In arid and semi-arid regions, improving water efficiency is imperative for the sustainable advancement of both forestry and agriculture. The water demand period for Xanthoceras sorbifolium Bunge, an economically and ecologically important tree grown in the Horqin Sandy Land of China, is unknown. This knowledge gap has hindered the development of optimized deficit irrigation (DI) schemes aimed at conserving water while maintaining yield and quality. To identify this key period and better understand the water-use dynamics of this species, we conducted a two-year field experiment (2021-2022). Eight irrigation treatments were applied across three key phenological stages: flowering (F), fruit setting to expansion (S), and fruit-expansion to maturity (M). The irrigation treatments included full-stage (FSM), two-stage (FS, FM, and SM), single-stage (F, S, and M), and no irrigation (NI). The application of DI decreased fruit yields by 8.36-58.01 % (p < 0.05), while two-stage irrigation significantly reduced water consumption and evapotranspiration compared with full-stage irrigation (p < 0.01). FS significantly improved water productivity (WP), irrigation water productivity (WPI), and fruit quality. All two-stage irrigation treatments demonstrated yield response factors (ky) < 1. The FS treatment reduced irrigation volume by 43.7 %, while the yield decreased by only 8.36 %, suggesting that the irrigation savings did not significantly compromise yield. In summary, the FS treatment is recommended as the most optimal irrigation schedule, followed by SM and FM, for the production of X. sorbifolium in drylands. This approach conserves water while minimally impacting productivity, thus representing a sustainable water management strategy.

The increasing frequency and severity of heatwaves pose a significant threat to forest viability, potentially inducing growth anomalies or mortality. However, the physiological mechanisms governing mature tree acclimation to recurrent heatwaves remain poorly understood. Here, automatic, continuous dendrometer and xylem sap flow sensors were used to assess the dynamics of stem growth (RGR), tree water deficit (TWD), and stem sap flow density (Js) in three mature Populus plantations (Tongzhou, TZ; Gaotang, GT; Wenxian, WX) across North China during summer heatwaves in 2023. During the initial heatwave, trees exhibited growth suppression while sustaining higher TWD. Relative to pre-heatwave levels, tree growth during the second heatwave still declined, but the magnitude of the decline varied regionally. Trees in GT with a sufficient 10-day recovery window showed greater growth recovery than the first heatwave period, whereas those in TZ and WX with insufficient recovery intervals (2-4 days) experienced persistent growth inhibition. Furthermore, environmental factors strongly affected RGR during repeated heatwave periods, whereas TWD and Js were associated with RGR during the second heatwave period. Specifically, high Js promoted growth, whereas increased TWD reduced growth. Collectively, this study emphasizes the importance of hydraulic regulation and adequate recovery windows in shaping thermal adaptive strategies, providing valuable insights to guide sustainable forest management and silvicultural practices under increasingly frequent compound extremes.

Thinning is widely used to improve stand structure in poplar plantations, but its short-term seasonal effects on leaf physiology remain unclear. We examined how different thinning intensities influence leaf water status, nutrient dynamics, and photosynthetic function of Populus tomentosa during dry and rainy seasons. Seasonal physiological adjustments were assessed using leaf functional traits, chlorophyll fluorescence, gas-exchange measurements, and nutrient stoichiometry. Thinning increased leaf relative water content and moderated seasonal variation in leaf water potential, indicating short-term buffering of leaf water status. Moderate thinning altered photochemical parameters in a season-dependent manner. In contrast, gas-exchange traits were driven mainly by seasonal conditions rather than thinning. Leaf nutrient stoichiometry exhibited clear seasonal contrasts, with N-P co-limitation during the dry season and N limitation during the rainy season. Structural equation modelling showed that tree growth was more strongly associated with photochemical traits in the dry season and with gas-exchange traits in the rainy season. Overall, these findings demonstrate that thinning primarily influences short-term seasonal coordination among leaf physiological processes rather than inducing rapid shifts in structural traits, highlighting the importance of seasonal context when evaluating thinning effects in poplar plantations.

Urban vegetation is increasingly exposed to the compound heat and drought stress due to global warming and urban heat island effects, yet heat tolerance and its linkage with drought resistance or photosynthesis remain unclear for plants in urban environments. We evaluated corresponding traits in nine woody species common to northern China's highly urbanized megacity cluster dominated by temperate continental monsoon climate. Specifically, we assessed: (1) temperature thresholds causing declines in the maximum quantum yield of photosystem II (Fv/Fm), (2) leaf resistance to xylem embolism and turgor loss, and (3) leaf gas exchange and biochemical efficiency of photosynthesis of field-grown, mature plants, but under lab conditions. We also recorded the in situ Fv/Fm and leaf temperature under contrasting air temperatures to assess whether and how plants maintained functional integrity of the photosynthetic apparatus under heat. We showed that stress tolerance and photosynthetic traits differed markedly among species. The overall weak heat tolerance of these plants resulted in significantly decreased Fv/Fm in four species under high air temperature, while species with a wider thermal safety margin, primarily determined by leaf temperature, retained greater functional integrity. Traits associated with leaf heat tolerance, drought tolerance, and photosynthetic efficiency were decoupled. Moreover, correlations were identified between heat tolerance traits and climatic metrics, indicating the variability in climate of species distributional range. Findings of this study add to the limited knowledge regarding the physiological resistance of urban greening plants and may provide reference during the establishment of green infrastructure in this region.

With climate change, drought-driven deep soil desiccation is an escalating threat to forest ecosystems. However, the interactions between plant performance and deep soil drying remain poorly understood. We integrated 0–6 m soil water profiles, fine root distributions, and plant physiological traits across an age gradient of poplar (Populus tomentosa and Populus euramericana) plantations, linking the spatiotemporal dynamics of dried soil layers (DSL) with tree eco-physiological responses. We propose the percentage of DSLT to root zone (PTR) as a new index accounting for DSL severity. Our results revealed that DSL severity followed a desiccation-to-alleviation trajectory, partially recovering around 25 years. Once PTR exceeded 50%, trees exhibited threshold responses of reduced growth rate and specific leaf area, regulation became more isohydric, and stomatal traits shifted toward smaller but more numerous apertures. Coarser roots remained in moist layers for water transport, while finer roots in drier layers to maximize uptake, reflecting combined drought tolerance and avoidance strategies. Our findings reveal the nonlinear development of DSL and identify a physiological threshold beyond which poplars initiate coordinated responses. We provide new insights into plant-soil water feedback mechanisms under long-term drying conditions.

Accurate assessment of tree transpiration is essential for plantation water management but is limited by the spatial heterogeneity of stem sap flow. We investigated Populus tomentosa using sapwood dyeing and thermal dissipation probes under full drip irrigation (DIFI) and rainfed (CK) treatments. Results showed that under DIFI, deep soil water maintained synchronized outer and inner sap flux density (SFD) with small seasonal variation in the ratio of outer-to-inner SFD (ℛₒᵢ), exhibiting an outer-xylem-prioritized, inner-engaged radial pattern. In contrast, CK experienced deep soil water deficit, leading to substantially lower inner SFD and irregularly fluctuating ℛₒᵢ, resulting in persistent inner-layer limitation and greater radial divergence. The outer SFD responded more strongly to vapor pressure deficit and radiation (R² = 0.64–0.67) than the inner SFD (R² = 0.39–0.50). For azimuthal variation, a mixed-effects model detected a significant but small overall azimuth effect (F₃, ₂₇ = 4.94, P < 0.01), with only the north–south contrast significant in pairwise comparisons; however, tree-to-tree variation was the dominant source of heterogeneity. Neglecting azimuthal variation resulted in a 21.6% mean deviation in whole-tree transpiration estimates, whereas using two and three orientations reduced the mean deviation to 12.8% and 7.0%, respectively. Sapwood dyeing revealed that hydraulic pathways shifted from distinct sectoral patterns at the base to widely distributed pathways with increasing height. We conclude that sap-flow measurement strategies should be adjusted to stand soil-water conditions. In water-limited stands, probes covering both the outer and inner xylem are crucial to capture dynamic radial heterogeneity, while combining measurements from two to three orientations across multiple trees is needed to minimize errors arising from azimuthal and individual variation. These insights provide a basis for optimizing transpiration models and water-saving management practices in plantation ecosystems.

Accurate prediction of canopy light interception is essential for advancing precision management in planted forests. However, conventional approaches that rely on manual pruning strategies are limited in scalability and fail to effectively capture the dynamic interactions between canopy structure and light distribution. We propose a novel Canopy Light Interception Prediction with Transformer-LSTM Network(CLIP-TLNet) that combines 3D spatial complexity quantification with temporal modeling to predict light distribution within individual tree canopies over time. Leveraging multi-sensor UAV-derived LiDAR point clouds and synchronized photometric measurements from triploid poplar plantations, we develop a regional canopy complexity algorithm based on multi-scale fractal dimension analysis, enabling precise quantification of structural heterogeneity. This complexity metric enhances model interpretability and improves learning performance by characterizing how canopy architecture influences light penetration. In parallel, the spatio-temporally decoupled Transformer-LSTM architecture effectively captures temporal trends while maintaining spatial awareness, yielding a Mean Absolute Percentage Error (MAPE) of 6.8% and outperforming the second-best CNN-LSTM model by a 33.6% reduction in Root Mean Square Error (RMSE).Ablation studies confirm that incorporating canopy complexity as a structural prior caused a 20.6% increase in MAPE upon its removal, underscoring its critical role in predictive performance. By enabling data-driven, complexity-aware pruning strategies and temporally optimized interventions, this framework bridges static structural assessment with dynamic environmental response modeling. It offers a powerful tool for precision silviculture and intelligent canopy management in plantation forestry.

Against the backdrop of intensifying drought risks driven by global climate change, the productivity enhancement of plantation forests increasingly relies on water management. However, how sustained water supplementation influences nutrient allocation and stoichiometric balance at the tree organ level remains a critical knowledge gap. Through a five-year experiment in a Populus tomentosa plantation, we examined how water supplementation interacts with stand development to shape nutrient allocation and stoichiometry across tree organs. Elevated soil moisture did not change organ carbon (C) concentrations, though it differentially affected nitrogen (N) and phosphorus (P) concentrations depending on the organ and the age of the trees. Leaf N:P patterns suggested a possible tendency toward N limitation during stand development. Stand-level C, N, and P stocks responded to water supplementation in an age-dependent manner, with significant increases under DIFI mainly emerging by age 5. These increases were primarily associated with biomass accumulation, yet pronounced P retention in woody biomass suggested increasing plant P demand during later stand development. Nutrient allocation shifted in two phases: before canopy closure, moisture-elevated trees prioritized leaves, while water-limited trees invested more in roots; after canopy closure, moisture-elevated stands allocated nutrients to stems for light competition, whereas water-limited stands maintained a root-conservation strategy. Our findings suggest that soil moisture and stand age jointly influence the transition from early resource-acquisition to later structural competition or resource conservation. Sustained water supplementation should therefore be coupled with stage-specific nutrient monitoring and adaptive fertilization to maintain long-term productivity and nutrient balance.

EcoSyn is a unified control-and-learning framework for coupled irrigation-forestry management under nonstationary climate drivers. The architecture comprises: first, a hierarchical adaptive ecostate machine (HAESM) that encodes hybrid dynamics, fusing discrete event triggers with continuous sensor fields to govern zone-level mode transitions; second, an eco-impact learning module (EILM) that optimizes a composite eco-impact index (EII) = omega(1) W-eff/W-req + omega(2) C-abs/C-max, where W-eff integrates effective water use and C-abs is a carbon-uptake functional of flux and vegetation indices; third, a distributed knowledge node network (DKNN) for federated, privacy-preserving parameter sharing across heterogeneous agro-ecological zones with edge execution, and, fourth, a climate-resilient feedback loop that introduces forward-weighted reinforcement using event-conditioned gains for anomaly and extremes handling. Online optimization ties HAESM transition policies to EILM's loss-regularized objective, while DKNN aggregation stabilizes local controllers against covariate shift. The full stack ingests multisource Internet of Things measurements and operates in a calibrated simulation with FAOSTATderived priors and grid-structured geospatial features. In six stress scenarios spanning demand surges, variable precipitation, soil degradation, and high evapotranspiration, EcoSyn attains 94.2% resource-allocation accuracy, 80.2% drought-response efficiency, and 89.7% anomaly resilience.

From 2016 to 2021, a field experiment was conducted in the North China Plain to study the long-term effects of drip irrigation and nitrogen coupling on the growth, biomass allocation, and irrigation water and fertilizer use efficiency of short-rotation triploid Populus tomentosa plantations. The experiment adopted a completely randomized block design, with one control (CK) and six water-nitrogen coupling treatments (IF, two irrigation levels × three nitrogen application levels). Data analysis was conducted using ANOVA, regression models, Spearman's correlation analysis, and path analysis. The results showed that the effects of water and nitrogen treatments on the annual increment of diameter at breast height (ΔDBH), annual increment of tree height (ΔH), basal area of the stand (BAS), stand volume (VS), and annual forest productivity (AFP) in short-rotation forestry exhibited a significant stand age effect. The coupling of water and nitrogen significantly promoted the DBH growth of 2-year-old trees (p < 0.05), but after 3 years of age, the promoting effect of water and nitrogen coupling gradually diminished. In the 6th year, the above-ground biomass of Populus tomentosa was 5.16 to 6.62 times the under-ground biomass under different treatments. Compared to the I45 treatment (irrigation at soil water potential of -45 kPa), the irrigation water use efficiency of the I20 treatment (-20 kPa) decreased by 88.79%. PFP showed a downward trend with the increase in fertilization amount, dropping by 130.95% and 132.86% under the I20 and I45 irrigation levels. Path analysis indicated that irrigation had a significant effect on the BAS, VS, AFP, and TGB of 6-year-old Populus tomentosa (p < 0.05), with the universality of irrigation being higher than that of fertilization. It is recommended to implement phased water and fertilizer management for Populus tomentosa plantations in the North China Plain. During 1-3 years of tree age, adequate irrigation should be ensured and nitrogen fertilizer application increased. Between the ages of 4 and 6, irrigation and fertilization should be ceased to reduce resource wastage. This work provides scientific guidance for water and fertilizer management in short-rotation plantations.

The accurate point cloud completion of individual tree crowns is critical for quantifying crown complexity and advancing precision forestry, yet it remains challenging in dense plantations due to canopy occlusion and LiDAR limitations. In this study, we extended the scope of conventional point cloud completion techniques to artificial planted forests by introducing a novel approach called Multi−feature Fusion Completion of Populus (MFCPopulus). Specifically designed for Populus Tomentosa plantations with uniform spacing, this method utilized a dataset of 1050 manually segmented trees with expert−validated trunk−canopy separation. Key innovations include the following: (1) a hierarchical adversarial framework that integrates multi−scale feature extraction (via Farthest Point Sampling at varying rates) and biologically informed normalization to address trunk−canopy density disparities; (2) a structural characteristics split−collocation (SCS−SCC) strategy that prioritizes crown reconstruction through adaptive sampling ratios, achieving a 94.5% canopy coverage in outputs; (3) a cross−layer feature integration enabling the simultaneous recovery of global contours and a fine−grained branch topology. Compared to state−of−the−art methods, MFCPopulus reduced the Chamfer distance variance by 23% and structural complexity discrepancies (ΔDb) by 33% (mean, 0.12), while preserving species−specific morphological patterns. Octree analysis demonstrated an 89−94% spatial alignment with ground truth across height ratios (HR = 1.25−5.0). Although initially developed for artificial planted forests, the framework generalizes well to diverse species, accurately reconstructing 3D crown structures for both broadleaf (Fagus sylvatica, Acer campestre) and coniferous species (Pinus sylvestris) across public datasets, providing a precise and generalizable solution for cross−species trees’ phenotypic studies.

Three-dimensional models of trees can help simulate forest resource management, field surveys, and urban landscape design. With the advancement of Computer Vision (CV) and laser remote sensing technology, forestry researchers can use images and point cloud data to perform digital modeling. However, modeling leafless tree models that conform to tree growth rules and have effective branching remains a major challenge. This article proposes a method based on 3D Gaussian Splatting (3D GS) to address this issue. Firstly, we compared the reconstruction of the same tree and confirmed the advantages of the 3D GS method in tree 3D reconstruction. Secondly, seven landscape trees were reconstructed using the 3D GS-based method, to verify the effectiveness of the method. Finally, the 3D reconstructed point cloud was used to generate the QSM and extract tree feature parameters to verify the accuracy of the reconstructed model. Our results indicate that this method can effectively reconstruct the structure of real trees, and especially completely reconstruct 3rd-order branches. Meanwhile, the error of the Diameter at Breast Height (DBH) of the model is below 1.59 cm, with a relative error of 3.8–14.6%. This proves that 3D GS effectively solved the problems of inconsistency between tree models and real growth rules, as well as poor branch structure in tree reconstruction models, providing new insights and research directions for the 3D reconstruction and visualization of landscape trees in the leafless stage.

In forestry data management and analysis, data integrity and analytical accuracy are of critical importance. However, existing techniques face a dual challenge: first, sensor failures, data transmission interruptions, and human errors lead to the prevalence of missing data in forestry datasets; second, the multidimensional heterogeneity and environmental complexity of forestry systems not only increase the difficulty of missing value estimation, but also significantly affect the accuracy of resolving the potential correlations among data. In order to solve the above problems, we proposed the L2 model using the aspen woodland as the experimental object. The L2 model consists of a complementary model and a predictive model. The L2 complementary model integrates low tensor tensor kernel norm minimisation (LRTC-TNN) to capture global consistency and local trends, and combines long and short-term memory and convolutional neural network (LSTM-CNN) to extract temporal and spatial features, which is effective in accurately reconstructing the missing values in forestry time-series data. We also optimised the LRTC-TNN model to handle multi-class data and incorporated a self-attention mechanism into the LSTM-CNN framework to improve performance in the case of complex missing data. The L2 prediction model adopts a dual attention mechanism (temporal attention mechanism and feature attention mechanism) based on LSTM to construct a stem diameter prediction model, which achieves high-precision prediction of stem diameter variation. Then we further analyzed the effects of various factors on stem diameter using SHAP (Shapley Additive Explanations).Experimental results demonstrate that our L2 significantly improves data completion accuracy while preserving the original structure and key characteristics of the data. Moreover, it enables a more precise analysis of the factors affecting stem diameter, providing a robust foundation for advanced forestry data analysis and informed decision making.

Short rotation plantation forestry (SRF) is being widely adopted to increase wood production, in order to meet global demand for wood products. However, to ensure maximum gains from SRF, optimised management regimes need to be established by integrating robust predictions and an understanding of mechanisms underlying tree growth. Hybrid ecophysiological models, such as potentially useable light sum equation (PULSE) models, are useful tools requiring minimal input data that meet the requirements of SRF. PULSE models have been tested and calibrated for different evergreen conifers and broadleaves at both juvenile and mature stages of tree growth with coarse soil and climate data. Therefore, it is prudent to question: can adding detailed soil and climatic data reduce errors in this type of model? In addition, PULSE techniques have not been used to model deciduous species, which are a challenge for ecophysiological models due to their phenology. This study developed a PULSE model for a clonal Populus tomentosa plantation in northern China using detailed edaphic and climatic data. The results showed high precision and low bias in height (m) and basal area (m2 center dot ha-1) predictions. While detailed edaphoclimatic data produce highly precise predictions and a good mechanistic understanding, the study suggested that local climatic data could also be employed. The study showed that PULSE modelling in combination with coarse level of edaphic and local climate data resulted in reasonably precise tree growth prediction and minimal bias.

Plantations are an important component of global forest coverage, but their performance is increasingly affected by water limitation due to climate change. Employing a rainfall exclusion facility, we report on the impacts of reduced rainfall on leaf water relations and organ morphological traits, in six Populus varieties commonly used for afforestation across North China. We exposed trees to 2 years of 50% rainfall exclusion and found that leaf hydraulic traits conferring drought resistance, including water potential thresholds triggering xylem embolism, leaf pressure-volume characteristics and metrics quantifying the risk of hydraulic dysfunction (i.e., hydraulic safety margin), were not improved, despite slightly but significantly decreased predawn leaf water potential and growth rate. Interspecific variation in response to rainfall exclusion was observed for some morphological traits, yet the adjustments were unlikely to benefit drought resistance. Overall, our results demonstrate an overall lack of physiological adaptive adjustments for leaves in response to rainfall reduction at early growth stage for these trees. If this response persists as trees age, the function of these trees will be potentially reduced due to increased risk of hydraulic failure, if the drying trend continues in their planting region.

Plant hydraulic traits primarily define the water regulation strategy, thus enabling a better understanding of vegetation structure, function and dynamics under varying hydro‐environments. Despite being intensively documented in woody species, the variation and correlation of hydraulic traits across herbaceous species remain largely understudied. Here, we report on the leaf hydraulics of nine herbs with contrasting growth forms (graminoid and forb). Traits quantifying drought resistance, including leaf water potential thresholds triggering xylem embolism (P x ), stomatal closure (P gs ) or leaf turgor loss point (P tlp ), and minimum conductance (g min ), together with leaf gas exchange, morphological traits and biomass allocation, were measured on pot‐grown plants. In addition, an in situ dry‐down was imposed on four representative species, with leaf gas exchange, water potential and level of xylem embolism being continuously monitored during dehydration to determine the dynamics of stomatal closure and leaf xylem embolism. We found that the studied graminoids tended to be more drought tolerant than forbs, although the difference in hydraulic safety margin for stomatal closure (HSM st ) did not differ significantly between these growth forms. Across species, P x was coordinated with P gs and P tlp , but was decoupled from gas exchange traits, including maximum photosynthetic rate and stomatal conductance. Furthermore, no correlations were found between hydraulic traits and specific leaf area or the ratio of aboveground to belowground biomass. For plants that experienced in situ dehydration, stomatal closure always preceded the onset of xylem embolism in leaves. Moreover, species exhibited a distinct stomatal regulation strategy during the dehydration despite belonging to the same growth form. Our findings contribute to the understanding of herb hydraulics, which will inform prediction on the dynamics of grassy ecosystems by providing traits data and guiding the classification of plant functional types in ‘grassy’ ecosystems. Read the free Plain Language Summary for this article on the Journal blog.

Soil pore structure plays an important role in the ecosystem. In recent years, researchers have begun utilizing deep learning to segment soil pores. However, when confronted with a large number of soil pore datasets that require annotation, the effort and time for manual labeling are limited and insufficient to accurately annotate the entire dataset. To address this issue, this paper proposes a weakly supervised soil pore segmentation method (WSSPS) based on traditional segmentation algorithms. WSSPS generates soil pore pseudo-labels through the traditional segmentation algorithm for pre-training in the upstream task. Subsequently, fine-tuning was performed in the downstream task using expert-defined labels that only accounted for 1.8% to 35.6% of the total dataset to obtain the final segmentation effect map. In this study, three traditional segmentation algorithms are utilized for comparison experiments in the upstream task, and they are also compared with each other and four supervised deep learning methods. The results demonstrate that WSSPS not only possesses better segmentation results than traditional and supervised methods, but also greatly reduces the amount of manual annotation. This study facilitates the application of deep learning in soil pore segmentation and provides image processing technical support for the advancement of modern soil research.

Purpose Fine roots and soil properties show distinct vertical patterns, reflecting their coupled responses to thinning and water-fertilizer management. This study aimed to elucidate soil-root interactions and provide insights for the sustainable management of plantations. Methods A split-plot design was established with three thinning intensities (no thinning, moderate, heavy) and three water-nitrogen treatments (control, irrigation, irrigation + nitrogen). Soil profiles (0–6 m) and fine roots were sampled to assess changes in soil moisture, nutrient dynamics, and fine root traits. Multivariate analyses were used to identify key regulatory drivers. Results Soil water content (SWC) peaked at 300–400 cm and was sensitive to management in the 20–500 cm layer. Thinning and irrigation increased SWC, whereas water-nitrogen input reduced it in mid-depth layers. Thinning enhanced nitrogen accumulation, while water-nitrogen input offset nitrogen loss but increased nitrate leaching risk. Fine root biomass density was highest in the 0–20 cm layer, with deeper layers remaining stable. Water-nitrogen addition increased specific root area, with SWC as the main determinant after thinning, and both phosphorus and SWC driving responses under fertilization. Conclusion Thinning improved water availability but constrained nutrients, while water-nitrogen input shifted fine roots toward an acquisitive strategy, highlighting management-specific soil-root interactions.

In order to explore the management strategies for cultivating and improving the stem quality of Populus tomentosa plantations under the background of climate change, this study focuses on P. tomentosa plantations over 10 years old in the North China Plain. Using linear mixed models and ordered logistic models, the impacts of cultivar, tree size, stand age, competition, and climate on the stem quality of P. tomentosa (including crown base height, tapering, branching grade, and straightness grade) were analyzed. The study found that: cultivar significantly affected all stem quality indicators (P < 0.05). Compared to other cultivars, the P. tomentosa f. yixianensis had a 23 % increase in branch height, an 8 % reduction in taper, and the risk of having poorer branches and stem form decreased by 96 % and 80 %, respectively. In addition, taller and bigger-diameter trees had better external stem quality. The impacts of competition-related indicators on stem quality were inconsistent: reduced canopy openness could improve stem quality by enhancing light competition, however, increased tree density increased the risk of deteriorating branching and straightness grade by 1.2 % and 0.9 %, respectively. Among all factors, cultivar and individual tree size had the greatest relative importance for various stem quality indicators, followed by competition-related factors, while stand age and climate factors have no significant impact on P. tomentosa stem quality (P > 0.05). Currently, climate change has little impact on the external stem quality characteristics during the cultivation of P. tomentosa plantations. Management strategies for stem quality can focus on cultivar selection and competition regulation. It is worth noting that increasing tree density in the North China Plain may not necessarily improve stem quality of P. tomentosa plantations, so caution is needed in the process of regulating competition.