker interactions between OC and minerals via cation bridges in tropical alkaline soils. There were no significant differences in the slope of the linear regression lines between soil types and land uses, except that the slope in Alfisols was greater than that in Vertisols, and all the slopes were consistently lower than that of Fujisaki et al. (2018), similar to that of the boundary line analysis for the whole dataset. Our results provide direct evidence that tropical alkaline soils have a substantially lower organic carbon storage capacity as MAOM than that of tropical acidic to neutral soils. Further study is necessary to elucidate the factors controlling the low OC storage capacity in this area.

Bacteria and fungi are key agents in plant residue decomposition, and their roles are determined by their taxonomic and functional compositions. However, the spatiotemporal patterns of microbial diversity, particularly functional traits, remain poorly understood. To close this gap, we conducted a 16-week field-based rice straw burial experiment coupled with amplicon sequencing. Random forest (RF) analysis revealed that soil chemical properties (available potassium (AK), pH, and soil organic matter (SOM)) and climate factors (MAP and MAT) were the main predictors of bacterial and fungal taxonomic composition, explaining 46.9% and 27.3% of the variation, respectively. In contrast, the functional composition related to straw decomposition was less influenced, with these factors explaining 0% of the variation for bacteria and 31.4% for fungi. The distance–decay relationship (DDR) model further showed significant spatiotemporal differences in the taxonomic composition between straw-decomposing fungi and bacteria, with fungi exhibiting greater variability, as indicated by a steeper slope (−2E−04) than that for bacteria (−9E−05). However, the functional composition related to straw decomposition showed no significant spatiotemporal variation. Our results demonstrate that the taxonomic variability of straw-degrading bacteria and fungi is shaped by distinct environmental factors, whereas their functional composition remains stable across space and time, reflecting functional redundancy in terms of straw decomposition.

Knowledge about the structural and functional microbial diversity influencing the biochemical properties and biological processes of soils in rice-jute based cropping systems in Eastern India is limited. In the present study, the Biolog EcoPlate technique was used to evaluate microbial metabolic diversity in long-term organically managed (OM) and conventional (CM) rice-jute based cropping systems for predicting soil quality in farmers' fields in Eastern India. The six cropping systems that were undertaken for this study were rice-lentil-jute (OM), rice-mustard-jute (OM), rice-potato-jute (OM), rice-lentil-jute (CM), rice-mustard-jute (CM), and rice-potato-jute (CM). The results of the present study revealed significant improvement in total soil organic carbon (SOC), microbial biomass carbon (MBC), labile C or permanganate oxidizable C (KMnO₄-C), soil enzymatic activities, carbon lability index (LI), carbon management index (CMI), and Soil Quality Index (SQI) in organic production systems compared to conventionally managed cropping systems. The average well colour development, utilisation of carbon substrates, Shannon-Weaver index (H), and Mcintosh (U) indices showed a significant variation among the cropping systems. Principal component analysis revealed a clear distinction between the organic and conventional production systems, suggesting that organically managed rice-jute based cropping systems have a more significant impact on microbial diversity compared to conventionally managed rice-jute based cropping systems. Furthermore, the results indicated a clear ranking of drivers, with management having a greater influence than crop rotation on soil abiotic and biotic properties, as even the rice-potato-jute system under organic management outperformed the rice-lentil-jute system under conventional practice. Among the cropping systems, rice-lentil-jute (OM) had the largest SOC, LI, CMI, Shannon-Weaver index, and Mcintosh indices, and SQI, followed by rice-mustard-jute (OM) and rice-potato-jute (OM). The present findings suggest that adopting the organically managed rice-lentil-jute system is pivotal for maintaining soil health and promoting agricultural sustainability in rice-jute based cropping systems and is highly recommended for the Eastern Indo-Gangetic Plains.

Soil health is central to sustainable agriculture and a key goal of regenerative and organic farming. However, monitoring changes in soil health remains challenging due to the lack of regionally relevant benchmarks and context-specific indicators. This study assessed the impacts of long-term and combined regenerative management practices on soil health across 87 California vineyards with diverse management histories, microclimates, and soils. Three key indicators, including aggregate stability, mineralizable carbon, and soil organic carbon, were used to develop region-specific soil health scoring functions. These were adapted from Cornell's Comprehensive Assessment of Soil Health and the Soil Health Institute frameworks. Indicator values generally trended lower than existing benchmarks, emphasizing the need for crop- and region-specific scoring systems. Results from mixed-effects models and fuzzy-set qualitative comparative analysis (fsQCA) indicate that long-term cover cropping (≥ 10 years) was the most consistent driver of high soil health scores, especially when combined with other practices. Livestock integration improved soil organic carbon and mineralizable carbon scores in under 10 years, showing potential to accelerate soil health benefits. Our results highlight the importance of tailoring practices to local soil and climate conditions. Findings also support the development of more flexible, regionally informed soil health frameworks.

Mapping of acid sulfate soils (ASS) has in the past focused mainly on ASS probability maps, which are very useful to avoid environmental damage caused by these soils. However, these maps do not indicate the ASS subtypes, which may have different environmental impacts depending on whether they are actively releasing acidity and metals (sulfuric soils) or have the potential to do so (hypersulfidic soils) if the sulfidic material within them is disturbed (oxidized). Additionally, there is a particular type of soil that is close to being classified as an ASS, but where the pH criterion is not fulfilled. This soil is referred to as para-ASS and may have a similar negative environmental impact as ASS. In the risk assessment of ASS, it is therefore crucial to know the location of ASS subtypes as well as para-ASS. In this study, we have created for the first time a multiclass map of ASS subtypes in Finland. Furthermore, four probability maps have been generated, one for each class. For this, the suitability of two machine learning methods for multiclass classification of different ASS subtypes has been evaluated. The methods are random forest (RF) and gradient boosting (GB), which showed very high capabilities for the classification of ASS in binary classification. RF has given the best results with F1-score values between 71% and 80% for the four classes. An accurate and realistic multiclass map of the ASS subtypes has been created using the RF model.