Search Results (22)

Soil heavy metal (HM) contamination has become a critical global environmental issue, predominantly caused by industrial and agricultural operations. This study focuses on Mount Wuyi, a UNESCO biodiversity hotspot and major tea production base, to examine vegetation-mediated soil HM accumulation under anthropogenic impacts. We analyzed nine HMs (Mn, Cu, Zn, Cd, Hg, As, Pb, Cr, Ni) across diverse vegetation types using geochemical indices and Positive Matrix Factorization (PMF) modeling. The findings revealed Mn and Zn were dominant elements, and Cr and Pb concentrations exceeded regional background values by 3.47 and 1.26 times, respectively. Cr, Cd, and Pb demonstrated significant pollution levels, while Cd and Hg posed the highest ecological risks. Vegetation type significantly influenced HM distribution patterns, with cultivated areas and shrublands (including tea gardens) accumulating higher concentrations of Cu, Cd, Pb, and Hg from agricultural and transportation sources. Notably, bamboo forests exhibited natural resistance to HM contamination. PMF analysis identified four primary pollution sources: urbanization (27.94%), transport–agriculture activities (21.40%), agricultural practices (12.98%), and atmospheric deposition (12.96%). These results underscore the need for implementing clean energy solutions, phytoremediation strategies, and tea-specific detoxification measures to maintain ecological security and agricultural sustainability in this ecologically significant region. Full article

The primary objective of this research was to develop an efficient method for accurately identifying and localizing weeds in ecological tea garden environments, aiming to enhance the quality and yield of tea production. Weed competition poses a significant challenge to tea production, particularly due to the small size of weed plants, their color similarity to tea trees, and the complexity of their growth environment. A dataset comprising 5366 high-definition images of weeds in tea gardens has been compiled to address this challenge. An enhanced U-Net model, incorporating a Double Attention Mechanism and an Atrous Spatial Pyramid Pooling module, is proposed for weed recognition. The results of the ablation experiments show that the model significantly improves the recognition accuracy and the Mean Intersection over Union (MIoU), which are enhanced by 4.08% and 5.22%, respectively. In addition, to meet the demand for precise weed management, a method for determining the center of weed plants by integrating the center of mass and skeleton structure has been developed. The skeleton was extracted through a preprocessing step and a refinement algorithm, and the relative positional relationship between the intersection point of the skeleton and the center of mass was cleverly utilized to achieve up to 82% localization accuracy. These results provide technical support for the research and development of intelligent weeding equipment for tea gardens, which helps to maintain the ecology of tea gardens and improve production efficiency and also provides a reference for weed management in other natural ecological environments. Full article

Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens. Full article

Understanding the correlation between soil chemical properties and tea quality is essential for the comprehensive management of ancient tea gardens. However, the specific links between these factors in ancient tea gardens remain underexplored. This study analyzes the soil chemical properties of four distinct research regions in Nanhua County to explore their effects on key chemical components in ancient tea garden teas, providing a scientific basis for improving the quality of ancient tea garden teas through soil management. Employing high performance liquid chromatography (HPLC) and inductively coupled plasma mass spectrometry (ICP-MS), the chemical components of tea and the chemical properties of the soil were meticulously quantified. Following these measurements, the integrated fertility index (IFI) and the potential ecological risk index (PERI) were evaluated and correlation analysis was conducted. The results revealed that ancient tea garden tea quality is closely linked to soil chemical properties. Soil’s total nitrogen (TN), total sulfur (TS), and available potassium (AK) negatively correlate with tea’s catechin gallate (CG) component and AK also with polyphenols. Most other soil properties show positive correlations with tea components. The research also evaluated soil heavy metals’ IFI and PERI. IFI varied significantly among regions. Hg’s high pollution index indicates ecological risks; Cd in Xiaochun (XC) region poses a moderate risk. PERI suggests moderate risk for XC and Banpo (BP), with other areas classified as low risk. Implementing reasonable fertilization and soil amelioration measures to enhance soil fertility and ensure adequate supply of key nutrients will improve the quality of ancient tea gardens. At the same time, soil management measures should effectively control heavy metal pollution to ensure the quality and safety of tea products. Insights from this study are crucial for optimizing soil management in ancient tea gardens, potentially improving tea quality and sustainability. Full article

Pest infestations in tea gardens are one of the common issues encountered during tea cultivation. This study introduces an improved YOLOv8 network model for the detection of tea pests to facilitate the rapid and accurate identification of early-stage micro-pests, addressing challenges such as small datasets and the difficulty of extracting phenotypic features of target pests in tea pest detection. Based on the original YOLOv8 network framework, this study adopts the SIoU optimized loss function to enhance the model’s learning ability for pest samples. AKConv is introduced to replace certain network structures, enhancing feature extraction capabilities and reducing the number of model parameters. Vision Transformer with Bi-Level Routing Attention is embedded to provide the model with a more flexible computation allocation and improve its ability to capture target position information. Experimental results show that the improved YOLOv8 network achieves a detection accuracy of 98.16% for tea pest detection, which is a 2.62% improvement over the original YOLOv8 network. Compared with the YOLOv10, YOLOv9, YOLOv7, Faster RCNN, and SSD models, the improved YOLOv8 network has increased the mAP value by 3.12%, 4.34%, 5.44%, 16.54%, and 11.29%, respectively, enabling fast and accurate identification of early-stage micro pests in tea gardens. This study proposes an improved YOLOv8 network model based on deep learning for the detection of micro-pests in tea, providing a viable research method and significant reference for addressing the identification of micro-pests in tea. It offers an effective pathway for the high-quality development of Yunnan’s ecological tea industry and ensures the healthy growth of the tea industry. Full article

China’s Yunnan Province, known for its tea plantations, faces significant challenges in smart pest management due to its ecologically intricate environment. To enable the intelligent monitoring of pests within tea plantations, this study introduces a novel image recognition algorithm, designated as YOLOv8n-WSE-pest. Taking into account the pest image data collected from organic tea gardens in Yunnan, this study utilizes the YOLOv8n network as a foundation and optimizes the original loss function using WIoU-v3 to achieve dynamic gradient allocation and improve the prediction accuracy. The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model’s complexity. The integration of the Efficient Multi-Scale Attention Module with Cross-Spatial Learning enables the model to have more flexible global attention. The research results demonstrate that compared to the original YOLOv8n model, the improved YOLOv8n-WSE-pest model shows increases in the precision, recall, mAP50, and F1 score by 3.12%, 5.65%, 2.18%, and 4.43%, respectively. In external validation, the mAP of the model outperforms other deep learning networks such as Faster-RCNN, SSD, and the original YOLOv8n, with improvements of 14.34%, 8.85%, and 2.18%, respectively. In summary, the intelligent tea garden pest identification model proposed in this study excels at precise the detection of key pests in tea plantations, enhancing the efficiency and accuracy of pest management through the application of advanced techniques in applied science. Full article

The decline in primary natural forests worldwide has intensified research on the effects of forest transformation on soil carbon (C), nitrogen (N), and phosphorus (P) cycles and stocks. However, the extent to which soil C, N, and P stocks and stoichiometry are affected by forest conversion remains unclear. Here, we examined the effects of forest transformation on soil nutrient storage capacity and stoichiometric characteristics in native broadleaf forests (BFs), plantation forests (PFs), tea gardens (TGs), cultivated lands (CLs), and urban artificial green spaces (GSs) at a county scale in subtropical China. The results showed that the other forest types exhibited significantly reduced soil C and N contents and stocks but increased soil P content and stock compared to BFs. The soil C:N:P stoichiometric ratios for BFs and the converted PFs, TGs, GSs, and CLs were sequentially decreased as follows: 444.8:24.2:1, 95.0:10.0:1, 30.2:3.9:1, 23.1:3.7:1, and 19.4:1.9:1, respectively. Within the altitude (AL) span of 180 to 1200 m surveyed, the AL decided the type of forest conversion and significantly influenced the stock levels and stoichiometric ratios of soil C, N, and P. The results of this study highlight the importance of the ecological management of TGs and the optimization of soil P production in CLs, TGs, and GSs. Full article

The spatial heterogeneity of potentially toxic elements (PTEs) in a typical green tea-producing area in Zhejiang was investigated with application of geostatistics. The positive matrix factorization (PMF) was conducted for analysis of pollution sources and risk assessment of the soil of the tea garden. The results revealed that 93.52% of the study area did not exceed the PTEs risk screening value in the soil pollution risk control standard of agricultural land. The results of the spatial heterogeneity analysis showed that Cd and Pb had moderate spatial auto-correlation, exhibiting similar spatial distribution patterns. The high-value locations were distributed in the southeast of the study area, while low-value locations were distributed in the southwest of the study area. The Cr, As, and Hg had strong spatial auto-correlation, while Cr and As had similar spatial distribution patterns whose high-value areas and low-value areas were concentrated in the west and center of the study area, respectively. The Cd, Pb, and As originated from the agricultural source, transportation source, and industrial source, respectively, while Cr and Hg were from the natural source on the basis of the results of the PMF model. The results of a potential ecological risk assessment revealed that five PTEs in the study area were of low potential risk. The single-factor ecological risk ranking was Cd > As > Hg > Cr > Pb. The overall ecological risk in the study area was slight. The human health risk model indicates that there was a non-carcinogenic risk for children in the study area, and the high-value area was concentrated in the northwest of the study area. It is concluded that emphasis shall be given to excessive Cd caused by agricultural sources in the southeast of the study area, and control and monitoring will be strengthened in the northwestern part of the study area. The relevant measures for prevention of soil pollution must be conducted. Full article

Tea (Camellia sinensis L.), an important economic crop in China, is highly favored by the population. Microorganisms can help plants acquire soil nutrients and cope with various stresses, and the diversity and structural composition of the rhizosphere microbial community of tea plants are crucial for ensuring the growth and quality of tea leaves. Therefore, we studied the differences in soil nutrients, enzyme activities and microbial communities between two different tea gardens (a tableland tea garden and an ancient tea plantation) in different ecological niches (rhizosphere and bulk soil), as well as the impacts they experienced. The results show that the soil pH levels in the ancient tea plantation were within the optimal range (4.5–5.5), and both rhizosphere and bulk soil nutrients in the ancient tea plantation were higher than those in the tableland tea garden, except for TP; the nutrients in the rhizospheres of ancient tea trees were more abundant. Moreover, higher enzyme activities were observed in the rhizosphere soil than those in the bulk soil in both tea gardens, and both the tableland and ancient tea garden soils were subjected to a certain degree of C&N limitations. The microbial communities of the two tea gardens were dominated by bacteria, but the α-diversity of the bacterial and fungal communities in the rhizosphere soil of the tableland tea garden was higher than that in the ancient tea plantation. The bacterial communities were largely dominated by Proteobacteria and Acidobacteriota, and the fungal communities were largely dominated by Ascomycota and Basidiomycota in the two tea gardens. The structure and composition of soil bacterial communities in the two tea gardens were similar, whereas significant differences were observed in the fungal communities. In addition, soil pH and SWC were the key factors influencing the fungal community in both the rhizosphere and bulk soil in the two tea gardens, whereas the bacterial community was more significantly affected by soil TN, NH₄⁺-N, SWC and DON. These findings provide essential foundational information for the preservation of ancient tea plantations, the ecological adaptability of ancient tea trees and the management of tableland tea gardens. Full article

To investigate the contents of carbon, nitrogen, and phosphorus in tea plantation soils and their ecological stoichiometric characteristics, as well as their response to environmental factors in high-latitude regions of China, soil samples from 0 to 20 cm depth were collected from tea plantations at different altitudes and cultivation years in the main tea-producing areas of Shaanxi Province. These samples were used to determine the soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents and to calculate their stoichiometric ratios. The findings revealed the following: the average soil SOC and TN content in tea gardens were 13.15 and 1.30 g·kg⁻¹, respectively, exceeding the national soil average. These values met the Class I tea garden fertility standards. However, the average soil TP content, at 0.45 g·kg⁻¹, fell below the national soil average, meeting the Class II tea garden fertility standards. In tea gardens, the average ratios of carbon to nitrogen (C:N), carbon to phosphorus (C:P), and nitrogen to phosphorus (N:P) in the soil were 10.42, 30.98, and 3.32, respectively. These ratios were all lower than the national soil average, indicating relatively high phosphorus availability but nitrogen deficiency in tea garden soils. As altitude increased, there was a decline in soil SOC content, C N, and C P ratios, followed by a subsequent increase. No significant changes were seen in TN, TP, and N P ratio in the soil, but there was an increase in SOC content, TN content, and C P ratio during cultivation. The N-to-P ratio initially increased before decreasing, while the C-to-N ratio decreased before increasing. Soil TP content did not change significantly. The study recommends careful nitrogen fertilizer application in tea garden management to balance nitrogen and phosphorus. Full article

Intercropping, a well-established agroecological technique designed to bolster ecological stability, has been shown to have a significant impact on soil health. However, the specific effects of tea/Trachelospermum jasminoides intercropping on the physicochemical properties and functional microbial community structure in practical cultivation have not been thoroughly investigated. In this study, we utilized high-throughput sequencing technology on the 16S/ITS rDNA genes to assess the impact of tea intercropping with T. jasminoides on the composition, diversity, and potential functions of the soil microbial community in tea gardens. The results indicated that the tea/T. jasminoides intercropping system significantly increased pH levels, soil organic matter, available nitrogen, phosphorus, potassium, and enzyme activity, ultimately augmenting soil nutrient levels. Furthermore, an in-depth analysis of the bacterial co-occurrence network and topological structure portrayed a more intricate and interconnected soil bacterial community in tea gardens. Remarkably, the abundance of beneficial genera, including Burkholderia, Mesorhizobium, Penicillium, and Trichoderma, underwent a substantial increase, whereas the relative abundance of pathogenic fungi such as Aspergillus, Fusarium, and Curvularia experienced a marked decline. Functional predictions also indicated a notable enhancement in the abundance of microorganisms associated with nitrogen and carbon cycling processes. In summary, the intercropping of tea and T. jasminoides holds the potential to enrich soil nutrient content, reshape the microbial community structure, bolster the abundance of functional microorganisms, and mitigate the prevalence of pathogenic fungi. Consequently, this intercropping system offers a promising solution for sustainable tea garden management, overcoming the limitations of traditional cultivation methods and providing valuable insights for sustainable agriculture practices. Full article

Biodiversity plays an important role in maintaining the ecological balance of the earth. The study of traditional knowledge related to biological resources is a hot issue in the field of international biodiversity conservation. Xishuangbanna is a key area of biodiversity and a cultural hotspot of international significance. According to the Technical Regulation for Classification, Investigation, and Inventory of Traditional Knowledge Relating to Biological Diversity issued by the Ministry of Ecology and Environment, we investigated and catalogued the traditional knowledge related to biodiversity of the Jino people who have lived in Xishuangbanna for generations, and collected 490 entries of traditional knowledge related to biodiversity of the Jino people. Drawing on the traditional knowledge diversity index calculation method proposed by Wang Guoping, the overall traditional knowledge α-diversity index of the Jino people is 0.63, indicating that the richness of the traditional knowledge of the Jinuo people is relatively high. The traditional culture related to biodiversity, the traditional knowledge related to agricultural genetic resources, and the traditional technology related to the sustainable utilization of biological resources are relatively rich and diverse. The diversity index is 0.86, 0.82 and 0.79, respectively. In addition, Xishuangbanna has invested a lot of energy in biodiversity protection, including the establishment of nature reserves, botanical gardens, zoos, ecological tea gardens and other species reserves, and the promulgation of laws and policies related to biodiversity protection, and has achieved remarkable results in in situ protection and ex situ protection. On the basis of analyzing the progress and achievements of biodiversity conservation in Xishuangbanna, this study points out that Xishuangbanna faces challenges such as the loss of traditional knowledge, insufficient conservation efforts, and great changes in land use, and puts forward corresponding suggestions. Full article

(1) Background: In managing ecological tea gardens, litter composed of pruned and fallen tea leaves from companion tree species is an important component of tea garden soil. The decomposition of litter plays a crucial role in regulating nutrient cycling in tea garden ecosystems. (2) Methods: This study employed the litterbag method to investigate chemical stoichiometry characteristics and enzyme activity changes during the decomposition process of pruned and fallen Camellia sinensis leaves from companion tree species in an ecological tea garden located in central Guizhou Province. (3) Results: With decomposition duration, the general trend of changes in the C/N and C/P ratios showed a decrease in the activity of UE (urease), AP (acid phosphatase), and PPO (polyphenol oxidase) followed by an increase, while CAT (catalase) and CEL (cellulase) activity decreased, then increased, and then decreased again. On the other hand, the N/P and the activity of SC (sucrase) first increased and then decreased. The C/N and the activities of UE, PPO, and AP generally reached their maximum values during the late decomposition stage (366–428 d), while the N/P and the CAT activity peaked during the mid-decomposition stage (305 d). In contrast, the activity of SC and CEL reached its maximum value during the early decomposition stage (123 d). The N/P ratios were significantly higher than those of the CS (C. sinensis) litter in the mixed treatment, while C/N and C/P ratios were significantly lower than those in the CS during decomposition for 184–366 days. The UE, CAT, AP, and SC activities of CBL (C. sinensis + B. luminifera) litter were significantly higher than those of the CS litter during decomposition. During the experiment, antagonistic effects were observed in the C/N and C/P ratios of the different litter types. Most mixed litter exhibited additive effects on enzyme activity, while a few showed nonadditive effects. For the nonadditive effects, most were antagonistic effects, mainly in the CPM (C. sinensis + C. glanduliferum) litter. A small portion, mainly observed in the CBL and CCG (C. sinensis + C. glanduliferum) litter, showed synergistic effects. (4) Conclusions: Selecting B. luminifera and C. glanduliferum to be part of the tree species composition in ecological tea gardens can produce positive mixed effects on enzyme activity during litter decomposition, increase nutrient return capacity, maintain tea garden fertility, and achieve the ecological development of tea gardens. Full article

Eco-efficiency is a significant target for evaluating the agricultural ecosystem and measuring sustainable agricultural development through quantitative analysis. It is also an essential part of constructing the ecological tea garden, which offers a directional function in realizing the green development of the tea industry. After measuring the eco-efficiency of China’s tea industry using the super-efficiency SBM model, this paper analyzes the spatial disequilibrium and dynamic evolution trend of the eco-efficiency in China’s tea industry through the method of Dagum Gini Coefficient and Kernel Density Estimation. The results show that the level of eco-efficiency in China’s tea industry was improved overall, and the spatial disequilibrium was significantly reduced. The differences within the tea region decreased as follows: tea regions in Southwest China, South China, south of the Yangtze River, and north of the Yangtze River; the overall difference in the eco-efficiency in the tea industry mainly comes from the contribution of the interregional difference in tea regions, and the second contribution comes from the intraregional difference in tea regions and the difference in super-variable density. The eco-efficiency of the tea industry has been improved both nationally and within the top four tea regions; the disequilibrium between areas and within the tea region has been largely alleviated, but there is still room to optimize the input–output structure and promote the eco-efficiency. Full article

In order to explore whether tea/tree intercropping plantations have positive effects on soil ecosystem services functions, the possible effects of intercropping cultivation of 151 different tea and other species’ intercropping setups were summarized and analyzed in terms of three aspects of soil ecological service functions (supply services, support services, and regulating services). An ArcGIS map was plotted to show the distribution of existing intercropping plantations in China up to June 2021. Furthermore, it was concluded that the benefits of intercropping tea plantations exceeded those of monocropping tea plantations in terms of soil ecosystem service functions, such as water retention capacity, mineral contents, effects on energy transformation, and regulating environmental conditions. Intercropping tea plantations were more sustainable than regular tea plantations because of the different degrees of variability and benefits in all three aspects mentioned above. However, tea and tree intercropping plantations often require careful planning and preliminary experimentation to determine the type of intercropping that will have positive impacts, especially in the long term. Full article