Search Results (246)

The spatial variation of forest ecosystem services at regional scales remains poorly understood, and few studies have explicitly analyzed how ecosystem services are distributed across different forest management types. This study assessed the spatial overlap between forest management types and ecosystem service hotspots in the Southeastern United States (SEUS) and the Pacific Northwest (PNW) forests. We used the InVEST suite of tools and GIS to quantify carbon storage and water yield. Carbon storage was estimated, stratified by forest group and age class, and literature-based biomass pool values were applied. Average annual water yield and its temporal changes (2001–2020) were modeled using the annual water yield model, incorporating precipitation, potential evapotranspiration, vegetation type, and soil characteristics. Ecosystem service outputs were classified to identify hotspot zones (top 20%) and to evaluate the synergies and tradeoffs between these services. Hotspots were then overlaid with forest management maps to examine their distribution across management types. We found that only 2% of the SEUS and 11% of the PNW region were simultaneous hotspots for both services. In the SEUS, ecological and preservation forest management types showed higher efficiency in hotspot allocation, while in PNW, production forestry contributed relatively more to hotspot areas. These findings offer valuable insights for decision-makers and forest managers seeking to preserve the multiple benefits that forests provide at regional scales. Full article

Against the backdrop of multifaceted strategies to combat climate change, understanding soil erosion’s role in carbon cycling is critical due to terrestrial carbon pool vulnerability. This study integrates bibliometric methods with visualization tools (CiteSpace, VOSviewer) to analyze 3880 Web of Science core publications (1994–2024, inclusive), constructing knowledge graphs and forecasting trends. The results show exponential publication growth, shifting from slow development (1994–2011) to rapid expansion (2012–2024), aligning with international climate policy milestones. The Chinese Academy of Sciences led productivity (519 articles), while the US demonstrated major influence (H-index 117; 52,297 citations), creating a China–US bipolar research pattern. It was also found that Dutch journals dominate this research field. A keyword analysis revealed a shift from erosion-driven carbon transport to ecosystem service assessments. Emerging hotspots include microbial community regulation, climate–erosion feedback, and model–policy integration, though developing country collaboration remains limited. Future research should prioritize isotope tracing, multiscale modeling, and studies in ecologically vulnerable regions to enhance global soil carbon management. This study provides a novel analytical framework and forward-looking perspective for the soil erosion research on soil carbon cycling, serving as an extension of climate change mitigation strategies. Full article

Managing soil-borne pathogens and diseases in plants is particularly challenging because the pathogens that cause them can persist in the soil for extended periods, often resulting in repeated crop damage in affected areas. These destructive diseases compromise plant health by weakening the root systems, which makes the plants more susceptible to environmental stress and nutritional deficiencies. Every year in the United States, a whopping $9.6 million is allocated to reverse the harmful effects of pesticides on humans, plants, animals, and the environment. On the contrary, disease-suppressive soils offer an effective strategy for controlling pathogens while ensuring the least contamination of the environment. These soils can be managed by both conventional and advanced methods, such as reduced tillage, crop rotation, organic amendments, nanoparticles, omics approaches, and biofumigation. However, these soils can be local in nature, and their properties might be disrupted by common agricultural practices like tillage and agro-chemical application. This review synthesizes the concepts and mechanisms of disease suppression in soils and explores the ways that can be improved through the management of soil health for enhanced plant health and yield. Full article

Accurate regional-scale mapping of soil organic matter (SOM) is crucial for land productivity management and global carbon pool monitoring. Current remote sensing inversion of SOM faces challenges, including the underutilization of temporal information and low feature selection efficiency. To address these limitations, this study developed an integrated framework combining multi-temporal Landsat imagery, field-measured SOM data, intelligent feature optimization, and machine learning. The framework employs two novel image-processing strategies: the Maximum Annual Bare-Soil Composite (MABSC) method to extract background spectral information and the Multi-temporal Feature Optimization Composite (MFOC) method to capture seasonal and environmental dynamics. These features, along with topographic covariates, were processed using an improved Feature-Optimized and Interpretable XGBoost (FOI-XGB) model for key variable selection and spatial mapping. Validation across two subtropical coastal mountainous regions at different scales in southeastern China demonstrated the framework’s effectiveness and robustness. Key findings include the following: (1) Both the MABSC-derived spectral bands and the MFOC-optimized indices significantly outperformed traditional single-season approaches. Their combined use achieved a moderate SOM inversion accuracy (R² = 0.42–0.44). (2) The FOI-XGB model substantially outperformed traditional feature selection methods (Pearson, SHAP, and CorrSHAP), achieving significant regional R² improvements ranging from 9.72% to 88.89%. (3) The optimal model integrating the MABSC-derived features, MFOC-optimized indices, and topographic covariates attained the highest accuracy (R² up to 0.51). This represents major improvements compared with using topographic covariates alone (R² increase of up to 160.11%) or the combined spectral features (MABSC + MFOC) alone (R² increase of up to 15.91%). This study provides a robust, scalable, and practical technical solution for accurate SOM mapping in complex environments, with significant implications for sustainable land management and carbon monitoring. Full article

Effective management of straw in rice (Oryza sativa L.)–rape (Brassica napus L.) rotation systems is essential for optimising resource efficiency and improving soil quality. This two-year study investigated the impact of seven straw treatment methods on soil organic carbon (SOC) dynamics. The treatments examined were as follows: (1) control (CK); (2) rice straw (SF); (3) rapeseed straw (YF); (4) rice-straw-derived biochar (SB); (5) rapeseed-straw-derived biochar (YB); (6) mixed straw (YSF); (7) mixed biochar (YSB). Soil properties, enzyme activities and carbon fractions were subsequently analysed. During the canola growing season, the application of rice straw biochar increased oxidisable carbon (ROC), dissolved organic carbon (DOC) and microbial biomass carbon (MBC) by 25.7%, 61.7% and 67.2%, respectively, compared to the control. Notably, SB was more effective than unprocessed rice straw (SF) at increasing SOC and ROC. Furthermore, SB demonstrated superior performance in enhancing ROC (56.4%), MBC (36.0%) and DOC (12.2%) compared to hybrid biochar (YSB). SB consistently exhibited a higher carbon accumulation trend than the rapeseed-derived treatments (YF, YB and YSB). The results of the study indicated that applying rice straw biochar during the oilseed rape growing season was effective in increasing variable carbon pools and soil organic carbon accumulation. Full article

Poisoning and suffocation accidents occurred frequently in the pre-treatment pool workshops of biogas plants, so this paper provided a multi-dimensional risk analysis model: Bow-Tie-Qualitative Comparative Analysis (QCA)-Bayesian Neural Network-Consequence Simulation. First, the reasons for biogas poisoning and suffocation accidents were clarified through Bow-Tie. Then, the QCA method explored the accident cause combination paths in management. Next, the frequency distribution of biogas poisoning and suffocation accidents in the pre-treatment pool workshop was predicted to be 0.61–0.66 using the Bayesian neural network model, and the uncertainty of the forecast outcome was given. Finally, the ANSYS Fluent 16.0 simulation of biogas diffusion in three different ventilation types and a grid-independent solution of the simulation were conducted. The simulation results showed the distribution of methane, carbon dioxide and hydrogen sulfide gases and the hazards of the three gases to workers were analyzed. In addition, according to the results, this paper discussed the importance and necessity of ventilation in pre-treatment pool workshops and specified the hazard factors in biogas poisoning and suffocation accidents in the pre-treatment pool workshops. Some suggestions on gas alarms were also proposed. Full article

Reclaiming coastal wetlands for agricultural purposes has led to intensified farming activities, which are anticipated to affect greenhouse gas (GHG) flux processes within coastal wetland ecosystems. However, how greenhouse gas exchanges respond to variations in agricultural reclamation activities across different years remains uncertain. To address this knowledge gap, this study characterized dynamic exchanges within the soil–plant–atmosphere continuum by employing continuous monitoring across four representative coastal wetland soil–vegetation systems in Jiangsu, China. The results show the carbon dioxide (CO₂) and nitrous oxide (N₂O) flux exchanges between the system and the atmosphere and soil–vegetation carbon pools, which revealed the drivers of carbon dynamics in the coastal wetland system. The four study sites, converted from coastal wetlands to agricultural lands at different times (years), generally act as CO₂ sinks and N₂O sources. Higher levels of CO₂ sequestration occur as the age of reclamation rises. In terms of time scale, crops lands were found to be CO₂ sinks during the growing period but became CO₂ sources during the crop fallow period. Although the temporal trend of the N₂O flux was generally smooth, reclaimed farmlands acted as net sources of N₂O, particularly during the crop-growing period. The RDA and PLS-PM models illustrate that soil salinity, acidity, and hydrothermal conditions were the key drivers affecting the magnitude of the GHG flux exchanges under reclamation. This study demonstrates that GHG emissions from reclaimed wetlands can be effectively regulated through science-based land management, calling for prioritized attention to post-development practices rather than blanket restrictions on coastal exploitation. Full article

Reasonable nitrogen fertilizer management and cultivation methods can enhance the nitrogen supply and carbon sequestration capabilities of soil, which is beneficial for meeting the growth requirements of crops and alleviating environmental issues. However, the existing research on optimizing nitrogen use efficiency and soil carbon sequestration in organic systems remains limited. Therefore, a field trial was conducted to elucidate the impacts of different cultivation patterns and nitrogen application rates on soil carbon and nitrogen pools, especially on how these factors affect the components of soil organic carbon. The treatments included conventional cultivation with low nitrogen treatment (CFN12), conventional cultivation with high nitrogen treatment (CFN18), organic cultivation with low nitrogen treatment (OFN12), and organic cultivation with high nitrogen treatment (OFN18). The results demonstrated that, relative to CFN18, OFN12 significantly increased the accumulation amounts of organic carbon and nitrogen in paddy soil. This was evident under multiple classifications of organic carbon, while it showed no advantage in the accumulation of mineral nitrogen. Notably, the organic cultivation mode increased the activities of enzymes involved in the carbon–nitrogen cycle in the cultivated layer and optimized the structure of humus, which gave the proportion of aggregates with a particle size greater than 0.5 mm more advantages. Correlation analysis demonstrated that the pertinent indices associated with soil carbon and nitrogen pools exhibited a highly significant positive correlation in the topsoil layer, accompanied by pronounced synergistic interactions among them. The PCA comprehensive scoring results indicate that OFN12 has the highest total score, indicating that it is beneficial for the improvement of soil fertility. This study offers practical insights for improving soil health, boosting plant growth, and enhancing climate mitigation through soil carbon storage, contributing to more sustainable agricultural practices. Full article

Soil carbon is a crucial component of the global carbon cycle, and carbon mineralization is influenced by various factors. However, there is a lack of systematic analyses on the responses of carbon mineralization in different soil types to the addition of exogenous organic matter. This study investigates the effects of compost addition on the mineralization and kinetic characteristics of soil carbon across three typical agricultural soils: paddy soil, black soil, and cinnamon soil. A 210-day incubation study was conducted with four treatments: Control (un-amended soil), R (soil + straw), R1M (soil + straw + low compost application rate), R2M (soil + straw + high compost application rate). The results showed that the CO₂ emission rates of the three soils were higher during the early stage (1–37 days) and decreased afterward. The CO₂ emission rates of the paddy soil and the black soil were significantly higher than those of the cinnamon soil. The addition of compost significantly increased both the CO₂ emission rate and the cumulative release of CO₂, especially in the R2M treatment. At the end of the incubation, the SOC contents were higher in the R2M treatment than in the Control for all three soils (p < 0.05), with the most notable increase in the cinnamon soil (60.93%). Compost addition significantly enhanced the active carbon pool (C_a), slow carbon pool (C_s), and potentially mineralizable carbon pool (C_p), while decreasing the mineralization rate (k_a) of the C_a, but the effect on the mineralization rate (k_s) of the C_s and mineralization entropy (C_m) varied by soil types. The k_s of the paddy soil was significantly reduced by 23.08% under the R1M and R2M treatments compared with the Control and R treatment. The k_s of the black soil was significantly increased by 59.52% under the R2M treatment compared with the Control. The k_s of the cinnamon soil was elevated considerably by 79.31% under the R2M treatment compared with the Control, R, and R1M treatments (averaging 0.29 × 10⁻² d), and the k_s of the paddy soil and black soil were significantly higher than those of the cinnamon soil under the R2M treatment. The C_m was significantly higher in the organic material added treatments than in the Control for the black soil and the paddy soil, but showed a higher value in the R treatment than in the R2M and Control for the cinnamon soil. In conclusion, compost addition stimulated soil carbon mineralization and improved the SOC content, especially in the cinnamon soil, while reducing the mineralization rate of the active carbon pool across the three soils. The mineralization rate of the slow carbon pool and the changes in mineralization entropy were dependent on soil types, primarily related to the initial soil nutrient contents, pH, and particle compositions. These findings offer valuable insights for managing the soil carbon pool in agricultural ecosystems. Full article

Fertilization management constitutes a critical determinant of agroecosystem productivity. Reasonable fertilization can increase the organic matter content in soil; however, the potential mechanism of how different fertilization regimes impact soil carbon sequestration is unclear. We hypothesized that the combined application of biochar and organic fertilizer would enhance soil carbon sequestration by improving soil physicochemical conditions, increasing microbial activity, and promoting the accumulation of stable forms of carbon. This study systematically investigated different regimes, including the application of chemical fertilizer alone (SCN), chemical fertilizer with biochar (SCB), chemical fertilizer with organic fertilizer (SCO), and chemical fertilizer with both biochar and organic fertilizer (SCBO), on soil physiochemical properties, enzyme activities, labile organic carbon fractions, microbial carbon fixation gene expression, and community composition. The results demonstrated that (1) the application of organic materials significantly enhanced soil nutrient levels and enzyme activities, with the best performance from SCBO; (2) the organic materials increased the labile soil organic carbon (SOC) content and the carbon pool management index, with SCO showing the highest at 69.82%; (3) SCB and SCBO improved the stability of soil carbon components by increasing the proportion of Aromatic C; and (4) the carbon fixation genes ACAT and sdhA exhibited the highest abundance in SCBO. In parallel, the relative abundance of Actinomycetota increased with the application of organic materials, reaching its peak in SCBO. Mantel testing revealed a strong correlation between microbial community composition and SOC, emphasizing the importance of SOC in microbial growth and metabolism. Moreover, the strong correlation between carbon fixation genes and aromatic carbon suggested that specific carbon forms, particularly aromatic structures, played a critical role in driving microbial carbon fixation processes. Full article

Given the worsening global climate change that drives drought frequency and irrigation water shortages, implementing water-conserving practices like alternate wetting and drying (AWD) is now critically urgent. Biochar is widely used for soil carbon sequestration. However, there is limited information on the effects of biochar on soil organic carbon (SOC) and its labile fractions in paddy fields, especially under AWD. A two-year field experiment was conducted with two irrigation regimes (CF: continuous flooding irrigation; AWD) as the main plots and 0 (B₀) and 20 t ha⁻¹ (B₁) biochar as sub-plots. AWD had no effect on the SOC and particulate organic carbon (POC) content, but increased the dissolved organic carbon (DOC), microbial biomass carbon (MBC), easily oxidizable organic carbon (EOC), light fraction organic carbon (LFOC), and carbon pool management index (CPMI) at 0–10 cm depths, by 24.4–56.4%, 12.6–17.7%, 9.2–16.8%, 25.6–28.1%, and 11.3–18.6%, respectively. Biochar increased SOC while also increasing DOC, MBC, EOC, LFOC, POC, and CPMI at 0–20 cm depths, by 18.4–53.3%, 14.7–70.2%, 17.4–22.3%, 10.2–27.6%, 95.2–188.3%, 46.6–224%, and 5.6–27.2, respectively, making SOC more labile under AWD. Our results highlight that biochar still holds great potential for improving soil quality and carbon sequestration under AWD, and the combination of biochar and AWD can achieve the synergistic optimization of the food–water–carbon sequestration trade-off, which is beneficial to sustainable agricultural production. Full article

The drive towards carbon neutrality has prompted the worldwide expansion of utility-scale solar facilities. Previous studies have reported the positive effects of solar facilities’ installation on pasture productivity and biodiversity in arid regions. However, our understanding of how solar facilities influence a wide range of ecosystem functions simultaneously, and the relative contributions of soil microbial attributes, remains incomplete. To address this gap, we assessed the changes in ecosystem multifunctionality following solar facility installation in an alpine desert grassland in the Qinghai–Tibet plateau by measuring twenty-three ecosystem function indicators comprising primary production, the soil nutrient pool, carbon cycling, nitrogen cycling, phosphorus cycling and oxidation–reduction. Furthermore, we estimated the soil microbial diversity, microbial indicator taxa and microbial activity to identify the primary driving factors. The results showed that solar facilities had positive effects on ecosystem multifunctionality; the positive effect size was higher in the initial installation period (31.4%) than in the constant running period (3.5%). The enhancements in ecosystem multifunctionality were mainly due to enhanced nutrient cycling induced by the increased abundance of fungal indicator taxa and microbial activity. Moreover, the structural equation model revealed distinct regulatory paths between the two periods and a transition in the primary driving factors of ecosystem multifunctionality from microbial indicator taxa to microbial activity. In conclusion, our study demonstrates the positive influence of solar facilities on multiple ecosystem functions, emphasizing the critical role of soil microbial mechanisms in regulating ecosystem multifunctionality. These findings provide valuable insights into soil biota-driven processes that could inform strategies aimed at enhancing soil health and ecosystem functionality in arid grasslands under human-managed systems. Full article

Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet the modern agricultural demand for rapid, non-destructive analysis. While significant progress has been made in spectral inversion for SOM prediction, its accuracy still lags behind traditional chemical methods. This study proposes a novel approach to predict SOM content by integrating spectral, texture, and color features using a three-branch convolutional neural network (3B-CNN). Spectral reflectance data (400–1000 nm) were collected using a portable hyperspectral imaging device. The top 15 spectral bands with the highest correlation were selected from 260 spectral bands using the Correlation Coefficient Method (CCM), Boruta algorithm, and Successive Projections Algorithm (SPA). Compared to other methods, CCM demonstrated superior dimensionality reduction performance, retaining bands highly correlated with SOM, which laid a solid foundation for multi-source data fusion. Additionally, six soil texture features were extracted from soil images taken with a smartphone using the gray-level co-occurrence matrix (GLCM), and twelve color features were obtained through the color histogram. These multi-source features were fused via trilinear pooling. The results showed that the 3B-CNN model, integrating multi-source data, performed exceptionally well in SOM prediction, with an R² of 0.87 and an RMSE of 1.68, a 23% improvement in R² compared to the 1D-CNN model using only spectral data. Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R² improvements ranging from 4% to 11%. This study demonstrates the potential of multi-source data fusion in accurately predicting SOM content, enabling rapid assessment at the field scale and providing a scientific basis for precision fertilization and agricultural management. Full article

It has become increasingly important to incorporate carbon metrics in the forest harvest planning process. The Generic Carbon Budget Model (GCBM) is a well-recognized tool to evaluate the potential impact of management decisions on carbon sequestration and storage, supporting sustainable forest management planning. Although GCBM is effective in carbon budgeting and estimating carbon metrics, its computational complexity makes it difficult to integrate into forest planning with multiple scenarios. In this regard, this study proposes using machine algorithms to expedite the output generated by GCBM. XGBoost was implemented to estimate the carbon pool and NEP in managed forests of Quebec. Furthermore, polynomial regression was also implemented to serve as a validation benchmark. Datasets with total sizes of 13.53 million and 7.56 million samples were compiled for NEP and carbon pool forecasting to run the model. The results indicate that XGBoost was able to accurately replicate the performance of the GCBM model for both NEP forecasting (R² = 0.883) and carbon pool estimation (R² = 0.967 for aboveground biomass). Although machine learning approaches are comparatively faster, GCBM still offers better accuracy. Hence, the decision on which method to use, either machine learning or GCBM, should be dictated by the specific objectives and the constraints of the project. Full article

Ecological restoration has increasingly been employed to reverse land degradation and increase carbon (C) sink, especially in ecologically fragile karst areas. Microbial necromass carbon (MNC) constitutes a critical pool within soil organic carbon (SOC), contributing substantially to long-term C sequestration through mineral stabilization. However, its distribution patterns across soil profiles and grassland restoration stages in karst areas remain unclear. To address this knowledge gap, the contents of bacterial necromass C (BNC), fungal necromass C (FNC), and their contributions to SOC were estimated based on glucosamine and muramic acid contents across the soil profile (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, and 80–100 cm) for four subalpine restoration stages (grazing enclosure for 5, 11, 17, and 25 years) in the karst region. Our findings demonstrated that both soil depth and grassland restoration stages effectively influenced the BNC and FNC contents. On average, the soil BNC, FNC, and total MNC at the depth of 80–100 cm reduced by 70.50%, 59.70%, and 62.18% compared with in topsoil (0–20 cm), respectively. However, the FNC/BNC ratio gradually increased with the increase in soil depth, which was 43.15% higher at 80–100 cm soil depth than in topsoil, suggesting that the accumulation efficiency of FNC was higher compared to BNC in the deep soil. The BNC, FNC, and MNC were positively correlated with the grassland restoration stage, while FNC/BNC ratio had a negative relationship with the restoration stage (R² = 0.45, p < 0.001). FNC contributed significantly more to SOC (28.6–36.4%) compared to BNC (7.7–9.9%) at all soil depths, indicating that soil fungal necromass has an essential effect on SOC sequestration. The results of the random forest model and distance-based redundancy analysis identified that pH, soil water content, and dissolved organic carbon were the three most essential predictors for the contribution of MNC to SOC. Our study highlights the importance of microbial necromass to SOC accumulation, providing significant scientific implications for the C pool management during the restoration of degraded grasslands in karst regions. Full article