Search Results (719)

Soil respiration (Rt), consisting of heterotrophic (Rh) and autotrophic respiration (Ra), plays a vital role in terrestrial carbon cycling and is sensitive to soil temperature and moisture. In dryland agriculture, plastic film mulching (PM) is widely used to regulate soil hydrothermal conditions, but its effects on Rt components and their temperature sensitivity (Q₁₀) across regions remain unclear. A two-year field study was conducted at two rain-fed maize sites: Anding (warmer, semi-arid) and Yuzhong (colder, drier). PM significantly increased Rt, Rh, and Ra, especially Ra, due to enhanced root biomass and improved microclimate. Yield increased by 33.6–165%. Peak respiration occurred earlier in Anding, aligned with maize growth and soil temperature. PM reduced Q₁₀ of Rt and Ra in Anding, but only Ra in Yuzhong. Rh Q₁₀ remained stable, indicating microbial respiration was less sensitive to temperature changes. Structural equation modeling revealed that Rt and Ra were mainly driven by soil temperature and root biomass, while Rh was more influenced by microbial biomass carbon (MBC) and dissolved organic carbon (DOC). Despite increased CO₂ emissions, PM improved carbon emission efficiency (CEE), particularly in Yuzhong (+67%). The application of PM is recommended to enhance yield while optimizing carbon efficiency in dryland farming systems. Full article

Accurate estimation of biomass higher heating value (HHV) is crucial for designing efficient bioenergy systems. In this study, we developed a Backpropagation artificial neural network (ANN) that predicts HHV from routine proximate/ultimate composition data. The network (9-6-6-1 architecture, trained for 15,000 epochs with learning rate 0.3 and momentum 0.4) was calibrated on 99 diverse Spanish biomass samples (inputs: moisture, ash, volatile matter, fixed carbon, C, H, O, N, S). The optimized ANN achieved strong predictive accuracy (validation R² ≈ 0.81; mean squared error ≈ 1.33 MJ/kg; MAE ≈ 0.77 MJ/kg), representing a substantial improvement over 54 analytical models despite the known complexity and variability of biomass composition. Importantly, in direct comparisons it significantly outperformed 54 published analytical HHV correlations—the ANN achieved substantially higher R² and lower prediction error than any fixed-form formula in the literature. A sensitivity analysis confirmed chemically intuitive trends (higher C/H/FC increase HHV; higher moisture/ash/O reduce it), indicating the model learned meaningful fuel-property relationships. The ANN thus provided a computationally efficient and robust tool for rapid, accurate HHV estimation from compositional data. Future work will expand the dataset, incorporate thermal pretreatment effects, and integrate the model into a user-friendly decision-support platform for bioenergy applications. Full article

Accurate assessments of aboveground biomass (AGB) stocks and their changes in extensive Miombo forests are challenging due to the lack of site-specific allometric equations (AEs). Terrestrial Laser Scanning (TLS) is a non-destructive method that enables the calibration of AEs and has recently been validated by the IPCC guidelines for carbon accounting within the REDD+ framework. TLS surveys were carried out in five non-contiguous 1-ha plots in two study sites in the wet Miombo forest of Katanga, in the Democratic Republic Congo. Local wood densities (WD) were determined from wood cores taken from 619 trees on the sites. After a careful checking of Quantitative Structure Models (QSMs) output, the individual volumes of 213 trees derived from TLS data processing were converted to AGB using WD. Four AEs were calibrated using different predictors, and all presented strong performance metrics (e.g., R² ranging from 90 to 93%), low relative bias and relative individual mean error (11.73 to 16.34%). Multivariate analyses performed on plot floristic and structural data showed a strong contrast in terms of composition and structure between sites and between plots within sites. Even though the whole variability of the biome has not been sampled, we were thus able to confirm the transposability of results within the wet Miombo forests through two cross-validation approaches. The AGB predictions obtained with our best AE were also compared with AEs found in the literature. Overall, an underestimation of tree AGB varying from −35.04 to −19.97% was observed when AEs from the literature were used for predicting AGB in the Miombo of Katanga. Full article

Climate change alters plant biomass allocation and aboveground–belowground trade-offs in grassland ecosystems, potentially affecting critical functions such as carbon sequestration. However, uncertainties persist regarding how precipitation gradients regulate (1) responses of aboveground biomass (AGB), belowground biomass (BGB), and total biomass in alpine grasslands, and (2) precipitation-mediated AGB-BGB allocation strategies. To address this, we conducted a large-scale field survey across precipitation gradients (400–700 mm/y) in the Sanjiangyuan alpine grasslands, Qinghai–Tibet Plateau. During the 2024 growing season, a total of 63 sites (including 189 plots and 945 quadrats) were sampled along five aridity classes: <400, 400–500, 500–600, 600–700, and >700 mm/y. Our findings revealed precipitation as the dominant driver of biomass dynamics: AGB exhibited equal growth rates relative to BGB within the 600–700 mm/y range, but accelerated under drier/wetter conditions. This suggests preferential allocation to aboveground parts under most precipitation regimes. Precipitation explained 31.71% of AGB–BGB trade-off variance (random forest IncMSE), surpassing contributions from AGB (17.61%), specific leaf area (SLA, 13.87%), and BGB (12.91%). Structural equation modeling confirmed precipitation’s positive effects on SLA (β = 0.28, p < 0.05), AGB (β = 0.53, p < 0.05), and BGB (β = 0.60, p < 0.05), with AGB-mediated cascades (β = 0.33, p < 0.05) dominating trade-off regulation. These results advance our understanding of mechanistic drivers governing allometric AGB–BGB relationships across climatic gradients in alpine ecosystems of the Sanjiangyuan Region on the Qinghai–Tibet Plateau. Full article

International efforts are underway to implement carbon neutrality policies in rapidly changing climate conditions. This situation has strongly demanded the discovery of novel carbon sinks. The Salix genus has attracted attention as a promising carbon sink owing to its rapid growth and efficient use as a biofuel in short-rotation cultivation. The present study aims to derive an allometric equation and conduct stem analysis as fundamental tools for estimating net primary productivity (NPP) in Salix pierotii Miq. stand, which is increasingly acknowledged as an important emerging carbon sink. The allometric equations derived showed a high explanatory rate and fitness (R² ranged from 0.74 to 0.99). The allometric equations between DBH and stem volume and biomass derived in the process of stem analysis also showed a high explanatory rate and fitness (R² ranged from 0.87 to 0.94). The NPPs calculated based on the allometric equation derived and stem analysis were 11.87 tonC∙ha⁻¹∙yr⁻¹ and 15.70 tonC∙ha⁻¹∙yr⁻¹, respectively. These results show that the S. pierotii community, recognized as the representative riparian vegetation, could play an important role as a carbon sink. In this context, an assessment of the carbon absorption capacity of riparian vegetation such as willow communities could contribute significantly to achieving carbon neutrality goals. Full article

Light Detection and Ranging (LiDAR) provides three-dimensional information that can be used to extract tree parameter measurements such as height (H), canopy volume (CV), canopy diameter (CD), canopy area (CA), and tree stand density. LiDAR data does not directly give diameter at breast height (DBH), an important input into allometric equations to estimate biomass. The main objective of this study is to estimate tree DBH using existing allometric models. Specifically, it compares three global DBH pantropical models to calculate DBH and to estimate the aboveground biomass (AGB) of the Lake Broadwater Forest located in Southeast (SE) Queensland, Australia. LiDAR data collected in mid-2022 was used to test these models, with field validation data collected at the beginning of 2024. The three DBH estimation models—the Jucker model, Gonzalez-Benecke model 1, and Gonzalez-Benecke model 2—all used tree H, and the Jucker and Gonzalez-Benecke model 2 additionally used CD and CA, respectively. Model performance was assessed using five statistical metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), percentage bias (MBias), and the coefficient of determination (R²). The Jucker model was the best-performing model, followed by Gonzalez-Benecke model 2 and Gonzalez-Benecke model 1. The Jucker model had an RMSE of 8.7 cm, an MAE of −13.54 cm, an MAPE of 7%, an MBias of 13.73 cm, and an R² of 0.9005. The Chave AGB model was used to estimate the AGB at the tree, plot, and per hectare levels using the Jucker model-calculated DBH and the field-measured DBH. AGB was used to estimate total biomass, dry weight, carbon (C), and carbon dioxide (CO₂) sequestered per hectare. The Lake Broadwater Forest was estimated to have an AGB of 161.5 Mg/ha in 2022, a Total C of 65.6 Mg/ha, and a CO₂ sequestered of 240.7 Mg/ha in 2022. These findings highlight the substantial carbon storage potential of the Lake Broadwater Forest, reinforcing the opportunity for landholders to participate in the carbon credit systems, which offer financial benefits and enable contributions to carbon mitigation programs, thereby helping to meet national and global carbon reduction targets. Full article

Accurate estimation of aboveground biomass (AGB) in tree–shrub communities is critical for quantifying forest ecosystem productivity and carbon sequestration potential. Although generalized allometric equations offer expediency in natural forest AGB estimation, their neglect of interspecific variability introduces methodological pitfalls. Precise AGB prediction necessitates resolving two biological constraints: phylogenetic conservation of allometric coefficients and ontogenetic regulation of scaling relationships. This study establishes an integrated framework combining the following: (1) phylogenetic signal detection (Blomberg’s K/Pagel’s λ) across 157 species’ allometric equations, revealing weak but significant evolutionary constraints (λ = 0.1249, p = 0.0027; K ≈ 0, p = 0.621); (2) hierarchical error decomposition of 9105 stems in a Mt. Wuyishan forest dynamics plot (15 species), identifying family-level error stratification (e.g., Theaceae vs. Myrtaceae, Δerror > 25%); (3) ontogenetic trajectory analysis of Castanopsis eyrei between Mt. Wuyishan and Mt. Huangshan, demonstrating significant biomass deviations in small trees (5–15 cm DBH, p < 0.05). Key findings resolve the following hypotheses: (1) absence of strong phylogenetic signals validates generalized models for phylogenetically diverse communities; (2) ontogenetic regulation dominates error magnitude, particularly in early developmental stages; (3) differential modeling is recommended: species-specific equations for pure forests/seedlings vs. generalized equations for mixed mature forests. This work establishes an error hierarchy: ontogeny > taxonomy > phylogeny, providing a mechanistic basis for optimizing forest carbon stock assessments. Full article

The biomass allocation patterns between aboveground and belowground are an essential functional trait for plant survival under a changing environment. The effects of arbuscular mycorrhizal fungi (AMF) communities on plant biomass allocation, particularly in degraded Festuca ovina grasslands in ecologically fragile karst areas, remain unclear. Therefore, we conducted a field investigation combined with a greenhouse experiment to explore the importance of AMF compared to bacteria and fungi for plant biomass allocation. The results showed that plant biomass in degraded grasslands exhibited allometric biomass allocation, contrasting with isometric partitioning in non-degraded grasslands. AMF, not bacteria or fungi, were the primary microbial mediators of grassland degradation effects on plant biomass allocation based on structural equation modeling. The greenhouse experiment demonstrated that the selected AMF keystone species from the field study performed according to ecological network analysis, particularly multi-species combinations, enhanced the belowground biomass allocation of F. ovina under rocky desertification stress compared to single-species inoculations, through decreasing soil pH, enhancing alkaline phosphatase (ALP) activity, and increasing the expression level of AMF-inducible phosphate transporter (PT4). This study highlights the critical role of the AMF community, rather than individual species, in mediating plant survival strategies under rocky desertification stress. Full article

Exploring the mechanisms by which green agricultural production reduces emissions and enhances carbon sequestration in soil can provide a scientific basis for greenhouse gas reduction and sustainable development in farmland. This study uses a combination of meta-analysis and field experiments to evaluate the impact of different agricultural management practices and climatic conditions on soil organic carbon (SOC) and the emissions of CO₂ and CH₄, as well as the role of microorganisms. The results indicate the following: (1) Meta-analysis reveals that the long-term application of organic fertilizers in green agriculture increases SOC at a rate four times higher than that of chemical fertilizers. No-till and straw return practices significantly reduce CO₂ emissions from alkaline soils by 30.7% (p < 0.05). Warm and humid climates in low-altitude regions are more conducive to soil carbon sequestration. (2) Structural equation modeling of plant–microbe–soil carbon interactions shows that plant species diversity (PSD) indirectly affects microbial biomass by influencing organic matter indicators, mineral properties, and physicochemical characteristics, thereby regulating soil carbon sequestration and greenhouse gas emissions. (3) Field experiments conducted in the typical green farming research area of Chenzhuang reveal that soils managed under natural farming absorb CH₄ at a rate three times higher than those under conventional farming, and the stoichiometric ratios of soil enzymes in the former are close to 1. The peak SOC (19.90 g/kg) in the surface soil of Chenzhuang is found near fields cultivated with natural farming measures. This study provides theoretical support and practical guidance for the sustainable development of green agriculture. Full article

Soil organic carbon (SOC) mineralization is the conversion of SOC to inorganic forms of carbon (C) by microbial decomposition and conversion. It plays an important role in global C cycling. Currently, most of the studies investigating the effects of different tree species on SOC mineralization focus on forest ecosystems, and few have focused on reservoir water-level drawdown zones. In this study, we used an indoor incubation method to investigate SOC mineralization in the plantation soils of Glyptostrobus pensilis, Taxodium Zhongshanshan, Taxodium distichum and CK (unplanted plantation) in the reservoir water-level drawdown zones. We aimed to explore the effects of different tree species on the process of SOC mineralization in the reservoir water-level drawdown zones by considering both the biological and chemical processes of the soil. The results showed that the rates of SOC mineralization in the G. pensilis and T. Zhongshanshan plantations were 47% and 37%, respectively, higher than those in CK (p < 0.05), whereas the rate of SOC mineralization in T. distichum soils did not differ from that in CK. The structural equation model’s results showed microbial biomass carbon (MBC) is a key driver of SOC mineralization, while SOC and dissolved organic carbon (DOC) concentrations are also important factors that affect SOC mineralization and follow MBC. Compared to soil biochemical properties, the bacterial community composition has relatively little effect on SOC mineralization. Planted forests can, to a degree, change the biochemical properties of the soil in the reservoir water-level drawdown zones, effectively improving soil pH, and significantly increasing the amount of potential soil C mineralization, the content of SOC and the diversity of the soil bacteria (p < 0.05). Full article

Forests are a key terrestrial carbon sink, storing carbon in biomass, the forest floor, and the mineral soil (SOC). Since Pinus radiata D. Don is the most widely planted forest species in Chile, it is important to understand how environmental and soil factors influence these carbon pools. Our objective was to evaluate the effects of climate and site variables on carbon stocks in adult radiata pine plantations across contrasting water and nutrient conditions. Three 1000 m² plots were installed at 20 sites with sandy, granitic, recent ash, and metamorphic soils, which were selected along a productivity gradient. Biomass carbon stocks were estimated using allometric equations, and carbon stocks in the forest floor and mineral soil (up to 1 m deep) were assessed. SOC varied significantly, from 139.9 Mg ha⁻¹ in sandy soils to 382.4 Mg ha⁻¹ in metamorphic soils. Total carbon stocks (TCS) per site ranged from 331.0 Mg ha⁻¹ in sandy soils to 552.9 Mg ha⁻¹ in metamorphic soils. Across all soil types, the forest floor held the lowest carbon stock. Correlation analyses and linear models revealed that variables related to soil water availability, nitrogen content, precipitation, and stand productivity positively increased SOC and TCS stocks. In contrast, temperature, evapotranspiration, and sand content had a negative effect. The developed models will allow more accurate estimation estimates of C stocks at SOC and in the total stand. Full article

Soil salinization severely limits global agricultural sustainability, particularly across the saline–alkaline landscapes of the Qinghai–Tibet Plateau. We examined how potassium fulvate (PF) modulates oat (Avena sativa L.) performance, soil chemistry, and rhizospheric microbiota in the saline–alkaline soils of the Qaidam Basin. PF markedly boosted shoot and root biomass, with the greatest response observed at 150 kg hm⁻². At the same time, it enhanced soil fertility by increasing organic matter, nitrate-N, ammonium-N, and available potassium, and improved ionic balance by lowering Na⁺ concentrations and the sodium adsorption ratio (SAR), while increasing Ca²⁺ levels and soil moisture content. Under the high-dose treatment (F2), endogenous fungal contributions declined sharply, exogenous replacements increased, and fungal α-diversity fell; multivariate ordinations confirmed that PF reshaped both bacterial and fungal communities, with fungi exhibiting the stronger response. We integrated three machine learning algorithms—least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—to minimize the bias inherent in any single method. We identified microbial β-diversity, organic matter, and Na⁺ and Ca²⁺ concentrations as the most robust predictors of the Soil Salinization and Alkalization Index (SSAI). Structural equation modeling further showed that PF mitigates salinity chiefly by improving soil physicochemical properties (path coefficient = −0.77; p < 0.001), with microbial assemblages acting as key intermediaries. These findings provide compelling theoretical and empirical support for deploying PF to rehabilitate saline–alkaline soils in alpine environments and offer practical guidance for sustainable land management in the Qaidam Basin. Full article

The focus of this study was on young populations of Kandelia obovata within a constructed coastal wetland in Haicang Bay, Xiamen, Southeast China. The objective was to systematically examine their allometric growth characteristics and carbon sequestration potential over an 8-year monitoring period (2016–2024). Allometric equations were developed to estimate biomass, and the spatiotemporal variation in both plant and soil carbon stocks was estimated. There was a significant increase in total biomass per tree, from 120 ± 17 g at initial planting to 4.37 ± 0.59 kg after 8 years (p < 0.001), with aboveground biomass accounting for the largest part (72.2% ± 7.3%). The power law equation with D²H as an independent variable yielded the highest predictive accuracy for total biomass (R² = 0.957). Vegetation carbon storage exhibited an annual growth rate of 4.2 ± 0.8 Mg C·ha⁻¹·yr⁻¹. In contrast, sediment carbon stocks did not show a significant increase throughout the experimental period, although long-term accumulation was observed. The restoration of mangroves in urban coastal constructed wetlands is an effective measure to sequester carbon, achieving a carbon accumulation rate of 21.8 Mg CO₂eq·ha⁻¹·yr⁻¹. This rate surpasses that of traditional restoration methods, underscoring the pivotal role of interventions in augmenting blue carbon sinks. This study provides essential parameters for allometric modeling and carbon accounting in urban mangrove afforestation strategies, facilitating optimized restoration management and low-carbon strategies. Full article

Acid phosphatase (ACPase) is an essential industrial enzyme, but its production via recombinant bacterial fermentation is often limited by insufficient dissolved oxygen control. This study optimized the aerobic fermentation of the ACPase-producing recombinant bacterium Bacillus subtilis 168/pMA5-Acp by refining the bioreactor’s aerodynamic structure using computational fluid dynamics (CFD) simulations. This was combined with fermentation kinetics modeling to achieve precise process control. First, the gas distributor structure of the 5 L bioreactor was optimized using CFD simulation results. Optimal mass transfer conditions were identified through comprehensive analysis of K_La in different reactor regions (aeration ratio: 1.142 VVm, K_La = 264.2 h⁻¹). The simulation results showed that the optimized oxygen transfer efficiency increased 2.49 fold compared to the prototype. Second, the process control issue was addressed by developing a BP (backpropagation) neural network model to predict K_La under alternative media conditions. The prediction error was less than 5%, and the model was combined with the logistic equation to construct the bacterial growth kinetic model (R² > 0.99). The experiments demonstrated that using the optimized reactor with a molasses–urea medium (molasses 7.5 g/L; urea 15 g/L; K₂HPO₄ 1.2 g/L; MgSO₄·7H₂O 0.25 g/L) reduced production costs while maintaining enzyme activity (215.99 U/mL) and biomass (OD₆₀₀ = 101.67) by 90.03%. This study provides an efficient and cost-effective process solution for the industrial production of ACPase and a theoretical foundation for bioreactor design and scale-up. Full article

In this paper, a class of two-delay predator–prey models with coefficient-dependent delay is studied. It examines the combined effect of fear-induced delay and post-predation biomass conversion delay on the stability of predator–prey systems. By analyzing the distribution of roots of the characteristic equation, the stability conditions for the internal equilibrium and the existence criteria for Hopf bifurcations are derived. Utilizing normal form theory and the central manifold theorem, the direction of Hopf bifurcations and the stability of periodic solutions are calculated. Finally, numerical simulations are conducted to verify the theoretical findings. This research reveals that varying delays can destabilize the predator–prey system, reflecting the dynamic complexity of real-world ecosystems more realistically. Full article