Search Results (40,204)

Optimization of nitrogen (N) management is critical for enhancing maize (Zea mays L.) productivity while maintaining soil health. The present study investigated the impact of split-application fertilization strategies on soil chemical properties and grain yield across three distinct soil types (calcaric fluvisol, luvic phaeozem, and stagnic phaeozem) in Mureș County, Romania, over three cropping seasons (2022–2024). Three fertilization variants were evaluated: the first treatment, designated V1, involved the application of 300 kg/ha NPK 20-20-0 + 300 kg/ha urea, the second treatment V2 utilized 300 kg/ha NPK 20-20-0 + 300 kg/ha NAC 27 N-calcium ammonium nitrate, and the third treatment V3 served as the baseline control, receiving (300 kg/ha NPK 20-20-0). Results indicated that significant differences were observed among the three experimental sites representing contrasting soil types for soil chemical properties and maize productivity. Calcaric fluvisol exhibited the highest production potential, attaining a mean yield of 11,702.78 kg/ha. The impact of N supplementation on soil N levels and maize yield was found to be significant. The variant receiving urea supplementation (V1) achieved the highest median yield of 9560 kg/ha in comparison to the 7420 kg/ha obtained in the control. A strong positive correlation was observed between N index and yield across all soil types (ρ = 0.93 to 0.97, p < 0.001). Fertilization significantly influenced soil pH, CaCO₃ content, nitrogen index, phosphorus availability, and maize yield, whereas humus content remained relatively stable among treatments. These findings indicate that a split-fertilization regime combining NPK with urea provides a favorable balance between productivity and cost-effectiveness and maize output in the Transylvanian Plateau. Full article

We study the extreme behavior of six central Chile glacier mass balance series facing significant retreats and ice wastage due to climate variability and change. This has led to reduced meltwater availability in dry seasons, increasing competition for downstream water resources. Understanding glacier mass balances is crucial for predicting future water availability in scenarios with higher water demands. We used Extreme Value Theory tools to analyze the data and identify extreme events. The main objective of this study is to statistically analyze glacier mass losses in Chile, using mass balance data collected from both national and international sources. The results show high heterogeneity in the extreme behavior of glaciers, with some showing an approximately exponential tail (Guanaco Glacier), others exhibiting stability with slight tails (Echaurren Norte and Mocho Glaciers) and one (Amarillo Glacier) with a highly unstable structure. The other analyzed glaciers (Juncal Norte and Juncal Sur) have slight and potentially limited tails. These results confirm the high importance of studying glaciers in the Andes in order to better understand their responses to climate change, an important and relevant aspect for the future management of glacier melt water resources. Full article

The Tietê River, heavily polluted by the largest Brazilian city (São Paulo), has significant ecological and socioeconomic importance. The effects of water-quality degradation on zooplankton diversity (taxonomic and functional) and limnological variables were evaluated through a comparison of the Tietê River’s main channel, one of its marginal lagoons and a low-impacted tributary. Samplings covered both a rainy and a dry season. Environmental conditions are distinctive, with the main river and lagoon classified as hypereutrophic and the tributary as oligo-mesotrophic. The zooplankton, an essential community for aquatic ecosystem functioning, also exhibited remarkable spatial variation. Richness varied between six (lagoon/dry) and 43 (tributary/rainy). There was a much higher abundance in the lagoon (mean = 6.5 × 10⁵), followed by the Tietê River (mean = 4.0 × 10⁴) and tributary (mean = 2.5 × 10³), and a higher taxonomic diversity (Shannon mean = 2.98) and functional richness (mean = 0.66) in the tributary, contrasting with the intermediate values in the Tietê River (means of 1.7 and 0.31, respectively) and lower in the lagoon (1.49 and 0.01). Zooplankton from the Tietê River and the lagoon positively correlated with pH, total solids, chlorophyll and phosphorus. Negative pollution’s effects on the zooplankton community were intensified by the lagoon’s lentic hydrodynamics. The low-impacted tributary seems to act as a refuge for the regional zooplankton biodiversity, taxonomically and functionally, which is restricted to highly tolerant species in the main river. Full article

The synergistic utilization of post-mining spaces and geothermal energy through underground seasonal thermal energy storage (USTES) provides a promising pathway for sustainable heating and the low-carbon redevelopment of mining regions. To advance the thermal management and reveal the thermo-hydraulic evolution patterns within these repurposed environments, this study proposes an integrated approach that utilizes post-mining roadways as heat storage reservoirs, within the scope of a single idealized case study. A comprehensive USTES heating system model was established to systematically evaluate operational characteristics and environmental impacts under diverse conditions assuming homogeneous rock properties and idealized thermal boundaries. Results demonstrate that the surrounding ground temperature and the low thermal conductivity of the rock mass contribute to limiting heat dissipation and maintaining stable seasonal storage performance. For a roadway with a 20,000 m³ water storage capacity and an optimal 3900 m² solar collector area, the system successfully satisfies the thermal demand of 30,000 m² of building area. The configuration achieves 1239 MWh of cumulative heat storage over a 245-day cycle, maintaining a direct heating-to-heat-pump-upgraded heating ratio of 1.02. Furthermore, the implementation of variable-frequency thermal management strategies demonstrates remarkable economic and environmental superiority, yielding a 35.8% cost reduction compared to coal-fired heating, an overall energy saving rate of 77.5% relative to electric heating systems and a 13.5% decrease in CO₂ emissions relative to gas-fired systems. This research provides fundamental design parameters for the synergistic exploitation of mineral and geothermal resources, advancing the development of green heating and the sustainable utilization of post-mining spaces. Full article

Accurate estimation of winter wheat aboveground biomass (AGB) is essential for crop growth monitoring and precision agricultural management. To reduce the effects of canopy structural complexity and spectral saturation on AGB estimation, this study evaluated winter wheat grown under different compost substitution ratios and planting densities. Based on unmanned aerial vehicle (UAV) multispectral and RGB imagery acquired over two growing seasons at four key growth stages, spectral vegetation indices, colour vegetation indices, and canopy structural features were extracted and integrated. Recursive feature elimination, Elastic Net, and support vector regression were used to construct stage-specific AGB estimation models. The optimal feature strategy varied among growth stages, indicating that AGB estimation requires stage-specific feature selection rather than a single fixed feature combination. The proposed framework achieved validation R² values of 0.872, 0.898, 0.867, and 0.895 at the jointing, booting, flowering, and grain-filling stages, respectively, and the corresponding RRMSE values were 12.5%, 12.1%, 14.3%, and 12.0%, respectively. Additional comparisons with PLSR, RF, and XGBoost based on the stage-specific optimal feature sets further confirmed the competitive performance of SVR under the present small-sample and multi-source feature conditions. Model improvement was more evident at the flowering and grain-filling stages. At these stages, the integration of selected spectral, colour, and structural features better represented canopy closure, spike-layer formation, and late-season biomass variation. Under the treatment combining 20% compost substitution with a planting density of 4.5 million plants ha⁻¹, winter wheat maintained relatively high AGB levels across growth stages. The novelty of this study lies in demonstrating that the effectiveness of multi-source UAV feature fusion for winter wheat AGB estimation is growth-stage dependent and is enhanced when coupled with feature selection. These findings provide a methodological reference for multi-temporal AGB monitoring and precision cultivation management under similar field conditions. Full article

Essential oils (EOs) from wild Calamintha nepeta (Portugal) populations collected in Portugal (Évora) were investigated in order to evaluate the impact of Mediterranean seasonal conditions on their phytochemical composition and biological activity. Essential oil GC-FID and GC-MS analyses revealed distinct seasonal chemotypes, with spring samples dominated by isopulegone/pulegone, whereas autumn samples contained higher proportions of isomenthone and menthol. Antioxidant activity was assessed through lipid peroxidation inhibition, DPPH radical scavenging and ferric reducing power assays, while antibacterial activity was evaluated against multidrug-resistant (MDR) clinical isolates. Seasonal differences were reflected in both EO chemical composition and bioactivity. Autumn samples displayed greater antioxidant potential, with Y1A showing the highest inhibition of lipid peroxidation (IC₅₀ = 0.85 mg/mL) and Y2A exhibiting the highest ferric reducing power. Conversely, spring samples were more active against MDR bacteria. Among them, Y1S showed the broadest antimicrobial spectrum, with MIC values ranging from 465 to 1767 μg/mL. The unusually wet spring season coincided with marked isopulegone accumulation (≈50%), while warmer autumn conditions favoured higher levels of isomenthone and menthol in the EOs. These findings highlight the importance of seasonal environmental conditions in determining the phytochemical profile and bioactive potential of C. nepeta EOs, providing valuable insights for their standardisation and valorisation in pharmaceutical, food and conservation-related applications. Full article

Industrial and domestic wastewaters, nonpoint pollution sources, and climate change affect stream ecosystems, water quantity, and quality. Within the scope of this study, the water quality of Nilüfer Stream was evaluated using the Water Quality Index (WQI), One-Way ANOVA, the Kruskal–Wallis Test, and Principal Component Analysis (PCA). In the study, 4686 water quality data from seven sampling stations between 2008 and 2024 were used. WQI results showed a distinct decrease in water quality from the upstream to the downstream of the Stream. Average WQI values for the stations were found to be between 140.83 and 487.83. The lowest WQI value was found at Station 1 and the highest WQI value was found at Station 7. According to WQI, the ranking of the stations by magnitude was St7 > St4 > St5 > St6 > St2 > St3 > St1. A statistically significant difference was observed between the stations in terms of WQI, ANOVA, and Kruskal–Wallis Test (p < 0.05), and water quality was found to be seasonally diverse. Generally, at stations (except for two stations), the seasonal WQI values ranked by magnitude were autumn > summer > winter > spring. The PCA showed that relationships among parameters originating from industrial wastewater associated with the textile, automotive, and metal industries were stronger (component loadings > 0.75), whereas the groups identified in the upstream basin indicated domestic pollution and agricultural pollution from fertilizers and pesticides. PCA conducted between meteorological parameters and the WQI values of the stations showed that climate change could be effective at only two stations. It was determined that the region located before the wastewater treatment plant (St4) was associated with precipitation, humidity, and evaporation, while the downstream region (St7) was related to wind speed. It was observed that water quality was more influenced by industrial, urban, and agricultural pollution sources than by climate change. Full article

The White-naped Crane (Antigone vipio), a first-class national protected bird species in China, exhibits a declining global population. To investigate the spatiotemporal patterns and drivers of wintering habitat suitability, data from 71 valid distribution sites were collected from 2015 to 2025 during the wintering period. Using the MaxEnt model, current and future (2050 and 2070) potential suitable habitat distributions were simulated under three climate scenarios: SSP126 (low emissions), SSP245 (medium emissions), and SSP585 (high emissions). The modeling yielded an average AUC value of 0.984, indicating high predictive accuracy. Key environmental variables influencing the wintering distribution of the White-naped Cranes include elevation, distance to major water, precipitation of the driest month, slope, temperature seasonality, and mean temperature of the wettest quarter. The current high-suitable area for the White-naped Cranes spans 5.64 × 10⁴ km² and is primarily distributed in the middle and lower reaches of the Yangtze River and in coastal wetlands along the North China. Among these, Hunan, Hubei, Jiangxi, and Anhui provinces contain relatively concentrated high-suitable areas for the species. Primarily influenced by elevation, distance to major water, precipitation of the driest month, and land-use classification, the suitable wintering habitat of the White-naped Cranes is projected to undergo significant contraction, shifting predominantly to the middle reaches of the Yangtze River. The most severe contraction is projected under the SSP585 scenario by 2070, with a reduction of 4.11 × 10⁵ km². Contraction areas are primarily concentrated along the Bohai and Yellow Sea coasts and in the middle and lower reaches of the Yangtze River, while minimal expansion occurs in Hubei, Anhui, and Zhejiang. The overall southwestward shift in the species’ distribution centroid may be associated with changes in elevation and distance to major water. Finally, habitat conservation strategies for the White-naped Cranes are proposed, providing a scientific basis for population protection and habitat management under future climate change. Full article

Watermelons account for 7% of the world’s fruit vegetable production. In the European market, Spain contributes around 35% of total watermelon supply, with the majority grown in greenhouses in Almería, Southern Spain. This study presents experimental results from the first agrivoltaic watermelon trial conducted in a raspa-y-amagado greenhouse during the 2024 growing season in Almería, Spain. Watermelons were cultivated under two shading treatments with 30% and 50% of the roof area covered with PV modules and compared against an unshaded control group. Throughout the experiment, temperature values in the 30% and 50% zones were $2.2$${}^{\circ }\mathrm{C}$ and $4.3$${}^{\circ }\mathrm{C}$ lower than in the control zone, respectively. The unshaded control zone and the 30% shading treatment maintained DLI conditions within the optimal range between 21/$\mathrm{m}$/day and 32/$\mathrm{m}$/day for most of the crop cycle, while the 50% shading zone remained largely above the minimum threshold of 15/$\mathrm{m}$/day required for adequate crop growth. No statistically significant differences were observed in fruit weight, rind width, fruit firmness, or soluble solids content at harvest. In addition, the experimentally measured irradiance data from this study were compared with simulations from a previously established irradiance model. The model was applied to the raspa-y-amagado greenhouse, and the experimental data were used to perform a long-term comparison between simulated and measured irradiance for 265 days of data. The irradiance model accurately reproduced shading effects from both the PV modules and greenhouse structure, achieving nRMSE values of 0.09, 0.18, and 0.27 for the control, 30% shading, and 50% shading zones, respectively. Full article

The world has already been facing food, nutrition, and security challenges for the last few decades. The coronavirus 2019, COVID-19, has a significant impact on food security and agriculture, such as affecting food demand and the food supply chain, with the greatest consequences on the most vulnerable population. This review provides a comprehensive overview of the effects of COVID-19 on global agriculture and food security, drawing on recent scientific publications, institutional reports, and policy documents from 2020 to 2026. The review examines the impact of the pandemic on cropping patterns, fruit and vegetable harvests, availability of farm inputs, connectivity of the agricultural system, food supply chains, food demand, and labor availability. Vegetable and fruit markets were most affected due to the spread of COVID-19. Due to the closing of markets and restaurants, produce distributors and farmers were required to transfer supplies entirely from the food production to the marketplace. These effects are additionally being felt in agriculture and food security. Almost 55% of researchers indicated that COVID-19 has the most impact on agriculture and its complete harvest during the season, and an additional 45% stated that COVID-19 has adversely affected food security. However, food has slowed down well to date in numerous nations. The spread of COVID-19 is beginning to disrupt the supply of agricultural products and food to consumers and the marketplace across and within borders. The different spring crops, such as sunflower, canola, maize, barley, spring wheat, and various field vegetables, cannot be grown during COVID-19. Consequently, COVID-19 has had a binding effect on the food supply chain and agriculture due to the disruption, which the government should have addressed promptly. Full article

Aerobiology, the science that studies atmospheric biological particles (including pollen, fungal spores, bacteria, and viruses), has undergone a profound transformation from a descriptive, observational discipline into a predictive, data-driven field, thanks to advances in statistical methods and environmental sensing technologies. Early research, based on classical statistical methods such as descriptive analysis, correlation analysis, and linear regression, established a fundamental understanding of seasonal dynamics and environmental relationships. However, the inherent complexity of aerosol biological systems—characterized by nonlinear interactions, spatiotemporal variability, and multiscale processes—has spurred the adoption of modern statistical techniques. These techniques include time-series analysis, generalized linear and additive models, spatial statistics, Bayesian inference, machine learning, and data assimilation, often combined with high-resolution environmental monitoring and sensor networks. In recent years, hybrid modeling approaches have emerged, combining mechanistic understanding of atmospheric transport and biological emissions processes with data-driven learning to improve the accuracy, robustness, and interpretability of predictions. This review comprehensively compares classical, modern, and hybrid statistical methods in air biology, exploring their theoretical foundations, practical applications, and inherent limitations. Furthermore, this review highlights emerging paradigms such as uncertainty quantification, causal inference, digital twins, and AI-driven real-time prediction systems. It also discusses challenges, including data heterogeneity, model interpretability, and cross-regional portability. By treating aerobiology as a complex adaptive environmental–biological system, this study highlights statistical methods that link observations to mechanisms and advance scalable, reliable, systems-oriented prediction frameworks for future research and applications. Full article

The efficiency of anaerobic digestion (AD) of lignocellulosic biomass is strongly determined by biomass yield, chemical composition, and bioavailability, all of which undergo substantial seasonal variation. However, integrated analyses linking these factors with AD performance, process kinetics, and energy-economic efficiency remain limited. This study aimed to evaluate the effect of seasonal variability in the chemical composition of Helianthus annuus biomass on AD efficiency from a technological and economic perspective. The novelty of this study lies in integrating seasonal changes in biomass composition with AD kinetics, CH₄ productivity per hectare, and CHP techno-economic performance to identify the optimal harvest window for Helianthus annuus. The experiments were conducted using biomass harvested from June to December. The results showed significant (p < 0.05) variability in biomass properties, including a progressive increase in lignocellulosic fractions over the growing season, with neutral detergent fiber (NDF) increasing from 30.58 ± 1.8 to 66.58 ± 3.1% TS and acid detergent lignin (ADL) from 5.13 ± 0.5 to 10.35 ± 0.9% TS, accompanied by a decline in substrate bioavailability. The maximum CH₄ yield of 258 ± 13 mL/g VS was obtained in August, with a process rate of 29.0 ± 3.4 mL/g VS·d and the highest utilization of methane potential, reaching 62.5 ± 3.8% (BMP_CH4/TBMP). Correlation and regression analyses indicated that ADL and NDF were the strongest empirical predictors of AD performance within the analyzed dataset, showing a negative association with both CH₄ production yield and kinetics (R² up to 0.86), whereas reducing sugars had a stimulatory effect. Multiple regression models showed high predictive performance, with R² = 0.889 for BMP_CH4. The highest energy and economic efficiency was achieved in summer. In August, CH₄ production reached 3214 ± 596 m³/ha, corresponding to 11.2 ± 2.1 MWh/ha of electricity and a net result of 1559 ± 417 EUR/ha. Increased lignification in the later part of the season led to reduced process efficiency and a deterioration of the economic balance. From a practical perspective, these results demonstrate that harvest scheduling should be based on the trade-off between biomass quantity and biodegradability rather than on biomass yield alone. Full article

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R²) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-X_II-CNN model, with R² = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis. Full article

This study explores the three-dimensional structure of dust aerosols over the Qaidam Basin using CALIPSO satellite observations from 2007 to 2022. The results show that polluted dust is the dominant aerosol type in this region. Dust activity peaks in spring, with its vertical extent reaching nearly 10 km. Dust Aerosol Optical Depth (DAOD) is relatively high in the northwest and central parts of the basin, with a spring peak of 0.25 and an autumn minimum of 0.12. DAOD has shown a notable decreasing trend over the past 16 years. In terms of vertical structure, dust aerosols are mainly concentrated below 4 km AGL, especially within the near-surface layer of 0–2 km, and their occurrence frequency declines as altitude increases. The dust layer thickness exhibits obvious seasonal variations, which are primarily controlled by changes in layer top height. The average thickness decreases from 1.53 km in spring to 0.61 km in winter, while the layer’s bottom height remains fairly stable. Analysis based on the LASSO-SHAP model indicates that potential evapotranspiration and friction velocity are the major factors affecting DAOD, highlighting the vital roles of surface dryness and near-surface dynamic forcing. Furthermore, investigation of typical dust events reveals distinct vertical stratification of dust transport. Low-level dust movement is restricted by basin terrain, whereas upper levels are governed by the westerlies. This study improves our understanding of the three-dimensional structure, seasonal evolution, and transport processes of dust aerosols in high-altitude arid basins. Full article

Mare milk is rich in nutrients and bioactive compounds, and its composition changes throughout lactation. This study investigated variations in the production, nutritional composition, and bioactive components of Yili mare milk across lactation stages. Twenty-six healthy grazing Yili mares were sampled on days 1, 10, 30, 60, 90, and 120 of lactation. Milk production, nutritional components, amino acids, fatty acids, minerals, vitamins, and immunologically active proteins were analyzed. Milk production peaked on day 30 and then declined. Colostrum contained significantly higher fat, protein, solids-not-fat, total solids, minerals, lactoferrin, lysozyme, and immunoglobulins than mature milk (p < 0.05), whereas lactose increased and stabilized after day 30. Essential amino acids peaked on day 30. As lactation progressed, saturated fatty acids decreased while polyunsaturated fatty acids increased. Vitamin profiles also varied across lactation, with ascorbic acid increasing during late lactation. β-casein content was higher during mid-lactation. In summary, colostrum is enriched in immunoactive proteins and minerals, whereas mature milk exhibits a more balanced amino acid and fatty acid profile. While these observed variations likely reflect the combined effect of lactation stage and seasonal pasture fluctuations under natural grazing, these findings provide practical insights into changes in milk composition in grazing Yili mares and may support the development of mare milk products under similar grazing systems. Full article