Search Results (719)

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

This study proposes an integrated framework for sustainable tropical agriculture by combining biochemical waste valorization with spatial carbon footprint estimation in ‘Phulae’ pineapple production. Peel and eye residues from fresh-cut processing were enzymatically converted into rare sugar, achieving average conversion efficiencies of 35.28% for peel and 37.51% for eyes, with a benefit–cost ratio of 1.56 and an estimated unit cost of USD 0.17 per gram. A complementary zero-waste pathway produced functional gummy products using vinegar fermented from pineapple eye waste, with the preferred formulation scoring a mean of 4.32 out of 5 on a sensory scale with 158 untrained panelists. For spatial carbon modeling, the Bare Land Referenced Algorithm (BRAH) and Otsu thresholding were applied to multi-temporal Sentinel-2 and THEOS imagery to estimate plantation age, which strongly correlated with field-measured emissions (r = 0.996). This enabled scalable mapping of plot-level greenhouse gas emissions, yielding an average footprint of 0.2304 kg CO₂ eq. per kilogram of fresh pineapple at the plantation gate. Together, these innovations form a replicable model that aligns tropical fruit supply chains with circular economy goals and carbon-related trade standards. The framework supports waste traceability, resource efficiency, and climate accountability using accessible, data-driven tools suitable for smallholder contexts. By demonstrating practical value addition and spatially explicit carbon monitoring, this study shows how integrated circular and geospatial strategies can advance sustainability and market competitiveness for the ‘Phulae’ pineapple industry and similar perennial crop systems. Full article

The construction and development of digital infrastructure have emerged as a crucial indicator of national competitiveness, which holds significant importance in driving the sustained growth of the national economy and the comprehensive advancement of society. This paper explores the impact of digital infrastructure on farm households’ scale management, aiming to reveal the role and potential of digital technology in agricultural modernization. Additionally, it seeks to offer a scientific foundation for the government to formulate agricultural policies and advance agricultural modernization. Using the OLS (Ordinary Least Squares) model, moderating effect model, and other methods, this study investigates how digital infrastructure affects farm households’ scale management based on micro-level research data of 2510 farm households from the CRRS (China Rural Revitalization Survey). The following conclusions are drawn: Firstly, the enhancement of digital infrastructure can motivate farm households to expand the land management area and increase the unit output of land. Secondly, farm households’ digital literacy positively moderates the effect of digital infrastructure on their land unit output; moreover, digital skills training for farm households exhibits a positive moderating effect on the influence of digital infrastructure on their management area. Finally, there is a heterogeneity in the impact of digital infrastructure on farm households’ scale management. Specifically, the promotion of farm households’ scale management is stronger in plain areas and weaker in hilly and mountainous areas; stronger for middle-aged and older and small-scale farm households; and weaker for youth groups and large-scale farm households. Based on this, this paper suggests increasing the investment in digital infrastructure construction, improving farm households’ digital literacy, carrying out digital skills training, and formulating differentiated regional policies for reference. Full article

Vegetation succession is a critical indicator of ecosystem structure and function and is often disrupted by the expansion of invasive species. However, ecosystem-scale studies elucidating invasion-driven succession mechanisms remain limited. This research focused on the Yancheng coastal salt marsh and analyzed the distribution variation of invasive species (Spartina alterniflora) and native species (Suaeda salsa and Phragmites australis) from 1987 to 2022 via the Google Earth Engine and random forest method. Logistic/Gaussian models were used to quantify land–sea distribution changes and vegetation succession trajectories. By integrating data on soil salinity, invasion duration, and fractional vegetation cover, generalized additive models (GAMs) were applied to identify the main factors influencing vegetation succession and to explore how Spartina alterniflora invasion affects the succession of salt marsh vegetation. The results indicated that the areas of Spartina alterniflora and Phragmites australis significantly increased by 3787.49 ha and 3452.60 ha in 35 years, respectively, contrasting with Suaeda salsa’s 82.46% decline. The FVC in the area has significantly increased by 42.10%, especially in the coexisted areas of different vegetation communities, indicating intensified interspecific competition. The overall trend of soil salinity was decreasing, with a decrease in soil salinity in native species areas from 0.72% to 0.37%. From the results of GAMs, soil salinity, tidal action, and invasion duration were significant factors influencing the distribution of native species, but salinity was not a significant factor affecting the Spartina alterniflora distribution. The findings revealed that the expansion of Spartina alterniflora changed the soil salinity and interspecific interactions, thereby altering the original plant community structure and establishing a new vegetation succession. This study enhances the understanding of the impacts of invasive species on ecosystems and offers theoretical support for salt marsh restoration. Full article

Common ragweed (Ambrosia artemisiifolia L.) has been identified as one of the most harmful invasive weed species in Europe due to its allergenic pollen and competitive growth in diverse habitats. In the first part of this review [Common Ragweed—Ambrosia artemisiifolia L.: A Review with Special Regards to the Latest Results in Biology and Ecology], its biological characteristics and ecological behavior were described in detail. In the current paper, control strategies are summarized, focusing on integrated weed management adapted to the specific habitat where the species causes damage—arable land, semi-natural vegetation, urban areas, or along linear infrastructures. A range of management methods is reviewed, including agrotechnical, mechanical, physical, thermal, biological, and chemical approaches. Particular attention is given to the spread of herbicide resistance and the need for diversified, habitat-specific interventions. Among biological control options, the potential of Ophraella communa LeSage, a leaf beetle native to North America, is highlighted. Furthermore, innovative technologies such as UAV-assisted weed mapping, site-specific herbicide application, and autonomous weeding robots are discussed as environmentally sustainable tools. The role of legal regulations and pollen monitoring networks—particularly those implemented in Hungary—is also emphasized. By combining traditional and advanced methods within a coordinated framework, effective and ecologically sound ragweed control can be achieved. Full article

This paper examines how demographic characteristics, institutional structures, and livelihood strategies shape household resilience to climate variability and drought in West Pokot County, one of Kenya’s most climate-vulnerable arid and semi-arid lands (ASALs). Using a mixed-methods approach, it combines household survey data with three statistical techniques: Multinomial Logistic Regression (MLR) assesses the influence of gender, age, and education on livestock ownership and livelihood choices; Multiple Correspondence Analysis (MCA) reveals patterns in institutional access and adaptive practices; and Stepwise Linear Regression (SLR) quantifies the relationship between resilience strategies and agricultural productivity. Findings show that demographic factors, particularly gender and education, along with access to veterinary services, drought-tolerant inputs, and community-based organizations, significantly shape resilience. However, trade-offs exist: strategies improving livestock productivity may reduce crop yields due to resource and labor competition. This study recommends targeted interventions, including gender-responsive extension services, integration of indigenous and scientific knowledge, improved infrastructure, and participatory governance. These measures are vital for strengthening resilience not only in West Pokot but also in other drought-prone ASAL regions across sub-Saharan Africa. Full article

Background: Optimal nutrition and training regimens are essential for athletes to maximize performance and recovery. Probiotic supplementation, through the modulation of the gut microbiota, and omega-3 fatty acids, known for their anti-inflammatory properties, may enhance physiological adaptations when combined with targeted training. This study evaluated the effects of probiotics and omega-3 supplementation, alongside ultra-short race pace training (USRPT), on performance metrics in competitive sprint swimmers. Methods: In this double-blind, placebo-controlled study, 60 male sprint swimmers (age: 19.2 ± 3.6 years; height: 182.2 ± 5.2 cm; weight: 81.6 ± 4.4 kg) with a minimum of five years of training experience, were randomly assigned to six groups (n = 10 per group): (1) Control (CON), (2) USRPT only, (3) Placebo + USRPT (PLA + USRPT), (4) Probiotics + USRPT (PRO + USRPT), (5) Omega-3 + USRPT (OMEGA + USRPT), and (6) Probiotics + Omega-3 + USRPT (PRO + OMEGA + USRPT). Over the eight-week intervention, the participants in PRO + USRPT consumed one multi-strain probiotic capsule daily (4.5 × 10¹¹ CFU) and a placebo capsule. Those in OMEGA + USRPT ingested 1000 mg of fish oil after lunch (500 mg EPA and 180 mg DHA per capsule) paired with a placebo capsule. The combined supplementation group (PRO + OMEGA + USRPT) received both probiotic and omega-3 capsules. The PLA + USRPT group consumed two starch capsules daily. The USRPT protocol was implemented across all the training groups, where the swimmers performed 17 sets of 25 m and 12.5 m sprints based on weekly recorded race times. Performance assessments included pre- and post-test measurements of sprint times (50 m and 100 m freestyle), vertical jump tests (both in water and on dry land), and other strength and endurance metrics (reaction time, agility T-test, sprint index, fatigue index, and velocity). Results: The combined intervention of probiotics and omega-3 with USRPT produced the greatest improvements in performance. The PRO + OMEGA + USRPT group reduced 50 m freestyle time by 1.92% (p = 0.002, pEta² = 0.286) and 100 m freestyle time by 2.48% (p = 0.041, pEta² = 0.229), demonstrating significant Time × Group interactions consistent with a synergistic effect. Additionally, the sprint index improved (pEta² = 0.139, p = 0.013) and reaction time decreased (pEta² = 0.241, p = 0.009) in the combined group, indicating enhanced anaerobic capacity and neuromuscular responsiveness compared to single interventions. Conclusions: This study suggests that combining probiotics and omega-3 supplementation with USRPT leads to synergistic improvements in sprint swimming performance, enhancing anaerobic power and recovery beyond what is achieved with individual interventions. This integrated approach may provide a practical strategy for competitive swimmers seeking to optimize their performance. Future studies should incorporate mechanistic markers, longer intervention durations, and diverse athlete populations to clarify further and extend these findings. Full article

Monoculture and legume–grass mixed cropping are the two most common planting methods, with mixed cropping generally demonstrating higher hay yield and superior nutritional quality compared to monoculture. However, research on legume–grass mixed cropping for establishing cultivated pastures in typical sand-fixing vegetation areas of the Mu Us Sandy Land remains scarce. These knowledge gaps have hindered the synergistic integration of forage production and ecological restoration in the region. This study conducted mixed cropping trials in the sand-fixing vegetation zone of the Mu Us Sandy Land using Dahurian wildrye (Elymus dahuricus), Mongolian wheatgrass (Agropyron mongolicum), and Standing milkvetch (Astragalus adsurgens) to investigate the effects of species combinations and planting ratios on forage productivity and nutritional quality, aiming to determine the optimal planting strategy. Results showed that in the first establishment year, the yield of all mixed cropping systems significantly exceeded that of monocultured Dahurian wildrye and Mongolian wheatgrass. All mixed cropping combinations exhibited land equivalent ratios (LER) and relative yield totals (RYT) below 1, indicating varying degrees of interspecific competition during the first year, with grass species generally demonstrating stronger competitive abilities than legumes. Mixed-cropped forages showed higher crude protein, crude fat, and crude ash content compared to monocultures, alongside lower neutral detergent fiber (NDF) and acid detergent fiber (ADF) levels, suggesting improved relative feed value (RFV). Among the combinations, E₅A₅ and E₆A₄ (5:5 and 6:4 ratio of Dahurian wildrye to Standing milkvetch) achieved higher RFV, with RFV gradually declining as the legume proportion decreased. In conclusion, both monoculture and legume–grass mixed cropping are viable in the Mu Us Sandy Land’s sand-fixing vegetation areas and the E₅A₅ combination (5:5 ratio of Dahurian wildrye to Standing milkvetch) as having the highest overall score, demonstrating that this mixed cropping ratio optimally balances yield and nutritional quality, making it the recommended planting protocol for the region. This mixed cropping system offers a theoretical foundation for efficiently establishing artificial pastures in the Mu Us Sandy Land, supporting regional pastoral industry development and desertification mitigation. Full article

Intercropping legumes offers a sustainable approach to enhance resource efficiency and yields, yet the effects of different legume densities and nitrogen addition levels on soil quality within such systems remain unclear. We conducted a comparative analysis of crop yield, nitrogen use efficiency, and soil quality between intercropping and monoculture systems, and further examined the effects of four planting densities (D1: 210 kg ha⁻¹, six rows; D2: 280 kg ha⁻¹, eight rows; D3: 350 kg ha⁻¹, ten rows) and four nitrogen application levels (N0: 0 kg ha⁻¹; N1: 80 kg ha⁻¹; N2: 160 kg ha⁻¹; N3: 240 kg ha⁻¹) within a jujube–alfalfa (Ziziphus jujuba Mill. and Medicago sativa L. respectively) intercropping system. The results showed that intercropping significantly enhanced land productivity within the agricultural system, with the highest yields (alfalfa: 13790 kg ha⁻¹; jujube: 3825 kg ha⁻¹) achieved at an alfalfa planting density of 280 kg ha⁻¹. While the intercropping systems generally improved productivity, an alfalfa planting density of 350 kg ha⁻¹ resulted in an actual yield loss due to excessive nutrient competition at higher densities. As the planting density of alfalfa increased, its competitive ratio declined, whereas the competitive ratio of jujube trees increased. Compared to monocropping systems, intercropping systems demonstrated a clear trend of enhanced nitrogen utilization efficiency and improved soil quality, particularly at an alfalfa planting density of 280 kg ha⁻¹. At an alfalfa density of 280 kg ha⁻¹, the intercropping system exhibited increases of 15.13% in nitrogen use efficiency (NUE), 46.60% in nitrogen partial factor productivity (NPFP), and 32.74% in nitrogen nutrition index (NNI), as well as improvements in soil quality of 19.53% at a depth of 0–20 cm and 15.59% at a depth of 20–40 cm, compared to the monoculture system. Further analysis revealed that nitrogen utilization efficiency initially increased and then decreased with a rising competitive ratio of alfalfa. Accordingly, soil quality was improved along with the enhanced nitrogen utilization efficiency. Thus, at an alfalfa planting density of 280 kg ha⁻¹, resource use efficiency and soil quality were maximized as a result of optimal interspecific competitiveness and the highest nitrogen use efficiency, with minimal influence from the application of nitrogen fertilizer. Full article

In recent years, remote sensing imagery has become indispensable for applications such as environmental monitoring, land use classification, and urban planning. However, the physical constraints of satellite imaging systems frequently limit the spatial resolution of these images, impeding the extraction of fine-grained information critical to downstream tasks. Super-resolution (SR) techniques thus emerge as a pivotal solution to enhance the spatial fidelity of remote sensing images via computational approaches. While deep learning-based SR methods have advanced reconstruction accuracy, their high computational complexity and large parameter counts restrict practical deployment in real-world remote sensing scenarios—particularly on edge or low-power devices. To address this gap, we propose LSANet, a lightweight SR network customized for remote sensing imagery. The core of LSANet is the large separable kernel attention mechanism, which efficiently expands the receptive field while retaining low computational overhead. By integrating this mechanism into an enhanced residual feature distillation module, the network captures long-range dependencies more effectively than traditional shallow residual blocks. Additionally, a residual feature enhancement module, leveraging contrast-aware channel attention and hierarchical skip connections, strengthens the extraction and integration of multi-level discriminative features. This design preserves fine textures and ensures smooth information propagation across the network. Extensive experiments on public datasets such as UC Merced Land Use and NWPU-RESISC45 demonstrate LSANet’s competitive or superior performance compared to state-of-the-art methods. On the UC Merced Land Use dataset, LSANet achieves a PSNR of 34.33, outperforming the best-baseline HSENet with its PSNR of 34.23 by 0.1. For SSIM, LSANet reaches 0.9328, closely matching HSENet’s 0.9332 while demonstrating excellent metric-balancing performance. On the NWPU-RESISC45 dataset, LSANet attains a PSNR of 35.02, marking a significant improvement over prior methods, and an SSIM of 0.9305, maintaining strong competitiveness. These results, combined with the notable reduction in parameters and floating-point operations, highlight the superiority of LSANet in remote sensing image super-resolution tasks. Full article

Background: The Port of Santa Marta, located on Colombia’s northern Caribbean coast, plays a vital role in the country’s maritime trade, particularly in the export of agricultural and perishable goods. This raises the question: how competitive is Santa Marta’s container terminal compared to national and regional ports, and what strategic factors shape its performance within the Colombia and Latin American maritime logistics system? Methods: This study evaluates the port’s competitiveness by applying Porter’s Extended Diamond Model. A mixed-methods ap-proach was employed, combining structured surveys and interviews with port stakeholders and operational data analysis. A competitiveness matrix was developed and examined using standardized residuals and L1 regression to identify critical performance gaps and strengths. Results: The analysis reveals several competitive advantages, including the port’s strategic location, natural deep-water access, and advanced infrastructure for refrigerated cargo. It also benefits from skilled labour and proximity to global shipping routes, such as the Panama Canal. Nonetheless, challenges remain in storage capacity, limited road connectivity, and insufficient public investment in hinterland infrastructure. Conclusions: While the Port of Santa Marta shows strong maritime capabilities and spe-cialized services, addressing its land-side and institutional constraints is essential for positioning it as a resilient, competitive logistics hub in the Latin American and Caribbean region. Full article

As a typical agricultural waste, the resource utilization of corn stover (CS) plays a crucial role in the coordinated optimization of ecological and economic benefits. In order to enhance the utilization of CS resources, Lentilactobacillus (L.) buchneri (LB) and different proportions of Artemisia argyi (AA) were added to CS to investigate the impact of additives on the fermentation quality and aerobic stability of corn stover silage (CSS). This study revealed that the separate addition of AA or LB in CS effectively improved the silage quality and aerobic stability. Specifically, LB exhibited the lowest pH value of 3.72 at 90 d of fermentation, while the NH₃-N content was 0.07 g/kg DM during the anaerobic fermentation stage and 0.19 g/kg DM during the aerobic exposure stage (p < 0.05). Mixing 30% AA increased the lactic acid content, lowered the pH, maintained a higher relative abundance of Lactobacillus, and reduced mycotoxin levels. In terms of aerobic stability, all AA-treated groups demonstrated superior performance compared to the LB treatment. Additionally, it was observed, that in the 30% AA group, Candida exhibited the highest relative abundance. Importantly, the addition of AA upregulated carbohydrate metabolism and lipid metabolism during the ensiling process, and their relative abundances remained high during aerobic exposure. Fully utilizing CS resources as feed to provide fiber and nutrients for ruminants can not only reduce the pressure on forage demand but meet the development needs of “grain-saving” animal husbandry, which is conducive to solving the contradictions of “human–animal competition for food” and “human animal competition for land”. Full article

This systematic review, registered in PROSPERO (CRD42025101243), aimed to evaluate how specific badminton shoe design features influence lower-limb biomechanics, injury risk, and sport-specific performance. A comprehensive search in six databases yielded 445 studies, from which 10 met inclusion criteria after duplicate removal and eligibility screening. The reviewed studies focused on modifications involving forefoot bending stiffness, torsional stiffness, lateral-wedge hardness, insole and midsole hardness, sole structure, and heel curvature. The most consistent biomechanical benefits were associated with moderate levels of forefoot and torsional stiffness (e.g., 60D) and rounded heel designs. Increased forefoot bending stiffness was associated with reduced foot torsion and knee loading during forward lunges. Torsional stiffness around 60D provided favorable ankle support and reduced knee abduction, suggesting potential protection against ligament strain. Rounded heels reduced vertical impact forces and promoted smoother knee–ankle coordination, especially in experienced athletes. Lateral-wedge designs improved movement efficiency by reducing contact time and enhancing joint stiffness. Harder midsoles, however, resulted in increased impact forces upon landing. Excessive stiffness in any component may restrict joint mobility and responsiveness. Studies included 127 male-dominated (aged 18–28) competitive athletes, assessing kinematics, impact forces, and coordination during sport-specific tasks. The reviewed studies predominantly involved male participants, with little attention to sex-specific biomechanical differences such as joint alignment and foot structure. Differences in testing methods and movement tasks further limited direct comparisons. Future research should explore real-game biomechanics, include diverse athlete populations, and investigate long-term adaptations. These efforts will contribute to the development of performance-enhancing, injury-reducing badminton shoes tailored to the unique demands of the sport. Full article

Forest shelterbelts and windbreaks play a vital role in protecting ecosystems, mitigating climate change effects, and enhancing agricultural productivity. These vegetative barriers serve as effective tools for soil conservation, reducing wind and water erosion while improving soil fertility. Additionally, they contribute to biodiversity preservation by providing habitat corridors for various plant and animal species. Their role in microclimate regulation, such as temperature moderation and increased humidity retention, further enhances agricultural yields and ecosystem stability. This study examines the historical evolution, design principles, and contemporary applications of forest shelterbelts and windbreaks, drawing insights from scientific research and case studies worldwide. It highlights the economic and environmental benefits, including improved air quality, carbon sequestration, and water management, making them crucial components of sustainable land use strategies. However, challenges such as land use competition, maintenance costs, and policy constraints are also analyzed, underscoring the need for integrated approaches to their management. Through a comprehensive bibliometric analysis of the existing literature and field studies, this paper emphasizes the necessity of strategic planning, community involvement, and adaptive policies to ensure the long-term sustainability of forest shelterbelts and windbreaks. The findings contribute to a broader understanding of their role in combating environmental degradation and promoting ecological resilience in the face of ongoing climate challenges. Full article

Water management is a long-standing source of dispute between the riparian states of Karnataka and Tamil Nadu. Recently, these disputes have intensified due to impacts from climate change and Bangalore’s rapid growth to megacity status. Despite well-defined national water governance instruments, competition between state actors and limited access to reliable hydrometric data have led to a fragmented regulatory regime, allowing unchecked exploitation of surface and groundwater resources. Meanwhile, subsidised energy for groundwater pumping incentivises the unsustainable irrigation of high-value, water-intensive crops, resulting in overextraction and harm to aquatic ecosystems. Here, we employ a water–energy–food–environment (WEFE) nexus approach to examine the socio-political, economic, and environmental factors driving unsustainable irrigation practices in the Cauvery River Basin (CRB) of Southern India. Our methodology integrates spatially explicit analysis using digitised irrigation census data, theoretical energy modelling, and crop water demand simulations to assess groundwater use patterns and energy consumption for irrigation and their links with governance and economic growth. We analyse spatio-temporal irrigation patterns across the whole basin (about 85,000 km²) and reveal the correlation between energy access and groundwater extraction. Our study highlights four key findings. First, groundwater pumping during the Rabi (short-rain) season consumes 24 times more energy than during the Kharif (long-rain) season, despite irrigating 40% less land. Second, the increasing depth of borewells, driven by falling water table levels, is a major factor in rising energy consumption. Third, energy input is highest in regions dominated by paddy cultivation. Fourth, water pumping in the Cauvery region accounts for about 16% of India’s agricultural energy use, despite covering only 4% of the country’s net irrigated area. Our study reinforces the existing literature advocating for holistic, catchment-wide planning, aligned with all UN Sustainable Development Goals. Full article