Search Results (943)

Background: Straw application (SP) is an important agronomic practice in sustainable agriculture, yet its effects on arbuscular mycorrhizal (AM) fungal communities in tea plantation soils remain poorly understood. Methods: This study investigated the responses of AM fungi to SP in tea plantations in south Henan, China, by assessing colonization characteristics, community composition, diversity, co-occurrence networks, and soil environmental drivers. Results: SP significantly increased the mycorrhizal colonization rate (MC), by 59.4%. High-throughput sequencing (26,865 sequences and 406 ASVs) revealed that SP reduced the dominance of Claroideoglomus (32.2% to 10.5%) and Glomus (51.01% to 46.7%) while enriching Paraglomus and Acaulospora. Although the α-diversity was unaffected, the β-diversity significantly shifted, indicating community homogenization under SP. Differential taxa analysis confirmed genus-specific responses, and co-occurrence networks showed a simplified topology (nodes: −18.4%; edges: −33.4%) but maintained stability, with increased module specialization (Z_i and P_i). Soil properties explained 80.0% of the variation in AM fungal parameters, with pH and available phosphorus (AP) as key drivers. SP shifted environmental filters from nitrogen/carbon-related factors to metal ions (Al³⁺ and Ca²⁺), altering geochemical conditions. Conclusions: SP selectively reshapes AM fungal communities by altering soil microenvironments and selectively modulating the AM fungal community while maintaining network stability. This study provides new insights into the microbial mechanisms of SP and a basis for sustainable, AMF-based tea plantation management. Full article

Leaf Area Index (LAI) is a fundamental parameter for characterizing the growth of tea (Camellia sinensis L.). However, in rugged mountainous regions, the combined effects of topographic relief and canopy structural heterogeneity severely constrain the accuracy of UAV-based multispectral LAI retrieval. This study develops an integrated framework combining topographic correction with interpretable machine learning to improve LAI estimation. We utilized a UAV multispectral dataset collected during the peak growing season from a typical tea-growing region in Fujian Province, China (altitude range: 58–186 m), comprising a total of 90 samples. Three topographic correction methods, including Sun–Canopy–Sensor (SCS), SCS with C correction (SCS+C), and Minnaert+SCS, were evaluated in combination with Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models. Results indicated that the SCS+C algorithm outperformed other methods by effectively accounting for direct and diffuse radiation components, thereby reducing topographic dependence while maintaining radiometric consistency across heterogeneous surfaces. The XGBoost model combined with SCS+C correction achieved the highest performance (R² = 0.8930, RMSE = 0.6676, nRMSE = 7.93%, MAE = 0.4936, Bias = −0.0836). SHapley Additive exPlanations (SHAP) analysis revealed a structure-dominated retrieval mechanism, in which red-band textural features (Correlation_R) exhibited higher importance than conventional vegetation indices. Compared with previous studies that primarily focus on either topographic correction or model development, this study provides quantitative insights into the underlying retrieval mechanisms. This framework improves the precision of tea LAI retrieval in complex terrains and provides a robust methodological basis for digital management in mountainous agriculture. Full article

Understanding the accumulation, ecological risk, and source interactions of potentially toxic elements (PTEs) in reservoir sediments is essential for protecting drinking water safety, yet such processes remain insufficiently understood in karst tea-plantation watersheds influenced by mixed anthropogenic activities. In this study, sediment cores collected from four sites (CY-1 to CY-4) during 2022–2024 were analyzed, and an integrated framework combining the Potential Ecological Risk Index (RI), Spearman correlation analysis, Principal Component Analysis (PCA), and Positive Matrix Factorization (PMF) was applied to evaluate contamination characteristics and quantify source contributions. The results revealed significant spatial–vertical heterogeneity of PTEs, with Zn (up to 153 mg/kg) and Cr (up to 64.6 mg/kg) showing the greatest variability, and strong co-enrichment among Cu, Zn, Pb, and Ni (r > 0.85, p < 0.01). Although the overall ecological risk was low (RI = 83.15–106.69), As contributed the highest proportion of risk (28–35%). PCA indicated distinct grouping patterns among elements, while PMF resolved three major sources: domestic sewage and agricultural runoff, agricultural and coal-combustion inputs, and industrial–traffic emissions. Notably, physicochemical parameters (TP, TN, and COD) played important roles in regulating the mobility and partitioning of PTEs by influencing nutrient-associated adsorption processes, organic matter complexation, and redox-related transformations. These findings highlight the multi-source-driven accumulation mechanisms of PTEs in karst reservoirs and provide a scientific basis for targeted pollution control and watershed management in agriculturally impacted regions. Full article

Excessive intake of sugar-sweetened beverages is a major source of dietary free sugars and is strongly associated with an increased risk of type 2 diabetes mellitus. Sweetened tea beverages, which are widely consumed across many Asian countries including Indonesia, represent an important target for sugar reduction. However, reducing sugar content often results in lower perceived sweetness and diminished consumer acceptance. This study examined the potential of floral aroma cues to support sugar reduction in sweetened green tea beverages. Formulations containing jasmine, rose, or lavender aroma were prepared at 100%, 80%, and 70% of the reference sugar level and evaluated by 182 panelists using hedonic rating, Just-About-Right (JAR) scaling with penalty analysis, and Rate-All-That-Apply (RATA) profiling combined with principal component analysis (PCA). Sugar reduction led to decreased perceived sweetness and liking in control samples, whereas jasmine and rose aromas significantly enhanced sweetness perception at reduced sugar levels. Notably, jasmine and rose maintained sweetness perception and overall liking at up to 30% and 20% sugar reduction, respectively. In contrast, lavender aroma provided limited sweetness enhancement and was associated with increased bitterness and astringency. Overall, these findings indicate that culturally congruent floral aromas, particularly jasmine and rose, can be strategically applied to support sugar reduction in sweetened tea beverages while maintaining consumer acceptance, contributing to sensory-driven reformulation strategies for supporting public health. Full article

The global rise in antimicrobial resistance has spurred increased interest in alternative antimicrobial agents, particularly essential oils (EOs). These oils are complex mixtures of volatile compounds that exhibit documented biological activity. This study evaluated antimicrobial and antibiofilm effects of selected EOs against clinically relevant bacterial and fungal pathogens. Antimicrobial activity against planktonic cells was assessed using disc diffusion assays with DMSO-diluted EO solutions against Escherichia coli (E.coli), Staphylococcus aureus (S.aureus), Pseudomonas aeruginosa, Klebsiella pneumoniae, and Candida albicans. Antibiofilm activity of E. coli and S. aureus was examined using ethanol-based EO formulations, with biofilm viability quantified by colony forming unit (CFU) enumeration. Cinnamon (Cinnamomum verum) oil showed the strongest and most consistent activity, inhibiting planktonic and biofilm models. Tea tree (Melaleuca alternifolia), lemongrass (Cymbopogon citratus), rosemary (Rosmarinus officinalis), rose (Rosa damascena), and jasmine (Jasminum officinale) oils showed significant planktonic antimicrobial effects, while jasmine oil (Jasminum officinale) demonstrated pronounced antibiofilm activity against S. aureus, including strong biofilm eradication in several replicates. In contrast, chamomile (Matricaria chamomilla) and sandalwood (Santalum austocaledonicum) oils showed limited or no activity. These findings highlight differences between planktonic and biofilm responses, emphasizing the importance of incorporating biofilm models into antimicrobial evaluation. Overall, Cinnamomum verum and Jasminum officinale oils may serve as complementary antimicrobial agents, warranting further investigation. Full article

Spring frosts pose a major threat to tea production, causing severe damage to tender spring buds and substantial economic losses. To support timely frost protection measures, this study develops an interpretable machine learning framework for next-day frost forecasting in a tea plantation in Danyang, eastern China. Leveraging nine years (2008–2016) of multi-source data—including high-resolution on-site meteorological observations and daily records from surrounding regional stations—we engineered a comprehensive set of predictive features capturing local microclimatic, regional synoptic, and short-term temporal dynamics. A two-stage feature selection approach, combining Spearman correlation screening with SHAP-based importance ranking, identified an optimal subset of 14 robust predictors. Among eight benchmarked models, XGBoost achieved the best performance on a chronologically held-out test set, yielding a CSI of 0.736, accuracy of 91.0%, F1-Score of 0.848 and AUC-ROC of 0.968. Ablation experiments demonstrated the added value of data integration: model performance improved from a CSI of 0.617 (using only local data) to 0.736 (with full multi-source inputs). SHAP interpretability analysis further revealed that the model’s predictions align with established frost formation physics, highlighting key drivers such as nocturnal cooling rate and regional humidity. This work demonstrates that integrating multi-scale meteorological data with interpretable machine learning offers a reliable, transparent, and operationally viable tool for frost risk management—providing actionable insights to enhance resilience in precision horticulture for perennial crops like tea. Full article

Real-time measurement of tea bud phenotypes via mobile devices is constrained by model lightweighting challenges, and research on non-contact measurement of tea bud phenotypes based on key points remains largely unexplored. Information on the growth posture of tea buds is an important basis for determining tea maturity grades, quality monitoring, and tea breeding. Therefore, this work develops a deep learning-enabled YOLOv8p-Tea model to estimate key point information of tea bud posture and automatically obtain three-dimensional point cloud information of tea buds by integrating depth information, thereby achieving in situ measurement of tea bud phenotypic parameters. Meanwhile, the model is trained and validated using a tea bud (one-bud-three-leaf) image dataset, and its effectiveness is demonstrated through experiments. Compared to the YOLOv8p-pose model, the model achieves a mAP50 of 98.3%, a P of 97%, and parameters of 0.72 M, with mAP50 and P improved by 1.5% and 1.9%, respectively, and the parameter count is reduced by 25%. To validate the accuracy of phenotypic extraction, the model was deployed on edge devices, and 30 tea buds with one bud and three leaves were randomly selected in a tea garden. The final in situ measurement results showed an MRE of 6.63%. Experimental findings indicate that the developed method is capable of not only effectively estimate tea bud posture but also accurately achieves in situ measurement of tea bud phenotypes, which holds potential applications for meeting the construction needs of smart tea gardens and optimizing tea breeding. Full article

Elemental migration and enrichment are important processes influencing tea plant growth and the assembly of rhizosphere bacterial communities within the rock–soil–plant continuum. This study explores how soil parent materials (granite, quartz schist, and sericite schist) are potentially associated with these processes and their observed associations with the elemental composition of tea leaves. Exploratory statistical analyses revealed distinct, lithology-specific biogeochemical patterns that serve as a foundation for hypothesis generation. In granite soils, chlorite correlated with the mobility of Cr, Pb, Cu, Ni, Mg, and Na, coinciding with shifts in the relative abundances of Verrucomicrobia, Armatimonadetes, and Chloroflexi. In quartz schist, kaolinite exhibited notable correlations with the dynamics of Pb, Cr, Ni, Zn, and As, which were statistically linked to Planctomycetes, Proteobacteria, and Acidobacteria. Complex mineral–microbe interactions were observed in sericite schist soils, where clay minerals (e.g., chlorite, illite) were closely associated with the migration of multiple elements (Pb, K, Ca, Cd, As, Al, Fe, Zn), paralleling structural variations in communities of Actinobacteria, Planctomycetes, Chloroflexi, and Proteobacteria. Potassium (K), calcium (Ca), and manganese (Mn) showed bioaccumulation tendencies in tea leaves across all lithologies, with an enrichment capacity order of Ca > K > Mn > Mg > Na > Al. Exploratory Classification and Regression Tree (CART) analysis suggested that the migration of K, Ca, Cu, Zn, and Hg corresponded most closely with their soil concentrations. Manganese (Mn) exhibited a mineral-associated trend, with kaolinite content as a potential correlate, while cadmium (Cd) migration was statistically linked to the relative abundance of Armatimonadetes. These findings highlight potential candidate relationships between mineralogy, microbes, and elemental mobility rather than confirming causal mechanisms, emphasizing the need for further validation in larger or experimental datasets. Full article

Soil respiration (Rs) plays an important role in the carbon (C) dynamics of terrestrial ecosystems and is strongly regulated by nitrogen (N) inputs. While the impact of N fertilization on Rs has been widely documented in conventional farmland ecosystems, its patterns and influencing factors in perennial tea plantation systems are still poorly understood. In the study, we conducted a 15-year field experiment in a representative tea plantation to investigate the effects of different N rates (0, 112.5, 225, and 450 kg N ha⁻¹ yr⁻¹) on Rs. Compared to the control (N0), soil pH decreased significantly (p < 0.05) by 6.07%, 11.82%, and 16.12% under N112.5, N225, and N450, respectively. Concurrently, cation exchange capacity (CEC), ammonium (NH₄⁺-N), nitrate (NO₃⁻-N), and available phosphorus (AP) increased with increasing N rates, whereas available potassium (AK) decreased. Soil microbial biomass carbon (MBC) initially increased and then decreased with increasing N rates, while dissolved organic carbon (DOC) content increased consistently. The Rs rate exhibited a distinct seasonal pattern with a single peak in August. The annual mean Rs rates were 2.79, 3.15, 4.06, and 3.85 μmol·m⁻²·s⁻¹ for the N0, N112.5, N225, and N450 treatments, respectively. Soil temperature explained 55.41% to 61.08% of the variation in Rs rates across N treatments, and a composite model incorporating both soil temperature and moisture further improved the prediction of Rs dynamics. Cumulative soil CO₂ emissions (CCEs) over the study period ranged from 10,427 to 14,221 kg CO₂-C ha⁻¹ across treatments and were significantly negatively correlated with soil pH, and positively correlated with DOC, MBC, and NO₃⁻-N content. A non-linear relationship between N application rate and CCEs was observed, highlighting the complexity of optimizing N management for balancing productivity and climate mitigation in tea plantation systems. These findings provide a theoretical basis for developing rational N fertilization strategies and improving the predictive capacity of C cycle models in agroecosystems. Full article

Robust quantification of greenhouse gas (GHG) balances in tea plantations is critical for evaluating their contribution to agricultural carbon neutrality. This study aimed to develop data-driven models to quantify soil organic carbon (SOC) sequestration and N₂O emissions in Chinese tea plantations, evaluate their net GHG balance at the national scale, and assess the mitigation potential under alternative nitrogen management scenarios. Using a comprehensive national dataset, we compared multiple machine learning (ML) approaches with a conventional multiple linear regression (MLR) model to simulate N₂O emissions and SOC changes in Chinese tea plantations. All ML models substantially outperformed the MLR model, with the Random Forest (RF) algorithm achieving the highest predictive accuracy. The RF models yielded R² values of 0.68 for N₂O emissions and 0.67 for SOC changes, with no significant prediction bias. Variable importance and marginal effect analyses revealed strong non-linear controls. Mineral N fertilizer input was the dominant driver of N₂O emissions, followed by organic N input, soil clay content, and SOC. In contrast, SOC dynamics were primarily regulated by organic carbon inputs, tea plantation age, climate variables, and soil pH. National-scale simulations indicated an average N₂O emission intensity of 9.03 kg N₂O ha⁻¹ yr⁻¹ and a mean SOC sequestration rate of 0.88 t C ha⁻¹ yr⁻¹. Overall, SOC sequestration offset N₂O emissions, rendering Chinese tea plantations a net GHG sink (−2525 Gg CO₂-eq yr⁻¹). Scenario analyses showed that mineral N reduction increased net GHG uptake by 1804 Gg CO₂-eq, while organic fertilizer substitution achieved a substantially larger mitigation potential of 5961 Gg CO₂-eq. By integrating SOC sequestration and N₂O emissions within a unified modeling framework and applying machine-learning-based national-scale simulations, this study provides a more comprehensive and data-driven quantification of GHG balances in tea ecosystems, offering a scientific basis for evaluating their role in agricultural carbon neutrality strategies. Full article

Thymus species play an important role in traditional medicine, food practices, and cultural heritage across the Balkan Peninsula, a region characterized by high floristic diversity and long-standing ethnobotanical traditions. This review provides a comprehensive synthesis of published ethnobotanical and ethnopharmacological data on the traditional uses of Thymus species in the Balkans. A systematic survey of literature published between 1900 and 2022 was conducted using major scientific databases and classical ethnobotanical sources, covering Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Greece, Kosovo, Montenegro, North Macedonia, Romania, Serbia, Slovenia, and the European part of Turkey. In total, 553 records of traditional use were documented. The results reveal that Thymus taxa are most frequently employed for the treatment of respiratory and gastrointestinal disorders, followed by applications related to the nervous, urinary, cardiovascular, and integumentary systems. Culinary uses such as spices, herbal teas, beverages, and food preservatives are widely reported, highlighting the close connection between medicinal and dietary traditions. The analysis also identifies significant taxonomic inconsistencies in the ethnobotanical literature, particularly the frequent misapplication of names such as T. serpyllum and T. vulgaris, which complicates the interpretation of traditional knowledge. Overall, the strong cross-cultural consistency of uses across Balkan countries supports the pharmacological relevance of Thymus species and aligns well with modern phytotherapeutic evaluations. This review underscores the importance of critically integrating ethnobotanical data, taxonomic accuracy, and contemporary phytotherapy to better understand and utilize the therapeutic potential of Thymus species. Full article

Background/Objectives: Precocious puberty in girls currently appears to be one of the main problems in pediatric endocrine gynecology. Early onset of menstruation (EOM) means that the age at which the first menstruation occurs is lower than the average/median for the population, which ranges from 12 to 13 years and depends primarily on ethnic origin. Depending on age and severity of symptoms, these disorders negatively affect girls’ quality of life in many areas, including school life, family relationships, and everyday life. Methods: This article provides a scoping review summarizing scientific evidence from human studies on the association between substances derived from green tea (flavanols) and soy (isoflavones) and precocious puberty in girls. Results: Despite the relatively small number of girls enrolled in the studies, available scientific evidence from randomized controlled trials (RCTs) suggests that polyphenols from decaffeinated green tea (DGTP) may contribute to lowering the age of first menstruation in girls living with obesity. The effect of soy isoflavones or soy in the context of premature menstruation in girls is unclear. Most studies report that it may have no effect on the age of first menstruation, while individual studies suggest that very early exposure to soy (< 4 months of age) may result in earlier puberty, and others suggest that higher consumption of soy isoflavones delays this process. Conclusions: Further well-designed intervention studies in humans are needed to better understand the endocrine and metabolic relationships regarding the role and importance of specific polyphenols in the pathogenic mechanisms of the development and treatment of precocious puberty in girls. Full article

The genera Pestalotiopsis, Neopestalotiopsis, and Pseudopestalotiopsis share highly similar morphological characteristics. Although species within these genera are recognized as plant pathogens, their pathogenicity can differ even on the same host plant, highlighting the importance of accurate genus-level identification for effective disease management. However, reliable discrimination among these genera based solely on morphology or internal transcribed spacer (ITS) amplicon length is difficult. Therefore, molecular approaches based on ITS sequence data are required for practical and reliable genus-level identification. This study aimed to develop genus-specific PCR primers through comparative genome analysis using genus-specific gene regions identified from available genomic data. The performance of these primers was evaluated using 49 isolates obtained from banana, Japanese andromeda, loquat, rubber, and tea. The primer sets achieved an overall identification accuracy of 97%. One strain could not be assigned to Pestalotiopsis, which exhibited morphological characteristics inconsistent with the genus and was positioned outside the main Pestalotiopsis clade in phylogenetic analyses, supporting the taxonomic validity of the primer-based identification. These results demonstrate that the developed primers provide a reliable and practical tool for genus-level identification and taxonomic assignment of these morphologically similar fungal pathogens, including direct detection from infected plant tissues. Full article

The tomato processing industry is among the most widespread food industries worldwide, generating residues that pose an important issue due to their abundance and the rising negative environmental impacts associated with waste. This paper summarizes potential products that can be obtained from these sources, with a focus on the production of a specific biopolymer, cutin, which has great potential as a food packaging material. It emphasizes the development of an integration proposal model for the biorefinery process of tomato pomace, in line with the zero-waste concept, by performing comparative techno-economic analysis (TEA) of two processing scenarios: (1) a biorefinery pathway that valorizes tomato pomace utilization by producing cutin and (2) an integrated process designed for the simultaneous production of cutin and phenolic antioxidants. The study identifies current research gaps and outlines strategic directions for potential integration pathways that can enhance not only the economic viability and profitability of the process but also its environmental benefits through more complete. The techno-economic analysis model for cutin extraction showed an internal rate of return (IRR) of only 2%, which is five times lower than the IRR achieved in our integrated model for cutin and phenolic compounds. Additionally, the payback time in the integrated approach improved significantly from 9.56 to 5.7 years. This paper assesses the potential of tomato pomace as a sustainable source for the production of high-value bioproducts that can economically justify investments in sustainable bioprocessing technologies and reduce waste through an integrated approach. Full article

The tea lace bug, Stephanitis chinensis, is an important pest in the southwest tea region in China. It has recently emerged in some parts of the tea areas, severely impacting the profitability of spring tea. To clarify the distribution dynamics of S. chinensis under current and future climate change, this study used the MaxEnt model and ArcGIS software to predict the distribution and dominant environmental factors of S. chinensis. The results show that the mean precipitation of the warmest quarter (Bio18), the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), and the variation coefficient of temperature (Bio4) are the dominant environmental factors affecting S. chinensis distribution. Under the current climatic conditions, the suitable habitats for S. chinensis are mainly distributed in East and South Asia, with only a small distribution in southern Europe, southeastern North America, and coastal areas of southeastern South America; the highly suitable habitats are primarily distributed in China, southern Japan, and southern South Korea. The total suitable area of S. chinensis accounts for approximately 28.58% of China’s land area. The high-suitability regions are primarily concentrated in the Guizhou, Chongqing, Sichuan, Hubei, Hunan, Shaanxi, and Jiangsu provinces. Under future climate conditions, the total suitable area of S. chinensis will increase to varying degrees, primarily expanding northward, with the extension of high-suitability areas mainly concentrated in Hubei, Anhui, and Henan. The migration distance of the geographical distribution center ranges between 32.27 km and 96.13 km, with a primary shift toward the northeast. This study predicts potential suitable areas for the tea lace bug under different climate change scenarios. Specifically, regions at the highest risk, such as the Hubei, Anhui, and Henan provinces, should enhance monitoring and early warning systems and implement timely prevention and control measures to ensure the safe production of tea. Full article