Search Results (1,576)

Forests worldwide are increasingly affected by climate-driven stressors and large-scale disturbances that impair tree physiology, disrupt water and carbon balance, and increase mortality risk. In this context, successful natural and artificial regeneration is essential for maintaining forest continuity, carbon storage, and biodiversity. However, regeneration outcomes depend not only on site conditions but also on biotic pressures, especially browsing by cervids in temperate and boreal forests. The aim of this review was to identify and synthesize evidence on how silvicultural methods can reduce browsing damage in forest regeneration and to assess how these methods influence the underlying drivers of cervid pressure through stand structure and forage availability. We examine mechanisms operating at two spatial scales: at the microscale, regeneration type, planting density, structural heterogeneity, planting stock, and how species mixture influences browsing probability and intensity; at the macroscale, how cutting systems and the spatial and temporal arrangement of harvests shape foraging landscapes by concentrating or dispersing browse resources and edge habitats. The reviewed evidence shows that dense, structurally diverse natural regeneration can dilute browsing pressure, whereas uniform artificial regeneration may increase repeated damage, and that species composition and mixture patterns can either protect or expose palatable species. We conclude that integrating microscale regeneration design with landscape-level harvest planning can strengthen stand resilience, reduce dependence on fencing, and support climate-adaptive forest development. To the best of our knowledge, no previous review has synthesized this evidence across both micro- and macroscale silvicultural contexts. Although most of the studies included in this review originate from Europe, we believe that the knowledge presented here is relevant to the majority of boreal and temperate forests worldwide. Full article

Generally, forest trees with medicinal value present diverse chemotypes considered key determinants of efficacy, safety, and commercial valuation. Such heterogeneity varies among tissues, genotypes, and seasons, and stress exposure. This review summarizes how regulatory controls and genome architecture affect the stability and synthesis of secondary metabolites in woody medicinally important taxa. Detailed haplotypic and chromosomal analyses have recently identified diverse and repeatable architectural drivers. Among these, LTR/transposon-mediated revamping, neofunctionalization, biosynthetic gene clusters, and tandem duplication play a special role in reshaping pathway capacity. The enzymatic regulation of these drivers translates this “capacity” into harvest-pertinent chemistry by employing conserved TF modules, hormone crosstalk, and emergent chromatin/epigenetic layers. Nevertheless, major parameters pertaining to the tissue-specific storage, transport, and compartmentalization of these chemotypes are contextualized with certain limitations. In this review, the integration of GWAS/eQTL/TWAS with multi-tissue is explained in addition to the replacement of a single reference with pangenome/haplotype frameworks, and explicit modeling of G × E further strengthen genotype-to-chemotype mapping. Therefore, in this review we summarize practical workflows for chemotype discovery utilizing staged validation models of heterologous reconstitution, isotope/spatial evidence, and chemistry. These findings were supported by data on saponins, alkaloids, iridoids, and defense response. Such an integration links mechanistic understanding to authentication, standardization, and sustainable utilization strategies in woody medicinal trees. Full article

The study aims to analyze comprehensive effects of site quality class (SQC), stand density index (SDI), and species composition (SC) on biomass growth. Based on 5872 observations from 2040 permanent sample plots of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantations from successive national forest resource inventories, five classical growth equations were employed and nonlinear regression and dummy variables were used for modeling. A dominant height (DH) growth model was first developed to determine SQC, followed by a series of stand biomass (SB) growth models incorporating SQC, SDI, and SC (pure vs. mixed stands). Growth differences among different classes or categories were analyzed using inflection age and optimal rotation age. The results show that Korf equation performed best for both DH and SB growth models; SDI contributed the most to SB growth, followed by SQC, with their interaction accounting for over half of the total contribution. Mixed stands grew faster than pure stands; higher SQC was associated with faster growth and earlier attainment of inflection age and optimal rotation age. The productivity increased with rising SDI, but the rate of increase gradually diminished. Different optimal rotation ages should be determined for pure and mixed stands across different SQCs. Reasonable adjustment of harvesting age and control of stand density represent the greatest potential for improving forest productivity. Full article

Forest harvesting transportation planning must balance operational efficiency with environmental sustainability, because timber transportation can cause both soil disturbance and carbon emissions. However, most vehicle routing studies primarily focus on economic objectives such as distance or cost minimization, whereas environmental impacts are often considered separately. The integrated optimization of ecological disturbance and carbon emissions remains limited in forest transportation planning. To address this gap, this study formulates a multi-vehicle routing optimization model for timber transportation that simultaneously minimizes transportation distance, makespan, soil disturbance, and CO₂ emissions within a hierarchical forest road network. An enhanced evolutionary algorithm, Eco-Constrained Lévy-flight Local Search NSGA-II (ECLS-NSGA-II), is proposed to improve convergence and maintain environmentally favorable routing solutions. Simulation experiments comparing ECLS-NSGA-II with NSGA-II, MOPSO, MOEA/D, and WS-GA demonstrate that the proposed method achieves superior performance across all objectives, producing shorter routes, lower completion times, and reduced CO₂ emissions while maintaining minimal ecological disturbance. Additional experiments on randomly generated networks further confirm the robustness of the proposed approach. These results indicate that the proposed framework provides an effective methodological tool for environmentally sustainable timber transportation planning in forest operations. Full article

The increasing demand for innovative strategies in sustainable food production—driven by rapid global population growth, particularly in sub-Saharan Africa (SSA)—necessitates urgent attention to agricultural resilience. Recent technological advancements have enhanced crop productivity, post-harvest preservation, and environmentally sustainable farming practices. However, three critical bottlenecks remain: (i) the lack of accurate, maize-specific yield prediction methods tailored to SSA; (ii) limited multimodal modeling approaches capable of capturing complex, nonlinear interactions among heterogeneous data sources; and (iii) a lack of explainability mechanisms, which render high-performing models “black boxes” and hinder stakeholder trust. To address these gaps, this study presents an explainable machine learning framework for smart maize yield prediction. We integrate multimodal SSA-specific soil, crop, and weather data to capture the multi-dimensional drivers of maize productivity. Six diverse algorithms—including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), categorical boosting (CatBoost), support vector machine (SVM), random forest (RF), and an artificial neural network (ANN) combined with a k-nearest neighbors (kNN)—were benchmarked to evaluate predictive performance. To ensure robustness against spatial heterogeneity, we employed a Leave-One-Plot-Out (LOPO) cross-validation strategy. Empirical results on unseen test data identify CatBoost as the best-performing model, achieving a coefficient of determination of ($R^2 =~76\%$), demonstrating its ability to capture complex, nonlinear relationships in agricultural data. To enhance transparency and stakeholder trust, we integrated Local Interpretable Model-agnostic Explanations (LIME), providing plot-level insights into the physiological and environmental drivers of maize yield. Together, these contributions establish a scalable and interpretable modeling framework capable of supporting data-driven agricultural decision-making in SSA. Full article

Timber sale prices are influenced by multiple tract, product, and transaction characteristics. This study evaluates the effects of species composition, product class, sale method, harvest type, timber quality, and average tree diameter on timber stumpage prices using timber sale records from Alabama between 1 January 2010 and 31 December 2019. Prices were modeled on a per weight unit basis using a generalized linear model with a Gamma distribution and logarithmic link. Results indicate that larger average diameters were consistently associated with higher prices across most product classes. Harvest type also influenced prices, with salvage operations yielding prices approximately 8.3% lower than thinning operations. Timber quality had a moderate effect: good-to-excellent quality timber sold for about 4.8% higher prices than poor-to-fair quality timber. Sale method was an important determinant of price outcomes. Negotiated sales generated significantly lower prices than sealed-bid sales, averaging approximately 17% lower overall. However, interaction analysis revealed that negotiated sales produced higher prices for mixed hardwood sawtimber, likely reflecting the diverse end uses of these products. Regional effects were also evident, with higher prices observed in the southwestern portion of the state, likely due to proximity to the Port of Mobile and associated export markets. These findings highlight the importance of both tract and transaction characteristics in determining timber prices and provide guidance for landowners and forest managers when selecting sale strategies and management practices. Full article

Mechanized timber harvesting on steep slopes causes soil disturbance; however, comprehensive post-harvest assessment remains challenging because terrain complexity and safety constraints render traditional field-based methods labor-intensive, spatially limited, and difficult to implement systematically. In this study, we developed and evaluated a convolutional neural network-based semantic segmentation model for detecting soil disturbances using high-resolution unmanned aerial vehicle (UAV) imagery in a steep-slope harvested area (2.50 ha, mean slope of 53.4%) in Republic of Korea. A U-Net semantic segmentation model was trained on manually annotated orthomosaic tiles incorporating RGB and digital elevation model (DEM) inputs. Ensemble predictions at an optimized threshold of 0.65 achieved Intersection over Union (IoU) of 0.55 and F1-score of 0.71. Although moderate, these values reflect the inherently challenging conditions of steep-slope forest terrain compared to similar studies conducted under gentler terrain. DEM-derived depth estimation enabled severity classification of the detected disturbances, with light disturbances predominating. Field validation using 38 pinboard measurements demonstrated reliable spatial detection (ρ = 0.567, RMSE = 6.45 cm). This approach provides an effective alternative to traditional monitoring practices in mountainous forests, where systematic trail planning is impractical, and may support evidence-based assessment of harvesting impacts for sustainable forest management. Full article

The collection of high-quality seeds to produce forest seedlings is closely linked with the time of harvesting. Climate warming is already having visible effects in all life stages of forest tree species, including the timing of seed maturation. The purpose of this study was to update the knowledge on seed collection timing and to identify the indicators of physiological maturity for three key Eastern European tree species—silver fir (Abies alba), European larch (Larix decidua), and small-leaved lime (Tilia cordata). Seeds and cones were collected from Romanian clonal seed orchards and evaluated at several stages of seed maturation using germination tests for European larch and tetrazolium viability tests for silver fir and small-leaved lime. The results revealed species-specific differences in seed maturation timing: in silver fir seed viability increased slightly from late August to early September, in European larch germination remained low (≈20%) regardless of harvest time, while small-leaved lime viability declined significantly after late August. These findings suggest that the harvest period observed during the study years occurred earlier than the traditionally recommended intervals and could be linked to recent warming trends. This study highlights the relevance of re-evaluating seed collection schedules under changing climatic conditions, while further multi-year studies are required to confirm these patterns and refine practical recommendations. Full article

Computed tomography (CT) enables non-destructive insight into internal log structure, yet fully automated interpretation of CT images remains limited by inconsistent annotations, boundary ambiguity, and insufficient spatial context in 2D slice-based analysis. These challenges restrict the industrial deployment of deep learning for wood quality assessment. This study applies artificial intelligence (AI) and deep learning to the automated analysis of computed tomography (CT) scans of wood logs for detecting internal qualitative features and segmenting bark. Using convolutional neural networks (CNNs), trained models accurately distinguish healthy and damaged regions and segment bark, including discontinuous parts. We introduce a novel pseudo-spatial representation by merging consecutive slices into red–green–blue (RGB) format, which improves prediction accuracy and model robustness across logs. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) highlights regions contributing most to defect detection, particularly knots. Comprehensive evaluation using Sørensen–Dice similarity coefficients and confusion matrices confirms the effectiveness of the proposed approach under industrial conditions. These findings demonstrate that AI-driven CT image analysis can address key limitations of current log-grading workflows and enable more reliable, objective, and scalable quality assessment for timber-dependent economies. Full article

Secondary salinization in mulched drip-irrigated cotton fields of arid oasis–desert transition zones in Xinjiang imposes coupled root-zone constraints, including salt-induced aggregate structural degradation and ionic stress. However, field evidence remains limited on whether integrating a structure-oriented soil conditioner with humic acid can generate stable improvements across growing seasons. A two-year field experiment with a randomized block design (three replicates) was conducted to evaluate four treatments: control (CK), polyacrylamide (PAM, 30 kg ha⁻¹), humic acid (HA, 450 kg ha⁻¹), and PAM + HA. Soil physical and chemical properties and aggregate-size distribution were determined after harvest, while enzyme activities and root traits were assessed at the flowering–boll stage. Structural equation modeling (SEM) and random forest (RF) analysis were used to explore soil–root–yield linkages and identify key soil predictors associated with yield variation. Treatment effects were most evident in the 0–20 cm layer, with PAM + HA showing the greatest overall improvement. In the topsoil, PAM + HA lowered soil pH from 8.35 to 7.88 in 2024 (p < 0.05), increased soil organic carbon (SOC) to 4.29 g kg⁻¹ in 2025 (p < 0.01), and increased NO₃⁻–N to 25.51 and 30.27 mg kg⁻¹ in 2024 and 2025, respectively (both p < 0.05). PAM + HA also enhanced cellulase activity from 6.17 to 16.85 mg glucose g⁻¹ 72 h⁻¹ in 2024 and increased seed cotton yield to 6683.69 and 5996.89 kg ha⁻¹ in 2024 and 2025, with a 51.0% yield increase over CK in 2024. SEM showed that root development had the strongest direct positive effect on yield (β = 0.79, R² = 0.63; goodness of fit (GOF) = 0.74), while random forest identified alkaline phosphatase, cellulase, and NO₃⁻–N as the main yield predictors (out-of-bag R² (OOB R²) = 0.672, p = 0.01). This study elucidated the effects of the combined application of a structure-oriented soil conditioner and humic acid on the root-zone environment of mulched drip-irrigated cotton fields in arid regions, providing a theoretical basis for the coordinated regulation of soil structural improvement and nutrient activation in saline–sodic cotton fields. Full article

Light availability is a key environmental factor influencing plant functional traits and ecological strategies. To investigate how natural populations of Iris pumila respond to contrasting irradiance, we conducted an in situ reciprocal transplant experiment using clonal genotypes from two natural populations, each originating from an open dune and a shaded forest habitat. Leaves collected from each of the replanted and transplanted genotypes were analyzed for structural (specific leaf area—SLA, leaf dry matter content—LDMC), physiological (specific leaf water content—SLWC, photosynthetic pigments) and biochemical (peroxidase—POD, glutathione reductase—GR, phenolics and anthocyanins) traits. Shade-grown individuals developed thinner leaves with higher SLA and chlorophyll content, enhancing light-harvesting efficiency, whereas sun-exposed plants exhibited greater LDMC, increased POD and GR activities and higher anthocyanin levels—traits consistent with enhanced photoprotection under high irradiance. All genotypes exhibited pronounced plasticity to light intensity, with habitat exerting a stronger influence on trait expression than population origin. To evaluate oxidative balance, we proposed the ODAC index (Oxidative Damage to Antioxidant Capacity), which integrates lipid peroxidation with antioxidant capacity. ODAC values revealed consistent population-level differences, with higher values in Dune genotypes across habitats, indicating a constitutively elevated oxidative load relative to antioxidant protection and suggesting differentiation in redox regulation between populations. Overall, leaf trait variation in I. pumila appears to be primarily driven by plastic responses to light conditions, while differentiation in oxidative physiology contributes to functional divergence between populations. Full article

This study evaluated the potential of Sentinel-2-derived NDVI (Normalized Difference Vegetation Index) for predicting within-field variability of winter wheat grain yield in central Lithuania during the 2024 growing season. Reliable within-field yield prediction remains challenging in regions with heterogeneous soils and limited region-specific models, particularly in northeastern Europe. Grain yield data were obtained from combine harvesters equipped with GPS yield monitoring across 13 fields with a total area of 283.6 ha. NDVI values were calculated for four half-monthly periods from March to May, corresponding to key phenological stages (from tillering to spike emergence). Spatial and temporal variability in NDVI–yield relationships was observed, with early May consistently showing the strongest correlations (r up to 0.49), particularly in lower-fertility fields, indicating its critical role in yield prediction. Machine learning models (Random Forest, XGBoost, and Deep Neural Networks), along with linear regression, were applied to predict yields based on NDVI from four growth stages. Random Forest achieved the highest predictive accuracy (MAE = 0.951 t/ha), outperforming the other models. The model also showed the highest correlation with observed yields (Pearson r = 0.717), indicating strong agreement between predicted and measured values. Feature importance analysis confirmed NDVI from 1 to 15 May as the most influential predictor across all models. Predicted yield maps closely matched observed patterns, with the largest discrepancies near field edges due to combine harvester effects. These findings highlight the utility of mid-season NDVI for precise estimation of within-field grain yield variability and demonstrate that Random Forest models can effectively capture the NDVI–yield relationship, particularly under heterogeneous field conditions. Full article

This study investigates how key planning variables—the number of wood assortments, the geometric shape of clear-cut areas, and the extraction (forwarding) distance—influence greenhouse gas (GHG) emissions. Twelve plots formed a heterogeneous sample with similar site type and soil moisture conditions. A Komatsu 931 harvester and a 855 forwarder, driven by the experienced operators, were used to ensure consistency in operator skill. For each plot, the isoperimetric quotient was computed to quantify how plot shape correlated with labor hours, fuel consumption, and the resulting volume of GHG emitted. The number of assortments extracted per plot ranged from three to fourteen product groups. The results show that plots with more complex shapes require significantly more operator time and fuel. Increasing the number of assortments amplifies handling time and fuel use. Longer extraction distances further exacerbate the emissions. These findings underscore the importance of integrating spatial geometry and wood assortment planning into harvest scheduling to enhance productivity and reduce the carbon footprint of forest operations. Recommendations for practitioners include prioritizing more compact treatment units, optimizing assortment grouping, and minimizing extraction distances as key strategies for precision forestry. Full article

Sustainable forest operations require operational planning tools that effectively integrate productivity, environmental conservation, and social acceptability, particularly within complex and environmentally sensitive forest systems. In Mediterranean small-scale forestry, harvesting decisions are frequently guided by expert judgment rather than by systematic and transparent planning frameworks. This reliance on subjective decision making can result in heterogeneous management practices and, in some cases, suboptimal operational outcomes. This study aims to validate a GIS-based Analytic Hierarchy Process (GIS–AHP) decision support system for the selection of harvesting and wood systems in the chestnut coppices of central Italy and to assess the robustness of its recommendations when expert judgments are provided by different stakeholder groups. The methodology integrates spatial data and multi-criteria analysis to evaluate the suitability of three extraction systems (forwarder, cable skidder, and cable yarder) and three wood systems (Cut-To-Length, Whole-Tree Harvesting, and Tree-Length) across 162 Forest Management Units (1332.5 ha), using weights elicited from four stakeholder categories (researchers, technicians, forest owners, and workers; n = 144). Results show statistically significant differences in mean suitability values among stakeholder groups for all systems; however, convergence at the operational decision level is high. The cable skidder is recommended over 94%–100% of the area depending on the stakeholder category, with full agreement among all groups in 87.7% of the Forest Management Units. For wood systems, Whole-Tree Harvesting is selected over 96.1% of the analysed area, with agreement in 95.1% of the Forest Management Units. Divergences are therefore limited and attributable to differences in AHP weighting structures. Overall, the findings demonstrate that the GIS–AHP approach provides stable and transferable recommendations despite variability in expert perspectives, supporting its applicability as a transparent and robust decision support tool for operational planning in chestnut coppices and similar Mediterranean forest systems. Full article

Low forage productivity of natural grasslands remains a major limitation for sustainable livestock production in the forest–steppe zone of Northern Kazakhstan, highlighting the need for high-yield, locally adapted forage systems. This study evaluated nine forage agrophytocenoses, including perennial grasses and legume–grass mixtures, established in 2024 and assessed over two growing seasons on leached chernozem soils. Plant height, stand density, and biomass yields were quantified at optimal harvest stages, with statistical differences tested using one-way ANOVA and Tukey’s HSD (p < 0.05). Legume-containing agrophytocenoses consistently outperformed natural grass cover and grass monocultures in canopy development and biomass accumulation. The highest productivity was achieved in Lolium multiflorum + Medicago sativa (I+A), Medicago sativa + Festuca arundinacea (A+TF), and Onobrychis viciifolia + Festulolium + Phleum pratense (S+F+T), reaching up to ~19.66 t ha⁻¹ green biomass and ~5.24 t ha⁻¹ dry matter. In contrast, Agropyron cristatum monoculture yielded minimally during establishment, while ryegrass mixtures with annuals declined in the second year. Optimized legume–grass agrophytocenoses represent the most productive and agronomically reliable strategy to enhance forage supply and improve environmental resilience in Northern Kazakhstan. Full article