Search Results (90)

Pedunculate oak (Quercus robur L.), an ecologically and economically important tree species has been significantly affected by oak dieback in recent years. Since one of the symptoms of oak dieback is crown defoliation, this research aimed to determine the quantity, quality, average tree value, and wood defects that influence grading in different stages of oak dieback indicated by tree crown defoliation degree. The research was conducted in a 62- and 116-year-old stand of the lowland Croatian forest. In total, 115 pedunculate oak trees were sampled and processed in 983 logs that were analyzed. The prescribed single-entry volume tables underestimate harvesting volume by 5.45% on site A and 6.16% on site B, while the calculation of net harvesting volume underestimates net volume by 0.26% on site A and overestimates net volume on site B by 4.59%. The analysis of wood defect presence showed that insect holes, rot, and covered knots were the main reasons for the degradation of quality class. Dead trees showed a decreased average tree value in DBH classes 32.5–42.5 cm compared to the healthy trees. Based on the findings of this research, tree crown defoliation degree could be used as a timber quality and average tree value indicator. Full article

Sugarcane yield prediction plays a pivotal role in enabling farmers to monitor crop development and optimize cultivation practices, guiding harvesting operations for sugar mills. In this study, we established three experimental fields, which were planted with three main sugarcane cultivars in Guangxi, China, respectively, implementing a multi-gradient fertilization design with 39 plots and 810 sampling grids. Multispectral imagery was acquired by unmanned aerial vehicles (UAVs) during five critical growth stages: mid-tillering (T1), late-tillering (T2), mid-elongation (T3), late-elongation (T4), and maturation (T5). Following rigorous image preprocessing (including stitching, geometric correction, and radiometric correction), 16 VIs were extracted. To identify yield-sensitive vegetation indices (VIs), a spectral feature selection criterion combining gray relational analysis and correlation analysis (GRD-r) was proposed. Subsequently, three supervised learning algorithms—Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM)—were employed to develop both single-stage and multi-stage yield prediction models. Results demonstrated that multi-stage models consistently outperformed their single-stage counterparts. Among the single-stage models, the RF model using T3-stage features achieved the highest accuracy (R² = 0.78, RMSE_V = 7.47 t/hm²). The best performance among multi-stage models was obtained using a GBDT model constructed from a combination of DVI (T1), NDVI (T2), TDVI (T3), NDVI (T4), and SRPI (T5), yielding R² = 0.83 and RMSE_V = 6.63 t/hm². This study highlights the advantages of integrating multi-temporal spectral features and advanced machine learning techniques for improving sugarcane yield prediction, providing a theoretical foundation and practical guidance for precision agriculture and harvest logistics. Full article

Selection forestry sustains timber production and stand structural complexity via partial harvesting. However, regeneration initiated by harvesting may function as fuel ladders, providing pathways for fire to reach the forest canopy. We sought potential mitigation approaches by simulating stand growth and potential wildfire behavior over a century in stands dominated by coast redwood (Sequoia sempervirens (Lamb. ex. D. Don) Endl.) on California’s north coast. We used the fire and fuels extension to the forest vegetation simulator (FFE-FVS) to compare group selection (GS) to single-tree selection silviculture with either low-density (LD) or high-density (HD) retention on a 20-year harvest return interval. These three approaches were paired with six options involving vegetation management (i.e., hardwood control or pre-commercial thinning (PCT)) with and without fuels treatments (i.e., prescribed fire or pile burning), or no subsequent vegetation or fuel treatment applied after GS, HD, or LD silviculture. Fuel treatment involving prescribed fire reduced hazardous fuel loading but lowered stand density and hence productivity. Hardwood control followed by prescribed fire mitigated potential wildfire behavior and promoted dominance of merchantable conifers. PCT of small young trees regenerating after selection harvests, followed by piling and burning of these cut trees, sustained timber production while reducing potential wildfire behavior by approximately 40% relative to selection silviculture without vegetation/fuel management, which exhibited the worst potential wildfire behavior. Full article

The Miombo woodlands are declining in both area and value, primarily due to over-harvesting of commonly preferred species. These forests, however, still contain several other species that are potentially of commercial importance. This study aimed to address the need for improved volume and biomass estimates for the sustainable management and utilization of two of the most abundant timber species in Mozambique’s Miombo woodlands: Brachystegia spiciformis (common name: Messassa) and Julbernardia globiflora (common name: red Messassa). Non-linear models were developed to estimate the merchantable wood volume under bark, heartwood volume, and biomass. The volume and biomass models for wood and heartwood volume, which included both diameter at breast height (DBH) and tree height as predictor variables, outperformed single-predictor models. However, the performance of some ratio models using DBH as the only predictor variable surpassed that of models using two predictor variables. The developed models are recommended for adoption by forest companies to increase economic and environmental benefits as they can refine harvest planning by improving the selection of trees for harvesting. Proper tree selection enhances the rate of recovery of high-quality timber from heartwood while observing sustainable forest management practices in Miombo and increasing the proportion of carbon removed from forests, which is subsequently stored in wood products outside the forest. Full article

The endophytic strain Amfr20 was isolated from roots of the olive tree var. Amfissa. Based on core-genome phylogenomic analyses, it was classified as Bacillus velezensis. The isolate showed positive results in numerous plant growth promoting traits, as well as in abiotic stress tolerance and in colonization related traits in vitro. Furthermore, the strain exhibited antifungal activity in vitro through diffusible and volatile compounds. Whole genome analysis revealed that the strain possesses large and various arsenals of secondary metabolite biosynthetic gene clusters involved in the bioagent’s functional properties, including plant growth promotion, colonization, and plant defense elicitation, as well as having the genomic potential for abiotic stress mediation. Based on TLC-bioautography, the ethyl acetate extracts of secreted agar-diffusible compounds from Amfr20 through single and dual cultures were found to be bioactive independently of the fungal pathogen’s interaction. The bacterial endophyte also proved efficient in suppressing the severity of anthracnose olive rot and gray mold post-harvest diseases on olive fruits and table grape berries, respectively. Lastly, Amfr20 beneficially affected Arabidopsis thaliana growth under normal and saline conditions, while boosting the plant development of Solanum lycopersicum through seed biopriming and root irrigation methods. The results of this multilevel study indicate that the novel endophyte Amfr20 Bacillus velezensis is a promising bioagent that should be exploited in the future as an ecological biopesticide and/or biostimulant. Full article

Mangrove ecosystems rank among the most productive on Earth. Conducting research on the biomass prediction model of mangroves, as well as achieving simple and efficient estimations of the biomass of mangrove plant organs and the overall biomass, is of utmost significance for evaluating the productivity of the mangrove ecosystem and offering guidance for the future planning, restoration, and management of mangroves. This study examines the biomass distribution characteristics of Kandelia obovata at the northern edge of its range in China and develops models for estimating the biomass of its various components and individual trees. The findings provide valuable references for accurately assessing the biomass of Kandelia obovata plantations in Zhejiang Province. We measured the biomass of different components (branches, leaves, roots) using the harvest method and employed independent variables, including basal diameter (D), tree height (H), diameter squared (D²), the product of diameter squared and height (D²H), and the product of basal diameter and height (DH). Dependent variables included the leaf, branch, root, and total biomass. We developed linear, quadratic, and power function regression equations, selecting the optimal models based on the coefficient of determination (R²), significance of regression, root mean square error (RMSE), and Akaike Information Criterion (AIC). The total biomass ranged from 0.100 to 0.925 Mg ha⁻¹, while the carbon stocks ranged from 0.038 to 0.377 Mg C ha⁻¹. Results indicated that branch biomass accounted for the highest proportion (47.44%~68.35%), while leaf biomass (8.61%~27.83%) and root biomass (23.04%~25.64%) were relatively lower. Similarly, branch carbon storage constituted the highest proportion (52.68%~77.79%), with leaf (8.70%~29.36%) and root carbon storage (13.51%~20.55%) being lower. The optimal model exhibited R² values ranging from 0.594 to 0.921 and significant F-tests (p < 0.001). Single variables D, D², and combined variables D²H and DH provided the best fits. Basal diameter (D) and tree height (H) effectively predict the biomass of Kandelia obovata across different ages, with combined variables DH and D²H enhancing model accuracy. The biomass estimation model for total biomass is: W_Total = 0.0584(DH)^1.3918 (R² = 0.908, F = 2459.87, RMSE = 0.448). This model serves as a reliable tool for estimating the biomass of Kandelia obovata mangroves at the northern edge of its distribution in China. Full article

Precise photogrammetric mapping of preharvest conditions in an apple orchard can help determine the exact position and volume of single apple fruits. This can help estimate upcoming yields and prevent losses through spatially precise cultivation measures. These parameters also are the basis for effective storage management decisions, post-harvest. These spatial orchard characteristics can be determined by low-cost drone technology with a consumer grade red-green-blue (RGB) sensor. Flights were conducted in a specified setting to enhance the signal-to-noise ratio of the orchard imagery. Two different altitudes of 7.5 m and 10 m were tested to estimate the optimum performance. A multi-seasonal field campaign was conducted on an apple orchard in Brandenburg, Germany. The test site consisted of an area of 0.5 ha with 1334 trees, including the varieties ‘Gala’ and ‘Jonaprince’. Four rows of trees were tested each season, consisting of 14 blocks with eight trees each. Ripe apples were detected by their color and structure from a photogrammetrically created three-dimensional point cloud with an automatic algorithm. The detection included the position, number, volume and mass of apples for all blocks over the orchard. Results show that the identification of ripe apple fruit is possible in RGB point clouds. Model coefficients of determination ranged from 0.41 for data captured at an altitude of 7.5 m for 2018 to 0.40 and 0.53 for data from a 10 m altitude, for 2018 and 2020, respectively. Model performance was weaker for the last captured tree rows because data coverage was lower. The model underestimated the number of apples per block, which is reasonable, as leaves cover some of the fruits. However, a good relationship to the yield mass per block was found when the estimated apple volume per block was combined with a mean apple density per variety. Overall, coefficients of determination of 0.56 (for the 7.5 m altitude flight) and 0.76 (for the 10 m flights) were achieved. Therefore, we conclude that mapping at an altitude of 10 m performs better than 7.5 m, in the context of low-altitude UAV flights for the estimation of ripe apple parameters directly from 3D RGB dense point clouds. Full article

Effective forest management necessitates spatially explicit information about tree species composition. This information supports the safeguarding of native species, sustainable timber harvesting practices, precise mapping of wildlife habitats, and identification of invasive species. Tree species identification and geo-location by machine learning classification of UAV aerial imagery offer an alternative to tedious ground surveys. However, the timing (season) of the aerial surveys, input variables considered for classification, and the model type affect the classification accuracy. This work evaluates how the seasons and input variables considered in the species classification model affect the accuracy of species classification in a temperate broadleaf and mixed forest. Among the considered models, a Random Forest (RF) classifier demonstrated the highest performance, attaining an overall accuracy of 83.98% and a kappa coefficient of 0.80. Simultaneously using input data from summer, winter, autumn, and spring seasons improved tree species classification accuracy by 14–18% from classifications made using only single-season input data. Models that included vegetation indices, image texture, and elevation data obtained the highest accuracy. These results strengthen the case for using multi-seasonal data for species classification in temperate broadleaf and mixed forests since seasonal differences in the characteristics of species (e.g., leaf color, canopy structure) improve the ability to discern species. Full article

The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0~1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester. Full article

The prevalence of apples as the most widely consumed fruit globally does not exempt them from storage-related issues, resulting in substantial harvest losses. A prominent concern is the development of rot due to various factors during storage. This research endeavors to examine the influence of agrotechnological methods on the longevity of apples and the incidence of rot throughout storage. Apple trees (Malus domestica Borkh. cv. Rubin) grafted on dwarfing rootstocks P60 were planted in 2010 in single rows with a spacing of 1.25 m between trees and 3.5 m between rows. Eight combinations of different growth control measures (manual, mechanical pruning, spraying, trunk cutting) were selected for the experiment. The implementation of mechanical pruning, in conjunction with trunk cutting and Ca-prohexadione spraying, as well as summer pruning, detrimentally impacted the shelf life of apples. Examination of the storage period revealed a loss of 33–40% of the crop due to rot. Conversely, manual pruning sustained a consistent level of phenolic compounds throughout the storage period. Other pruning methods resulted in a notable increase in phenolic compounds, ranging from 67% to a two-fold rise compared to the compounds present at harvest. However, the integration of mechanical pruning with subsequent manual pruning not only significantly augmented the yield of apples but also yielded a shelf life akin to that of manually pruned apples. Following the analysis of the results, it is advisable to conduct mechanical pruning of the apples intended for storage along with supplementary manual pruning. Full article

A soil removal device for the root-soil complex of Gentian imitating the percussion function of a woodpecker was designed to improve the soil removal efficiency of harvesting devices for rhizome-type traditional Chinese herbal medicines. Based on the physical parameters of roots and the root-soil complex of Gentian, the structure parameters of the striking arm and the actual profile of the cam are determined according to the physical parameters when the woodpecker knocks on the tree. The key parameters that affect the working performance of the soil removal device and their suitable value ranges have been identified through the impact test and analysis of the root-soil complex of Gentian. The mass of the striking hammer, the swing angle of the striking arm, and the rotation speed of the cam were taken as the experimental factors and the soil removal rate and the energy consumption per hammer percussion were taken as the experimental indicators. The ternary quadratic orthogonal regression combination experiment was carried out using Design-Expert. The regression model of the influence factors and evaluation indicators was established through the analysis of variance. The interaction effects of the influence factors on the indicators were analyzed using the response surface method. Using multiobjective optimization method, the optimal parameter combination was obtained as that of the mass of the striking hammer of 0.9 kg, the swing angle of the striking arm of 47°, and the rotation speed of the cam of 100 r/min, then the soil removal rate was the maximum and the energy consumption of single-hammer knocking was the minimum, with the values of 89.12% and 31.21 J, respectively. This study can provide a reference for the design and optimization of soil removal devices for rhizome-type traditional Chinese herbal medicines. Full article

This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees’ influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China. Full article

Understanding the effects of various forest management systems, including logging practices, on soil properties is essential for implementing sustainable management strategies. In Malaysia, two types of forest management systems were commonly used: Malayan Uniform System (MUS) and Selective Management System (SMS) practices. However, their effects on soil quality remained elusive, especially after decades of recovery. To address this need, we selected three plots for the MUS and SMS in Johor, Malaysia, to assess soil properties in logged-over forest plots. All the plots were natural forest reserves. Soil properties analyzed include soil acidity, electrical conductivity, cation exchange capacity, selected nutrient contents, and soil compaction. Generally, the results of the study indicate that forests logged using the SMS exhibit superior soil quality compared to those logged using the MUS according to several key soil properties. Specifically, significantly higher cation exchange capacity, potassium content, calcium content, and magnesium content with lower soil compaction was observed in the SMS when compared to MUS plots. In short, the SMS enhances soil quality more effectively than the MUS, even with a shorter logging cycle. This is because the SMS does not harvest all trees and distributes the impact of harvesting more evenly over time, rather than concentrating it at a single time point. Ultimately, this highlights that the SMS can play a significant role in promoting sustainable forest management practices by preserving soil quality. Full article

Silvicultural treatment and the forest harvesting operations using different methods can lead to an increase in the production of runoff and sediment by changing the canopy and soil surface where they are conducted. In order to investigate this issue, sampling plots were established in the Namkhaneh district of the Kheyrud forest with three replications for every treatment: control stand and tree harvesting systems using single-selection cuttings and group-selection cuttings. The amount of runoff and sediment was collected and estimated from precipitation over a period of one year. Also, some soil physical properties such as bulk density, penetration resistance, sand, silt, and clay content, soil moisture, and soil organic matter were measured. The results showed that tree harvesting systems has a significant effect on runoff, the runoff coefficient, and sediment but the season (growing season and fall) and the combined effect of tree harvesting systems and the season have no significant effect on the runoff coefficient and sediment. The mean runoffs of each rainfall event for the control, single-tree, and group-selection treatments were 5.67, 8.42, and 10.28 mm, respectively, and the sediment amounts were 3.42, 6.70, and 11.82 gr/m², respectively. Furthermore, the total annual erosion amounts of the control, selection, and grouping treatments were 0.427, 0.838, and 2.178 t/ha, respectively. The bulk density, penetration resistance, and percentage of sand and silt were positively related and the percentages of clay and organic matter were negatively related with the amount of runoff and sediment. In the method of individual selection cuttings, the damage to the forest in terms of the amount of runoff and soil erosion was less than for the group-selection cuttings. Forest harvesting by the selection method (single-selection and group-selection) has caused different changes in the vegetation canopy. The final summary of our results could be the advice to predominantly use the single-selection method in high-slope stands. Full article

All-natural rubber is harvested from rubber trees (Hevea brasiliensis Muell. Arg.) by traditional tapping knives, so rubber tapping still heavily relies on labor. Therefore, this study explored a novel, hand-held mechanical rubber tapping machine for rubber tree harvesting. In this study, a mechanical tapping cutter with a vertical blade and adjustable guide was first described. The response surface method was applied to evaluate factors affecting the tapping effect. The experimental values were in close agreement with the predicted value. Machine-tapped latex was comparable in quality to hand-tapped latex. Based on the single-factor results, the response surface method (RSM) and the center combined rotation design (CCRD) optimization method were adopted to explore the influence of three factors influencing vertical blade height (A), cutting force (B), and spiral angle (C) on the tapping effect. Regarding the cutting rate of the old rubber line (Y₁), cutting time (Y₂), latex flow rate (Y₃), and average cutting current (Y₄) as evaluation indexes of the tapping effect, an optimization scheme was determined. The quadratic model fits for all the responses. The test results showed that the main factors affecting Y₁, Y₂, Y₃, and Y₄ were A and B, B, A and C, and B, respectively. Under optimal conditions, the influencing factors of A, B, and C were 10.24 mm, 51.67 N, and 24.77°, respectively, when the evaluation index values of Y₁, Y₂, Y₃, and Y₄ were 98%, 8.65 mL/5 min, 9.00 s, and 1.16 A. The range of the relative error between the experimental and predicted results was from −11.11% to 11.11%. According to the optimized treatment scheme, a comparison test was designed between mechanical and manual rubber tapping tools. To verify the availability and effect of the mechanical tapping method preliminarily, the important rubber tapping evaluation indexes included bark thickness, bark excision, latex flow time, cutting time, ash content, and cutting depth, which were selected to serve as a comparison test. There was no significant difference between hand and mechanical methods, except ash content (p < 0.05) and cutting time (p < 0.01). The mechanical tapping machine proposed in this study is meaningful to improve cutting efficiency, practicality, and operability. Furthermore, it provides crucial theoretical references for the development of intelligent tapping machines. Full article