Search Results (15)

Yuexi County, a key tea-producing area in eastern Dabie Mountain, may face potential heavy metal(oid) (HM) contamination risks due to nearby mining and intensive agricultural activities. This study investigated seven HMs (As, Cd, Cr, Hg, Ni, Pb, and Zn) in paired soil–tea samples using multiple analytical approaches, including the geoaccumulation index (I_geo), the potential ecological risk index (RI), bioconcentration factor (BCF), and positive matrix factorization (PMF) with Monte Carlo simulation for health risk assessment. Results showed that Zn (82.65 mg/kg) and Cd (0.15 mg/kg) were the most enriched HMs in soils with higher I_geo values than other HMs. PMF analysis identified four major HM sources: mining and transportation (27.75%), agricultural activities (26.90%), natural soil parent material (26.17%), and industrial emissions (19.18%). Tea plants exhibited selective HM absorption, with Hg showing the highest bioaccumulation (BCF = 0.45), while As, Cr, and Pb had minimal uptake (BCF < 0.05). Although health risk assessments confirmed that both non-carcinogenic and carcinogenic risks from soil and tea consumption were within safe limits for adults and children, Cr and Ni required special attention due to their risk contributions. Overall, ecological and health risks in the region were found to be low. These findings provide important scientific support for pollution monitoring, risk management, and overcoming trade barriers in tea-growing regions with acidic soils. Future research should integrate HM speciation analysis with seasonal monitoring to further optimize tea plantation management strategies. Full article

Soil chemical properties and soil microbial communities are the key factors affecting the content of tea. The mechanism by which altitude changes soil’s chemical properties and microbial community structure to affect tea content is unclear. This study was conducted on a typical tea plantation in the Fenghuang Mountains of Chaozhou, China. It systematically revealed the relationship between soil chemical properties and microbial communities with tea quality components between different altitudes (396 m/517 m/623 m). We discovered that soil pH and soil Catalase activity appeared to decrease and then increase with altitude, and soil SOM content and soil Acid Phosphatase activity were significantly higher at mid-altitude. Soil TP and TK content were lowest at high altitudes (0.20 mg/kg, 5.98 mg/kg). Non-significant differences were found in the spatial composition of microbial communities at different altitudes. The abundance of fungi (Sobol index) was significantly higher (p < 0.05) at low altitudes than in other altitude groups. Redundancy analysis indicated that soil pH and TP are drivers of changes in bacterial community structure. The abundance of Fibrobacteres, a key functional group of bacteria, showed a decreasing trend with increasing altitude, and Stachybotrys (fungi) likewise had the lowest abundance at high altitude (p < 0.05). The catechin, theanine, and caffeine content of tea leaves accumulated the least at high altitude (12.91%, 0.39%, 2.88%). Fibrobacteres and Stachybotrys, as well as soil TK and TP content, were strongly associated with the accumulation of major contents in tea leaves. Meanwhile, fungal abundance was significantly and positively correlated with theanine (p < 0.05). This study enhances our understanding of soil chemical property–soil microbial community–tea tree interactions. By exploring the differences in soil key nutrient content and the abundance of functional flora driving tea quality at different altitudes, it provides a basis for the precise microecological management of tea gardens. Full article

The monoculture planting in terraced tea plantations has led to severe soil degradation, which poses a significant threat to the growth of tea plants. However, the mechanisms by which intercropping systems improve soil health through the regulation of soil microbial communities at the micro-topographical scale of terraced tea plantations (i.e., terrace surface, inter-row, and terrace wall) remain unclear. This study investigates the effects of intercropping Ophiopogon japonicus in a five-year tea plantation on the soil physicochemical properties, enzyme activities, and microbial community structure and functions across different micro-topographical features of terraced tea plantations in Wuyi Mountain. The results indicate that intercropping significantly improved the soil organic matter, available nutrients, and redox enzyme activities in the inter-row, terrace surface, and terrace wall, with the effects gradually decreasing with increasing distance from the tea plant rhizosphere. In the intercropping group, tea leaf yield increased by 13.17% (fresh weight) and 19.29% (dry weight) compared to monoculture, and the disease indices of new and old leaves decreased by 40.63% and 38.7%, respectively. Intercropping strengthened the modularity of bacterial networks and the role of stochasticity in shaping bacterial communities in different micro-topographic environments, in contrast to the patterns observed in fungal communities. The importance of microbial phyla such as Proteobacteria and Ascomycota in different micro-topographical features was significantly regulated by intercropping. In different micro-topographical zones of the terraced tea plantation, beneficial bacterial genera such as Sinomonas, Arthrobacter, and Ferruginibacter were significantly enriched, whereas potential fungal pathogens like Nigrospora, Microdochium, and Periconia were markedly suppressed. Functional annotations revealed that nitrogen cycling functions were particularly enhanced in inter-row soils, while carbon cycling functions were more prominent on the terrace surface and wall. This study sheds light on the synergistic regulatory mechanisms between micro-topographical heterogeneity and intercropping systems, offering theoretical support for mitigating soil degradation and optimizing management strategies in terraced tea agroecosystems. Full article

Distinguishing vegetation types from satellite images has long been a goal of remote sensing, and the combination of multi-source and multi-temporal remote sensing images for vegetation classification is currently a hot topic in the field. In species-rich mountainous environments, this study selected four remote sensing images from different seasons (two aerial images, one WorldView-2 image, and one UAV image) and proposed a vegetation classification method integrating hierarchical extraction and object-oriented approaches for 11 vegetation types. This method innovatively combines the Random Forest algorithm with a decision tree model, constructing a hierarchical strategy based on multi-temporal feature combinations to progressively address the challenge of distinguishing vegetation types with similar spectral characteristics. Compared to traditional single-temporal classification methods, our approach significantly enhances classification accuracy through multi-temporal feature fusion and comparative experimental validation, offering a novel technical framework for fine-grained vegetation classification under complex land cover conditions. To validate the effectiveness of multi-temporal features, we additionally performed Random Forest classifications on the four individual remote sensing images. The results indicate that (1) for single-temporal images classification, the best classification performance was achieved with autumn images, reaching an overall classification accuracy of 72.36%, while spring images had the worst performance, with an accuracy of only 58.79%; (2) the overall classification accuracy based on multi-temporal features reached 89.10%, which is an improvement of 16.74% compared to the best single-temporal classification (autumn). Notably, the producer accuracy for species such as Quercus acutissima Carr., Tea plantations, Camellia sinensis (L.) Kuntze, Pinus taeda L., Phyllostachys spectabilis C.D.Chu et C.S.Chao, Pinus thunbergii Parl., and Castanea mollissima Blume all exceeded 90%, indicating a relatively ideal classification outcome. Full article

Wuyi Mountain in China is listed on the World Natural and Cultural Heritage List. With the vigorous development of urban cultural tourism, the sustainable development of heritage sites has become the focus of academic and industry circles, among which the rational use and scientific planning of natural resources have become increasingly prominent. In this context, in-depth research on resource development and protection strategies in the Wuyishan area has important practical significance and theoretical value. Therefore, this paper presents a case study of the tourist tea garden landscape design practice at the Wuyistar Chinese Tea Garden, located in Wuyishan City. This paper underscores the significance of incorporating the site’s existing natural environment resources, particularly its plant resources, into the tea garden landscape design, while adhering to principles within the framework of world heritage. The research method includes extensive field surveys combined with GIS analysis and biodiversity surveys, covering the topography and slope of the tea plantation, current natural resources, statistics on the number of tourists after completion, and the related benefits of local enterprises. These planning concepts are realized through a series of infrastructure measures, which are divided into four angles: restoring mountains and rivers, rereading cultural context, sorting out style and appearance, and improving functions. The design practice is carried out in different areas. Simultaneously, the creation of a distinctive tourist destination enables tourists to fully engage with nature and tea culture, while simultaneously fostering the growth of cultural tourism in world heritage sites. This study proposes a planning practice case, which provides a framework and ideas for designing tea gardens. From the aspects of resource protection and utilization, cultural inheritance and display, and tourism service improvement, it provides a model and method that can be used as a reference for the landscape design and planning of similar tea gardens, which will help promote the healthy development of the Chinese tea culture tourism industry. It also provides useful practical experience for the protection and development of world heritage sites. Full article

Agricultural activities such as fertilization and cultivation constitute a substantial source of non-point source (NPS) nitrogen (N) in aquatic ecosystems. Precise quantification of fluxes across diverse land uses and identification of critical source areas are essential for effectively mitigating nitrogen loads. In this study, the Soil Water Assessment Tool (SWAT) was employed to accurately model the watershed hydrology and total nitrogen (TN) transport in the Zhongtian River Basin, i.e., an agricultural watershed characterized by low mountainous terrain. The simulation results indicated that the average TN load intensity within the watershed was 21.34 kg ha⁻¹ yr⁻¹, and that TN load intensities for paddy fields and tea plantation were 34.96 and 33.04 kg ha⁻¹ yr⁻¹, respectively. Agricultural land, which covered 32.06% of the area, disproportionately contributed 52.88% of the N output in the watershed. Pearson and redundancy analysis (RDA) underscored land use as the primary driver of nitrogen emissions, with a contribution exceeding 50%. Building on a high-precision simulation analysis, a suite of best management practices (BMPs) was established. These findings highlight the superior performance of engineered BMPs over agricultural BMPs, with TN load reduction rates of 12.23 and 27.07% for filter strips and grassed waterways, respectively. Among three agricultural BMPs, the effect of fertilizer reduction was the most pronounced, achieving reductions of 6.44% for TN and 21.26% for nitrate. These results suggest that optimizing fertilizer management and implementing engineered BMPs could significantly reduce nitrogen pollution in agricultural watersheds, providing valuable insights for sustainable agricultural practices and water quality management. Full article

Accurate identification of tea plantation distribution is critical for optimizing agricultural practices, informing land-use policies, and preserving ecological balance. However, challenges persist in mountainous regions with persistent cloud cover and heterogeneous vegetation, where conventional methods relying on single-source remote sensing features face limitations due to spectral confusion and information redundancy. This study proposes a novel framework integrating multisource remote sensing data and feature optimization to address these challenges. Leveraging the Google Earth Engine (GEE) cloud platform, this study synthesized 108 spectral, textural, phenological, and topographic features from Sentinel-1 SAR and Sentinel-2 optical data. SVM-RFE (support vector machine recursive feature elimination) was employed to identify the optimal feature subset, prioritizing spectral indices, radar texture metrics, and terrain parameters. Comparative analysis of three classifiers, namely random forest (RF), support vector machine (SVM), and decision tree (DT), revealed that RF achieved the highest accuracy, with an overall accuracy (OA) of 95.03%, a kappa coefficient of 0.95. The resultant 10 m resolution spatial distribution map of tea plantations in Shiyan City (2023) demonstrates robust performance in distinguishing plantations from forests and farmlands, particularly in cloud-prone mountainous terrain. This methodology not only mitigates dimensionality challenges through feature optimization but also provides a scalable solution for large-scale agricultural monitoring, offering critical insights for sustainable land management and policy formulation in subtropical mountainous regions. Full article

This study focused on examining the early stages of tea cultivation (1, 3, and 5 years) in mountainous tea plantations. It specifically aimed to investigate the changes in soil micro-ecology at different locations (inter-row, terrace surfaces, and terrace walls). It was revealed that as tea tree cultivation progressed over the years, bacterial diversity and co-occurrence networks annually decreased in different locations. The results of soil physicochemical index analysis showed that the soil’s available nutrients and the activities of cellulase and protease increased. Furthermore, the amplitude of variation of these indexes in the inter-row soil was significantly higher than that on the terrace surfaces and the terrace walls (p < 0.05). Alterations occurred in the soil microbial community structure, with an enrichment of bacterial genera such as Sinomonas, Granulicella, and Sphingomonas, as well as fungal genera such as Trichoderma, Penicillium, and Talaromyces; an increase in the proportion of plant pathogenic fungi (Cladosporium, Fusarium, and Curvularia) was observed in the inter-row soil. The results of soil microbial function prediction showed that nitrification and nitrogen fixation decreased, but denitrification increased (p < 0.05). In conclusion, cultivating tea trees in mountainous terraced plantations significantly impacted the soil microbial community, accelerated the metabolism of soil organic matter, disrupted soil nitrogen cycling functions, and increased the presence of plant pathogenic fungal pathogens. Moreover, the changes in the structure and functions of the soil microbial community demonstrate a spatial distance effect across different terrace locations. Full article

The intercropping of cover crops has been adopted in several agroecosystems, including tea agroecosystems, which promotes ecological intensification. Prior studies have shown that growing cover crops in tea plantations provided different ecological services, including the biocontrol of pests. Cover crops enrich soil nutrients, reduce soil erosion, suppress weeds and insect pests, and increase the abundance of natural enemies (predators and parasitoids). We have reviewed the potential cover crops that can be incorporated into the tea agroecosystem, particularly emphasizing the ecological services of cover crops in pest control. Cover crops were categorized into cereals (buckwheat, sorghum), legumes (guar, cowpea, tephrosia, hairy indigo, and sunn hemp), aromatic plants (lavender, marigold, basil, and semen cassiae), and others (maize, mountain pepper, white clover, round-leaf cassia, and creeping indigo). Legumes and aromatic plants are the most potent cover crop species that can be intercropped in monoculture tea plantations due to their exceptional benefits. These cover crop species improve crop diversity and help with atmospheric nitrogen fixation, including with the emission of functional plant volatiles, which enhances the diversity and abundance of natural enemies, thereby assisting in the biocontrol of tea insect pests. The vital ecological services rendered by cover crops to monoculture tea plantations, including regarding the prevalent natural enemies and their pivotal role in the biocontrol of insect pests in the tea plantation, have also been reviewed. Climate-resilient crops (sorghum, cowpea) and volatile blends emitting aromatic plants (semen cassiae, marigold, flemingia) are recommended as cover crops that can be intercropped in tea plantations. These recommended cover crop species attract diverse natural enemies and suppress major tea pests (tea green leaf hopper, white flies, tea aphids, and mirid bugs). It is presumed that the incorporation of cover crops within the rows of tea plantations will be a promising strategy for mitigating pest attacks via the conservation biological control, thereby increasing tea yield and conserving agrobiodiversity. Furthermore, a cropping system with intercropped cover crop species would be environmentally benign and offer the opportunity to increase natural enemy abundance, delaying pest colonization and/or preventing pest outbreaks for pest management sustainability. Full article

The continuous transformation from biodiverse natural forests and mixed-use farms into monoculture rubber plantations may lead to a series of hazards, such as natural forest habitats fragmentation, biodiversity loss, as well as drought and water shortage. Therefore, understanding the spatial distribution of rubber plantations is crucial to regional ecological security and a sustainable economy. However, the spectral characteristics of rubber tree is easily mixed with other vegetation such as natural forests, tea plantations, orchards and shrubs, which brings difficulty and uncertainty to regional scale identification. In this paper, we proposed a classification method combines multi-source phenology characteristics and random forest algorithm. On the basis of optimization of input samples and features, phenological spectrum, brightness, greenness, wetness, fractional vegetation cover, topography and other features of rubber were extracted. Five classification schemes were constructed for comparison, and the one with the highest classification accuracy was used to identify the spatial pattern of rubber plantations in 2014, 2016, 2018 and 2020 in Xishuangbanna. The results show that: (1) the identification results are in consistent with field survey and rubber plantations area generally shows a first increasing and then decreasing trend; (2) the Overall Accuracy (OA) and Kappa coefficient of the proposed method are 90.0% and 0.86, respectively, with a Producer’s Accuracy (PA) and User’s Accuracy (UA) of 95.2% and 88.8%, respectively; (3) cross-validation was employed to analyze the accuracy evaluation indexes of the identification results: both PA and UA of the rubber plantations stay stable over 85%, with the minimum fluctuation and best stability of UA value. The OA value and Kappa coefficient were stable in the range of 0.88–0.90 and 0.84–0.86, respectively. The method proposed provides reliable results on spatial distribution of rubber, and is potentially transferable to other mountainous areas as a robust approach for rapid monitoring of rubber plantations. Full article

Tea is a special economic crop that is widely distributed in tropical and subtropical areas. Timely and accurate access to the distribution of tea plantation areas is crucial for effective tea plantation supervision and sustainable agricultural development. Traditional methods for tea plantation extraction are highly dependent on feature engineering, which requires expensive human and material resources, and it is sometimes even difficult to achieve the expected results in terms of accuracy and robustness. To alleviate such problems, we took Xinchang County as the study area and proposed a method to extract tea plantations based on deep learning networks. Convolutional neural network (CNN) and recurrent neural network (RNN) modules were combined to build an R-CNN model that can automatically obtain both spatial and temporal information from multitemporal Sentinel-2 remote sensing images of tea plantations, and then the spatial distribution of tea plantations was predicted. To confirm the effectiveness of our method, support vector machine (SVM), random forest (RF), CNN, and RNN methods were used for comparative experiments. The results show that the R-CNN method has great potential in the tea plantation extraction task, with an F1 score and IoU of 0.885 and 0.793 on the test dataset, respectively. The overall classification accuracy and kappa coefficient for the whole region are 0.953 and 0.904, respectively, indicating that this method possesses higher extraction accuracy than the other four methods. In addition, we found that the distribution index of tea plantations in mountainous areas with gentle slopes is the highest in Xinchang County. This study can provide a reference basis for the fine mapping of tea plantation distributions. Full article

As natural ecosystems in most parts of the world come under increasing human influence, fragmentation is becoming the major driving factor of the global biodiversity crisis. Therefore, connectivity between habitat patches is becoming even more important. China began building national parks with the primary purpose of protecting nationally representative natural ecosystems and maintaining the integrity of their structure, processes and functions. Research is necessary to improve the internal connectivity of national parks and to propose suggestions for existing functional zoning and biological corridors. In this study, Qianjiangyuan National Park was selected as an example park, and landscape fragmentation was evaluated exponentially and simulated visually. The habitat characteristics of protected species in the region, morphological spatial pattern analysis and the delta of the probability of connectivity were used together to identify key habitat patches and their importance levels in the study area. Potential habitat corridors in the region were then obtained using least-cost path analysis and gravity modeling methods based on the distribution of key habitat and the migration costs of target species. The results of this study show that the disturbed landscape of the study area is dominated by tea plantations and drylands, with central roads being an important factor affecting the overall landscape connectivity. In terms of the distribution of key habitat patches, the mountains have a high value. In terms of area, their size is not directly proportional to their importance for maintaining landscape connectivity in the region, but large area patches are generally of higher importance. In terms of distance, key habitats that are closer to each other have a stronger correlation and a greater possibility for species migration. Combined with the functional zoning of Qianjiangyuan National Park, the setting of strictly protected areas and recreational areas is reasonable, and traditional use areas and ecological conservation areas could be appropriately adjusted according to the distribution of key habitats. The important corridor in the middle of the ecological conservation area is crucial for the overall connectivity of the national park, and the connectivity between strict protected areas will depend on successful protection of the ecological conservation area. Full article

Habitat conversion in mountain areas threatens their biodiversity. The effect on biodiversity of creating a mountain landscape with a network of forest fragments and a cultivated habitat matrix is poorly documented in China. Bird communities in forest fragments and tea plantations were censused by field observations in two years (2018–2019) in three tea-growing locations, Anxi, Beifeng, and Wuyishan in Fujian Province, south-eastern China. Out of a potential pool of 247 forest-associated bird species, we detected the presence of 82, mostly resident species, 32–47 of those regularly visiting tea plantations. Species-accumulation curves indicated the near-completeness of the census. The Rényi diversity profiles indicated a more diverse community in forest fragments than nearby tea plantations at Anxi and Beifeng, but the tea plantations at Wuyishan supported a more diverse bird community than the forest. Avian communities in tea plantations were a significantly nested subset of the forest communities. Tea plantations can provide resources for forest-associated birds, but the effectiveness of preserving avian diversity depends on natural forest fragments and can be enhanced by landscape-scale management, when the biocontrol potential of birds can also be enhanced. Full article

Sustainable agricultural practices necessitate accurate baseline data of crop types and their detailed spatial distribution. Compared with field surveys, remote sensing has demonstrated superior performance, offering spatially explicit crop distribution in a timely manner. Recent studies have taken advantage of remote sensing time series to capture the variation in plant phenology, inferring major crop types. However, such an approach was rarely used to extract detailed, multiple crop types spanning a large area, and the impact of topography has yet to be well analyzed in mountainous regions. This study aims to answer two questions in crop type extraction: (i) Is it feasible to accurately map multiple crop types over a large mountainous area with phenology-based modeling? (ii) What are the effects of topography in such modeling? To answer the questions, phenological metrics were extracted from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite time series, and the random forests classifier was used to map 12 crop types in South China (236,700 km²), featuring a subtropical monsoon climate and high topographic variation. Our study revealed promising results using MODIS EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) time series, although EVI outperformed NDVI (overall accuracy: 85% versus 81%). The spectral and temporal metrics of plant phenology significantly contributed to crop identification, where the spectral information exhibited greater importance. The increase of slope led to a decrease in model accuracy in general. However, uniformly distributed tree plantations (e.g., tea-oil camellia, gum, and tea trees) being cultivated on large slopes (>15 degrees) achieved accuracies greater than 80%. Full article

Understanding the driving factors of land-use spatio-temporal change is important for the guidance of rational land-use management. Based on land-use data, household surveys and social economic data in 2000, 2005, 2010, and 2015, this study adopted the Binary Logistic Regression Model (BLRM) to analyze the driving factors of land-use spatio-temporal change in a large artificial forest area in the Ximeng County, Yunnan province, in Southwest China. Seventeen factors were used to reflect the socio-economic and natural environment conditions in the study area. The results show a land use pattern composed of forestland, dry cropland, and rubber plantation in Ximeng County. Over the past fifteen years, the area of artificial forests increased rapidly due to the “Grain for Green” policy, which has led to increases in rubber plantations, tea gardens, eucalyptus forests, etc. In contrast, the area of natural forest and dry cropland decreased due to reclamations for farming and constructions. The BLRM approach helped to identify the main driving factors of land-use spatio-temporal change, which includes land-use policies (protection of basic farmlands and natural reserves), topography (elevation and slope), accessibility (distance to the human settlements), and potential productivity (fertility and irrigation). The study revealed the relationship between land-use spatio-temporal change and its driving factors in mountainous Southwest China, providing a decision-making basis for rational land-use management and optimal allocation of land resources. Full article