Background Grassland soils on the Qinghai–Xizang Plateau (QXP) represent an important terrestrial carbon reservoir and are affected by grazing, nitrogen (N) deposition, and climate variability. However, the interactive effects of grazing and N addition on soil organic carbon (SOC) stocks across soil depths remain poorly understood. We conducted a three-year factorial field experiment in an alpine meadow on the QXP to examine the effects of three stocking rates and four N-addition rates on plant biomass, plant functional composition, and SOC stocks at 0–30 cm depth. Climatic variables were obtained from an on-site weather station and were treated as observational interannual covariates rather than experimental factors. Results During the experimental period, a stocking rate of 8 sheep ha–1 was associated with higher SOC stocks at selected soil depths and sampling years. N addition mitigated some negative effects of grazing on plant aboveground biomass (AGB), but had negligible net effects on SOC stocks. Grazing weakened negative associations between interannual climatic covariates and SOC stocks. The contributions of grazing, climatic covariates, plant functional composition, and soil properties to SOC stocks varied with soil depth. Plant functional composition, particularly the AGB ratio of traditional herbage (TH) to functional native herbage (FNH), made an important contribution to SOC stocks in the upper soil layers, whereas the contributions of climatic covariates and soil properties increased with depth. Conclusions Stocking rates at 8 sheep ha–1 were compatible with higher SOC stocks under the conditions of this three-year experiment, but long-term and multi-site studies are needed to evaluate their broader applicability.
Soil erosion and deposition reshape carbon pools, but how geomorphic redistribution regulates microbial communities and CO2 flux remain unclear. In tropical island watersheds, intense rainfall, steep terrain, and coarse-textured soils may intensify soil redistribution and amplify variability in surface carbon dynamics. This study evaluated how erosion-deposition patterns were associated with soil physicochemical conditions, microbial assemblages, and soil CO2 flux along four transects within the Tengqiao River watershed, Baoting, Hainan Island, China. The Unit Stream Power-based Erosion/Deposition (USPED) model revealed spatial heterogeneity in erosion and deposition rates, ranging from − 7.90 to 22.61 t ha− 1 yr− 1 across the 20 sampling sites. Compared with erosion sites, deposition sites tended to show higher nutrient contents, SOC concentration and surface SOC stock, together with lower bulk density (BD). Microbial community composition and co-occurrence networks also differed between erosion and deposition sites. Deposition sites favored taxa linked to nutrient cycling, whereas erosion sites were associated with stress-tolerant taxa. Across the measured months, soil CO2 fluxes were significantly higher in the rainy season than in the dry season. Partial least squares structural equation modeling (PLS-SEM) showed that erosion-deposition dynamics reduced soil water content (SWC) and pH, increased BD, and significantly reduced SOC. Soil physical condition (SWC, BD) and pH were further associated with significant decreases in bacterial and fungal diversity, and bacterial diversity showed a significant positive association with soil CO2 flux. Erosion-deposition processes were associated with surface soil redistribution, microbial community restructuring, and seasonal variation in soil CO2 flux. In this coarse-textured tropical island watershed, erosion-deposition dynamics reduced SOC and altered soil physical and chemical conditions, thereby weakening bacterial and fungal diversity. The significant linkage between bacterial diversity and soil CO2 flux suggests that bacterial assemblages may play an important role in mediating short-term carbon release. These results highlight the need to integrate geomorphic and microbial processes when assessing carbon responses in erosion-prone ecosystems.
Grassland carbon sequestration plays a critical role in regulating terrestrial carbon cycling and mitigating climate change. Annual forage overseeding is increasingly used to improve soil fertility and enhance soil organic carbon (SOC) storage in managed grasslands; however, how this management practice affects the formation and stabilization of particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) under contrasting grassland management regimes remains poorly understood. We conducted a field experiment from 2023 to 2025 in a perennial cultivated grassland in southern China evaluate the effects of annual grass, annual legume, and legume–grass mixture overseeding under grazing and exclosure. We measured belowground biomass, root C and N pools, SOC, total nitrogen (TN), microbial biomass carbon and nitrogen (MBC and MBN), POC and MAOC. Our results showed that, compared with the control, SOC, MBC, and MBN increased significantly across all overseeding treatments, whereas TN increased significantly under annual legume and legume–grass mixture overseeding. Annual legume–grass mixture overseeding increased belowground biomass by approximately 15
As an emerging industry paradigm, the alignment between policy regulation of the low-altitude economy and regional achievements in pollution and carbon emission reduction constitutes a critical issue for promoting industrial green transformation and achieving sustainable development. Using panel data from 28 Chinese provinces over the period 2008–2023, this study systematically examines the nonlinear impact of low-altitude economic policy intensity on regional pollution and carbon emission reduction and its underlying mechanisms. Additionally, it analyzes the driving effects of the low-altitude economy on spatial transformation from pathway restructuring, spatial asymmetry, and risk resonance. The findings reveal that:(1) The results indicate that the impact of low-altitude economy policy intensity on pollution and carbon emission levels exhibits a nonlinear pattern, an estimated empirical transition point of approximately 2.896. (2) Low-altitude economy policy intensity first weakens and then strengthens environmental regulation, while its promoting effect on green innovation gradually diminishes, thereby shaping its pollution and carbon emission reduction effects. (3) A stronger low-altitude airspace flight foundation accelerates the crossing of the turning point and strengthens environmental benefits. (4) The spatial effects are uneven. Provinces such as Sichuan, Jilin, Shaanxi, and Zhejiang exhibit heterogeneous transitions in the marginal environmental effects of low-altitude economy development during key development stages. (5) Spatial transformation is asymmetric, with stronger pollution and carbon reduction effects in regions with better ecological endowments or higher climate risk exposure. (6) Public attention to the low-altitude economy and carbon market returns exhibit a negative risk resonance over short-to-medium-term horizons (100–150 days; 12–24 cycles), indicating time-scale dependence. Overall, the results reveal stage-dependent and spatially differentiated environmental consequences of the low-altitude economy, providing new evidence for understanding the carbon-management implications of emerging spatial economic activities.
Predicting enteric methane (CH₄) emissions from forage-based ruminant systems is challenging because nutrient-methane relationships may vary across forage and experimental contexts. This study evaluated whether forage nutritional characteristics provide transferable predictive information across heterogeneous multi-study datasets. We analyzed 244 observations from 22 independent studies using eight nutritional predictors: dry matter digestibility (DMD), acid detergent lignin (ADL), acid detergent fibre (ADF), crude protein (CP), neutral detergent fibre (NDF), CP: NDF ratio, lignification index, and fibre: protein ratio. Six machine-learning approaches were evaluated using leave-one-study-out cross-validation. Feature importance, pure-versus-mixture comparisons, nutrient-methane correlations, and sensitivity analyses were used to examine configuration-dependent relationships. A Proportion-Weighted Study Ensemble (PWSE) analysis was additionally conducted as a secondary evaluation of configuration-aware representation. Cross-study prediction remained challenging across all models. Random Forest provided the strongest performance (R² = -0.375; RMSE = 29.88 mL/g DM; MAE = 23.34 mL/g DM), followed by Gradient Boosting (R² = -0.389; RMSE = 30.02 mL/g DM). DMD was the dominant predictive feature, with a mean importance of 0.626 across the tree-based models. However, its relationship with methane was strongly configuration-dependent: DMD was positively correlated with methane in pure forage systems (r = 0.684, p < 0.001) but negatively correlated in mixture systems (r = -0.809, p < 0.001), with the difference between correlations statistically distinguishable by Fisher’s r-to-z analysis (p < 0.001). Mixture systems had higher crude protein and lower ADL than pure systems, and methane emissions were also higher in mixture systems (51.39 vs. 43.61 mL/g DM; p = 0.033). PWSE showed weak predictive performance and substantial sensitivity to study concentration. Methane variation is better characterized by configuration-dependent nutritional relationships than by isolated nutrient effects. The contrasting DMD-CH4 relationships demonstrate that the predictive meaning of a nutritional trait can change across forage contexts. These findings support context-aware machine learning, study-level validation, and improved evidence representation in future methane modelling.
