Search Results (4,754)

Long-term balanced mineral fertilization is essential for sustainable sweet cherry production under variable climatic conditions. This seven-year field study (2016–2022) evaluated the effects of NP, NPK, and NPKMg fertilization including the control on six parameters: trunk cross-sectional area (TCSA), fruit yield (FY), crop load (CL), fruit diameter (FD), water-soluble dry matter content (BRIX), and cherry leaf spot incidence (CLS) in two sweet cherry cultivars (‘Vera’ and ‘Carmen’). TCSA increased continuously in both cultivars, while fertilization effects on growth, FY, CL, and FD varied among years and were significantly higher under NPK and NPKMg treatments compared with the control, particularly in specific years. Leaf spot incidence was reduced in the NPKMg treatment in epidemic years, although strong interannual and cultivar-dependent variability was observed, with ‘Carmen’ being more susceptible than ‘Vera’. Correlation and regression analyses revealed significant relationships among key traits, particularly for CL vs. FY, FD vs. CLS, TCSA vs. CLS, and BRIX vs. CL, indicating strong vegetative–generative interactions. Principal component analyses further showed that tree and fruit traits as well as disease incidence were structured along a limited number of integrated multivariate components explaining most of the variance. In conclusion, balanced fertilization improved productivity and partly reduced disease incidence, but treatment effects were strongly influenced by complex multivariate interactions and interannual climatic variability. These findings highlight the importance of integrative analytical approaches to optimize nutrient management under Central European conditions. Full article

Background/Objectives: Systemic embolization is a common and serious complication of infective endocarditis (IE). This study evaluated the association between vegetation morphology and embolic events and assessed whether echocardiographic parameters provide incremental discriminatory value beyond clinical variables. Methods: We conducted a retrospective cohort study including 164 consecutive adults hospitalized with definite IE between 2011 and 2025 at a regional referral center. Vegetation presence, size, and mobility were assessed using transthoracic (TTE) and transesophageal echocardiography (TEE), according to clinical indication. The primary endpoint was overall in-hospital embolic burden, including embolic events present at admission, occurring during hospitalization, or incidentally detected during diagnostic work-up. Associations were analyzed using univariate and multivariable logistic regression, and model discrimination was evaluated using receiver operating characteristic (ROC) analysis. Results: Embolic events occurred in 96 patients (58.5%). Vegetations were identified in 68.3% of patients and were more frequent among those with embolization (78.1% vs. 54.4%). Mobile vegetations were more common in patients with embolic events (77.1% vs. 27.9%, p < 0.001), as were vegetations > 10 mm (61.5% vs. 38.2%, p = 0.006). Compared with non-mobile vegetations ≤ 10 mm, mobile vegetations ≤ 10 mm were associated with higher odds of embolization (OR 5.4), and mobile vegetations > 10 mm showed a similar association (OR 7.14). In multivariable analysis, vegetation mobility remained independently associated with embolic events. The clinical model demonstrated moderate discrimination (area under the curve [AUC] 0.71), which improved with the addition of vegetation mobility (AUC 0.81; p = 0.005) and size > 10 mm (AUC 0.79; p = 0.016), with no significant difference between the enhanced models. Conclusions: Both vegetation mobility and size > 10 mm were associated with overall in-hospital embolic burden and may provide complementary information for embolic risk stratification. These findings should be considered exploratory and require confirmation in prospective studies with standardized imaging and validation procedures. Full article

The leaf area index (LAI) is a key parameter reflecting vegetation canopy structure and growth status. This study systematically compares the performance of spectral and texture features derived from Sentinel-2A imagery for LAI retrieval in winter wheat and maize. Multiple vegetation indices and gray-level co-occurrence matrix (GLCM) texture features were extracted, and three types of texture indices—Normalized Difference Texture Index (NDTI), Ratio Texture Index (RTI), and Difference Texture Index (DTI)—were constructed. Modeling was performed using Partial Least Squares Regression (PLSR) and Gaussian Process Regression (GPR). Results show that red-edge vegetation indices and mean texture features (e.g., NDVI_M) are robust predictors for both crops, with correlation coefficients reaching 0.87 for winter wheat and 0.83 for maize. Texture indices further enhance the representation of canopy structural information; the optimal NDTI achieved |R| > 0.88 for both crops, though the specific feature pairs were crop-specific. Using the proposed two-stage feature optimization strategy combined with GPR, the LAI estimation accuracy for winter wheat reached R² = 0.87 with RMSE = 0.41 on an independent test set, while for maize the accuracy was R² = 0.75 with RMSE = 0.38. The strategy significantly improved accuracy for winter wheat (uniform canopy) but yielded limited gains for maize (heterogeneous canopy), largely due to differences in canopy architecture. This study demonstrates that integrating multi-dimensional features with nonlinear modeling enhances LAI estimation accuracy. By providing a side-by-side comparative evaluation across two contrasting crop canopies, this study underscores the necessity of crop-adaptive feature selection and modeling strategies. The findings offer practical guidance rather than a universal model for large-scale crop monitoring in agricultural remote sensing. Full article

The increasing need to reduce agrochemicals has intensified the search for sustainable alternatives in crop production. Insect frass, a by-product of insect rearing, has recently emerged as a promising organic fertilizer. In the present study, the effects of Tenebrio molitor frass (TMF) on plant growth and productivity were evaluated in three vegetable crops, cucumber (cv. Aisopos), pepper (cv. Lamuyo), and lettuce (cv. Paris Island), under greenhouse conditions. Experimental plants were grown in pots under two substrate fertility levels (fertilized and non-fertilized peat, hereafter referred to as “rich” and “poor” soil) and received TMF at two rates (1% and 2% w/w), applied either once or twice. Plant height and weight, fruit number and weight, and total production per plant were recorded. TMF application, applied as a soil amendment, enhanced plant growth and yield of the treated plants compared to the control, although the magnitude and consistency of the response varied among crops, soil types, and measured parameters. A clear dose-dependent response was not observed, as the 2% rate did not consistently outperform the 1% rate. Likewise, splitting the same total amount of TMF into two applications did not significantly improve plant performance. The response to the TMF application varied among crops in terms of growth and yield parameters. Lettuce recorded the strongest response, while cucumber and pepper exhibited more moderate improvements. Notably, TMF significantly increased growth and productivity even at the lowest application rates under poor soil conditions. These findings demonstrate that TMF is a promising low-input organic fertilizer under the tested conditions and highlight the importance of optimizing application rate and strategy for sustainable vegetable production. Full article

Leaf Area Index (LAI) is a key biophysical parameter for characterizing terrestrial vegetation dynamics and land surface processes. Time-series MODIS LAI products are widely used in ecological and land-related research, but cloud contamination and sensor noise lead to widespread spatio-temporal gaps, limiting their ability to support long-term, consistent vegetation monitoring over large areas. To address this issue, this study proposes a novel self-supervised LAI reconstruction framework (SSLAI) for generating gap-free and ecologically consistent LAI datasets across China. The framework integrates cross-modal environmental fusion, multi-scale spatio-temporal modeling, and adaptive phenological constraints to ensure the reconstructed LAI aligns with realistic vegetation growth rhythms. SSLAI outperforms seven traditional and state-of-the-art deep learning methods, maintaining a root mean square error (RMSE) below 0.20 even with 16 missing time windows. Field validation confirms its high accuracy, with a coefficient of determination (R²) of 0.885 and an RMSE of 0.477. Furthermore, SSLAI’s response to meteorological changes aligns with ecological principles, demonstrating favorable physical interpretability and ecological rationality. The reconstructed LAI exhibits superior spatial completeness and temporal consistency compared with MODIS, VIIRS, and GLASS products, and performs robustly under variable climatic conditions. This study provides an effective self-supervised solution for MODIS LAI gap-filling over large regions, and the generated high-quality LAI dataset can serve as a reliable data foundation for vegetation dynamics monitoring, land surface modeling, and global change research. Full article

Traditional methods for measuring rice yield are often labor-intensive, time-consuming, and difficult to implement at scale. Conversely, remote sensing-based yield prediction models typically exhibit limited applicability across diverse genetic materials. In this study, we propose a high-precision yield prediction approach that integrates UAV-based time-series imagery with dynamic process clustering. Field experiments were conducted over two years involving 630 rice germplasm accessions in Rugao and Huaian, Jiangsu Province. UAV-mounted RGB and multispectral cameras were employed to acquire canopy imagery throughout the rice growth period. A range of features, including spectral reflectance, vegetation indices, canopy height (CH), and canopy volume (CV), were extracted from the UAV data. The K-Shape clustering algorithm was applied to dynamically group the temporal growth curves, enabling the construction of a cluster-based yield prediction model. Among the vegetation indices, the Enhanced Vegetation Index (EVI2) demonstrated the best performance (R² = 0.73, RMSE = 599.53 kg/hm²). Models based on temporal features of CH and CV showed satisfactory accuracy (R² = 0.70, RMSE = 640.96 kg/hm²). Notably, a dual-modal model combining vegetation indices with structural parameters significantly improved predictive performance (R² = 0.80, RMSE = 511.42 kg/hm²). This study demonstrates that multi-feature cluster analysis enhances the accuracy and robustness of yield prediction models across diverse genotypes. The proposed methodology provides valuable technical support for high-yield rice breeding initiatives. Full article

This study quantifies how different land-use types influence surface soil characteristics (0–5 cm) and the dynamics of soil organic carbon (SOC) and nitrogen in the mountainous ecosystems of the Rila Mountains. Across 54 forest and agricultural plots, pH, bulk density, coarse fraction, C:N ratio, SOC, total nitrogen (TN), and their respective stocks were assessed using standard analytical methods and statistical tests (Shapiro–Wilk, ANOVA, Kruskal–Wallis, correlation and regression analysis). Land use significantly affected all soil parameters except pH. Forest soil showed lower bulk density and lower SOC stocks compared with grasslands. Unmown meadows exhibited the highest SOC and TN concentrations and stocks, while potato fields recorded the highest bulk density and elevated TN stocks, reflecting intensive management impacts on surface soil properties. Forest soils displayed species-specific patterns, with Scots pine and Silver fir showing comparatively lower SOC and TN stocks attributable to historical degradation and site limitations. As the study focused on the uppermost soil layer (0–5 cm), the results should be interpreted more as indicators of surface soil dynamics rather than as estimates of total topsoil carbon and nutrient storage. Correlation analysis revealed strong positive relationships among SOC, TN, and the C:N ratio, and strong negative relationships between SOC and both bulk density and coarse fraction in managed agricultural lands. The findings demonstrate that minimizing soil disturbance and maintaining permanent vegetation cover—particularly through conservation of unmanaged grasslands—offer great capacity for enhancing the soil organic matter accumulation in mountainous ecosystems. Full article

The presence of soil hydrocarbon parameters (SHPs), including total petroleum hydrocarbons (TPHs), total organic carbon (TOC; %), and soil toxicity (EC50; mg L⁻¹), can affect vegetation in several ways. This study assessed the impact of SHPs on vegetation in the Niger Delta using field-measured, leaf-scale hyperspectral data acquired across the region. Red-edge position (REP) and four hyperspectral vegetation indices (HVIs)—mND705, photochemical reflectance index (PRI), Normalised Difference Vegetation Vigour Index (NDVVI₈₄₄,₄₄₇; a vegetation vigour index), and modified DATT (MDATT; a chlorophyll-sensitive red-edge index)—were used to quantify chlorophyll content in the vegetation types of Awolowo grass, elephant grass, mango trees, oil palm trees, and mangrove vegetation and to explore their variation with SHPs. The results show that mangrove vegetation was the most impacted by TPHs (R = −0.683), while mango vegetation was the most impacted by TOC (R = −0.725), based on Pearson correlation coefficients derived from the mND705 index. Similarly, mango and mangrove vegetation showed the strongest responses to soil toxicity (EC50; mg L⁻¹), based on Spearman correlation coefficients (r_s = 0.657 and r_s = 0.870, respectively) using the MDATT index. These findings highlight species-specific physiological responses to soil hydrocarbon contamination and demonstrate the applicability of red-edge-based hyperspectral techniques for assessing vegetation stress in complex coastal ecosystems such as the Niger Delta. Full article

The environmental water balance is threatened due to the massive extraction of freshwater resources for daily human consumption around the world. This study endeavors to incorporate Visual MODFLOW, remote sensing and GIS techniques to establish a computational simulation of groundwater flow and quantify groundwater availability in the Siliguri Planning Area, which is facing rapid urbanization and high population growth. The basic parameters of MODFLOW modeling, such as observation heads and wells, boundary conditions and layer properties, are prepared from data issued by different sources. A model was designed to enhance our understanding of the three-dimensional hydrogeologic system of aquifers and simulate current and future groundwater behavior. Model performance was evaluated using more statistical indicators, including mean absolute error (MAE = 0.386 m), root mean square error (RMSE = 0.466 m) and coefficient of determination (R² = 0.9826), which indicate good agreement between observed and simulated groundwater levels. Recharge is primarily controlled by monsoonal precipitation and LULC characteristics, with agricultural and vegetated areas contributing 60–70% of total recharge, while built-up areas contribute less than 20%. Temporal analysis indicates localized groundwater decline at a rate of 0.16–0.18 m/year in urbanized zones. The groundwater recharge in the study area ranges from 5000 to 10,000 hectare-meters (ham), while groundwater extraction ranges from 1000 to 1500 ham. Overall, the net groundwater availability across all layers is 10,430 hectare-meters (ham). The findings may help groundwater authorities and associated organizations better comprehend the possible state of groundwater resources and put adaptation plans into place to prevent the loss of the water resources. Full article

Urban design that improves microclimate can significantly enhance the ecological livability of human settlements, while the climate-adaptive wisdom of applying local water-net landscapes to modern urban renewal requires further validation. To investigate the optimization mechanism of waterscape on microclimate comfort, this study focuses on the public space of Xinsheng District in the Suzhou water-net region. By integrating continuous incremental multi-scenario form design, computational fluid dynamics (CFD) multi-physics simulation, and climate sensation evaluation, we reproduce the spatial differentiation of microclimate and comfort gradients across multi-hour periods during hot summer daytime within the built-up environment involving waterbodies, vegetation, and buildings. Consequently, an indicator of comfort improvement efficiency (CIE) is proposed to measure the spatial effectiveness of per-unit-area water surface expansion on climate sensation. Results show that when controlling other morphological parameters and designing three incremental waterbody scenarios—no water surface, 50% water, and 100% waterscape—the relative comfort area expanded across all time periods as water increased. This implies that waterscape variations exert a positive effect on microclimate suitability. However, during the expansion of water area at each time, the CIE was higher in the 0–50% initial stage of water surface increase compared to the 50–100% later morphological stage. Therefore, this study reveals the stepwise nonlinear trend by which increased water area in the built-up environment improves the climate suitability of waterfront spaces. Furthermore, under constraints of equivalent area and other geometric forms, a more dispersed and networked waterscape was found to be a superior spatial strategy. This confirms the microclimate wisdom of the water-net landscape in the Jiangnan locality, providing form optimization guidance for ecologically oriented urban renewal design. Full article

In saline–alkali environments, soil salinity imposes severe abiotic stress on maize growth by inhibiting root activity and nutrient uptake. Traditional destructive sampling methods struggle to enable cross-growth stage, large-scale dynamic fertilizer effect assessment. This study, conducted in saline–alkali farmlands of Inner Mongolia, utilized UAV multispectral remote sensing to extract 20 vegetation indices and 40 texture parameters, constructing a multi-source feature set. An ensemble learning framework integrating Random Forest (RF), Decision Tree (DTR), AdaBoost and Gradient Boosting Regression (GBR) was developed to achieve precise monitoring of maize plant height, leaf area index (LAI), and yield. In addition, the study aimed to evaluate the dynamic effects of seven fertilizer treatments (six controlled-release composite fertilizers, T1–T6, and conventional CK) and to identify the optimal fertilization scheme, with particular emphasis on comparing the two best-performing treatments, T1 and T2. Results showed that: (1) The ensemble model improved prediction robustness, with R² values of 0.88, 0.76, and 0.76 for plant height, LAI, and yield across the entire growth cycle, respectively. The integration of texture features effectively mitigated spectral saturation during peak growth stages (e.g., tasseling and filling). (2) For fertilizer evaluation, T1 performed best in growth and yield at jointing, tasseling, and filling stages, with a yield increase rate of up to 40.18% at the jointing stage. Although T2 slightly outperformed T1 in yield increase at maturity (15.42%), T1 was identified as the optimal fertilizer scheme for the region based on whole-growth-stage growth performance, measured yield, LAI, and yield increase rate. These results demonstrate that UAV-based multi-source feature fusion combined with ensemble learning provides an effective and non-destructive approach for fertilizer evaluation and precision nutrient management in saline–alkali regions. Full article

This study investigated the effects of nutrient solution magnetization, droplet size, and spray control strategy on the growth characteristics of aeroponically grown lettuce. Two experimental groups were established. Group A employed a two-factor design under timer control to evaluate the effects of magnetic field intensity and droplet size, whereas Group B adopted an incomplete block design to examine the combined effects of magnetic field intensity, droplet size, and spray control mode, including timer control and temperature–humidity-based intelligent control. The interaction between magnetic field intensity and droplet size significantly affected root length, shoot growth traits, and the root-to-shoot ratio. Magnetic field intensity significantly influenced root length, shoot length, canopy area, biomass, and the root-to-shoot ratio, while droplet size primarily affected canopy area and biomass. Spray control strategy had a highly significant effect on the root-to-shoot ratio, and intelligent control improved biomass allocation compared with timer control. The results from Group B generally confirmed the trends observed in Group A, indicating that moderate magnetic field intensity and an appropriate droplet size were more favorable for lettuce growth than excessively high magnetic field intensity or unsuitable droplet size. Multi-objective optimization indicated that a moderate magnetic field intensity of approximately 260 mT, a droplet size of approximately 57.55 μm, and temperature–humidity-based intelligent control provided the most favorable balance between shoot growth and root-to-shoot allocation. These findings provide a preliminary reference for parameter selection in aeroponic system design and protected vegetable production under controlled conditions. However, the optimized parameters should be further validated across different lettuce cultivars, growth stages, and larger-scale production systems. Full article

Conventional Interferometric Synthetic Aperture Radar (InSAR)-based sub-canopy topography retrieval models often suffer from insufficient characterization of scattering mechanisms, strong nonlinearity, and poor parameter convergence. To address these issues, this study proposes an improved Interferometric Water Cloud Model (IWCM) that integrates Fractional Vegetation Cover (FVC) to retrieve sub-canopy topography. The proposed method accounts for both volume and ground scattering and introduces FVC as a constraint to improve the model’s physical realism. In addition, this study utilizes InSAR observations derived from TanDEM-X dual-baseline data, which enhance the information content of the measurements by providing multiple independent interferometric observations. A two-step nonlinear least squares optimization strategy is further employed to enhance the convergence of model parameter estimation. The proposed method was validated in the forested region of Genhe City, Inner Mongolia. Airborne LiDAR-derived surface elevation data were used for assessment. The results indicate that, compared with the original InSAR-derived Digital Elevation Model (DEM), the accuracy of the retrieved sub-canopy topography improves by 39.04%. Furthermore, compared with the previously proposed Normalized Difference Vegetation Index (NDVI)-based method, under their respective optimal initial extinction coefficient conditions (${\mu }_0$), an additional accuracy improvement of 11.69% is achieved. These results demonstrate that the proposed method effectively reduces the influence of the forest canopy on interferometric phase observations and improves the capability of sub-canopy topography reconstruction in complex forest environments. The method also provides a new approach for dual-baseline and multi-baseline InSAR-based sub-canopy topography retrieval. Full article

In the context of increasing consumer interest in functional food products and the use of vegetable oils with enhanced biological value, the development of mayonnaise emulsions with improved structural, rheological, and performance characteristics is highly relevant. Safflower oil is of considerable interest as a fat base for mayonnaise due to its high content of polyunsaturated fatty acids and antioxidant components. However, its application in emulsion systems requires scientifically substantiated optimization of formulation parameters to ensure product stability and the desired rheological properties. The aim of this study was to optimize the formulation parameters of mayonnaise based on safflower oil using response surface methodology. The independent variables were the mass fraction of safflower oil, the content of egg powder, and skimmed milk powder. The response functions included apparent viscosity (Y₁), consistency coefficient (Y₂), and emulsion stability (Y₃), which comprehensively characterize the structural and rheological behavior and stability of mayonnaise emulsions. Experimental studies were carried out using a rotatable central composite design. The rheological properties of the mayonnaise emulsions were determined by rotational rheometry, and emulsion stability was assessed by centrifugation. Based on the experimental data, second-order quadratic regression models were developed, adequately describing the effects of formulation factors and their interactions on the studied parameters. It was established that the apparent viscosity, consistency coefficient, and emulsion stability of mayonnaise emulsions depend nonlinearly on the formulation factors and are determined by their combined effect. The maximum response values are achieved at an optimal ratio of fat and protein phases rather than at the extreme concentrations of individual components. As a result of optimization, a mayonnaise formulation based on safflower oil was proposed, ensuring high emulsion stability and balanced rheological characteristics. The developed technological scheme confirms the practical feasibility of the optimized formulation and its potential application in the production of functional mayonnaise sauces. Full article

Agronomic biofortification offers an environmentally friendly way to improve crop nutrition. The biofortification of vegetables with iodine has attracted increasing attention due to its significance for human health. Hydrogen sulphide (H₂S) is a gaseous signalling molecule that affects many physiological and biochemical processes in plants. Lettuce (Lactuca sativa L.) plants were cultivated under controlled greenhouse conditions. Foliar applications of potassium iodate (KIO₃) and hydrogen sulphide (H₂S, supplied by sodium hydrosulphide (NaHS)) were applied separately and together (H₂S + KIO₃). Evaluations included growth parameters, photosynthetic pigments, biochemical metabolites, antioxidant enzyme activities, plant hormone levels, and mineral nutrient contents. All treatments resulted in significant changes in plant growth and physiological traits compared to the control. The combined application resulted in greater responses across several parameters; however, these observations do not demonstrate a causal or mechanistic interaction between the treatments. The combined application increased plant fresh weight by ~42% and leaf area by ~35% compared to the control. Total chlorophyll content approximately doubled (≈100% increase), while SOD, POD, and CAT activities increased by up to ~160%, ~13%, and ~40%, respectively. Proline and sucrose contents increased by approximately 100% and 85%. Hormonal changes included increases in indole-3-acetic acid (~44%) and cytokinins (~55%), and a decrease in abscisic acid (~20%). In addition, several macro- and micronutrients in leaves and roots were affected by the treatments. The combined application of KIO₃ and H₂S was associated with greater responses across several measured parameters than either compound alone; however, these observations do not demonstrate a causal or mechanistic interaction between the two compounds. Furthermore, as the experiment was conducted under non-stress greenhouse conditions, the observed physiological responses should be interpreted as changes in metabolic and regulatory processes rather than direct evidence of enhanced stress tolerance. Overall, the results indicate that foliar application of KIO₃ and H₂S can influence growth and physiological traits of lettuce under controlled conditions. Full article