Search Results (79)

Fraxinus spp. is one of the most important salt-alkali resistant tree species in the Yellow River region of China. However, the limited number of superior families and individuals, as well as the lack of a well-established parent selection system for hybrid breeding, have seriously constrained the improvement of seed orchards and the construction of advanced breeding populations. To address these issues, this study investigated 22 full-sib families of Fraxinus spp., using SSR molecular markers to calculate the genetic distance (GD) between parents. Combined with combining ability analysis, the study aimed to predict heterosis in offspring growth traits and select superior families and individuals through multi-trait comprehensive evaluation. The results showed the following: (1) Tree height (TH), diameter at breast height (DBH), and volume index (VI) exhibited extremely significant differences among families, indicating rich variation and strong selection potential. (2) The phenotypic and genotypic coefficients of variation for TH, DBH, and VI ranged from 4.34% to 16.04% and 5.10% to 17.73%, respectively. Family heritability was relatively high, ranging from 0.724 to 0.818, suggesting that growth is under strong genetic control. (3) The observed and expected heterozygosity of 15 parents were 0.557 and 0.410, respectively, indicating a moderate level of heterozygosity. Nei’s genetic diversity index and Shannon’s information index were 0.488 and 0.670, respectively, indicating relatively high genetic diversity. GD between parents ranged from 0.155 to 0.723. (4) Correlation analysis revealed significant or highly significant positive correlations between family heterosis and growth traits, combining ability, and GD, with specific combining ability (SCA) showing the strongest predictive power. Regression analysis further demonstrated significant linear correlations between GD and heterosis of TH and VI, and between SCA and heterosis of TH, DBH, and VI, establishing a GD threshold (≤0.723) and SCA-based co-selection strategy. In addition, four superior Fraxinus families and 11 elite individuals were selected. Their genetic gains for TH, DBH, and VI reached 2.28%, 3.30%, and 9.96% (family selection), and 1.98%, 2.11%, and 4.00% (individual selection), respectively. By integrating genetic distance (GD) and quantitative genetic combining ability (SCA), this study established a quantifiable prediction model and proposed the “GD–SCA dual-index parent selection method”, offering a new paradigm for genetic improvement in tree breeding. Full article

Sustainable Forest Management (SFM) requires the building of relationships among diameter increment, shape, and size (ISS), and increment–age variables to identify critical changes in forest structure and dynamics. This understanding is essential for maintaining forest productivity, structural and species diversity, stability, and sustainability. This study focused on measuring, reporting, and modeling these relationships for Araucaria angustifolia (Bertol.) Kuntze, across various diameters and three stands, located at different rural properties in southern Brazil. A random sample of 186 individual trees was acquired; the trees were measured for multiple dendrometric variables, and several morphometric indices were calculated. Additionally, two cores were extracted from each tree using an increment borer, enabling the measurement of growth rings and annual diameter increments. These were modeled using generalized linear models to assess the relationships among them and to quantify changes in forest structure and dynamics. The results revealed the dominance of A. angustifolia and a decline in the increment rate with increasing age, shape, and size in both old and young trees, indicating potential risks to the structure and dynamics of these unmanaged forests. Therefore, the models constructed in this study can guide conservation-by-use efforts and ensure the long-term continuity and productivity of forest remnants at selected rural properties, where A. angustifolia trees are predominant. Full article

Background: Single-step genomic BLUP (ssGBLUP) has gained increasing interest from forest tree breeders. ssGBLUP combines phenotypic and pedigree data with marker data to enhance the prediction accuracy of estimated breeding values. However, potential errors in determining progeny relationships among open-pollinated species may result in lower accuracy of estimated breeding values. Unknown parent groups (UPG) and metafounders (MF) were developed to address missing pedigrees in a population. This study aimed to incorporate MF into ssGBLUP models to select the best parents for controlled mating and the best progenies for cloning in a tree breeding population of Eucalyptus globulus. Methods: Genetic groups were defined to include base individuals of similar genetic origin. Tree growth was measured as total height (TH) and diameter at breast height (DBH), while disease resistance was assessed through heteroblasty (the transition from juvenile to adult foliage: ADFO). All traits were evaluated at 14 and 21 months. Two genomic multi-trait threshold linear models were fitted, with and without MF. Also, two multi-trait threshold-linear models based on phenotypic and pedigree information (ABLUP) were used to evaluate the increase in accuracy when adding genomic information to the model. To test the quality of models by cross-validation, the linear regression method (LR) was used. Results: The LR statistics indicated that the ssGBLUP models without MF performed better, as the inclusion of MF increased the bias of predictions. The ssGBLUP accuracy for both validations ranged from 0.42 to 0.68. Conclusions: The best model to select parents for controlled matings and individuals for cloning is ssGBLUP without MF. Full article

With the intensification of global climate change and human activities, coniferous species as the main components of natural forests in the Altay Mountains are facing the challenges of aging and regeneration. This study systematically analyzed structural heterogeneity and regeneration of three coniferous stand groups, Larix sibirica Ledeb. stand group, Abies sibirica Ledeb.-Picea obovata Ledeb.-Larix sibirica mixed stand group, and Picea obovata stand group, respectively, across western, central, and eastern forest areas of the Altay Mountains in Northwest China based on field surveys in 2023. Methodologically, we integrated Kruskal–Wallis/Dunn’s post hoc tests, nonlinear power-law modeling (diameter at breast height (DBH)–age relationships, validated via R², root mean square error (RMSE), and F-tests), static life tables (age class mortality and survival curves), and dynamic indices. Key findings revealed structural divergence: the L. sibirica stand group exhibited dominance of large-diameter trees (>30 cm DBH) with sparse seedlings/saplings and limited regeneration; the mixed stand group was dominated by small DBH individuals (<10 cm), showing young age structures and vigorous regeneration; while the P. obovata stand group displayed uniform DBH/height distributions and slow regeneration capacity. Radial growth rates differed significantly—highest in the mixed stand group (average of 0.315 cm/a), intermediate in the P. obovata stand group (0.216 cm/a), and lowest in the L. sibirica stand group (0.180 cm/a). Age–density trends varied among stand groups: unimodal in the L. sibirica and P. obovata stand groups while declining in the mixed stand group. All stand groups followed a Deevey-II survival curve (constant mortality across ages). The mixed stand group showed the highest growth potential but maximum disturbance risk, the L. sibirica stand group exhibited complex variation with lowest risk probability, while the P. obovata stand group had weaker adaptive capacity. These results underscore the need for differentiated management: promoting L. sibirica regeneration via gap-based interventions, enhancing disturbance resistance in the mixed stand group through structural diversification, and prioritizing P. obovata conservation to maintain ecosystem stability. This multi-method framework bridges stand-scale heterogeneity with demographic mechanisms, offering actionable insights for climate-resilient forestry. Full article

This study examines the differences in growth patterns, biomass accumulation, and carbon storage between planted European beech and Norway spruce in the Western Carpathians, Slovakia. Two approaches were used to analyze young forest trees and stands: destructive tree sampling and repetitive tree measurements. Biomass modeling was conducted for individual tree components and entire trees, demonstrating that stem diameter and height were strong predictors of biomass. Notably, beeches exhibited greater tree biomass than spruces when analyzed at the same stem diameter, whereas the opposite trend was observed when tree height was used as the predictor. At the stand level, biomass modeling incorporated the mean diameter, mean height, or stand age. Two primary tree components were analyzed: woody parts, which store carbon long term, and foliage, which stores carbon for shorter periods. Stand age emerged as the most reliable predictor, providing real-time estimates of biomass and carbon storage. At a maximum modeled stand age of 12 years, beech biomass stock was 18 Mg ha⁻¹, compared to 58 Mg ha⁻¹ for spruce (uniform tree spacing of 2.0 × 2.0 m for both species was considered). Correspondingly, carbon storage values were 9 Mg ha⁻¹ for beech and 29 Mg ha⁻¹ for spruce, demonstrating a threefold difference in favor of spruce. The study also examined the biomass transition to necromass, specifically foliage litter loss. Over 12 years, spruce stands shed 10.3 Mg ha⁻¹ of needle litter, while beech stands lost 5.4 Mg ha⁻¹. A 12-year-old beech stand fixed-carbon (necromass in form of foliage litter was not included) equivalent to about 30 Mg CO₂ per ha, while a spruce stand of the same age fixed nearly 107 Mg CO₂ per ha. The carbon storage in live trees translates into financial values about EUR 2000 per ha for beech and over EUR 7000 per ha for spruce, highlighting an economic advantage for spruce in carbon sequestration markets as part of climate sustainability efforts. However, in practice, these differences could be partly reduced through denser (more than double) planting of beech compared to spruce. Full article

To address the inefficiency and significant errors in the manual measurement of phenotypic parameters of Phoebe zhennan seedlings, a non-destructive automated method based on a 3D point cloud was proposed for extracting phenotypic parameters of stem and leaves following stem and leaf segmentation. First, the processed point cloud image was aligned using the Sample Consensus Initial Aligment (SAC-IA) and Iterative Closest Point (ICP) algorithms to generate a three-dimensional model of the seedlings. The stem point cloud was extracted from the model using the median normalized growth vector-based search (MNVG) method, with the current growth vector refined based on previous growth points and vectors. These corrective processes enhanced the accuracy of stem extraction. The leaves were separated from the stem through streamlined projection, after which the remaining leaf point cloud was individually extracted using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The extracted stem data were used to measure stem length and stem diameter, and for each extracted leaf, leaf length, width, and area were measured, yielding accuracies of 97.7%, 93.2%, 96.4%, 88.02%, and 85.84%, respectively. The results of this study provide a valuable reference for forest breeding and the cultivation of high-quality tree seedlings. Full article

Forest ecosystems play a pivotal role in the global carbon cycle and climate change mitigation. Forest aboveground biomass (AGB), a critical indicator of carbon storage and sequestration capacity, has garnered significant attention in ecological research. Recently, uncrewed aerial vehicle-borne laser scanning (ULS) technology has emerged as a promising tool for rapidly acquiring three-dimensional spatial information on AGB and vegetation carbon storage. This study evaluates the applicability and accuracy of UAV-LiDAR technology in estimating the spatiotemporal dynamics of AGB and vegetation carbon storage in Robinia pseudoacacia (R. pseudoacacia) plantations in the gully regions of the Loess Plateau, China. At the sample plot scale, optimal parameters for individual tree segmentation (ITS) based on the canopy height model (CHM) were determined, and segmentation accuracy was validated. The results showed root mean square error (RMSE) values of 13.17 trees (25.16%) for tree count, 0.40 m (3.57%) for average tree height (AH), and 320.88 kg (16.94%) for AGB. The regression model, which links sample plot AGB with AH and tree count, generated AGB estimates that closely matched the observed AGB values. At the watershed scale, ULS data were used to estimate the AGB and vegetation carbon storage of R. pseudoacacia plantations in the Caijiachuan watershed. The analysis revealed a total of 68,992 trees, with a total carbon storage of 2890.34 Mg and a carbon density of 62.46 Mg ha⁻¹. Low-density forest areas (<1500 trees ha⁻¹) dominated the landscape, accounting for 94.38% of the tree count, 82.62% of the area, and 92.46% of the carbon storage. Analysis of tree-ring data revealed significant variation in the onset of growth decline across different density classes of plantations aged 0–30 years, with higher-density stands exhibiting delayed growth decline compared to lower-density stands. Compared to traditional methods based on diameter at breast height (DBH), carbon storage assessments demonstrated superior accuracy and scientific validity. This study underscores the feasibility and potential of ULS technology for AGB and carbon storage estimation in regions with complex terrain, such as the Loess Plateau. It highlights the importance of accounting for topographic factors to enhance estimation accuracy. The findings provide valuable data support for density management and high-quality development of R. pseudoacacia plantations in the Caijiachuan watershed and present an efficient approach for precise forest carbon sink accounting. Full article

This study addresses the challenges posed by the complex topography and forest structure in karst mountainous areas, as well as the difficulties in estimating forest stock using traditional methods. We propose a method that integrates optical remote sensing data from Sentinel-2 into airborne LiDAR data to estimate forest stock in karst areas. First, an Allometric Growth Model correlating tree height and diameter at breast height (DBH) in karst areas was developed based on field measurements. Tree height information extracted from LiDAR data was then combined with the binary wood volume model specific to fir trees in Guizhou Province to calculate the individual tree biomass of fir trees. In addition, this study evaluated the robustness of three machine learning methods, the Random Forest Regression Model, K-Nearest Neighbors Regression Model, and Backpropagation Neural Network Model, in estimating forest stock in karst mountainous areas. The results indicate the following: (1) The Allometric Growth Model based on field data showed strong predictive power for DBH and can be used for large-scale estimation. (2) The distribution characteristics of individual tree biomass and plot biomass under different site conditions revealed the distribution pattern of fir trees in the study area, providing important information for understanding the growth status of forest stock in the region. (3) The Random Forest Regression Model demonstrated exceptional accuracy, generalization capability, and robustness in the estimation of forest stock within karst mountainous regions. This study provides an effective technical tool for estimating forest stock in karst areas and under complex terrain conditions and has significant scientific value and practical implications for the monitoring and management of forest ecosystem carbon sinks. Full article

Forest plot surveys are vital for monitoring forest resource growth, contributing to their sustainable development. The accuracy and efficiency of these surveys are paramount, making technological advancements such as Simultaneous Localization and Mapping (SLAM) crucial. This study investigates the application of SLAM technology, utilizing LiDAR (Light Detection and Ranging) and monocular cameras, to enhance forestry plot surveys. Conducted in three 32 × 32 m plots within the Tibet Autonomous Region of China, the research compares the efficacy of LiDAR-based and visual SLAM algorithms in estimating tree parameters such as diameter at breast height (DBH), tree height, and position, alongside their adaptability to forest environments. The findings revealed that both types of algorithms achieved high precision in DBH estimation, with LiDAR SLAM presenting a root mean square error (RMSE) range of 1.4 to 1.96 cm and visual SLAM showing a slightly higher precision, with an RMSE of 0.72 to 0.85 cm. In terms of tree position accuracy, the three methods can achieve tree location measurements. LiDAR SLAM accurately represents the relative positions of trees, while the traditional and visual SLAM systems exhibit slight positional offsets for individual trees. However, discrepancies arose in tree height estimation accuracy, where visual SLAM exhibited a bias range from −0.55 to 0.19 m and an RMSE of 1.36 to 2.34 m, while LiDAR SLAM had a broader bias range and higher RMSE, especially for trees over 25 m, attributed to scanning angle limitations and branch occlusion. Moreover, the study highlights the comprehensive point cloud data generated by LiDAR SLAM, useful for calculating extensive tree parameters such as volume and carbon storage and Tree Information Modeling (TIM) through digital twin technology. In contrast, the sparser data from visual SLAM limits its use to basic parameter estimation. These insights underscore the effectiveness and precision of SLAM-based approaches in forestry plot surveys while also indicating distinct advantages and suitability of each method to different forest environments. The findings advocate for tailored survey strategies, aligning with specific forest conditions and requirements, enhancing the application of SLAM technology in forestry management and conservation efforts. Full article

Biomass serves as a crucial indicator of plant productivity, and the development of biomass models has become an efficient way for estimating tree biomass production rapidly and accurately. This study aimed to develop a rapid and accurate model to estimate the individual aboveground biomass of Salix suchowensis. Growth parameters, including plant height (PH), ground diameter (GD), number of first branches (NFB), number of second branches (NSB) and aboveground fresh biomass weight (FW), were measured from 892 destructive sample trees. Correlation analysis indicated that GD had higher positive correlations with FW than PH, NFB and NSB. According to the biological features and field observations of S. suchowensis, the samples were classified into three categories: single-stemmed type, first-branched type and second-branched type. Based on the field measurement data, regression models were constructed separately between FW and each growth trait (PH, GD, NFB and NSB) using linear and nonlinear regression functions (linear, exponential and power). Then, multiple power regression and multiple linear regression were conducted to estimate the fresh biomass of three types of S. suchowensis. For the single-stemmed plant type, model M1 with GD as the single parameter had the highest adj R², outperforming the other models. Among the 16 constructed biomass-estimating equations for the first-branched plant type, model M32 FW = ^0.010371 × PH^1.15862 × GD^1.250581 × NFB^0.190707 was found to have the best fit, with the highest coefficient of determination (adj R² = 0.6627) and lowest Akaike Information Criterion (AIC = 5997.3081). When it comes to the second-branched plant type, the best-fitting equation was proved to be the multiple power model M43 with PH, GD, NFB and NSB as parameters, which had the highest adj R² value and best-fitting effect. The stability and reliability of the models were confirmed by the F-test, repeated k-fold cross-validation and paired-sample t-tests. The models developed in this study could provide efficient tools for accurately estimating the total aboveground biomass for S. suchowensis at individual tree levels. The results of this study could also be useful for optimizing the economic productivity of shrub willow plantations. Full article

Castanopsis hystrix, a precious tree species in Southeast Asia, has the advantages of rapid growth and high-quality wood materials. However, there are problems such as its long breeding cycle and low efficiency, and being time-consuming, which greatly restricts the industrial development of C. hystrix. Performing genome selection (GS) for growth and wood traits for the early selection of superior progeny has great significance for the rapid breeding of new superior varieties of C. hystrix. We used 226 clones in the main distribution and 479 progenies within 23 half-sib families as experimental materials in this study. Genotyping datasets were obtained by high-throughput re-sequencing technology, and GS studies were conducted on the growth (tree height (H), diameter at breast height (DBH)) and wood (wood density (WD), fiber length (FL), and fiber length–width ratio (LWR)) traits. The coefficient of variation (CV) of five phenotypic traits ranged from 10.1% to 22.73%, the average CV of growth traits was 19.93%, and the average CV of wood traits was 9.72%. The Pearson correlation coefficients between the five traits were almost all significantly positive. Based on the Genomic Best Linear Unbiased Prediction (GBLUP) model, the broad-sense heritabilities of growth traits were higher than those of wood quality traits, and the different number of SNPs had little effect on the heritability estimation. GS prediction accuracy first increased and then reached a plateau at around 3K SNPs for all five traits. The broad-sense heritability of these five traits was significantly positively correlated with their GS predictive ability (r = 0.564, p < 0.001). Bayes models had better GS prediction accuracy than the GBLUP model. The 15 excellent progeny individuals were selected, and their genetic gain ranged from 0.319% to 2.671%. These 15 superior offspring individuals were 4388, 4438, 4407, 4468, 4044, 4335, 4410, 4160, 4212, 4461, 4052, 4014, 4332, 4389, and 4007, mainly from three families F5, F6, and F11. Our research lays out the technical and material foundation for the rapid breeding of new superior varieties of C. hystrix in southern China. Full article

Bog bilberry (Vaccinium uliginosum L.) is considered a highly valued non-wood forest product (NWFP) species with edible and medicinal uses in East Asia. It grows in the northeastern forests of China, where stand attributes and structure jointly determine its population characteristics and individuals’ growth. Mapping the regional distributions of its population characteristics can be beneficial in the management of its natural resources, and this mapping should be predicted using machine learning modeling to obtain accurate results. In this study, a total of 60 stands were randomly chosen and screened to investigate natural bog bilberry populations in the eastern mountains of Heilongjiang and Jilin provinces in northeastern China. Individual height, canopy cover area, and fresh weight all increased in stands at higher latitudes, and shoot height was also higher in the eastern stands. The rootstock grove density showed a polynomial quadratic distribution pattern along increasing topographical gradients, resulting in a minimum density of 0.43–0.52 groves m⁻² in stands in the southern part (44.3016° N, 129.4558° E) of Heilongjiang. Multivariate linear regression indicated that the bog bilberry density was depressed by host forest tree species diversity; this was assessed using both the Simpson and Shannon–Wiener indices, which also showed polynomial quadratic distribution patterns (with a modeling minimum of 0.27 and a maximum of 1.21, respectively) in response to the increase in latitude. Structural equation models identified positive contributions of tree diameter at breast height and latitude to shoot height and a negative contribution of longitude to the bog bilberry canopy area. Random forest modeling indicated that dense populations with heavy individuals were distributed in eastern Heilongjiang, and large-canopy individuals were distributed in Mudanjiang and Tonghua. In conclusion, bog bilberry populations showed better attributes in northeastern stands where host forest trees had low species diversity, but the dominant species had strong trunks. Full article

Forests make an important contribution to the global carbon cycle and climate regulation. Caijiachuan watershed false acacia (Robinia pseudoacacia Linn.) plantation forests have been created for 30 years, but a series of problems have arisen due to the irrationality of the density involved at that time. To precisely assess the contribution of R. pseudoacacia plantations with different densities to this cycle, we measured the diameter at breast height (DBH), tree height (H), biomass, and carbon stocks in trees, shrubs, herbs, litter, and soil across different density ranges, denoted as D1 = 900–1400, D2 = 1401–1900, D3 = 1901–2400, D4 = 2401–2900, and D5 = 2901–3400 trees ha⁻¹. In order to achieve the purpose of accurately estimating the biomass, carbon stocks and the contribution rate of each part in different densities of R. pseudoacacia plantations were measured. The results are as follows: (1) Both DBH and H decreased with increasing density, and field surveys were much more difficult and less accurate for H than DBH. Based on the two allometric growth models, it was found that the determination coefficient of the biomass model that incorporated both H and DBH (0.90) closely resembled that of the model using only DBH (0.89), with an error margin of only 0.04%. (2) At the sample scale, stand density significantly affected R. pseudoacacia stem biomass and total biomass. At the individual plant scale, stand density significantly affected R. pseudoacacia organ biomass. Increasing stand densities promoted the accumulation of vegetation biomass within the sample plot but did not improve the growth of individual R. pseudoacacia trees. The stem biomass constituted the majority of the total R. pseudoacacia biomass (58.25%–60.62%); the total R. pseudoacacia biomass represented a significant portion of the vegetation biomass (93.02%–97.37%). (3) The total carbon stock in the sample plots tended to increase with increasing stand density, indicating a positive correlation between density and the carbon stock of the whole plantation forest ecosystem. Hence, in future R. pseudoacacia plantations, appropriate densities should be selected based on specific objectives. For wood utilization, a planting density of 900–1400 trees ha⁻¹ should be controlled. For carbon fixation, an initial planting density of 2900–3400 trees ha⁻¹ should be selected for R. pseudoacacia. This study provides theoretical support for local forest management and how to better sequester carbon. Full article

Precise estimates of dendrometric and morphometric variables are indispensable for effective forest resource conservation and sustainable utilization. This study focuses on modeling the relationships between shape (morphometric), dimension (dendrometric) and density (N) to assess the sustainability of forest resources. It sheds light on the current state of site characteristics, reproduction, and the structure of Araucaria angustifolia trees at selected forest remnants across multiple sites in Santa Catarina, Southern Brazil. Individual trees and their dendrometric variables, such as the diameter at breast height (d), height (h), crown base height (cbh), annual periodic increment (API) in growth rings, and morphometric variables, including four radii of the crown in cardinal directions, were evaluated. These measurements allowed us to calculate various morphometric indices and crown efficiency, enabling the assessment of both vertical and horizontal structural conditions. Statistical analysis confirmed a positive relationship of the crown volume (cv) and crown surface area (csa) with the crown length (cl). Conversely, the crown efficiency, density, increment rate, and reproductive structure production declined. These morphometric relationships emphasize the complex dynamics within these forest ecosystems, irrespective of the chosen site, indicating that horizontal and vertical forest structures have stagnated and have been characterized by limited change in the last ten years. Such results raise concerns about sustainability, highlighting the need for proper conservation measures and sustainable forest management practices. Our findings underscore the need for substantial adjustments in the structure and dynamics of the forest, particularly on selected rural properties where this tree species is abundant, to ensure long-term sustainability. Full article

Maintaining permanent forest canopy cover and eventually harvesting timber by predetermined target diameter are often considered as a prototype for future management of the oak natural forest. However, target diameters and harvest age based on average forest growth rates from wide geographical areas often hamper improved management of oak forests. In this study, based on the sampling of 129 target trees from 51 oak natural secondary forest plots in Hunan Province, China, an individual-tree DBH (diameter at breast height) growth model of oak target trees was developed, and the site type (41 levels) was related to the model as random effects by a nonlinear mixed-effects approach. Moreover, the 41 site types were clustered into four site type groups (STG1, STG2, STG3, and STG4) by the K-means clustering algorithm to improve the model performance and practicality. With the help of the model, the five target diameters (including 24, 30, 40, 50, and 60 cm) were simulated in each of the four STGs, and the minimum target diameter was determined for each STG based on the theory of quantitative maturity. In the four STGs, the harvest age of the 24 cm diameter target ranged from 30 to 51 years; the harvest age of the 60 cm target diameter ranged from 131 to 220 years, with the oaks failing to reach 60 cm in the lowest-quality STG4; the minimum target diameter ranged from 21 cm to 29 cm. Results showed that lower-quality sites exclude higher target diameters from optimal harvesting strategies, in contrast to the higher target diameter as a more reasonable strategy in higher quality sites, and that the minimum target diameter is significantly influenced by site conditions. Therefore, it is necessary to develop a diverse target-diameter-harvesting strategy adapted for the complex site conditions of oak forests in Hunan Province towards site-specific timber management to improve the sustainability of timber production in oak forests. Full article