Search Results (13,075)

The sustainability of rural areas depends on effective wastewater management to reduce human impact on the environment, including the risk of pollution to surface water, groundwater, and soil from human waste. However, organized sanitation systems, which include pipeline networks and wastewater treatment plants in rural communities with low population densities, often have very low profitability and cost-efficiency, which greatly reduces their acceptance and residents’ willingness to pay. This study examines the economic profitability and cost-efficiency of selected on-site household sewage collection and treatment systems operating under real economic conditions in Poland. An evaluation was conducted on seven contemporary models of individual bioreactors, as well as a standard anaerobic septic tank equipped with drainage filters. Additionally, all options were tested on permeable and poorly permeable soils. For each variant, investment costs, as well as operation and maintenance expenses, were calculated. Financial evaluation utilized indicators of economic profitability and cost-efficiency, including the Payback Period, Net Present Value, Benefits–Cost Ratio, and Dynamic Generation Costs. The potential financial benefits included savings from avoiding the use of holding septic tanks and sewage transport by slurry wagons. All the studied designs of on-site sanitary sewage management showed significant economic feasibility and cost-efficiency. Full article

An unmanned aerial vehicle (UAV) multi-spectral system provides a monitoring platform to rapidly obtain crop spectral information that can reflect crop nitrogen status for the generation of dynamic variable-rate nitrogen (VRN). To improve the accuracy of VRN prescription maps, a method of generating VRN prescription maps on the basis of the vegetation index was proposed, and the effects of UAV flight time and altitude on VRN prescription maps were analyzed. The experimental site was located in Dacaozhuang, Hebei Province, China, and the experimental crop was winter wheat (Lunxuan 145). The flight altitudes of the UAV system were set to 50, 70 and 90 m. The flight times were set to 8:00 a.m., 11:00 a.m., 2:00 p.m. and 5:00 p.m. local time. The flight area was 1.18 ha with a 60° rotation angle under a three-span center pivot irrigation system with an overhang. UAV flight missions were executed during the jointing, heading, and grain filling phases of winter wheat. There were 90 management zones with pie shapes in total, which were composed of a 10° angle in the rotation direction and 4 sprinklers along the lateral direction. The vegetation indices (VIs) which are closely related to crop nutrient status were selected and used to generate distribution maps, which were superimposed with the management zones to generate VRN prescription maps. The results demonstrated that the red-edge soil adjusted vegetation index (RESAVI) was relatively more sensitive to the nitrogen status of winter wheat than the other VIs were. The RESAVI distributions were stable during periods with a solar elevation angle greater than 50° (11:00 a.m.–2:00 p.m. local time), and the VRN prescription maps were similar, with the overlap percentage of the same fertilization grade being greater than 80% and the relative error of the fertilization amount being less than 5%. Compared with that at 2:00 p.m., the overlap percentage of the same fertilization grade was 56.6% in both seasons at 8:00 a.m., whereas flights at 5:00 p.m. exhibited overlaps of 70.9% and 44.6% in the 2023 and 2024 seasons, respectively. Conversely, the flight altitude had little influence on the fertilizer amount and VRN prescription maps. The difference in the amount of fertilizer used was less than 3% at different flight altitudes. The required time is half of that for a 50 m flight when the flight altitude is 70 m and one third of that when the flight altitude is 90 m. Our study recommended operating the UAV multi-spectral system at solar elevation angles greater than 50° when generating VRN prescription maps of winter wheat, and the flight height can be adjusted according to the field area and the endurance time of the UAV. Full article

The agricultural sector has a significant impact on the global carbon cycle, contributing substantially to greenhouse gas (GHG) emissions through various practices and processes. This review paper examines the significant role of the agricultural sector in the global carbon cycle, highlighting its substantial contribution to GHG emissions through diverse practices and processes. The study explores the trends and spatial distribution of agricultural GHG emissions at both the global level and within the European Union (EU). Emphasis is placed on the principal gases released by this sector—methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂)—with detailed attention to their sources, levels, environmental impacts, and key strategies to mitigate and control their effects, based on the latest scientific data. The paper further investigates emissions originating from livestock production, along with mitigation approaches including feed additives, selective breeding, and improved manure management techniques. Soil-derived emissions, particularly N₂O and CO₂ resulting from fertilizer application and microbial activity, are thoroughly explored. Additionally, the influence of various agricultural practices such as tillage, crop rotation, and fertilization on emission levels is analyzed, supported by updated data from recent literature. Special focus is given to the underlying mechanisms that regulate these emissions and the effectiveness of management interventions in reducing their magnitude. The research also evaluates current European legislative measures aimed at lowering agricultural emissions and promoting climate-resilient, sustainable farming systems. Various mitigation strategies—ranging from optimized land and nutrient management to the application of nitrification inhibitors and soil amendments are assessed for both their practical feasibility and long-term impact. Full article

The Loess Plateau (China) is an ecologically fragile region where understanding the impact of forest naturalness on soil carbon content is critical for ecological restoration and enhancing carbon sequestration. This study investigates this relationship in the Cuiying Mountain area (Yuzhong County, Lanzhou City), a representative landscape of the semi-arid Loess Plateau. The Cuiying Mountain ecosystem is characterized by coniferous forests and Gray-cinnamon soils. We assessed forest naturalness using several key indicators: herb coverage, shrub coverage, tree biodiversity, and stand structural attributes. The results revealed a generally low level of forest naturalness at Cuiying Mountain. Although herb coverage was high, shrub coverage was minimal (2.1%), and tree biodiversity was low (Shannon index = 0.09). The stand structure was simple, characterized by considerable variation in individual tree sizes and a single canopy layer (mean mingling degree = 0.14). This structural simplicity aligns with the area’s history of plantation management. Furthermore, analysis of soil physicochemical properties and their relationship with plant diversity identified plant diversity as a significant factor influencing soil carbon content. The strongest correlation was observed between plant species number and topsoil organic carbon (r = 0.77), indicating a particularly pronounced effect of plant diversity on surface soil organic carbon. In summary, while forest naturalness at Cuiying Mountain is generally low, increased plant diversity enhances the accumulation of litter/root exudates and carbonates, suggesting that enhancing plant diversity is an effective strategy for increasing total soil carbon content. This study provides valuable insights for refining ecological restoration practices and strengthening the soil carbon sink function in forest ecosystems across the Loess Plateau and similar semi-arid regions. Full article

Soil aggregates are critical determinants of soil erosion resistance and nutrient retention capacity, while freeze–thaw cycles (FTCs) induce the structural reorganization of soil aggregates, thereby altering soil stability and influencing soil organic carbon (SOC) sequestration. This study was located in the Minjia River Basin in the typical seasonal freeze–thaw areas of the Loess Plateau and aimed to quantify the effects of FTCs on soil aggregate stability and SOC content under different land use types. Farmland, grassland, and forestland with more than 20 years of usage in the region were selected, and a 0–20 cm soil layer was subjected to seven FTCs (−8 °C to 20 °C), followed by wet and dry sieving classification, focusing on soil aggregate distribution, aggregate stability, mean weight diameter (MWD), geometric mean diameter (GMD), aggregate particle fractal dimension (APD), and SOC content of the aggregate. The results showed that soil aggregates in all land use types were dominated by macroaggregates (>2 mm), with the proportion in forestland (61–63%) > grassland (54–58%) > farmland (38–51%). FTCs enhanced aggregate stability across all land use types, especially in farmland. Concurrently, FTCs reduced the SOC content in all aggregate size fractions, with reduction rates ranging from farmland (9.00–21%) to grassland (4–26%) to forestland (5–31%). Notably, FTCs significantly increased the contribution of 2–5 mm water-stable (WS) aggregates to SOC sequestration, with increment rates of 86% (farmland), 80% (grassland), and 86% (forestland). Furthermore, FTCs altered the correlation between SOC content and aggregate stability. Specifically, the positive correlations of SOC with MWD and GMD were strengthened in aggregates < 0.5 mm but weakened in aggregates >0.5 mm. These findings advance our understanding of the coupled mechanisms underlying soil erosion and carbon cycling across land uses under freeze–thaw, providing a theoretical basis for ecosystem restoration and optimized soil carbon management in cold regions. Full article

Water-extractable carbon is thought to originate from labile organic carbon pools and has been used as an active carbon indicator for soil evaluation in numerous studies. This study aims to explore the regional variation patterns of water-extractable organic carbon (WEOC) and the environmental impact factors associated with it. It examines the variability of WEOC under different climatic conditions and land use types, including grasslands and woodlands, thereby enhancing our understanding of WEOC. We measured the WEOC in the surface soil layers (0–10 cm) of woodlands and grasslands in arid and semi-arid regions. Additionally, we analyzed the effects of varying climatic conditions and land use types on WEOC based on data from literature research. WEOC distribution patterns diverged spatially from soil organic carbon (SOC). WEOC fractions decreased with increasing precipitation, and surface soil WEOC accumulation was observed in arid regions. This accumulation was more pronounced in forest-land, resulting in a more marked divergence in WEOC concentrations between woodlands and grasslands in arid regions. We inferred that the inconsistent correlation between WEOC and SOC across regions arises from their distinct distribution patterns along environmental humidity gradients. Owing to the climate sensitivity of WEOC, its surface soil accumulation in arid areas may increase the vulnerability of soil ecosystems, rendering them more susceptible to environmental disturbances. Such susceptibility could drive organic carbon loss and soil quality degradation. These findings hold promise for improving our understanding of WEOC dynamic, and will also give insight into refining soil carbon balance models and soil management strategies to address environmental changes. Full article

Accurate land cover mapping is essential for environmental monitoring and agricultural management. Sentinel-2 imagery, with high spatial resolution and open access, provides valuable opportunities for operational classification. Convolutional neural networks (CNNs) have demonstrated state-of-the-art results, yet their adoption is limited by high computational demands and limited methodological transparency. This study proposes a lightweight CNN for soil–vegetation classification, in Dolj County, Romania. The architecture integrates three convolutional blocks, global average pooling, and dropout, with fewer than 150,000 trainable parameters. A fully documented workflow was implemented, covering preprocessing, patch extraction, training, and evaluation, addressing reproducibility challenges common in deep leaning studies. Experiments on Sentinel-2 imagery achieved 91.2% overall accuracy and a Cohen’s kappa of 0.82. These results are competitive with larger CNNs while reducing computational requirements by over 90%. Comparative analyses showed improvements over an NDVI baseline and a favorable efficiency–accuracy balance relative to heavier CNNs reported in the literature. A complementary ablation analysis confirmed that the adopted three-block architecture provides the optimal trade-off between accuracy and efficiency, empirically validating the robustness of the proposed design. These findings highlight the potential of lightweight, transparent deep learning for scalable and reproducible land cover monitoring, with prospects for extension to multi-class mapping, multi-temporal analysis, and fusion with complementary data such as SAR. This work provides a methodological basis for operational applications in resource-constrained environments. Full article

Subsoil compaction is a major constraint in sugarcane production, limiting yields and reducing resource-use efficiency. This study presents WireDepth, an innovative cloud-connected monitoring system that leverages edge computing and IoT technologies for real-time, spatially aware analysis and visualization of subsoiling depth. The system integrates ultrasonic, laser, inclinometer, and potentiometer sensors mounted on the subsoiler, with on-board microcontroller processing and dual wireless connectivity (LoRaWAN and NB-IoT/LTE-M) for robust data transmission. A cloud platform delivers advanced analytics, including 3D depth maps and operational efficiency metrics. System accuracy was assessed using 300 reference depth measurements, with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) calculated per sensor. The inclinometer and potentiometer achieved the highest accuracy (MAPE of 0.92% and 0.84%, respectively), with no significant deviation from field measurements (paired t-tests, p > 0.05). Ultrasonic and laser sensors exhibited higher errors, particularly at shallow depths, due to soil debris interference. Correlation analysis confirmed a significant effect of depth on sensor accuracy, with laser sensors showing the strongest association (Pearson r = 0.457, p < 0.001). Field validation in commercial sugarcane fields demonstrated that WireDepth improves subsoiling precision, reduces energy waste, and supports sustainable production by enhancing soil structure and root development. These findings advance precision agriculture by offering a scalable, real-time solution for subsoiling management, with broad implications for yield improvement in compaction-affected systems. Full article

Identifying the key drivers behind the spatiotemporal dynamics of ecosystem service functions is essential for clarifying how ecosystems respond to environmental changes. Such insights deepen our understanding of the evolution of complex ecological processes and service functions, and provide critical references for ecological governance, policy-making, and the pursuit of high-quality development pathways. In this study, the Remote Sensing Ecological Index (RSEI) was first constructed for the upstream basin of the Danjiangkou Reservoir using satellite imagery (2015 and 2024). We then employed the InVEST model to quantify six ecosystem service functions and their corresponding services: water purification (total nitrogen and total phosphorus), soil retention (soil erosion), water yield, carbon storage, and habitat provision (habitat quality). Finally, this study analyzes the driving mechanisms as well as the coupling coordination degree between the RSEI and six ecosystem service functions. From 2015 to 2024, the area classified as “excellent” in RSEI significantly expanded from 263.34 km² (3.22%) to 2566.21 km² (31.38%), reflecting a substantial enhancement in ecological quality throughout the upstream basin. There is no serious imbalance in the coupling and coordination relationship between RSEI and the value of various ecosystem service functions. Although improvements in ecosystem quality generally enhanced overall ecosystem service functions, competition among certain services was still evident in localized areas. Future ecological management should, therefore, prioritize not only the protection of ecosystem quality but also the scientific allocation of service supply and demand, the optimization of human–land relationships, and the promotion of a virtuous ecosystem cycle. Full article

Rhizosphere hypoxia, caused by soil compaction and waterlogging, is a major constraint on agricultural productivity. It severely impairs crop growth and yield by inhibiting root aerobic respiration, disrupting energy metabolism, and altering the rhizosphere microecology. Micro-nano bubbles (MNBs) show significant potential for alleviating rhizosphere hypoxia due to their unique physicochemical properties, including large specific surface area, high oxygen dissolution efficiency, prolonged retention time, and negative surface charge. This paper systematically reviews the key characteristics of MNBs, particularly their enhanced mass transfer capacity and system stability, and outlines mainstream preparation methods such as cavitation, electrolysis, and membrane dispersion. And the multiple alleviation mechanisms of MNBs—including continuous oxygen release, improvement of soil pore structure, and regulation of rhizosphere microbial communities—are clarified. The combination of MNBs aeration and subsurface drip irrigation can increase soil aeration by 5%. When applied in soilless cultivation and conventional irrigation systems, MNBs enhance crop yield and nutrient use efficiency. For example, tomato yield can be increased by 12–44%. Furthermore, the integration of MNBs with water–fertilizer integration technology enables the synchronized supply of oxygen and nutrients, thereby optimizing the rhizosphere environment efficiently. This paper sorts out the empirical effects of MNBs in soilless cultivation and conventional irrigation, and provides directions for solving problems such as “insufficient oxygen supply to deep roots” and “reactive oxygen species (ROS) stress in sensitive crops”. Despite these significant advantages, the industrialization of MNBs still needs to overcome challenges including high equipment costs and insufficient precision in parameter control, so as to promote large-scale agricultural application and provide an innovative strategy for the management of rhizosphere hypoxia. Full article

Urban densification intensifies the heat island effect, threatening ecological security. Green spaces, as crucial spatial elements in regulating the urban thermal environment, remain poorly understood in terms of their morphological characteristics and regulatory mechanisms, with a lack of systematic quantification and recognition of diurnal variations. This study, focusing on Shanghai’s main urban area, constructs physiological, physical, and morphological variables of green spaces based on high-resolution remote sensing data and the MSPA landscape morphology analysis framework. By integrating machine learning models with the SHAP interpretation algorithm, it analyses the influence mechanism of green spaces on Land Surface Temperature (LST) and its non-linear characteristics from the perspective of diurnal variation. The results indicate the following: (1) Green spaces exhibit pronounced diurnal variation in LST influence. Daytime cooling is primarily driven by vegetation cover, vegetation activity, and surface albedo through evapotranspiration and shading; night-time cooling depends on soil moisture and green space spatial structure and is achieved via thermal storage-radiative heat dissipation and cold air transport. (2) Green space indicators exhibit pronounced nonlinearity and threshold effects on LST. Optimal cooling efficiency occurs under moderate vegetation activity and moderate humidity conditions, whereas extreme high humidity or high vegetation activity may induce heat retention effects. (3) Day–night thermal regulation mechanisms differ markedly. Daytime cooling primarily depends on vegetation transpiration and shading to suppress surface warming; night-time cooling is dominated by soil thermal storage release, longwave radiation dissipation, and ventilation transport, enabling cold air to diffuse across the city and establishing a stable, three-dimensional nocturnal cooling effect. This study systematically reveals the distinct diurnal cooling mechanisms of high-density urban green spaces, providing theoretical support for refined urban thermal environment management. Full article

Wildfires pose a growing threat to ecosystems, infrastructure, and public safety, particularly in the province of British Columbia (BC), Canada. In recent years, the frequency, severity, and scale of wildfires in BC have increased significantly, largely due to climate change, human activity, and changing land use patterns. This study presents a comprehensive, data-driven approach to wildfire prediction, leveraging advanced machine learning (ML) and deep learning (DL) techniques. A high-resolution dataset was constructed by integrating five years of wildfire incident records from the Canadian Wildland Fire Information System (CWFIS) with ERA5 reanalysis climate data. The final dataset comprises more than 3.6 million spatiotemporal records and 148 environmental, meteorological, and geospatial features. Six feature selection techniques were evaluated, and five predictive models—Random Forest, XGBoost, LightGBM, CatBoost, and an RNN + LSTM—were trained and compared. The CatBoost model achieved the highest predictive performance with an accuracy of 93.4%, F1-score of 92.1%, and ROC-AUC of 0.94, while Random Forest achieved an accuracy of 92.6%. The study identifies key environmental variables, including surface temperature, humidity, wind speed, and soil moisture, as the most influential predictors of wildfire occurrence. These findings highlight the potential of data-driven AI frameworks to support early warning systems and enhance operational wildfire management in British Columbia. Full article

Global soil moisture has undergone significant changes in recent decades due to climate change and vegetation greening. However, the seasonal and climate zonal variations in soil moisture dynamics at different depths, driven by both climate and vegetation, remain insufficiently explored. This study provides a comprehensive analysis of the global patterns in rootzone and surface soil moisture and leaf area index (LAI) across different seasons and climate zones, utilizing satellite observations from 1982 to 2020. We investigate how climatic factors and LAI influence soil moisture variations and quantify their dominant contributions. Furthermore, by employing key vegetation phenological indicators, namely the peak of growing season (POS) and the corresponding maximum LAI (LAI_MAX), we assess the feedback effects of vegetation phenology on soil moisture dynamics. The results indicate that the greening trend (as reflected by LAI increases) from 2000 to 2020 was significantly stronger than that observed during 1982–1999 across all seasons and climate zones. Both rootzone and surface soil moisture shifted from a decreasing (drying) trend (1982–1999) to an increasing (wetting) trend (2000–2020). From 1982 to 2020, the LAI induced moistening trends in both surface and rootzone soil moisture. In arid and temperate zones, precipitation drove rootzone soil moisture increases only during the summer. Among all seasons and climate zones, solar radiation induced the strongest surface soil drying in tropical summers, with a rate of −0.04 × 10⁻³ m³m⁻³/Wm⁻². For rootzone soil moisture, LAI dominated over individual climatic factors in winter and spring globally. In contrast, solar radiation became the primary driver during summer and autumn, followed by precipitation. For surface soil moisture, precipitation exhibited the strongest control in winter, but solar radiation surpassed it as the dominant factor from spring through autumn. In the tropical autumn, the sensitivity of rootzone and surface soil moisture to POS (and LAI_MAX) was highest, at 0.059 m³m⁻³·d⁻¹ (0.256 m³m⁻³/m²m⁻²) and 0.052 m³m⁻³·d⁻¹ (0.232 m³m⁻³/m²m⁻²), respectively. This research deepens the understanding of how climate and vegetation regulate soil moisture across different climate zones and seasons. It also provides a scientific basis for improving global soil moisture prediction models and managing water resource risks in the context of climate change. Full article

Accurate estimation of carbon sequestration at the single-tree scale is essential for understanding forest carbon dynamics and supporting precision forestry under global carbon-neutral goals. Traditional allometric models often neglect environmental variability, while data-driven machine learning approaches suffer from limited interpretability. To bridge this gap, we developed a hybrid prediction framework that integrates a Structural Causal Model (SCM) with an Enhanced Long Short-Term Memory (LSTM) network. Using 47-year observation data (1975–2022) of Mongolian oak (*Quercus mongolica* Fisch. ex Ledeb.) from the Laoyeling Ecological Station, the SCM was applied to infer causal relationships among growth and environmental factors, while the Enhanced-LSTM combined multiscale convolution and self-attention modules to capture nonlinear temporal dependencies. Results showed that the proposed SCM-Enhanced-LSTM achieved the highest predictive performance (R² = 0.944, RMSE = 0.079 kg, MAE = 0.064 kg), outperforming Bi-LSTM and XGBoost models by over 20% in accuracy and maintaining robustness under noise perturbations. Causal analysis identified soil moisture and stem diameter as the dominant drivers of carbon increment. This study provides a transparent, interpretable, and high-precision framework for single-tree carbon sequestration prediction, offering methodological support for fine-scale forest carbon accounting and sustainable management strategies. Full article

Tea plantations are one of the most intensive land-use systems in subtropical China, but the long-term effects on soil microbial functioning remain insufficiently understood. This study combined extracellular enzyme activity, ecoenzymatic stoichiometry, and partial least squares path modeling (PLS-PM) to assess the impacts of forest-to-tea conversion and plantation age on microbial nutrient acquisition and metabolic limitations. The results showed that tea plantations had significantly higher activities of carbon (C)-, nitrogen (N)-, and phosphorus (P)-acquiring hydrolases compared to adjacent pine forests, and oxidase activity increased significantly with plantation age, reaching a fivefold higher level in the oldest plantation. Soil acidification, decreased soil organic carbon, and shifts in microbial composition (decline in bacteria and actinomycetes, increase in fungi) were the main drivers of these changes. The study indicates that tea planting intensifies microbial limitations on carbon and phosphorus and shifts microbial metabolism toward oxidative pathways, which may destabilize soil carbon pools and reduce long-term fertility. These findings highlight the importance of balanced nutrient management in tea plantation practices. However, the study is limited by the short duration of field sampling. Future research should focus on long-term monitoring to better understand the sustained impacts of tea cultivation on soil microbial functions and explore the role of different management practices in mitigating these effects. Full article