Search Results (215)

Soil microbiomes play a central role in nutrient cycling and ecosystem stability in forestry ecosystems. Here, we present an exploratory analysis of rhizosphere bacterial communities from eucalyptus plantations in two ecologically distinct Brazilian regions. Using 16S rRNA amplicon sequencing followed by ASV inference and phylogenetic reconstruction, we observed distinct taxonomic composition pattern between samples. Dominant phyla across both samples were Actinomycetota, Pseudomonadota, Acidobacteriota, and Bacillota, with Actinomycetota more abundant in TL (44.0%) than in ES (26.5%), and Acidobacteriota and Verrucomicrobiota more represented in ES. The family Streptomycetaceae and the genus Streptantibioticus were strongly enriched in ES (19.6% and 18.6%, respectively), whereas Solirubrobacteraceae, Pseudonocardiaceae, and Nocardiaceae were preferentially associated with TL. The Eldorado do Sul (ES) sample was characterized by higher observed richness and phylogenetic diversity, whereas Três Lagoas (TL) sample displayed relatively greater community evenness. Beta diversity metrics were consistent with high compositional dissimilarity between samples, with a limited fraction of ASVs forming a shared core microbiome. Despite this taxonomic variation, PICRUSt2-based predictions suggested a broadly conserved set of dominant metabolic pathways across samples. Predicted MetaCyc pathways were largely associated with central carbon metabolism, amino acid biosynthesis, and energy production. At the same time, variation in predicted metabolic profiles was observed between samples. The ES sample showed higher relative representation of pathways related to chitin degradation, purine metabolism, and nitrifier denitrification, whereas the TL sample displayed higher relative representation of pathways associated with alternative TCA variants, glyoxylate metabolism, menaquinol biosynthesis, and aromatic compound degradation. Overall, this exploratory analysis suggests that substantial taxonomic variation may coexist with a relatively conserved predicted functional framework across contrasting eucalyptus plantation systems. These observations should be interpreted as hypothesis-generating and highlight the need for future studies incorporating replicated sampling and direct functional measurements. Full article

Artificial intelligence (AI), cloud computing, robotics, automation, and remote sensing technologies are all contributing to digital transformation in forestry. Improving on low-accuracy Global Navigation Satellite Systems (GNSS) positioning affected by multipath error and interception under forest canopies is critical for integrating smart and digital technologies into equipment in forest operations. In an era where lidar-derived individual tree locations are now increasingly available in digital forest inventories, a possible alternative approach to positioning resources such as people or equipment accurately could be to match locally-measured tree positions and attributes in the forest with an existing global reference map based on prior remote sensing missions, effectively using the trees themselves as satellites to circumvent the need for GNSS-based positioning. We evaluated a lidar-based alternative to GNSS positioning using predicted tree positions from local terrestrial laser scanning (TLS) matched with a global stem map derived from prior airborne laser scanning (ALS), a methodology we refer to as TreePS. The horizontal error of the TreePS system was estimated using 154 permanent single-tree inventory plots on the University of Idaho Experimental Forest with two different workflows based on two common R packages (lidR v. 4.3.0, FORTLS v. 1.6.2) using either spatial coordinates or spatial plus stem DBH predicted using one or both segmentation routines and a custom matching algorithm. Mean TreePS error using lidR for below and above-canopy segmentation had mean error of 1.04 and 2.04 m with 93.5% and 91.6% of plots with viable match solutions on spatial and spatial plus DBH matching. The second workflow with both FORTLS (TLS point cloud) and lidR (ALS point cloud) had errors of 1.09 and 2.67 m but only 57.9% and 54.2% of plots with solutions using spatial and spatial plus DBH, respectively. There is room for improvement in the matching algorithm but the TreePS methodology and similar feature-matching solutions may be useful for below-canopy positioning of equipment, people or other resources under dense forests and other GNSS-degraded environments to help advance smart and digital forestry. Full article

With the expansion of global oil and gas resource exploration and development into deep and ultra deep layers, the efficient development of deep carbonate rock fracture cave reservoirs has become the key to ensuring energy security. However, this type of reservoir commonly faces high temperatures, high salinity, and extremely strong heterogeneity, leading to increasingly severe water content spikes caused by dominant water flow channels. Although the existing traditional inorganic plugging agent has good temperature resistance, it has the defects of great brittleness and easy cracking, while the organic polymer gel is prone to degradation failure under high temperature and high salt environments. In order to solve the above problems, a new biochar-enhanced inorganic composite gel system was constructed by using biochar prepared from agricultural and forestry waste pyrolysis as a functional enhancement component. Through rheological testing, high-temperature and high-pressure mechanical experiments, long-term thermal stability evaluation, and dynamic sealing experiments of fractured rock cores, the reinforcement and toughening laws and rheological control mechanisms of biochar on inorganic matrices were systematically studied. Research has found that a biochar content of 0.5 wt% can significantly improve the micro pore structure of the matrix. By utilizing its micro aggregate filling effect and interfacial chemical bonding, the compressive strength of the solidified body can be increased to over 2 MPa, and there is no significant decline in strength after aging at 130 °C for 30 days. More importantly, the unique “adsorption slow-release” mechanism of biochar effectively stabilizes the hydration reaction kinetics at high temperatures, extending the solidification time of the system to 15 h and solving the problem of flash condensation in deep well pumping. This system exhibits excellent shear thinning characteristics and crack sealing ability, and presents a unique “yield reconstruction” toughness sealing feature. This study elucidates the multidimensional strengthening mechanism of biochar in inorganic cementitious materials, providing technical reference for stable oil and water control in deep fractured reservoirs. Full article

Bioconversion of lignocellulosic biomass into edible, nutrient-rich products using low-cost forestry waste offers substantial ecological and economic benefits. Composting forestry waste as a substrate for oyster mushroom (Pleurotus ostreatus) cultivation is an effective recovery strategy. However, the specific microbial-driven mechanisms by which nitrogen sources regulate lignocellulose degradation and compost quality during forestry waste composting for Pleurotus ostreatus substrate preparation remain to be elucidated. We evaluated three organic nitrogen sources (bran, soybean meal, and chicken manure) and one inorganic source (diammonium phosphate, DAP) during composting of forest-waste-based substrates. Composting performance and cultivation outcomes were assessed using physicochemical analyses, lignocellulose degradation measurements, high-throughput sequencing of bacterial 16S rRNA and fungal ITS, and biological efficiency. Organic nitrogen sources enhanced compost temperature and lignocellulose degradation by providing sustained nitrogen release, promoting stable colonization of core microbial communities and cooperative bacteria–fungi networks. In contrast, inorganic nitrogen resulted in slower heating, minimal lignocellulose degradation (0.75%), and unstable, competition-dominated microbial networks. Nitrogen sources indirectly shaped microbial communities by regulating the C/N ratio, pH, and electrical conductivity. Lignocellulose degradation and bacterial diversity significantly influenced mushroom biological efficiency, with bacterial diversity strongly regulating degradation rates. The forest waste–bran treatment achieved the highest biological efficiency (78.35%). These findings offer a practical strategy for optimizing forestry waste bioconversion into fungal protein. Full article

In healthy ecosystems, large raptors such as the White-tailed Eagle perform the essential roles of predators, bioindicators, and umbrella species. Despite their importance, many species of raptors are globally endangered, and similarly, in the Republic of Moldova, 13 species of diurnal birds of prey went extinct in the last 7 decades. The White-tailed Eagle (Haliaeetus albicilla) is the only example of a raptor that has regionally made a demographic and distributional comeback after decades of absence. Following this comeback, a national monitoring scheme during 2014–2025, including a nest counting survey in 2022–2024, has been implemented to understand what the current national situation of the species is and its ecological preferences and threats, together with the fundamental ecological context that allowed the breeding population to adapt to an ever-changing landscape. Field research conducted over 12 years confirmed the breeding of eight pairs, with data indicating a minimum of 19–23 nesting pairs. Pairs generally avoid human-dominated landscapes, preferring higher coverage of wetlands and forests, but current data suggests frequent occupancy of suboptimal territories and increasing tolerance towards human activity and infrastructure. Although currently small, the breeding population experiences high breeding success with no negative outcomes recorded. However, droughts and forestry activities in the proximity of the nests potentially reduced and delayed breeding success. Current forestry and fish farming practices increase the vulnerability of the few known breeding pairs to habitat degradation, poaching, and deforestation. To improve the conservation status of this endangered raptor in the Republic of Moldova, as close as possible to Least Concern status, it is crucial to implement multi-purpose buffer zones around active nests during the breeding season and to further survey the breeding population and assess any demographic trends. Full article

Urbanisation is a major driver of ecological change, altering the composition and functioning of ecosystems through land use conversion, pollution, and environmental fragmentation. Although some authors reported that air pollutants could be absorbed and detoxified by the endophytic microbiome of urban trees, the specific mechanisms by which urban air pollution shapes the endophytic microbiome and, consequently, the trees’ capacity for pollutant degradation, remain largely unexplored. The aim of the present study was to: (1) analyse the structure of endophytic bacteriome of the phyllosphere of three widely planted ornamental tree species—Tilia tomentosa, Fraxinus excelsior, and Pinus nigra, growing at four locations within the city of Plovdiv, Bulgaria, with different anthropogenic load; and (2) assess the effects of host species and urban environmental exposure on bacteriome diversity, taxonomic composition, and functional capacity. Functional profiling based on 16S rRNA gene sequencing revealed enrichment of the metabolic pathways associated with nitrogen cycling, carbon metabolism, and hydrocarbon degradation, particularly in samples originating from more urbanised or polluted locations. These predicted functional traits suggest that endophytic bacteria may actively contribute to detoxification processes within plant tissues. Tilia tomentosa and Fraxinus excelsior were enriched in nitrogen and carbon cycling pathways, including denitrification, methanol oxidation, and methanotrophy—functions associated with oxidative stress mitigation and nutrient regulation. In contrast, Pinus nigra showed higher relative abundance of chemoheterotrophy, ureolysis, and sulphur respiration, indicating a more conservative and stress-tolerant microbiome. Although the study involved only one settlement, these results suggest that endophytic communities may contribute to urban tree sustainability by supporting ecosystem functions under stress conditions. By integrating microbial ecology with urban environmental assessment, this research provides new insights into the adaptive potential of endophytic microbiota in urban forests and highlights their importance in the sustainable management of green infrastructure through microbiome-informed strategies. Full article

Harmonised land evaluation frameworks are essential for sustainable land planning and policy development. Assessing land suitability is crucial for predicting agricultural and forestry potential but also for mitigating land degradation risks. Current land suitability maps in Portugal vary greatly in scale and methodology. This study presents the first nationally consistent framework to produce a harmonised land suitability map for mainland Portugal at a 1:100,000 scale following a recently updated WRB soil map. The latter was obtained by integrating legacy soil data with delineated land units according to soil-forming factors (climate, lithology, and relief). These land units were used to derive key land qualities, subsequently classified into constraint levels. Following FAO land evaluation principles, four land suitability levels for agriculture and forestry were assigned to 125 land units across three representative areas in southern Portugal. Relief and lithology emerged as main drivers of land suitability. Marginal agricultural lands are largely dominant (65.1–78.0%), followed by non-suitable lands (14.8–28.3%). Forestry suitability is mostly confined to moderate (61.5–69.4%) and marginal (30.6–37.4%) classes, reflecting the higher adaptability of forestry systems. High consistency was observed between the derived suitability classes and the latest land use/land cover map of Portugal. The framework enables decision-makers to identify areas suitable for intensive production while safeguarding lands vulnerable to degradation. It also provides a transferable tool for adaptive landscape management and sustainable land allocation, supporting policy development under changing environmental conditions in Mediterranean regions. Full article

Climate-smart forestry (CSF) is a comprehensive approach that aims to sustainably enhance wood productivity (production), improve forest resilience and adaptation, sequester carbon (mitigation), and support broader development goals. This strategy is profoundly linked with Forest Genetic Resources (FGR), which are crucial for the adaptive capacity and long-term sustainability of forest ecosystems in the face of the escalating climatic changes. Climate change presents significant risks, including increased air temperatures, altered precipitation regimes, and a rise in extreme weather events, leading to tree mortality, shifts in vegetation distribution, and a potential loss of critical forest functions and services, such as carbon sequestration capacity. While forests have inherent resilience, the rapidity and magnitude of projected changes may exceed their natural adaptive capacity, potentially resulting in local extinction and degradation of ecosystems. This review explores various facets of the interplay between CSF and FGR, emphasizing their role in sustainable forest management. Key areas of focus include: (1) Genetic Diversity, (2) Genotype Selection and Breeding, (3) Modern Breeding Techniques, (4) Molecular Breeding, (5) Genomic Prediction (GP), (6) Breeding Programs, (7) Silvicultural Practices, (8) Adaptation Mechanisms, (9) Phenotypic Plasticity, (10) Migration, particularly Assisted Gene Flow (AGF) and (11) Reproductive Material Management. Ultimately, the study highlights the crucial role of FGR in the resilience of forest ecosystems and proposes future actions for their integration into CSF strategies, including in situ and ex situ conservation, assisted migration, advanced research and development, community involvement, and supportive policy frameworks, all vital for the long-term sustainability and vitality of forest ecosystems in a changing climate. Full article

Forestry operations expose workers to a high risk of health constraints, accidents, and injuries. We are trying to protect them and implement many effective countermeasures; nevertheless, the development of new forestry machines remains a long process, with limited safety and ergonomic feedback, usually provided only at a late stage in the design process. In this study, we propose a practical digital ergonomics workflow that combines inertial motion capture, standardized risk scoring, and digital human modelling to improve and shorten human-centered and safer design of forestry machinery. We validated the approach in a field pilot on a prototype milling–spraying device for standing trees. Two experienced operators performed a full work-cycle (carry → install → operate → dismantle → return), during which their whole-body kinematics were captured in real forest conditions. These were then evaluated using kinematic metrics, RULA, OWAS, and a heart-rate-based load index. Based on these ergonomical and risk findings, we translate motion-derived risk ‘hotspots’ into real redesign targets (grip/handle geometry, weight distribution, support elements, and control layout), outlining an updated forestry-specific DHM/HDT (digital human modeling; human digital twin) framework that explicitly incorporates terrain and environmental constraints to accelerate the iteration of safer prototypes. The updated digital modeling framework will be used in the design of the new, more complex machine—“Semi-autonomous system for optimizing degraded soils by deep injection”. This machine contains a much more complex and advanced structure, including a tractor with an attachment tool for specialized deep soil injection. We suppose that using motion capture data, human digital twins, and digital human models can effectively support designing and the development process to avoid human-related construction nonconformities of this complex machine even before the final machine prototype is produced for functional field testing. Full article

Localization and mapping remain critical challenges for Unmanned Ground Vehicles (UGVs) operating in unstructured natural environments, such as forests and agricultural fields. While Visual SLAM (VSLAM) and Visual–Inertial SLAM (VI-SLAM) have matured significantly in structured and urban scenarios, their extension to outdoor natural domains introduces severe challenges, including dynamic vegetation, illumination variations, a lack of distinctive features, and degraded GNSS availability. Recent advances in Deep Learning have brought promising developments to VSLAM- and VI-SLAM-based pipelines, ranging from learned feature extraction and matching to self-supervised monocular depth prediction and differentiable end-to-end SLAM frameworks. Furthermore, emerging methods for adaptive sensor fusion, leveraging attention mechanisms and reinforcement learning, open new opportunities to improve robustness by dynamically weighting the contributions of camera and IMU measurements. This review provides a comprehensive overview of Visual and Visual–Inertial SLAM for UGVs in unstructured environments, highlighting the challenges posed by natural contexts and the limitations of current pipelines. Classic VI-SLAM frameworks and recent Deep-Learning-based approaches were systematically reviewed. Special attention is given to field robotics applications in agriculture and forestry, where low-cost sensors and robustness against environmental variability are essential. Finally, open research directions are discussed, including self-supervised representation learning, adaptive sensor confidence models, and scalable low-cost alternatives. By identifying key gaps and opportunities, this work aims to guide future research toward resilient, adaptive, and economically viable VSLAM and VI-SLAM pipelines, tailored for UGV navigation in unstructured natural environments. Full article

This study addresses the need for effective bamboo monitoring in smart forestry as UAV imagery and AI-based methods continue to advance. Bambusa stenostachya (thorny bamboo), commonly found in the badland regions of southern Taiwan, spreads rapidly due to its strong reproductive capacity and extensive rhizome system, often causing forestland degradation and challenges to sustainable management. An automated detection approach is therefore required to capture bamboo dynamics and support forest resource assessment. We use a dual-component framework for detecting bamboo forests and individual bamboo clumps from high-resolution UAV orthomosaic imagery. The first component performs semantic segmentation using U-Net or SegFormer to extract bamboo forest areas and generate a corresponding forest mask. The second component independently applies instance segmentation using YOLOv8-Seg and Mask R-CNN to delineate and localize individual bamboo clumps. The dataset was collected from Compartment 43 of the Qishan Working Circle in Kaohsiung, Taiwan. Experimental results show strong model performance: bamboo forest segmentation achieved an F1-score of 0.9569, while bamboo clump instance segmentation reached a precision of 0.8232. These findings demonstrate the promising potential of deep learning-based segmentation techniques for improving bamboo detection and supporting operational forest monitoring. Full article

In the context of climate change and greenhouse gas emissions, the forestry sector holds significant potential to contribute to global mitigation efforts. One of the primary drivers of deforestation is land expansion for livestock production. However, both sectors are closely linked to issues of food security and food sovereignty, with the livestock sector playing a crucial role in ensuring food availability. Integrating these two sectors through silvopastoral systems offers a promising solution that supports forest conservation while simultaneously addressing the global food crisis. Among the leading initiatives in forest conservation is REDD+, a mechanism under the UNFCCC that has proven effective in reducing deforestation and forest degradation, as well as in enhancing carbon stock conservation. Following the ratification of Article 6 of the Paris Agreement in 2024, REDD+ has gained recognition as a viable approach for generating international carbon credits. Given the intersection of the livestock and forestry sectors, and the potential of carbon credits to advance the goals of the Paris Agreement, silvopastoral systems could be considered for inclusion in REDD+ strategies under the framework of Article 6. Full article

This study explores the potential of pine bark—a highly accessible and underexploited by-product of forestry and food processing—as a renewable raw material for rigid polyurethane (PUR) foam production. Under optimal extraction conditions, water-soluble extractives rich in carbohydrates were isolated from biomass with a yield of 25% and subsequently condensed with propylene carbonate (PC) to produce bio-based polyols. The polyols synthesized at a PC/OH molar ratio ranging from 1 to 5 were incorporated into rigid PUR foam formulations as substitutes for commercial polyether polyols. The foams containing bio-polyols synthesized at a PC/OH ratio of 3 demonstrated the highest compressive strength and thermal insulation performance, exceeding those of the reference material by 30% and 9%, respectively, and exhibited enhanced thermo-oxidative stability. Incorporation of extracted bark up to 10 wt% as a filler in the PUR matrix led to a decrease in mechanical properties to the level of the reference foam and a 19% reduction in thermal insulation capacity, without affecting the closed-cell content. Cone calorimetry revealed that both filled and unfilled bio-polyol-based PUR foams exhibited lower degradation rate, heat release rate, and total smoke release compared with the reference material, indicating reduced flammability and a lower tendency toward fire propagation. Full article

Dyes are widely used in textile processing and are frequently discharged without adequate treatment, posing risks to aquatic ecosystems through reduced water quality, toxicity to organisms, and long-term environmental degradation. To address the need for sustainable remediation solutions, this study investigated the use of pine bark (Pinus pinaster), an abundant forestry byproduct, as a low-cost biosorbent for textile dye removal. Powdered (<0.5 mm) and granular (>1 mm) bark fractions were washed, dried, and modified through iron impregnation (10 wt.% Fe) via sonication in an FeCl₃·6H₂O solution, with one iron-coated variant subsequently subjected to thermal treatment at 400 °C under nitrogen (1 h) and hydrogen (3 h). Adsorption performance was evaluated using synthetic effluents containing Sirius Blue, Astrazon Red, and Sirius Yellow, individually and as a ternary mixture (80 mg/L each), with added NaCl and NaHCO₃ to simulate realistic conditions. Thermally treated granular iron-coated bark showed the highest removal efficiency, achieving >90% dye elimination within 24 h without detectable iron leaching, along with strong iron retention (~80%) and a 53% thermal-treatment yield. Maximum adsorption reached 15.51 mg/g at 5.0 g/L, while lower adsorbent doses increased capacity (26.8 mg/g) but reduced overall removal (~83%). Kinetic analysis was dose-dependent: the pseudo-first-order model provided the best fit at 5.0 g/L, reflecting the rapid approach to equilibrium, whereas the Elovich model fitted best at 2.5 g/L (R > 0.99), consistent with heterogeneous surface interactions under limited adsorbent availability. These results demonstrate the potential of thermally treated iron-coated pine bark as an efficient and sustainable biosorbent for textile wastewater treatment. Full article

Community-managed forests within agroforestry landscapes are vital for both carbon sequestration and agricultural sustainability. This study assesses the Hariyali Community Forest (HCF) in western Nepal, emphasizing its role in carbon storage within a Sal (Shorea robusta)-dominated lowland forest containing diverse native and medicinal species. Stratified field inventories combined with satellite-derived biomass and land-use/land-cover data were used to quantify carbon stocks and spatial trends. In 2022, the mean aboveground carbon density was 165 tC ha⁻¹, totaling approximately 101,640 tC (~373,017 tCO₂e), which closely matches satellite-based trends and indicates consistent carbon accumulation. Remote sensing from 2015–2022 showed a net tree cover gain of 427 ha compared to a 2000 baseline of 188 ha, evidencing effective community-led regeneration. The 615 ha Sal-dominated landscape also sustains agroforestry, small-scale horticulture, and subsistence crops, integrating livelihoods with conservation. Temporary carbon declines between 2020 and 2022, linked to localized harvesting and management shifts, highlight the need for stronger governance and local capacity. This study, among the first integrated carbon assessments in Nepal’s lowland Sal forests, demonstrates how community forestry advances REDD+ (Reducing Emissions from Deforestation and Forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks in developing countries) objectives while enhancing rural resilience. Linking field inventories with satellite-derived biomass and land-cover data situates community forestry within regional environmental change and SDG (Sustainable Development Goals) targets (13, 15, and 1) through measurable ecosystem restoration and livelihood gains. Full article