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In recent years, machine learning methods have garnered significant attention in the field of crop recognition, playing a crucial role in obtaining spatial distribution information and understanding dynamic changes in planting areas. However, research in smaller plots within mountainous regions remains relatively limited. This study focuses on Shangzhou District in Shangluo City, Shaanxi Province, utilizing a dataset of high-resolution remote sensing images (GF-1, ZY1-02D, ZY-3) collected over seven months in 2021 to calculate the normalized difference vegetation index (NDVI) and construct a time series. By integrating field survey results with time series images and Google Earth for visual interpretation, the NDVI time series curve for maize was analyzed. The Random Forest (RF) classification algorithm was employed for maize recognition, and comparative analyses of classification accuracy were conducted using Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Artificial Neural Network (ANN). The results demonstrate that the random forest algorithm achieved the highest accuracy, with an overall accuracy of 94.88% and a Kappa coefficient of 0.94, both surpassing those of the other classification methods and yielding satisfactory overall results. This study confirms the feasibility of using time series high-resolution remote sensing images for precise crop extraction in the southern mountainous regions of China, providing valuable scientific support for optimizing land resource use and enhancing agricultural productivity. Full article

Accurately assessing the carbon sink intensity of China’s ecosystem is crucial for achieving carbon neutrality. However, existing ecosystem process models have significant uncertainties in net ecosystem productivity (NEP) estimates due to the lack of or insufficient description of phenological regulation. Although plant developmental factors have been proven to significantly influence autumn phenology, they have not been systematically incorporated into autumn phenology models. In this study, we modified the autumn phenology model (cold-degree-day, CDD) by incorporating the growing-season gross primary productivity (GPP) and the start of growing season (SOS) and used it as a constraint to improve the CASA model for quantifying NEP across China from 2003 to 2021. Validation results showed that the CDD model incorporating developmental factors significantly improved the simulation accuracy at the end of the growing season (EOS). More importantly, compared with flux tower observations, the NEP derived from the improved CASA model based on the above phenology model showed a 15.34% reduction in root mean square error and a 74% increase in the coefficient of determination relative to the original model. During the study period, China’s multiyear average total NEP was 489.67 ± 38.27 Tg C/yr, with the highest found in evergreen broadleaf forests and the lowest detected in shrublands. Temporally, China’s NEP demonstrated an overall increasing trend with an average rate of 1.75 g C/m²/yr². However, the growth rate of NEP remained far below concurrent carbon emissions from fossil fuel combustion totally, especially for eastern China, while the northeastern regions performed relatively better. The improved regional carbon flux estimation framework proposed in this study will provide important support for developing future climate change mitigation strategies. Full article

Mapping the high-precision spatiotemporal dynamics of soil organic carbon (SOC) in croplands is crucial for enhancing soil fertility and carbon sequestration and ensuring food security. We conducted field surveys and collected 1121 soil samples from cropland in Changzhi, northern China, in 2010 and 2020. Random Forest (RF) models combined with 19 environmental covariates were used to map the topsoil (0–20 cm) SOC in 2010 and 2020, and uncertainty maps were used to calculate the dynamic changes in cropland SOC between 2010 and 2020. Finally, RF and Structural Equation Modeling (SEM) were employed to explore the effects of climate, vegetation, topography, soil properties, and agricultural management on SOC variation in croplands. Compared to the prediction model using only natural variables (RF_C), the model incorporating agricultural management (RF_A) significantly improved the simulation accuracy of SOC. The coefficient of determination (R²) increased from 0.77 to 0.85, while the Root Mean Square Error (RMSE) decreased from 1.74 to 1.53 g kg⁻¹, and the Mean Absolute Error (MAE) was reduced from 1.10 to 0.94 g kg⁻¹. The uncertainty in our predictions was low, with an average value of only 0.39–0.66 g kg⁻¹. From 2010 to 2020, SOC in the Changzhi croplands exhibited an overall increasing trend, with an average increase of 1.57 g kg⁻¹. Climate change, agricultural management, and soil properties strongly influence SOC variation. Mean annual precipitation (MAP), drainage condition (DC), and net primary productivity (NPP) were the primary drivers of SOC variability. Our findings highlight the effectiveness of agricultural management for predicting SOC in croplands. Overall, the study confirms that improved agricultural management has great potential to increase soil carbon stocks, which may contribute to sustainable agricultural development. Full article

The vaccinia virus (VV) is extensively utilized as a vaccine vector in the treatment of various infectious diseases, cardiovascular diseases, immunodeficiencies, and cancers. The vaccinia virus Tiantan strain (VVTT) has been instrumental as an irreplaceable vaccine strain in the eradication of smallpox in China; however, it still presents significant adverse toxic effects. After the WHO recommended that routine smallpox vaccination be discontinued, the Chinese government stopped the national smallpox vaccination program in 1981. The outbreak of monkeypox in 2022 has focused people’s attention on the Orthopoxvirus. However, there are limited reports on the safety and toxic side effects of VVTT. In this study, we employed a combination of transcriptomic analysis and machine learning-based feature selection to identify key genes implicated in the VVTT infection process. We utilized four machine learning algorithms, including random forest (RF), minimum redundancy maximum relevance (MRMR), eXtreme Gradient Boosting (XGB), and least absolute shrinkage and selection operator cross-validation (LASSOCV), for feature selection. Among these, XGB was found to be the most effective and was used for further screening, resulting in an optimal model with an ROC curve of 0.98. Our analysis revealed the involvement of pathways such as spinocerebellar ataxia and the p53 signaling pathway. Additionally, we identified three critical targets during VVTT infection—ARC, JUNB, and EGR2—and further validated these targets using qPCR. Our research elucidates the mechanism by which VVTT infects cells, enhancing our understanding of the smallpox vaccine. This knowledge not only facilitates the development of new and more effective vaccines but also contributes to a deeper comprehension of viral pathogenesis. By advancing our understanding of the molecular mechanisms underlying VVTT infection, this study lays the foundation for the further development of VVTT. Such insights are crucial for strengthening global health security and ensuring a resilient response to future pandemics. Full article

The long-term development of the manufacturing industry relies on sustainable tax management, which plays a key role in optimizing production costs. While artificial intelligence models have been applied to tax-related predictions, research on their application for predicting tax management levels is quite limited, with no studies focused on the manufacturing industry in China. To enhance digital innovation in corporate management, this study applies interpretable artificial intelligence models to predict the tax management level, which helps decision-makers maintain it within a sustainable range. The ratio of total tax expense to total profits (ETR) is used to represent the tax management level, which is predicted using decision trees, random forests, linear regression, support vector regression, and artificial neural networks with eight input features. Comparisons among the developed models indicate that the random forest model exhibits the best performance in terms of prediction accuracy and generalization capability. Additionally, the Shapley additive explanations (SHAP) technique is integrated with the developed model to enhance the interpretability of its predictions. The SHAP results reveal the importance of the input features and also highlight the dominance of certain features. The results show that the ETR from the previous year holds the greatest importance, being more than twice as significant as the second most important factor, whereas the effect of board size is negligible. Moreover, benefiting from the local interpretations using SHAP values, this approach aids managers in making rational tax management decisions. Full article

Western Tianshan National Nature Reserve in Xinjiang, China stands out for its uniqueness and high biodiversity, including lichens. This study aims to characterize lichen diversity and compare distribution patterns of different life forms, substratum affinities and photobiont types. Surveys were conducted from June to August 2024 using stratified sampling methods at elevation ranging from 1100 m to 3400 m in the study area. Morphological, anatomical and chemical studies revealed 173 lichen species from 24 families and 58 genera, of which 100 species were identified as crustose, 46 as foliose and 27 as fruticose. Among the different habitat groups, strictly saxicolous lichens were dominant with 89 species, followed by corticolous lichens with 44 species and terricolous lichens with 40 species. The total species richness of lichens has a bimodal pattern: one peak appears at a low altitude (1701–2000 m) and the other at a high altitude (2901–3200 m). Among the three substratum categories studied, the species richness of terricolous lichens showed a unimodal relationship with elevation, and the saxicolous lichen had a bimodal pattern. The species richness of corticolous lichens was highest at lower and medium elevations and decreased at higher elevations. With respect to photobiont type, the species richness of cyanolichens showed a unimodal relationship with elevation. Maximum richness occurred at 2700 m, contrary to the chlorolichens, which had a bimodal pattern. Species richness of all three growth forms of lichens showed a bimodal pattern related to elevation. Among the three morphological types, crustose and foliose species richness had their highest values of 38 and 19, respectively, at 1701–2000 m, and fruticose lichens peaked with a maximum of 13 species at 2301–2600 m. The species richness of crustose lichens is lowest between altitudes 2300 and 2600 m, while the lowest species richness of fruticose and foliose lichens occurs at 2001–2300 m and elevations above 2900 m. Full article

This study aims to analyze the natural regeneration characteristics and the key factors of Quercus forests, providing a theoretical foundation for maintaining the ecological stability of Quercus forests in northern China. In June and July 2023, 17 square plots of five Quercus species in Beijing were surveyed, and seedling regeneration and environmental factors (site, stand and soil factors) were measured. Pearson correlation and random forest algorithms were used to identify the relevant and key environmental factors affecting seedling regeneration density (Seedling 1, Seedling 2, Seedling 3). The natural regeneration capabilities of the five Quercus species in the Beijing area vary, with Quercus aliena and Quercus variabilis being stronger, while Quercus mongolica, Quercus acutissima and Quercus dentata are relatively weaker. Correlation analysis showed that Seedling 1 has no significant correlation with environmental factors; Seedling 2 is significantly negatively correlated with Pielou’s evenness (J) and exchangeable calcium (ECa) (p < 0.05); Seedling 3 is significantly positively correlated with species richness (S), Shannon–Wiener index (H), stand volume (M), and litter layer thickness (LT) (p < 0.05), and significantly negatively correlated with Pielou’s evenness (J) (p < 0.01). The random forest algorithm indicated that the regeneration of Seedling 1 is mainly affected by stand factors, while the regeneration of Seedling 2 and Seedling 3 is more influenced by soil and site factors. The Quercus forests in the Beijing region exhibit a rich species composition and demonstrate a certain capacity for natural regeneration. However, seedling growth is more constrained by soil and site factors in the later stages. Therefore, in the management of Quercus forests, environmental factors can be regulated during the seedling growth stage to create more suitable conditions for regeneration. Full article

The semi-humid evergreen broadleaved forest (SEBF) is the zonal vegetation type of western subtropical regions in China. Under human and natural disturbance, the area of SEBFs is severely shrinking, with remaining fragments scattered across mountains of the Central Yunnan Plateau. To explore the mechanisms of community assembly and species maintenance in the severely fragmented SEBFs, we selected three sites—Jinguangsi Provincial Nature Reserve, Huafoshan Scenic Area, and Qiongzhusi Forest Park—across the range of this vegetation type, and sampled a total of 42 plots of forest dominated by Castanopsis orthacantha Franch., the most widely distributed community type of SEBFs. We compared the species richness and composition of the communities of different age classes, employed the net relatedness index to characterize the phylogenetic structure of communities, and used Mantel tests and partial Mantel tests to quantify the impacts of spatial distance, age class, and habitat factors (including climate, topography, and soil) on species turnover across different spatial scales (i.e., intra- and inter-site) for trees, shrubs, and herbs, respectively. The results indicated the following: (1) In the young stage, the C. orthacantha communities exhibited a species richness statistically lower than those in middle-aged and mature communities. Notably, the difference in species richness among age classes was merely significant for shrub and herb species. Moreover, the phylogenetic structure changed towards over-dispersion with increasing community age. (2) The age class of the community played a pivotal role in determining taxonomic β diversity in the tree layer, while climate and soil factors significantly influenced β diversity in the shrub and herb layers of the communities. (3) Environmental filtering emerged as the predominant force shaping community assembly at the intra-site scale, whereas spatial distance was the primary determinant at the inter-site scale. Meanwhile, dispersal limitation versus biological interaction seemed to dominate the community dynamics of the C. orthacantha communities in the early versus middle and old ages, respectively. Our results highlight the variability in community assembly processes across different spatial and temporal scales, providing insights into the priority of the conservation and restoration of severely degraded zonal SEBFs. Expanding research to broader scales and other SEBF types, as well as considering the impacts of climate change and human activities, would provide further insights into understanding the mechanisms of community assembly and effective conservation strategies. Full article

With rapid urbanization, improving school quality in compulsory education is critical for optimal educational resource allocation. This study integrates a random forest machine learning model, GIS spatial analysis, and a spatial econometric model to examine the spatiotemporal differentiation of school quality in Dalian, China, in 2016 and 2020, as well as its relationships with the construction land development cycle, population density, and housing prices. The findings reveal a core–periphery structure, with overall school quality on the rise and basic facility configuration exerting a stronger impact than teacher strength. Among internal resources, per capita sports venue area (PCSFA) and per capita teaching equipment value (PCTRE) contribute most significantly to school quality, while high-quality clusters in traditional educational hubs, university-covered areas, and transitional zones spur improvements in surrounding schools. The population density, housing prices, and the construction land development cycle all positively correlate with school quality, highlighting the need for coordinated action among urban planners, education authorities, and housing regulators to ensure that land development, housing affordability, and school facility investments advance equitable access to quality education. These results provide a novel perspective on compulsory education quality assessment and offer a valuable foundation for guiding education policies and urban development strategies. Full article

The area of Cunninghamia lanceolata forests in China is expansive, the soil PhytOC(phytolith-occluded organic carbon) stock of Cunninghamia lanceolata forests is a vital carbon reservoir on the global scale. Soil from the Cunninghamia lanceolata forests was collected, and the soil physicochemical indexes and phytoliths and PhytOC content were measured to explore the accumulation characteristics of PhytOC in the 0–10, 10−20, and 20−30 cm soil layers at different stand ages. The results are as follows: (1) soil phytolith content (11.98–32.60 g·kg⁻¹), PhytOC content (0.48−1.10 g·kg⁻¹), PhytOC/TSOC (1.90%−6.93%), soil PhytOC stock (0.446−1.491 t·hm⁻²), and mature forest > middle−aged forest > Huitou−sha forest > young forest. The soil PhytOC accumulation was significantly affected by stand age. Huitou−sha is not an advantageous afforestation way of Cunninghamia lanceolata. (2) the soil physicochemical properties and stand conditions had significant effects on soil PhytOC accumulation. High−silicon, carbon-rich, acidic soil environment and appropriate thinning are conducive to phytolith formation and PhytOC sequestration. (3) the accumulation potential of soil PhytOC in the Cunninghamia lanceolata forest is relatively large, and its importance as a forest carbon sink cannot be ignored. Soil PhytOC stock in Cunninghamia lanceolata forests of different stand ages will lay a foundation for accurate estimation of forest carbon sink. Full article

The Chinese government’s rural rejuvenation program depends on improving the national Rural Emergency Management Capability (REMC). To increase the resilience of Chinese rural areas against external dangers, REMC and its driving elements must be effectively categorized and evaluated. This study examines the variations in REMC levels and driving factors across different cities and regions, revealing the spatial distribution patterns and underlying mechanisms. To improve REMC in Chinese rural areas, this research employs the Projection Pursuit Method to assess REMC in 280 cities from 2006 to 2020. Additionally, we identify 22 driving factors and use the Random Forest algorithm from machine learning to analyze their impact on REMC. The analysis is conducted at both national and city levels to compare the influence of various driving factors in different regions. The findings show that China’s REMC levels have improved over time, driven by economic growth and the formation of urban clusters. Notably, some underdeveloped regions demonstrate higher REMC levels than more developed areas. The four most significant driving factors identified are rural road density, rural Internet penetration, per capita investment in fixed assets, and the density of township health centers. At the city level, rural Internet penetration and the e-commerce turnover of agricultural products have particularly strong driving effects. Moreover, the importance of driving factors varies across regions due to local conditions. This study offers valuable insights for the Chinese government to enhance REMC through region-specific strategies tailored to local circumstances. Full article

Traditional methods for estimating tea yield mainly rely on manual sampling surveys and empirical estimation, which are labor-intensive and time-consuming. Accurately estimating fresh tea production in different seasons has become a challenging task. It is possible to estimate the seasonal yield of tea at the field scale by using the spatial resolution of 10 m, 5-day revisit period and rich spectral information of Sentinel-2 imagery. This study integrated Sentinel-2 images and uncrewed aerial vehicle (UAV) RGB imagery to develop six regression models at the field scale, which were employed for the estimation of seasonal and annual fresh tea yields of the Yunlong Tea Cooperatives in Yixiang Town, Pu’er City, China. Firstly, we gathered fresh tea production data from 133 farmers in the cooperative over the past five years and obtained UAV RGB and Sentinel-2 imagery. Secondly, 23 spectral features were extracted from Sentinel-2 images. Based on the UAV images, the parcel of each farmer was positioned and three topographic features of slope, aspect, and elevation were extracted. Subsequently, these 26 features were screened using the random forest algorithm and Pearson correlation analysis. Thirdly, we applied six different regression algorithms to establish fresh tea yield models for each season and evaluated their estimation accuracy. The results showed that random forest regression models were the optimal choice for estimating spring and summer yields, with the spring model achieving an R² value of 0.45, an RMSE of 40.38 kg/acre, and an rRMSE of 40.79%. Similarly, the summer model achieved an R² value of 0.5, an RMSE of 78.46 kg/acre, and an rRMSE of 39.81%. For autumn and annual yield estimation, voting regression models demonstrated superior performance, with the autumn model achieving an R² value of 0.42, an RMSE of 70.6 kg/acre, and an rRMSE of 39.77%, and the annual model attained an R² value of 0.47, an RMSE of 168.7 kg/acre, and an rRMSE of 34.62%. This study provides a promising new method for estimating fresh tea yield in different seasons at the field scale. Full article

In the face of the realistic background of population loss, resource constraints and factor constraints, it explores the high-quality development of forest areas, aiming to analyze the impacts of population exodus from forest areas on economic growth and high-quality development. The focus is on China’s key state-owned forest areas, especially in Heilongjiang Province, Jilin Province and Inner Mongolia Autonomous Region. A set of indicators centered on the concept of green development is constructed and designed to measure the dynamics of high-quality economic development and the impact of population outflow in the three provinces and regions from 2000 to 2022. The study found that (1) Heilongjiang Province excels in high-quality economic development, followed by Jilin Province, while the Inner Mongolia Autonomous Region lags behind. During the observation period, the high-quality development trend in the forest areas showed a steady upward trend until 2014 and then began to slow down or even decline. (2) The pulling effect of population mobility on economic growth in forest areas has gradually weakened; nevertheless, the effective agglomeration of labor and capital is still a key factor driving economic growth. (3) Population exodus poses an obstacle to high-quality economic development. Increased population mobility has a negative impact on both economic growth and high-quality development, exacerbating economic fluctuations, and is not conducive to stability and high-quality development. It is worth noting that although the population base plays a fundamental role in economic growth, the direct effect of population on development is not significant. On the contrary, there is a negative correlation between population outflow and high-quality economic development. Based on the findings, strategic recommendations are made with the aim of promoting a shift from a growth model that relies on population numbers to a development model that focuses on the improvement of population quality in forest areas, thereby realizing a fundamental innovation in the economic development model. This study enriches the theory of regional development in forest areas and is of great significance in promoting sustainable development in forest areas. Full article

The response of trees to climate is crucial for the health assessment and protection of forests in alpine regions. Based on samples of Pinus tabuliformis and Abies fargesii, two typical evergreen coniferous species with distinct elevation differences in the vertical vegetation zones of the Qinling Mountains, we have developed two tree-ring width chronologies for the southern slope of the central Qinling Mountains in central China. The correlation analysis results showed that the radial growth of P. tabuliformis and A. fargesii responded to different climatic factors. Water stress caused by temperature in May of the current year was the main limiting factor for radial growth of P. tabuliformis, while precipitation in September of the previous year and the current year had a negative impact on A. fargesii, with lag effects of temperature and precipitation during the previous growing season. Spatial correlation and comparative analysis indicated that the P. tabuliformis chronology responded to extreme dry and wet events on a regional scale. Interannual and multidecadal periodic signals recorded by tree rings suggested that the hydrological and climatic changes on the southern slope of the central Qinling Mountains were teleconnected with the Pacific and Atlantic Oceans, including El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO). Our results provide new evidence for a hydroclimatical response study inferred from tree rings on the southern slope of the central Qinling Mountains. Full article

Forest aboveground biomass (AGB) is not only the basis for forest carbon stock research, but also an important parameter for assessing the forest carbon cycle and ecological functions of forests. However, there are various uncertainties in the estimation process, limiting the accuracy of AGB estimation. Therefore, we extracted the spectral features, vegetation indices and texture factors from remote sensing images based on the field data and Landsat 8 OLI remote sensing images in Southern China to quantify the uncertainties. Then, we established three AGB estimation models, including K Nearest Neighbor Regression (KNN), Gradient Boosted Regression Tree (GBRT) and Random Forest (RF). Uncertainties at the plot scale and models were measured by using error equations to analyze the influences of uncertainties at different scales on AGB estimation. Results were as follows: (1) The R² of the per-tree biomass model for Cunninghamia lanceolata was 0.970, while the uncertainty of the residual and parameters for per-tree biomass model was 4.62% and 4.81%, respectively; and the uncertainty transferred to the plot scale was 3.23%. (2) The estimation methods had the most significant effects on the remote sensing models. RF was more accurate than other two methods, and had the highest accuracy (R² = 0.867, RMSE = 19.325 t/ha) and lowest uncertainty (5.93%), which outperformed both the KNN and GBRT models (KNN: R² = 0.368, RMSE = 42.314 t/ha, uncertainty = 14.88%; GBRT: R² = 0.636, RMSE = 32.056 t/ha, uncertainty = 6.3%). Compared to KNN and GBRT, the R² of RF was enhanced by 0.499 and 0.231, while the uncertainty was decreased by 8.95% and 0.37%, respectively. The uncertainty associated with the scale of remote sensing models remains the primary source of uncertainty when compared to the plot scale. On the remote sensing scale, RF is the model with the best estimation effect. This study examines the impact of both plot-scale and remote sensing model-scale methodologies on the estimation of AGB for Cunninghamia lanceolata. The findings aim to offer valuable insights and considerations for enhancing the accuracy of AGB estimations. Full article