Search Results (1,608)

In Mediterranean small-scale forestry, the adoption of highly mechanized CTL systems remains limited by fragmented forest lots, variable stand conditions, and high machine costs. This case study evaluated the operational productivity and economic feasibility of harvester-based felling and processing in two Mediterranean conifer stands in Southern Italy. A harvester was monitored in Calabrian pine and silver fir stands using a time-motion approach. Processing represented the dominant productive phase, while moving accounted for about one-third of productive machine time. Under the observed site conditions, the Calabrian pine showed higher gross productivity and lower unit time consumption than silver fir. The economic analysis indicated that feasibility was strongly dependent on gross productivity, benchmark motor-manual costs, and harvested lot volume, with more favourable break-even conditions in Calabrian pine. Full article

Non-industrial private forestlands (NIPF) have often been subjected to logging practices that remove the highest quality trees of the highest value species, leaving behind less-desirable stems and species; a practice termed high-grading or selective harvesting. Timber stand improvement (TSI) can be used to correct high-grading practices by removing poorly-formed or low-value tree species in order to promote the growth of higher value trees and species. The felled trees may be removed for biomass fuel or left in place. At study sites in southwestern Virginia, we monitored tree growth across experimental TSI with biomass removal, TSI cut-and-leave felled stems, and control plots in mixed-pine hardwood forests from 2012–2025, measuring diameter at breast height (DBH) for multiple species. Scarlet Oak (Quercus coccinea) and Yellow Poplar (Liriodendron tulipifera) had the largest growth increments during the study period, while Black Gum (Nyssa sylvatica) and Hickory species (Carya spp.) showed consistently low growth. Larger trees tended to grow at faster rates, consistent with allometric expectations. The two TSI treatments had similar growth increments and were 60–100% higher than control plots over the tree blocks of treatments in this study. Mortality at the longest-term measured block was more than twice as high as TSI plots. These results suggest that TSI can reduce competition for light and nutrients promoting diameter growth, whereas untreated plots may experience resource limitations that suppress growth and increase mortality. The study provides a baseline for understanding forest dynamics and highlights the importance of management interventions in maintaining productivity and structural diversity in selectively-logged forests. Full article

Italian “Agricultural Engineering”, while evolving toward the broader, interdisciplinary field of “Biosystems Engineering” (which also includes the study of biomasses/biomaterials, field and forest mechanization in difficult contexts and advanced post-harvest agri-food technologies), suffers from a structural critical issue due to its historical academic placement within the Agricultural rather than the Engineering departments. This positioning limits the depth of the technical subjects proposed to the students and does not facilitate the necessary collaboration with core engineering disciplines in research and didactics activities, thereby potentially slowing innovation in crucial fields like agro-bio-energies, precision agriculture and field robotics. To address this misalignment and foster inter-departmental synergy, this study proposes adopting the Contamination Lab (C-Lab) model as the archetype of a possible framework of academic and professional networking involving and centered on Agricultural Engineering. C-Labs (transdisciplinary platforms proposed by the Italian Ministry of University and Research) function as experiential laboratories, gathering students from Engineering, Agronomy, Computer Science, and Economics to collaboratively develop solutions to real-world challenges posed by industry stakeholders. The integration of a permanent, thematic C-Lab focused on agri-forestry and food machinery, supported by methodologies for enhancing creativity in technical fields, such as design thinking, represents an effective (and necessary) strategy to give Agricultural Engineering greater visibility in the Italian (and international) scenario and, prospectively, relocate it to the center of any research involving the technological and technical aspects of agriculture, forestry and food production. It is concluded that this initiative can serve as an institutional bridge for hybrid training, which is essential for aligning academic competencies with the growing demands for innovation and multidisciplinary professionalism in the national agri-food tech sector. Full article

In temperate climates, diversifying rotations with overwintering cover crops provides many benefits, including reducing nutrient losses, restoring soil organic matter, and managing weeds. However, there is limited understanding of where and when cover crops have been planted, especially relative to harvested winter crops, such as wheat and alfalfa. In this study, we use Sentinel-1 and Sentinel-2 satellite data to map winter land cover, including cover crops, across three sites in the Lower Peninsula of Michigan using random forest models. Our results show overall moderate accuracy (60–80%) across all three sites, with individual-level accuracies varying by region and land cover type. Generally, models that combined Sentinel-1 and Sentinel-2 bands, polarizations, and indices performed better than models that relied on one sensor alone. F1 scores for cover crop mapping were moderate, with the highest accuracies achieved for mapping any cover crop (0.77) compared to individual cover crop species—cereal rye (0.72) or ryegrass (0.50). Considering which bands and time periods were the most important for the classification, we found that vegetation indices developed using the red edge bands in the earlier part of the growing season were particularly important for classification accuracy. This work suggests that readily available Sentinel-1 and Sentinel-2 satellite data can be used to accurately map winter land cover, including cover crops, in the US Midwest. Full article

Rational fertilization directly affects the fruit quality of the Korla fragrant pear. However, the variation patterns of fruit appearance and texture indicators under different N-P₂O₅-K₂O ratios are complex, and redundancy among high-dimensional indicators restricts the practical application of quality discrimination and fertilization traceability. In this study, Korla fragrant pear fruits harvested under eight fertilization treatments (including the control) were selected as research materials. Significant differences existed in nutrient composition and application rate among treatments: no N-P₂O₅-K₂O was applied in the CK treatment; for treatments H1–H7, nitrogen (N) application rate ranged from 396.36 to 524.2 g·plant⁻¹, phosphorus (P₂O₅) from 326.08 to 652.17 g·plant⁻¹, and potassium (K₂O) from 450.67 to 1200.08 g·plant⁻¹, with the most prominent differences observed in P-K ratios and application rates. On this basis, 12 appearance and flesh texture indicators were determined, including single-fruit weight, longitudinal diameter, transverse diameter, fruit shape index, pericarp thickness, sclereid content, hardness, adhesiveness, cohesiveness, springiness, gumminess and chewiness. Three machine-learning algorithms, namely Random Forest (RF), Extreme Learning Machine (ELM) and K-Nearest Neighbor (KNN), were used to construct fruit quality discriminant models. The results showed that the RF model achieved the optimal discriminative performance, with accuracy values of 0.876 and 0.865 for the training and validation sets, respectively. Seven core quality indicators, including sclereid content and longitudinal diameter, were screened via feature-importance intersection analysis. The reconstructed RF model based on this indicator set exhibited nearly no loss in discriminative accuracy despite a ~42% reduction in indicator quantity, providing theoretical and technical support for quality grading, fertilization traceability and precision fertilization of Korla fragrant pear. Full article

Accurate vineyard yield estimation is essential for harvest planning, resource allocation, and economic decision-making, particularly under conditions of high spatial variability. Traditional sampling-based methods are labor-intensive, destructive, and prone to error, especially in high-productivity free-canopy systems. This study developed and evaluated predictive models for commercial irrigated vineyards of Carménère and Chardonnay in Chile’s Maule Region across two growing seasons (2023–2025). Structural yield components, physiological measurements, and UAV-derived multispectral indices (NDVI, GNDVI, NDRE) were collected from georeferenced sampling grids. Modeling approaches included linear regression, stepwise selection, and machine learning algorithms (Random Forest, Multilayer Perceptron). Validation results showed that cluster number was the primary driver of yield variability, explaining up to 40% of variation. Incorporating physiological and spectral variables improved accuracy, with the best models (least squares and MLP) achieving R² values up to 0.66 and reducing errors to 12–15%. Spatial yield maps reproduced intra-vineyard variability patterns, demonstrating that integrating plant-level and canopy-level data substantially enhances yield prediction. These findings provide a robust framework for precision viticulture applications. Full article

Rice is one of the world’s most important food crops, feeding over half of the global population and being crucial for food security. Accurate, timely mapping of rice fields is essential for precision agriculture, yet conventional methods relying on static samples fail to capture dynamic farmers’ planting decisions. To address this, we propose the Multi-Source Dynamic Sample Generation and Phenology-Guided Feature Selection Framework for In-Season Rice Identification (MSDF-RiceID) using multi-source remote sensing imagery. It incorporates two key innovations: (i) a rule-based sample updating mechanism based on historical rice maps and a dynamic threshold algorithm, and (ii) phenology-guided feature optimization through exponential weighting. Developed specifically to handle complex cropping patterns and high cloud cover in Hunan Province, MSDF-RiceID integrates these innovations within a grid-search-optimized Random Forest classifier to produce reliable monthly rice distribution maps. In-season samples corresponding to transplanting dates in April (DOY 100, 120), June (DOY 160), and July (DOY 184), differentiated as early-, middle-, and late-rice crops. The optimal feature set combined Sentinel-1 (PRI, VH, VH_VV), Sentinel-2 (NDYI, PSRI, NDBI, NDWI), and MODIS (NDVI, EVI, NDBI, LSWI) indices. Accuracy increased seasonally, with F1-score rising from 0.82 in May to 0.97 at harvest. Cross-region validation in Taishan (Guangdong) and Panjin (Liaoning) showed that the earliest identifiable stage (F1-score > 0.9) occurred earlier than in Hunan due to Hunan’s more complex triple-cropping phenology, highlighting the model’s strong transferability. Furthermore, MSDF-RiceID outperformed existing products (TWDTW-Rice and EARice10), increasing overall accuracy by 0.12–0.18, Kappa by 0.23–0.35, and F1-score by 0.09–0.15. These results demonstrate its effectiveness for in-season, large-scale, and dynamic rice mapping under persistent cloud cover, thereby providing direct support for precision agricultural management in heterogeneous cropping systems. Full article

Timely and accurate assessment of rice productivity, encompassing grain yield, aboveground biomass (AGB), and harvest index (HI), is essential for harvest planning, supply chain coordination, and food security. This study evaluates the feasibility of predicting all three productivity components using satellite and weather time series data while examining trade-offs between forecast accuracy and operational lead time. Five machine learning models (CatBoost, Gaussian Process Regression (GPR), Random Forest, Ridge regression, and TabPFN) were compared across six in-season prediction windows (December to May) using Sentinel-2 vegetation indices (Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red Edge 2 (CIRE2), Land Surface Water Index (LSWI)), weather variables (minimum and maximum temperature and radiation), and agronomic records from 256 commercial and experimental rice fields in southern New South Wales, Australia, over four growing seasons (2022–2025) using leave-one-year-out cross-validation. Rolling in-season forecasts were evaluated across December–May; March was selected for further analysis as a practical window that balances accuracy and timeliness for decision-making, with minimal additional error reduction in later months closer to harvest. TabPFN had the lowest RMSE for yield prediction (RMSE = 1.85 t ha⁻¹, $r=0.72$), Ridge had the lowest RMSE for AGB (RMSE = 3.05 t ha⁻¹, $r=0.77$), while tree-based models yielded the lowest RMSE for derived HI (RMSE ≈ 0.07). HI prediction showed weak regional relationships, with direct prediction yielding $\left|r\right|\approx 0.24$ and derived HI (predicted yield divided by predicted AGB) showing $r\approx 0$. Although strong correlations ($r>0.9$) between HI and vegetation indices were observed within individual site-seasons, consistent with other studies, these relationships were highly variable across site-seasons, reflecting the difficulty of inferring HI from canopy reflectance when biotic and/or abiotic stresses decouple AGB accumulation from grain filling. Both direct and derived HI approaches yielded comparable errors, indicating that satellite and weather data lack information content for regional-scale HI prediction. These findings support satellite-based yield and AGB forecasting for operational use. Full article

Accurate estimation of weekly banana production is relevant for harvest, packing, and logistics planning at the farm level. This study compared Random Forest, XGBoost and Long Short-Term Memory (LSTM) models for estimating the number of banana boxes processed weekly on a commercial banana farm in Naranjal canton, Ecuador. The dataset comprised 156 weekly records from January 2022 to December 2024 and integrated meteorological, edaphological and operational variables. Records from 2022 and 2023 were used for model training and hyperparameter selection, while the 52 weekly records from 2024 were retained as an unseen chronological hold-out test set. XGBoost achieved the best numerical performance on the 2024 hold-out set, followed closely by Random Forest, whereas LSTM showed weaker predictive performance given the available data. Bootstrap confidence intervals supported a cautious interpretation of the numerical differences between the tree-based models. Feature-importance analysis identified harvested bunches as the dominant operational predictor, followed by autoregressive production features and selected management-, soil-, and weather-related variables. Because harvested bunches are available only after the weekly harvest operation, the proposed model should be interpreted as a same-week production estimation or nowcasting tool rather than as a strict multi-week-ahead forecasting model. The augmented Dickey–Fuller and KPSS tests jointly supported treating the weekly target series as stationary for the purposes of the present modeling workflow. The results are limited to one farm and three production years; therefore, external validation across additional farms, seasons, and explicit ahead-of-time forecast horizons is required before broader deployment. Full article

Tropical forests of the Amazon support exceptional biodiversity while sustaining the livelihoods, cultures, and food systems of Indigenous communities. In Loreto, Peru, hunting remains central to both subsistence and market economies, yet its sustainability depends on ecological dynamics and sociocultural systems that shape harvest behavior. Here, we evaluate the potential for sustainable wildlife management in the Maijuna–Kichwa Regional Conservation Area (MKRCA) by integrating a spatially explicit biodemographic model of hunting with a targeted review of Maijuna hunting practices, governance, and economic context. Using participatory mapping data from 19 hunters in the community of Sucusari, we parameterized a model to estimate species-specific depletion under current and projected hunting scenarios. Model results suggest that current harvest rates are largely sustainable, with localized depletion near settlements but relatively intact populations across the broader landscape, supported by access to remote hunting areas and nearby source populations. The literature review reveals that Maijuna sociocultural systems, including territorial hunting norms, seasonal mobility, food-sharing practices, and species-specific taboos, may function as informal management institutions that distribute hunting pressure and limit overexploitation. Together, these findings suggest that both ecological conditions and sociocultural institutions in Sucusari are conducive to sustainable wildlife management if supported by adaptive co-management approaches. However, external pressures, particularly a proposed highway, may fragment existing source–sink dynamics and pose a significant risk to long-term sustainability. Full article

Restoration-oriented forest management is increasingly recognized as an important strategy for enhancing long-term carbon sequestration and rehabilitating degraded peri-urban forest landscapes. This study presents a scenario-based assessment of projected carbon sequestration trajectories under a National Reserve Forest Project implemented in peri-urban Wuhan, central China. Thirteen silvicultural models were grouped into three management pathways: intensive plantation cultivation, transformation of existing degraded stands, and tending of young and middle-aged forests. Carbon sequestration was evaluated over a 40-year assessment period (2024–2063) using a Biomass Expansion Factor-based accounting framework incorporating above- and belowground biomass, harvested wood products, and conservative baseline deductions consistent with national and provincial methodologies. The results indicate a sustained long-term increase in projected carbon sequestration despite periodic short-term declines associated with planned thinning and harvesting cycles. Transformation-oriented pathways contributed the largest cumulative project-scale sequestration and generally exhibited relatively strong area-normalized sequestration performance compared with intensive plantation and tending pathways. Intensive plantation systems displayed greater temporal fluctuation associated with shorter rotation cycles and repeated harvesting events. The analysis also highlights the importance of distinguishing between area-normalized sequestration efficiency and cumulative project-scale contribution, as models with moderate per-hectare performance generated substantial total carbon benefits because of their larger implementation area. The findings suggest that restoration-oriented management of existing degraded stands may provide a relatively stable long-term carbon-sequestration pathway in peri-urban forest systems where land availability for large-scale afforestation is constrained. The study also demonstrates the applicability of conservative scenario-based accounting frameworks for restoration-oriented forest carbon assessment and planning under data-limited conditions. Full article

This study evaluates the influence of harvesting methods and seasonal variability on the physicochemical and antioxidant properties of Tetragonisca fiebrigi honey produced in the tropical dry forest of Bolivia. Despite the growing interest in stingless bee honey, studies addressing the combined effects of seasonality and collection practices in this region remain scarce. Honey samples were collected during winter and spring using three approaches: conventional, optimized (based on good manufacturing practices), and direct racking from natural nests. Physicochemical parameters (pH 4.60–6.15; moisture 28–34%; water activity 0.69–0.75) and sugar composition (glucose 10.60–29.03 g/100 g; fructose 9.01–21.97 g/100 g; sucrose 0.70–3.23 g/100 g) showed variability primarily associated with season rather than harvesting method. Bioactive compounds exhibited a marked seasonal effect, with higher total phenolic content (up to 11.03 mg GAE/100 g), flavonoids (up to 23.08 mg QE/100 g), and antioxidant capacity (DPPH up to 1.33 mol TE/100 g; ORAC up to 25.93 mol TE/100 g) in spring samples. Multivariate analysis (PCA) revealed that honey variability is structured along bioactive and physicochemical axes, with samples obtained using the optimized method showing reduced dispersion and greater compositional consistency. These results indicate that while seasonality governs the compositional and functional properties of T. fiebrigi honey, improved harvesting practices contribute to reducing variability and enhancing product standardization. This study provides one of the first comprehensive datasets on Bolivian stingless bee honey and highlights its potential as a functional food, supporting the development of species-specific quality criteria and sustainable meliponiculture in tropical dry forest ecosystems. Full article

Medicinal plants grown outside their native forest habitat may produce phytochemical profiles that differ from wild-harvested material, yet the ecological mechanisms underlying these differences remain poorly synthesized across disciplines. This review proposes that the forest understory functions as a multi-signal elicitation system in which canopy light filtering, arbuscular mycorrhizal fungi (AMF), and above-ground biotic interactions collectively shape secondary metabolite profiles. AMF-mediated induced systemic resistance and above-ground biotic interactions operate through confirmed jasmonate-mediated pathways. Sunfleck-driven reactive oxygen species signaling is hypothesized but untested, and the red-to-far-red ratio modulated phytochrome B pathway characterized in Arabidopsis remains unconfirmed in shade-tolerant species. Using three saponin-rich medicinal plants (Panax vietnamensis, Panex quinquefolius, and Paris polyphylla) as case studies, we formalize this as a testable chemical terroir hypothesis with three falsifiable predictions. We also translate it into an ecological co-cultivation design principle with three production levels and a two-step operational framework, and identify priority experiments, analytical methods, and implementation challenges needed for validation. These contributions bridge forest ecology and medicinal plant science while identifying critical evidence gaps requiring resolution before field implementation. Full article

Background: The artificial cultivation of Dendropanax dentiger under forest understory conditions offers a sustainable alternative to wild harvesting, yet the metabolic adaptations underlying transplantation stress and recovery remain poorly understood. Objectives: In this study, we performed a comparative metabolomics analysis of different organs (leaves, current-year stems, three-year-old stems, and roots) from wild D. dentiger plants and those transplanted to the understory. Methods and Results: Metabolite annotation and classification revealed that over 60% of the metabolites fell into the categories of lipids and lipid-like molecules, organoheterocyclic compounds, phenylpropanoids, and polyketides. Further differential analysis of metabolites showed that understory transplantation significantly altered the metabolic profiles of all organs, exhibiting organ-specific response patterns. For the metabolite components in the organs of transplanted and wild D. dentiger, these metabolites were mainly classified into eight categories: alkaloids and derivatives; benzenoids; lignans, neolignans and related compounds; lipids and lipid-like molecules; organic acids and derivatives; organoheterocyclic compounds; phenylpropanoids and polyketides; and organic oxygen compounds. Notably, the contents of (-)-asarinin, (Z)-1-(methylthio)-5-phenyl-1-penten-3-yne, and stearidonic acid (SDA, 18:4n-3) were higher in transplanted plants than in wild plants, indicating the potential of understory cultivation for the targeted extraction of these bioactive compounds. Conclusion: These findings provide a metabolomics basis for optimizing the artificial cultivation and quality control of D. dentiger. This study highlights the value of metabolomics in understanding the metabolic composition of D. dentiger and offers a reference for its artificial cultivation. Full article

A nondestructive, rapid, and portable detection method for moisture content (MC) in living tree trunks remains unavailable. Tree radar, developed based on ground-penetrating radar (GPR) technology, represents a promising approach for tree trunk MC detection owing to its high penetration depth and low susceptibility to environmental interference. However, its application to living tree MC detection is constrained by curvature-induced wave propagation complexity, interspecific structural heterogeneity and the limited availability of labeled MC samples obtained through destructive coring, collectively resulting in poor model performance. The study proposed a novel GPR-based MC detection method employing a multi-scale one-dimensional convolutional neural network integrated with an attention mechanism and mixed data augmentation (mixed-MS1DCNNAM). GPR amplitude data extracted from the first 6.5 ns of B-scan signals were used to capture MC-related features via a custom program developed in MATGPR. A mixed model for four tree species with 15–30 cm diameters at breast height (DBH) achieved an R² of 0.7908 and an RMSE value of 0.1059, outperforming traditional models, with test metrics calculated at the tree level by averaging predictions from five directional GPR scans per tree. Furthermore, three DBH-specific sub-models (15–20 cm, 20–25 cm, and 25–30 cm) and four single-species sub-models were developed, yielding improved performance (R² ≥ 0.7246, RMSE ≤ 0.1033; RMSE ≤ 0.0959, MAE ≤ 0.0626, except for European white birch). These results highlighted the effectiveness of stratification by DBH class and tree species. Overall, this study effectively addresses aforementioned challenges and establishes a generalizable nondestructive approach for living trees under field conditions, facilitating sustainable forest management in tree growth monitoring, forest disaster monitoring, harvested timber storage and wood quality assessment. Full article