State-owned Forest Farm Research Articles

Effects of Soil Microorganisms on Carbon Sequestration under Different Mixed Modification Models in Pinus massoniana L. Plantation

In forests, microbial populations in the soil can directly influence the decomposition of carbon from surface plants, promoting carbon storage and stability. However, in sustainable forest management, it is still unclear how soil microorganisms under different plantation types affect organic carbon sequestration and whether the mechanisms of influence are the same. In this research, we focused on four mixed forests and pure Pinus massoniana-planted forest in the state-owned forest farm of Dushan County. Three replicated plots were set up for each model, and soil samples were collected from different layers (0–20 cm, 20–40 cm, and 40–60 cm), totaling 45 samples. We elucidated the effects of soil microorganisms on carbon sequestration under five mixed modification models of P. massoniana and further explored the mechanisms by which microbial functional communities regulate soil carbon sequestration under different mixed models through molecular sequencing and collinear network analysis. Variance analysis indicated that the soil organic carbon (SOC) of the same soil layer varied significantly, and there were also significant differences in the composition of soil bacterial and fungal microbial communities. Moreover, the bacterial community was more sensitive to changes in the vegetation environment, while the fungal community structure was more resistant to changes in the soil environment. Correlation analysis indicated that the diversity and composition of the bacterial community had more positive effects on soil organic carbon than those of the fungal community. Linear fitting and redundancy analysis (RDA) showed that particulate organic carbon (POC) in soil had the strongest correlation with SOC content. Soil microorganisms affected the storage and stability of soil carbon mainly by regulating the conversion of litter (carbon sources) into POC. The soil environment of different mixed models had different effects on soil carbon accumulation. Both correlation and collinearity network analyses indicated that soil microbial functional groups could enhance carbon storage by regulating readily oxidizable carbon (EOC) and POC content in mixed forest plantations. The results of our study provide a sound basis for replanting a reasonable forest model structure to improve forest carbon storage.

An efficient and accurate deep learning method for tree species classification that integrates depthwise separable convolution and dilated convolution using hyperspectral data

ABSTRACT Addressing accuracy and computational complexity challenges in hyperspectral image classification for small sample and multi-species scenarios, we developed DSC-DC, a lightweight convolutional neural network. This model is based on Depthwise Separable Convolution and Dilated Convolution and was trained using the Teakettle Experimental Forest dataset (USA). In this study, DSC-DC achieved an overall accuracy (OA) of 99.83%, average accuracy (AA) of 99.64%, and Kappa coefficient of 0.9996. Compared to Support Vector Machine and K-Nearest Neighbors, it demonstrated markedly higher OA (3.88% to 7.55%) and AA (30.71% to 34.09%). Compared to Inception-V3, ResNet50, and MSR-3DCNN, DSC-DC marginally outperformed in accuracy (OA: 0.06% to 0.31%; AA: 0.32% to 3.64%) while reducing the training time by 3.5, 5, and 35 times, and the prediction time by 2, 3, and 17 times, respectively. Moreover, DSC-DC exhibits slightly superior accuracy and efficiency compared to a 5-layer optimal structure of the 3D-CNN model. The application of the DSC-DC model to the hyperspectral dataset from the Jiepai branch of the Gaofeng State Owned Forest Farm in the Guangxi province, China, further demonstrated the reliability, versatility, and practical potential of this model. This study provides a reliable and efficient reference solution for small-sample and multi-tree classification tasks.

Study of the Sustainability of a Forest Road Network Using GIS-MCE

An environmentally friendly and economically efficient forest road network is the key to sustainable forest management, and it is essential to evaluate the sustainability of an established forest road network and to optimize the forest road network based on sustainability criteria. This study proposes a sustainability evaluation method for forest road networks. The evaluation index system was constructed by selecting 12 indices from the social, economic, and ecological dimensions. To improve the credibility of the weighting of the evaluation indicators, the evaluation indicators were assigned subjective and objective weights by the analytic hierarchy process and the entropy weight method, respectively, and the game theory idea was used to comprehensively assign the weights. The study area was chosen to be the Taohuajiang State-Owned Forest Farm, which is a national forest park and is also an ecological public welfare forest. The development of the forest road network in the social, economic, and environmental dimensions was calculated by adopting the evaluation method proposed in this study, and the sustainability of the forest road network in each subdivision of the study area was determined, to provide a detailed theoretical basis for the management to carry out the upgrading of the sustainability of the forest road network. The results derived from the evaluation methodology show that the sustainable development of the forest road network in the study area is “excellent”. At the same time, the level of sustainability of the forest road network in different areas of the study area was also evaluated. Finally, this paper puts forward policy recommendations for optimizing the sustainable development of forest road networks. The original sustainability evaluation method in this paper provides a scientific tool for assessing the degree of sustainable development of forest road networks, which contributes to optimizing the forest road networks.

Temporal and Spatial Analysis of the Changes in the Supply of Ecological Products in State-Owned Forest Farms after the Reform

To reveal the changes in the supply efficiency of forest farm ecological products after the reform of state-owned forest farms, this study uses the ecological product accounting method to calculate the physical quantity and value of state-owned forest farm ecological products using data from 2015 and 2020. Through exploratory spatial data analysis, the spatial distribution of the supply efficiency of ecological products of state-owned forest farms and its primary index are studied to explore the temporal and spatial evolution of ecological products from state-owned forest farms in China. The results show the following: (1) Through the reform of state-owned forest farms, the supply capacity of forest farms’ ecological products has been greatly improved, and the physical quantity and value quantity of ecological products have shown an increasing trend, with growth rates of 31.7% and 24.2%, respectively. (2) There is strong spatial heterogeneity in the supply efficiency of ecological products from state-owned forest farms in various provinces, and the overall distribution pattern is gradually decreasing from the northwest to the southeast coastal provinces. (3) There is a negative correlation between the supply capacity of ecological products and provincial GDP. The supply capacity of ecological products in provinces with high per capita GDP is relatively low. (4) The comprehensive index of the supply efficiency of ecological products and its first-level index of state-owned forest farms are characterized by different degrees of agglomeration in space, which are mainly represented by three distribution modes: “H–H”, “L–L”, and “L–H”. Therefore, it is necessary to clarify the ecological function orientation of state-owned forest farms, improve the endogenous power of ecological product supply, actively explore and connect social ecological needs, and achieve coordinated progress in terms of ecological environmental protection and economic development.

Extraction of tree crown parameters of high-density pure Cunninghamia lanceolata plantations by combining the U-Net model and watershed algorithm.

As one of the important timber species in China, Cunninghamia lanceolata is widely distributed in southern China. The information of tree individuals and crown plays an important role in accurately monitoring forest resources. Therefore, it is particularly significant to accurately grasp such information of individual C. lanceolata tree. For high-canopy closed forest stands, the key to correctly extract such information is whether the crowns of mutual occlusion and adhesion can be accurately segmented. Taking the Fujian Jiangle State-owned Forest Farm as the research area and using the UAV image as the data source, we developed a method to extract crown information of individual tree based on deep learning method and watershed algorithm. Firstly, the deep learning neural network model U-Net was used to segment the coverage area of the canopy of C. lanceolata, and then the traditional image segmentation algorithm was used to segment the individual tree to obtain the number and crown information of individual tree. Under the condition of maintaining the same training set, validation set and test set, the extraction results of the canopy coverage area by the U-Net model and traditional machine learning methods [random forest (RF) and support vector machine (SVM)] were compared. Then, two individual tree segmentation results were compared, one using the marker-controlled watershed algorithm, and the other using the combination of the U-Net model and marker-controlled watershed algorithm. The results showed that the segmentation accuracy (SA), precision, IoU (intersection over union) and F1-score (harmonic mean of precision and recall) of the U-Net model were higher than those of RF and SVM. Compared with RF, the value of those four indicators increased by 4.6%, 14.9%, 7.6% and 0.05, respectively. Compared with SVM, the four indicators increased by 3.3%, 8.5%, 8.1% and 0.05, respectively. In terms of extracting the number of trees, the overall accuracy (OA) of the U-Net model combined with the marker-controlled watershed algorithm was 3.7% higher than that of the marker-controlled watershed algorithm, with the mean absolute error (MAE) being decreased by 3.1%. In terms of extracting crown area and crown width of individual tree, R2 increased by 0.11 and 0.09, mean squared error decreased by 8.49 m2 and 4.27 m, and MAE decreased by 2.93 m2 and 1.72 m, respectively. The combination of deep learning U-Net model and watershed algorithm could overcome the challenges in accurately extracting the number of trees and the crown information of individual tree of high-density pure C. lanceolata plantations. It was an efficient and low-cost method of extracting tree crown parameters, which could provide a basis for developing intelligent forest resource monitoring.

Rural tourism and evolving identities of Chinese communities in forested areas

Rural China has faced considerable challenges in recent decades, leading to the dissolution of many state-owned forest farm communities and threatening sustainable development. There remains a need to understand the drivers and characteristics associated with these changes, including the influence of rural tourism on combating forest recession. This study addresses these concerns by constructing and applying a regional sociology framework to investigate the link between tourism and the evolution of communities in rural forested areas. Focusing on forest farm communities in central China, recent findings indicate that tourism has complicated social relations, leading to a transformation of identity from a predominantly status-based, industrial, and cultural identity to a comprehensive regional identity. The study aims to enrich tourism and identity theory, providing theoretical and practical guidance for understanding and enhancing forest community development and sustainability through rural tourism in China and elsewhere.

Result of a year-long animal survey in a state-owned forest farm in Beijing, China.

Background Artificial forest can have great potential in serving as habitat to wildlife, depending on different management methods. As the state-owned forest farms now play a new role in ecological conservation in China, the biological richness of this kind of land-use type is understudied. Once owned by a mining company, a largest state-owned forest farm, Jingxi Forest Farm, has been reformed to be a state-owned forest farm with the purpose of conservation since 2017. Although this 116.4 km2 forest farm holds a near-healthy montaine ecosystem very representative in North China, a large proportion of artificial coniferous forest in the forest farm has been proven to hold less biodiversity than natural vegetation. This situation, however, provides a great opportunity for ecological restoration and biodiversity conservation. Therefore, from November 2019 to December 2020, we conducted a set of biodiversity surveys, whose results will serve as a baseline for further restoration and conservation. New information Here, we report the result of a multi-taxa fauna diversity survey conducted in Jingxi Forest Farm mainly in year 2020 with explicit spatial information. It is the first survey of its kind conducted in this area, revealing a total of 19 species of mammals, 86 birds, four reptiles, two amphibians and one fish species, as well as 101 species of insects. Four species of mammals are identified as data-poor species as they have less than 100 occurrence records with coordination in the GBIF database. One species of insect, representing one new provincial record genus of Beijing, is reported.

Human Resource Allocation in the State-Owned Forest Farm of China for the Changing Climate

Global climate change has become a hot topic in today’s international political, economic, environmental and diplomatic arenas. China has implemented a series of strategies, measures and actions to cope with climate change, which has promoted industrial transformation and human resource adjustment in China’s state-owned forest areas. However, little is known about the role of current human resource allocation in adaptation to climate change in the state-owned forest farm of China. To address these gaps, this study calculated the current situation of human resource structure and the contribution rate of three industries to the allocation of human resources and the evaluation model of coordinated fitness to the climate changes in key state-owned forest farms. The results show that: (1) The current situation of talent in key state-owned forest areas shows a shortage of total amount, a shortage of high-level and highly educated talents, and aging of talents. (2) The coefficient of structural deviation increased and the coefficient of structural-change synergy kept decreasing, indicating that the coordination between human resource allocation and industrial structure in key state-owned forest areas nowadays is only at the intermediate level of synergistic fitness. The paper highlights the trained-professional human resource and the industrial structure changes in the context of climate change as the main limited factors for the key state-owned forest farms of China. Increasing the education investment for climate change and the economic income for the employees are suggested to be promoted for policy makers in future.

Analysis of biomass compatibility model of Chinese fir in Jiangle, Fujian Province

Aiming at the problem of incompatibility of biomass models of forest organs, taking Chinese fir in Fujian Jiangle State-owned Forest Farm as the research object, based on selecting the optimal independent model of each organ, the biomass compatibility model of Chinese fir was established with a three-level joint control scheme. The results show that the compatibility equation system based on the whole plant biomass can effectively solve the problem of incompatibility in the whole plant biomass, each sub-biomass and between sub-biomass. Besides, except for the leaf biomass model, all other biomass models have good fitting effect, which is of great significance to the guidance of the analysis of local Chinese fir biomass.

Dual-Concentrated Network With Morphological Features for Tree Species Classification Using Hyperspectral Image

At present, deep learning is a hot topic in the field of the classification of hyperspectral image (HSI), and it has aroused wide attention. However, in fine-grained classification tasks, such as tree species classification, the uncertain spectrum remains the major factor restraining the classification performance. In order to solve the dilemma of forest tree species classification, a Dual-concentrated Network with Morphological Features (DNMF) is proposed. Firstly, mathematical morphology is used to extract the morphological features of HSI. Then, coarse-grained information is extracted from the original hyperspectral data, and fine-grained information is extracted from morphological features. After that, both morphological representations and spectral inputs are fed into DNMF, and the overall evaluation index and visual image are obtained. The advantage of DNMF is that it decouples the spatial and spectral information, and a multi-source information fusion process is then simulated. Accordingly, DNMF obtains high tree species classification accuracy. In order to verify the superiority of DNMF, we choose Gaofeng State-owned Forest Farm (GSFF) in Guangxi Province and the Belgium dataset which was collected near the western part of Belgium as the research area. Related experiments demonstrate that the DNMF model achieves clearly better classification performance over other competitive baselines.

Above-Ground Biomass Estimation of Plantation with Complex Forest Stand Structure Using Multiple Features from Airborne Laser Scanning Point Cloud Data

Accurate forest above-ground biomass (AGB) estimation is important for dynamic monitoring of forest resources and evaluation of forest carbon sequestration capacity. However, it is difficult to depict the forest’s vertical structure and its heterogeneity using optical remote sensing when estimating forest AGB, for the reason that electromagnetic waves cannot penetrate the canopy’s surface to obtain low vegetation information, especially in subtropical and tropical forests with complex layer structure and tree species composition. As an active remote sensing technology, an airborne laser scanner (ALS) can penetrate the canopy surface to obtain three-dimensional structure information related to AGB. This paper takes the Jiepai sub-forest farm and the Gaofeng state-owned forest farm in southern China as the experimental area and explores the optimal features from the ALS point cloud data and AGB inversion model in the subtropical forest with complex tree species composition and structure. Firstly, considering tree canopy structure, terrain features, point cloud structure and density features, 63 point cloud features were extracted. In view of the biomass distribution differences of different tree species, the random forest (RF) method was used to select the optimal features of each tree species. Secondly, four modeling methods were used to establish the AGB estimation models of each tree species and verify their accuracy. The results showed that the features related to tree height had a great impact on forest AGB. The top features of Cunninghamia Lanceolata (Chinese fir) and Eucalyptus are all related to height, Pinus (pine tree) is also related to terrain features and other broadleaved trees are also related to point cloud density features. The accuracy of the stepwise regression model is best with the AGB estimation accuracy of 0.19, 0.76, 0.71 and 0.40, respectively, for the Chinese fir, pine tree, eucalyptus and other broadleaved trees. In conclusion, the proposed linear regression AGB estimation model of each tree species combining different features derived from ALS point cloud data has high applicability, which can provide effective support for more accurate forest AGB and carbon stock inventory and monitoring.

Formamide deionized accelerates the somatic embryogenesis of Cunninghamia lanceolata

Aim of the study: To improve the efficiency of the somatic embryogenesis (SE) in Cunninghamia lanceolata. Area of the study: The study was conducted at Nanjing Forestry University (Nanjing, China). Material and methods: Immature cones of C. lanceolata, genotype 01A1 which was planted in Yangkou State-owned Forest Farm (Fujian, China), were used to induced callus. These calli were used to induce SE, concentration gradients of 0 g/L, 0.01134 g/L, 0.1134 g/L, 1.1134 g/L and 11.34 g/L of FD was added, to explore the optimal concentration for promoting SE of C. lanceolata. Main results: Low concentration of FD promoted the maturation of somatic embryos, while high concentration of FD lead to browning of embryogenic callus. The seedling rate and rooting number of seedlings induced by different concentrations of FD were significantly different. Research highlights: This study may aid in the rapid maturation of C. lanceolata somatic embryos and is useful for accelerated C. lanceolata breeding. Keywords: C. lanceolata; Formamide Deionized; Somatic embryogenesis; Seedling rate. Abbreviations used: FD (Formamide Deionized), FD0 (the concentration of 0 g/L FD), FD0.01134 (the concentration of 0.01134 g/L FD), FD0.1134 (the concentration of 0.1134 g/L FD), FD1.134 (the concentration of 1.134 g/L FD), FD11.34 (the concentration of 11.34 g/L FD).

A climate sensitive mixed-effects diameter class mortality model for Prince Rupprecht larch (Larix gmelinii var. principis-rupprechtii) in northern China

Forest mortality is an important variable commonly included as one of the predictors in the growth and yield models that are the fundamental decision-making tools in forest management. The existing forest mortality models for Larch (Larix gmelinii var. principis-rupprechtii) forest, which plays key roles in maintaining forest ecosystem functions and reducing atmospheric carbon concentration through the sequestration, are based on the traditional modeling methods, and these models do not account for the effects of climate on the forest mortality. Developing climate sensitive mortality models are useful for formulating effective forest management strategies in the context of climate change. We developed the diameter class mortality models using the data of 102 temporary sample plots distributed across the Guandi Mountain National Forest Park and Wutai Mountain Boqiang State-owned Forest Farm in the Shanxi Province of northern China. Four commonly used mortality functions were used to fit the data. Among many climatic and dendrometric variables evaluated, number of trees per diameter class (NC), mean annual precipitation (MAP); median diameter class (MDC), and mean temperature difference (DT) contributed significantly highly to the mortality variations. Then, these variables were selected as predictors to develop the two-level nonlinear mixed-effects mortality models applicable to the diameter class levels. The random effects at the levels of both the sample plots and stands with different site quality (blocks) were taken into account to build the models. Compared to other three models derived from the Poisson; Zero-inflated Poisson; Hurdle Poisson functions, the two-level Logistic mixed-effects mortality model better explained the effects of climate variables on the forest mortality of Larch. Tree mortality increased with increasing NC and MAP, however, MDC and DT had the opposite effects with diameter class increasing. Modeling the random effects at the block-level alone led to significantly high correlations among the residuals, and correlations were significantly reduced when the random effects were modeled at both the block- and sample plot-levels. Excessive rain during the growing season decreased the rate of tree mortality, especially for diameter classes with high number of trees. Findings from this study can be combined with the knowledge of the adaptive management to reduce the risks and uncertainties associated with forest management decision.

Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images

Airborne hyperspectral remote sensing data with both rich spectral and spatial features can effectively improve the classification accuracy of vegetation species. However, the spectral data of hundreds of bands brings about problems such as dimensional explosion, which poses a huge challenge for hyperspectral remote sensing classification based on classical parameters models. Deep learning methods have been used for remotely sensed images classification in recent years, but the popular HSI datasets including Kennedy Space Center, Indian Pines, Pavia University scene and Salinas scene, have low spatial resolution, significant differences between categories, and regular boundaries. When applied to the classification of forestry tree species, the accuracy often decreases because the spectral response of different plants of the same family and genus are very similar, especially under the fragmented species distribution, complex topography and the occluded canopy. So we collect new data sets, selected Gaofeng State Owned Forest Farm in Guangxi province in south China as the research area and adopted the airborne hyperspectral data obtained by the LiCHy system of the Chinese Academy of Forestry to explore an improved three-dimensional convolutional neural network(3D-CNN) model for tree species classification. The proposed model uses raw data as input without dimension reduction or feature screening, and simultaneously extracts spectral and spatial features. After the 3D convolutional layer, the captured high-level semantic concept is a joint spatial spectral feature representation, so we can turn it into a one-dimensional feature as a new input to learn a more abstract level of expression. The widely used earlystop method is also used to prevent overfitting. The proposed model is a lightweight, generalized, and fast convergence classification model, by which the short-time and large-area of multiple tree species classification with high-precision can be realized. The result shows that the 3D-1D CNN model can shorten the training time of the 3D CNN model by 60% and achieve a classification accuracy of 93.14% within 50 ha in 6.37 min, which provides a basis for the classification of tree species, the mapping of forest form and the inventory of forest resources.

Stand density extraction and analysis of plantations based on QuickBird and Worldview-2 images

Stand density is one of the important forest structure parameters. QuickBird and Worldview-2 high spatial resolution remote sensing images were compared. The Jiangle state-owned forest farm in Fujian Province was the experimental area. A spectral local maximum filtering method was used to extract the number of individual trees in a mountain forest. Nonlinear quadratic polynomial regression models were established for the number of local maximum points and the actual stand density. The near-infrared II waveband of Worldview-2 imagery was used to extract the pure Chinese fir stands with an accuracy of 72.5%, R2 = 0.502, and RMSE = 35.77 and the Masson pine stands with an accuracy of 78.35%, R2 = 0.754, RMSE = 41.46, while the accuracy of all unclassified stands was only 0.2907. The results showed that the classification of tree species can improve the accuracy of modeling. The quadratic polynomial model using Worldview-2 image data achieved better results. Stand density in the Jiangle state-owned forest farm was extracted using the NIR II band after fusion of Worldview-2. A stand density planning map was constructed using the best models applied to the different forest types. The method of combining high-spatial resolution imagery and sampling plots to estimate stand density was proven to be feasible.

Tree Species Distribution Change Study in Mount Tai Based on Landsat Remote Sensing Image Data

Located in the Mount Tai state-owned forest farm, this study adopted Landsat multispectral remote sensing data in 2000 and 2016 on the GEE (Google Earth Engine) platform and selected four phases of images each year according to the phenological period. By dealing with the current situation map of forestry resources in 2000 and the field survey data in 2016, the samples of tree species distribution in 2000 and 2016 were obtained. On the basis of topographic correction with the empirical rotation model, this study used the random forest (RF) classifier to classify tree species from remote sensing images in 2000 and 2016, achieving high classification accuracy. The results showed that, after 16 years of evolution, the percentage of pine species in the forest decreased from 55.69% to 50.22%, with a percentage decrease as high as 5.47%. The percentage of black locust (Robinia pseudoacacia) increased from 10.15% in 2000 to 13.75% in 2016, with an increase of 3.60%. Quercus also had a positive growth in the area. This result reflected the expansion of black locust.

Discussion on Investigation and Preventive Measures of Forest Fire Risk under the New Situation—Taking China Huizhou State-owned Forest Farm and Provincial Nature Reserve as an Example

Forest fire is the biggest potential safety hazard of forest resources, and fire damage to forest resources is great. Therefore, forest fire prevention is an important content in the management and protection of forest resources. In order to effectively protect forest resources, scientific and effective measures must be taken to reduce forest fires. Based on this, this paper analyzes the troubleshooting of forest fire risk and discusses the relevant solutions.

Evaluaton of the Quality of Timber Forest in Jiangle State-owned Forest Farm Based on Matter-element Analysis

Evaluaton of the Quality of Timber Forest in Jiangle State-owned Forest Farm Based on Matter-element Analysis

Construction of ecological corridors in Changli County based on ecological sensitivity and ecosystem service values.

Ecological corridors provide essential routes for biological migration, which would promote species exchange among different habitats. The construction of corridors is beneficial to alle-viate the damages caused by the fragmentation of landscapes due to the acceleration of urbanization, with significance to biodiversity conservation. In this study, we located the ecological sources with the results of ecological sensitivity evaluation and ecosystem service value analysis in Changli County based on its land use status in 2015. Then, we produced the potential corridors with the minimal cumulative resistance model (MCR) and the minimum cost path method, and identified the key corridors with the gravity model. We put forward the ecological corridor system including four important corridors and two general corridors to underpin the scientific knowledge for the ecological corridor construction project and biodiversity conservation. The results showed that the ecological sensitivity of this area was relatively moderate and ecosystem services value was relatively low. Both the ecological sensitivity and ecosystem services were lower in the middle area but higher in around areas. The ecological sources were mainly distributed in the northern Jieshi Mountain Scenic Area, the eastern coastal state-owned forest farm and the western water source conservation area, which were highly coincident with the nature reserves determined by Ecological Environment Construction and Protection Planning (2011-2030) in the study area. The total length of the corridors was 112.66 km, within which the length of important ones was 47.61 km and that of general ones was 65.05 km. The best width of the corridors was around 30 to 60 m. The ecological corridors and ecological sources constituted an annular closed area, which would effectively promote species migration and the exchange of material and energy.

Effects of soil warming on specific respiration rate and non-structural carbohydrate concentration in fine roots of Chinese fir seedlings

A field mesocosm experiment with Chinese fir (Cunninghamia lanceolata) seedlings was conducted in Chenda State-Owned Forest Farm, Sanming, Fujian Province. The effects of soil warming (ambient +5 ℃) on specific respiration rates and nonstructural carbohydrate (NSC) concentrations in fine roots were measured by the ingrowth core method, to reveal the belowground responses and the adaptability of Chinese fir to global warming. The results showed that soil warming caused significant changes of fine root NSC in the second year. The NSC and starch concentrations in 0-1 mm fine roots, and the NSC and sugar concentrations in 1-2 mm fine roots decreased signifi-cantly in January. The NSC, sugar and starch concentrations in 0-1 mm roots and the starch concentration in 1-2 mm roots increased in July. Soil warming had no significant effect on fine root NSC in the third year. The specific root respiration rate of the 0-1 mm roots significantly increased in July of the second year but significantly decreased in July of the third year in the warmed plots. Compared with the 0-1 mm roots, soil warming had no significant effect on the specific root respiration rate of the 1-2 mm roots. In conclusion, the responses of fine root respiration to soil warming depended on the duration of warming. Fine root respiration partly acclimated to soil warming with increasing duration of soil warming, which kept fine root NSC being relatively stable.