Search Results (637)

Grazing affects soil organic carbon (SOC) through plant removal, livestock trampling, and manure deposition. However, the impact of grazing on SOC is also influenced by multiple factors such as climate, soil properties, and management approaches. Despite extensive research, the mechanisms by which grazing intensity influences SOC density in grasslands remain incompletely understood. This study examines the effects of varying grazing intensities on SOC density (0–30 cm) dynamics in temperate grasslands of northern China using field surveys and experimental analyses in a typical steppe ecosystem of Inner Mongolia. Results show that moderate grazing (3.8 sheep units/ha/yr) led to substantial consumption of aboveground plant biomass. Relative to the ungrazed control (0 sheep units/ha/yr), aboveground plant biomass was reduced by 40.5%, 36.2%, and 50.6% in the years 2016, 2019, and 2020, respectively. Compensatory growth failed to fully offset biomass loss, and there were significant reductions in vegetation carbon storage and cover (p < 0.05). Reduced vegetation cover increased bare soil exposure and accelerated topsoil drying and erosion. This degradation promoted the downward migration of SOC from surface layers. Quantitative analysis revealed that moderate grazing significantly reduced surface soil (0–10 cm) organic carbon density by 13.4% compared to the ungrazed control while significantly increasing SOC density in the subsurface layer (10–30 cm). Increased precipitation could mitigate the SOC transfer and enhance overall SOC accumulation. However, it might negatively affect certain labile SOC fractions. Elucidating the mechanisms of SOC variation under different grazing intensities and precipitation regimes in semi-arid grasslands could improve our understanding of carbon dynamics in response to environmental stressors. These insights will aid in predicting how grazing systems influence grassland carbon cycling under global climate change. Full article

Field bindweed (Convolvulus arvensis L.) is a competitive herbaceous perennial weed that reduces productivity in irrigated pastures. Grazing might reduce competition by field bindweed when it begins growth in the spring, thereby encouraging encroachment by desirable grass species during the summer. To test this hypothesis, a two-year study was conducted in two adjacent, privately owned, irrigated, warm-season perennial grass pastures (replicates) that were heavily infested with field bindweed. Study sites were near Tucumcari, NM, USA. The fields were grazed with exclosures to evaluate ungrazed management. Aboveground biomass of field bindweed, other weeds, and perennial grass were measured, and field bindweed plants were counted in May of 2018 and 2019. There was no difference between years for any variable. Other weed biomass and field bindweed biomass and plant numbers were reduced (p < 0.05) by grazing (61.68 vs. 41.67 g bindweed biomass m⁻² for ungrazed and grazed management, respectively, and 108.5 and 56.8 bindweed plants m⁻² for ungrazed and grazed management, respectively). Otherwise, perennial grass production was unaffected by either year or management. These results indicate that grazing can be an effective tool to reduce field bindweed competition in warm-season perennial grass pastures. Full article

Accurate estimation aboveground biomass (AGB) in winter wheat is crucial for yield assessment but remains challenging to achieve non-destructively. Unmanned aerial vehicle (UAV)-based remote sensing offers a promising solution at the plot level. Traditional field sampling methods, such as random plant selection or full-quadrat harvesting, are labor intensive and may introduce substantial errors compared to the canopy-level estimates obtained from UAV imagery. This study proposes a novel method using Fractional Vegetation Coverage (FVC) to adjust field-sampled AGB to per-plant biomass, enhancing the accuracy of AGB estimation using UAV imagery. Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. The aim was to evaluate the performance of multimodal data in estimating winter wheat leaves, spikes, stems, and total AGB. Results demonstrated that (1) FVC-adjusted per-plant biomass significantly improved correlations with most indicators, particularly during the filling stage, when the correlation between leaf biomass and NDVI increased by 56.1%; (2) RF and NN models outperformed SVM, with the optimal accuracies being R² = 0.709, RMSE = 0.114 g for RF, R² = 0.66, RMSE = 0.08 g for NN, and R² = 0.557, RMSE = 0.117 g for SVM. Notably, the RF model achieved the highest prediction accuracy for leaf biomass during the flowering stage (R² = 0.709, RMSE = 0.114); (3) among different water treatments, the R² values of water and drought treatments were higher 0.723 and 0.742, respectively, indicating strong adaptability. This study provides an economically effective method for monitoring winter wheat growth in the field, contributing to improved agricultural productivity and fertilization management. Full article

Salvia pratensis L. is a valuable medicinal plant rich in bioactive compounds, yet its allelopathic potential remains underexplored. This study evaluated allelopathic effects and total phenolic (TPC) and flavonoid (TFC) contents of water extracts from the dry aboveground biomass of S. pratensis. To assess their selectivity and potential application in sustainable weed management, extracts at five different concentrations were tested on the germination and early growth of lettuce, radish, tomato, and carrot. The results demonstrated that the phytotoxic effects of S. pratensis extracts were both concentration- and species-dependent. Higher extract concentrations significantly inhibited germination and seedling growth, while lower concentrations of extracts stimulated shoot elongation by up to 30% compared to the control. Phytochemical analysis revealed that S. pratensis extracts contain notable TPC and TFC contents, with their concentrations increasing consistently with the extract concentration. Correlation analysis showed that higher TPC and TFC contents were strongly negatively correlated with germination and seedling growth parameters. Radish exhibited the highest sensitivity to the extracts, while lettuce was the most tolerant. Further research under field conditions is needed to assess the efficacy, selectivity, and practical potential of S. pratensis extracts in sustainable crop production systems. Full article

Pinus yunnanensis, a native tree species in southwest China, is shading-tolerant and ecologically significant. Light has a critical impact on plant physiology, and decapitation improves canopy light penetration and utilization efficiency. The study of allometric relationships is well-known in forestry, forest ecology, and related fields. Under control (full daylight exposure, 0% shading), L1 (partial shading, 25% shading), L2 (medium shading, 50% shading), and L3 (serious shading, 75% shading) levels, this study used the decapitation method. The results confirmed the effectiveness of decapitation in annual P. yunnanensis and showed that the main stem maintained isometric growth in all shading treatments, accounting for 26.8% of the individual plant biomass, and exhibited dominance in biomass allocation and high shading sensitivity. These results also showed that lateral roots exhibited a substantial biomass proportion of 12.8% and maintained more than 0.5 of higher plasticity indices across most treatments. Moreover, the lateral root exhibited both the lowest slope in 0.5817 and the highest significance (p = 0.023), transitioning from isometric to allometric growth under L1 shading treatment. Importantly, there was a positive correlation between the biomass allocation of an individual plant and that of all components of annual P. yunnanensis. In addition, the synchronized allocation between main roots and lateral branches, as well as between main stems and lateral roots, suggested functional integration between corresponding belowground and aboveground structures to maintain balanced resource acquisition and architectural stability. At the same time, it has been proved that the growth of lateral roots can be accelerated through decapitation. Important scientific implications for annual P. yunnanensis management were derived from these shading experiments on allometric growth. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

To investigate the remediation efficiency of different plant species on cadmium (Cd)-contaminated soil, this study conducted a pot experiment with two woody species (Populu adenopoda and Salix babylonica) and two herbaceous species (Artemisia argyi and Amaranthus hypochondriacus). Soils were collected from an abandoned coal mine and adjacent pristine natural areas within the dam-adjacent section of the Three Gorges Reservoir Area to establish three soil treatment groups: unpolluted soil (T1, 0.18 mg·kg⁻¹ Cd), a 1:1 mixture of contaminated and unpolluted soil (T2, 0.35 mg·kg⁻¹ Cd), and contaminated coal mine soil (T3, 0.54 mg·kg⁻¹ Cd). This study aimed to investigate the growth status of plants, Cd accumulation and translocation characteristics, and the relationship between them and soil environmental factors. Woody plants exhibited significant advantages in aboveground biomass accumulation. Under T3 treatment, the Cd extraction amount of S. babylonica (224.93 mg) increased by about 36 times compared to T1, and the extraction efficiency (6.42%) was significantly higher than other species. Among the herbaceous species, A. argyi showed the maximum Cd extraction amount (66.26 mg) and extraction efficiency (3.11%) during T2 treatment. While A. hypochondriacus exhibited a trend of increasing extraction amount but decreasing extraction efficiency with increasing concentration. With the exception of S. babylonica under T1 treatment (BCF = 0.78), the bioconcentration factor was greater than 1 in both woody (BCF = 1.39–6.42) and herbaceous species (BCF = 1.39–3.11). However, herbaceous plants demonstrated significantly higher translocation factors (TF = 1.58–3.43) compared to woody species (TF = 0.31–0.87). There was a significant negative correlation between aboveground phosphorus (P) content and root Cd (p < 0.05), while underground nitrogen (N) content was positively correlated to aboveground Cd content (p < 0.05). Soil total N and available P were significantly positively correlated with plant Cd absorption, whereas total potassium (K) showed a negative correlation. This study demonstrated that woody plants can achieve long-term remediation through biomass advantages, while herbaceous plants, with their high transfer efficiency, are suitable for short-term rotation. In the future, it is suggested to conduct a mixed planting model of woody and herbaceous plants to remediate Cd-contaminated soils in the tailing areas of reservoir areas. This would synergistically leverage the dual advantages of root retention and aboveground removal, enhancing remediation efficiency. Concurrent optimization of soil nutrient management would further improve the Cd remediation efficiency of plants. Full article

Forests provide habitat for diverse species and play a key role in mitigating climate change. Remote sensing enables efficient monitoring of many forest attributes across vast areas, thus supporting effective and efficient management strategies. This study aimed to identify an effective combination of remote sensing sensors for estimating biophysical variables in Mediterranean and temperate forests that can be easily translated into an operational context. Aboveground biomass (AGB), canopy height (CH), and forest canopy cover (FCC) were estimated using a combination of optical (Sentinel-2, Landsat) and radar sensors (Sentinel-1 and TerraSAR-X/TanDEM-X), along with records of past forest disturbances and topography-related variables. As a reference, lidar-derived AGB, CH, and FCC were used. Model performance was assessed not only with standard approaches such as out-of-bag sampling but also with completely independent lidar-derived reference datasets, thus enabling evaluation of the model’s temporal inference capacity. In Mediterranean forests, models based on optical imagery outperformed the radar-enhanced models when estimating FCC and CH, with elevation and spectral indices being key predictors of forest structure. In contrast, in denser temperate forests, radar data (especially X-band relative heights) were crucial for estimating CH and AGB. Incorporating past disturbance data further improved model accuracy in these denser ecosystems. Overall, this study underscores the value of integrating multi-source remote sensing data while highlighting the limitations of temporal extrapolation. The presented methodology can be adapted to enhance forest variable estimation across many forest ecosystems. Full article

Accurate quantification of above-ground biomass (AGB) and carbon sequestration is vital for monitoring terrestrial ecosystem dynamics, informing climate policy, and supporting carbon neutrality initiatives. However, conventional methods—ranging from manual field surveys to remote sensing techniques based solely on 2D vegetation indices—often fail to capture the intricate spectral and structural heterogeneity of forest canopies, particularly at fine spatial resolutions. To address these limitations, we introduce ForestIQNet, a novel end-to-end multimodal deep learning framework designed to estimate AGB and associated carbon stocks from UAV-acquired imagery with high spatial fidelity. ForestIQNet combines dual-stream encoders for processing multispectral UAV imagery and a voxelized Canopy Height Model (CHM), fused via a Cross-Attentional Feature Fusion (CAFF) module, enabling fine-grained interaction between spectral reflectance and 3D structure. A lightweight Transformer-based regression head then performs multitask prediction of AGB and CO₂e, capturing long-range spatial dependencies and enhancing generalization. Proposed method achieves an R² of 0.93 and RMSE of 6.1 kg for AGB prediction, compared to 0.78 R² and 11.7 kg RMSE for XGBoost and 0.73 R² and 13.2 kg RMSE for Random Forest. Despite its architectural complexity, ForestIQNet maintains a low inference cost (27 ms per patch) and generalizes well across species, terrain, and canopy structures. These results establish a new benchmark for UAV-enabled biomass estimation and provide scalable, interpretable tools for climate monitoring and forest management. Full article

Accurate biomass estimation is important for forest management and climate change mitigation. This study evaluates the potential of using LiDAR (Light Detection and Ranging) data, acquired through Unmanned Aerial Vehicles (UAVs), for estimating above-ground and total biomass in Eucalyptus globulus and Pinus pinaster stands in central and northern Portugal. The acquired LiDAR point clouds were processed to extract structural metrics such as canopy height, crown area, canopy density, and volume. A multistep variable selection procedure was applied to reduce collinearity and select the most informative predictors. Multiple linear regression (MLR) models were developed and validated using field inventory data. Random Forest (RF) models were also tested for E. globulus, enabling a comparative evaluation between parametric and machine learning regression models. The results show that the 25th height percentile, canopy cover density at two meters, and height variance demonstrated an accurate biomass estimation for E. globulus, with coefficients of determination (R²) varying between 0.86 for MLR and 0.90 for RF. Although RF demonstrated a similar predictive performance, MLR presented advantages in terms of interpretability and computational efficiency. For P. pinaster, only MLR was applied due to the limited number of field data, yet R² exceeded 0.80. Although absolute errors were higher for Pinus pinaster due to greater biomass variability, relative performance remained consistent across species. The results demonstrate the feasibility and efficiency of UAV LiDAR point cloud data for stand-level biomass estimation, providing simple and effective models for biomass estimation in these two species. Full article

Accurate estimation of forest aboveground biomass (AGB) and understanding its ecological drivers are vital for carbon monitoring and sustainable forest management. However, AGB estimation using remote sensing is hindered by signal saturation in high-biomass areas and insufficient attention to ecological structural factors. Focusing on Guangdong Province, this study proposes a novel approach that spatially extrapolates airborne LiDAR-derived Forest structural parameters and integrates them with Sentinel-1/2 data to construct an AGB prediction model. Results show that incorporating structural parameters significantly reduces saturation effects, improving prediction accuracy and AGB maximum range in high-AGB regions (R² from 0.724 to 0.811; RMSE = 10.64 Mg/ha; max AGB > 180 Mg/ha). Using multi-scale geographically weighted regression (MGWR), we further examined the spatial influence of forest type, age structure, and species mixture. Forest age showed a strong positive correlation with AGB in over 95% of the area, particularly in mountainous and hilly regions (coefficients up to 1.23). Species mixture had positive effects in 87.7% of the region, especially in the north and parts of the south. Natural forests consistently exhibited higher AGB than plantations, with differences amplifying at later successional stages. Highly mixed natural forests showed faster biomass accumulation and higher steady-state AGB, highlighting the regulatory role of structural complexity and successional maturity. This study not only mitigates remote sensing saturation issues but also deepens understanding of spatial and ecological drivers of AGB, offering theoretical and technical support for targeted carbon stock assessment and forest management strategies. 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

The integration of different agroecological practices could significantly mitigate the impact of climate change. Therefore, a 2-year field experiment on organic zucchini was carried out to study the effects of clover (Trifolium alexandrinum L.) cover crop management (green manure, GM vs. flattening using a roller crimper, RC), compared to a control without cover (CT). This agroecological practice was tested in combination with the following different fertilizer treatments: T1. compost produced by co-composting coal mining wastes with municipal organic wastes compost plus urea; T2. compost produced with the same matrices as T1, replacing urea with lawn mowing residues; T3. non-composted mixture of the industrial matrices; T4. on-farm compost obtained from crop residues. The GM management showed the highest marketable yield and aboveground biomass of zucchini, with both values higher by approximately 38% than those recorded in CT. The T1, T2, and T3 treatments showed higher SOC values compared to T4 in both years, with a gradual increase in SOC over time. The residual effect of fertilization on SOC showed a smaller reduction in T3 and T4 than in T1 and T2, in comparison with the levels recorded during the fertilization years, indicating a higher persistence of the applied organic matter in these treatments. The findings of this study pointed out that combining organic fertilization and cover cropping is an effective agroecological practice to maintain adequate zucchini yields and enhance SOC levels in the Mediterranean environment. Full article

Although the benefits of rational grazing by polygastric animals are well known, little is understood about how chicken grazing affects soil biological health and its capacity to store organic matter. This study aimed to assess the impact of long-term free-range chicken grazing in an olive grove on the soil chemical and biochemical properties, including the total organic carbon (TOC), total nitrogen (TN), microbial biomass (Cmic), basal respiration, and microbial community structure, as well as the soil’s capability to stock organic carbon and total nitrogen. A field experiment was conducted in an olive grove grazed by chickens for over 20 years, with the animal load decreasing with distance from the poultry houses. At 20 m, where the chicken density was highest, the soils showed reduced OC and TN contents and a decline in fungal biomass. This was mainly due to the loss of both aboveground vegetation and root biomass from intensive grazing. At 50 m, where grazing pressure was lower, the soil OC, TN, and microbial community size and activity were similar to those in a control, ungrazed area. These findings suggest that high chicken density can negatively affect soil health, while moderate grazing allows for the recovery of vegetation and soil organic matter. Rational management of free-range chicken grazing, particularly through the control of chicken density or managing grazing time and frequency, is therefore recommended to preserve soil functions and fertility. Full article

Grassland biomass yield reflects a complex interaction of management intensity and environmental factors, yet quantifying the relative role of practices such as mowing and fertilization remains challenging. In this study, we introduce a multi-class machine learning framework to predict above-ground biomass on 150 permanent grassland plots across eight years (2009–2016) in Germany’s Biodiversity Exploratories and to evaluate the influence of key management variables. Following rigorous data cleaning, imputation of missing nitrogen values, feature standardization, and encoding of categorical practices, we trained CatBoost classifiers optimized via Bayesian hyperparameter search and mitigated class imbalance with ADASYN oversampling. We assessed model performance under binary, three-class, four-class, and five-class quantile-based categorizations, achieving test accuracies of 0.76, 0.57, 0.42, and 0.38, respectively. Across all schemes, mowing frequency and mineral nitrogen input emerged as the dominant predictors, while secondary variables such as drainage and conditioner use contributed as well. These results demonstrate that broad biomass categories can be forecast reliably from standardized management records, whereas finer distinctions necessitate additional environmental information or automated sensing to capture nonlinear effects and reduce reporting bias. This work shows both the potential and the limits of machine learning for informing sustainable grassland management and explainability thereof. Frequent mowing and higher mineral nitrogen inputs explained most of the predictable variation, enabling a 76% accurate separation of low and high biomass categories. Predictive accuracy fell below 60% for finer class resolutions, indicating that management records alone are insufficient for detailed yield forecasts without complementary environmental data. Full article