Search Results (1,309)

Wildfires can significantly alter soil physicochemical conditions and microbial communities in forest ecosystems. This study aimed to characterize the culturable soil fungal community and evaluate biological activity in Banco Totumo Bijibana, a protected dry tropical forest in Atlántico, Colombia, affected by a wildfire in 2014. Twenty soil samples were collected for microbiological (10 cm depth) and physicochemical (30 cm) analysis. Basal respiration was measured using Stotzky’s method, nitrogen mineralization via Rawls’ method, and fungal diversity through culture-based identification and colony-forming unit (CFU) counts. Diversity was assessed using Simpson, Shannon–Weaver, and ACE indices. The soils presented low organic matter (0.70%) and nitrogen content (0.035%), with reduced biological activity as indicated by basal respiration (0.12 kg C ha⁻¹ d⁻¹) and mineralized nitrogen (5.61 kg ha⁻¹). Four fungal morphotypes, likely from the genus Aspergillus, were identified. Simpson index indicated moderate dominance, while Shannon–Weaver values reflected low diversity. Correlation analysis showed Aspergillus-3 was positively associated with moisture, whereas Aspergillus-4 correlated negatively with pH and sand content. The species accumulation curve reached an asymptote, suggesting an adequate sampling effort. Although no control site was included, the findings provide a baseline characterization of post-fire soil microbial structure and function in a dry tropical ecosystem. Full article

In drought-prone regions without irrigation systems, effective agrotechnologies such as subsoiling are crucial for enhancing soil infiltration and water retention. However, the effects of subsoiling can vary depending on crop type and environmental conditions. Despite previous research, there is limited understanding of the contrasting responses of C3 (sunflower) and C4 (maize) crops to subsoiling under drought stress. This study addresses this knowledge gap by assessing the effectiveness of subsoiling as a drought mitigation practice on Haplic Chernozem in Northern Bulgaria, integrating ground-based and remote sensing data. Soil physical parameters, leaf area index (LAI), canopy temperature, crop water stress index (CWSI), soil moisture, and yield were evaluated under both conventional tillage and subsoiling for the two crops. A variety of optical and radar descriptive remote sensing products derived from Sentinel-1 and Sentinel-2 satellite data were calculated for different crop types. Consequently, the use of machine learning, utilizing all the processed remote sensing products, enabled the reasonable prediction of LAI, achieving a coefficient of determination (R²) after a cross-validation greater than 0.42 and demonstrating good agreement with in situ observations. Results revealed differing responses: subsoiling had a positive effect on sunflower, improving LAI, water status, and slightly increasing yield, while it had no positive effect on maize. These findings highlight the importance of crop-specific responses in evaluating subsoiling practices and demonstrate the added value of integrating unmanned aerial systems (UAS) and satellite-based remote sensing data into agricultural drought monitoring. Full article

Stable oxygen isotopes in tree rings (δ¹⁸O) serve as important proxies for climate change and offer unique advantages for climate reconstruction in arid and semi-arid regions. We established an annual δ¹⁸O chronology spanning 1964–2023 using Juniperus excelsa tree-ring samples collected from the Alborz Mountains in Iran. We analyzed relationships between δ¹⁸O and key climate variables: precipitation, temperature, Palmer Drought Severity Index (PDSI), vapor pressure (VP), and potential evapotranspiration (PET). Correlation analysis reveals that tree-ring δ¹⁸O is highly sensitive to hydroclimatic variations. Tree-ring cellulose δ¹⁸O shows significant negative correlations with annual total precipitation and spring PDSI, and significant positive correlations with spring temperature (particularly maximum temperature), April VP, and spring PET. The strongest correlation occurs with spring PET. These results indicate that δ¹⁸O responds strongly to the balance between springtime moisture supply (precipitation and soil moisture) and atmospheric evaporative demand (temperature, VP, and PET), reflecting an integrated signal of both regional moisture availability and energy input. The pronounced response of δ¹⁸O to spring evaporative conditions highlights its potential for capturing high-resolution changes in spring climatic conditions. Our δ¹⁸O series remained stable from the 1960s to the 1990s, but showed greater interannual variability after 2000, likely linked to regional warming and climate instability. A comparison with the δ¹⁸O variations from the eastern Alborz Mountains indicates that, despite some differences in magnitude, δ¹⁸O records from the western and eastern Alborz Mountains show broadly similar variability patterns. On a larger climatic scale, δ¹⁸O correlates significantly and positively with the Niño 3.4 index but shows no significant correlation with the Arctic Oscillation (AO) or the North Atlantic Oscillation (NAO). This suggests that ENSO-driven interannual variability in the tropical Pacific plays a key role in regulating regional hydroclimatic processes. This study confirms the strong potential of tree-ring oxygen isotopes from the Alborz Mountains for reconstructing hydroclimatic conditions and high-frequency climate variability. Full article

Given that the Sunkoshi River watershed (located in the southern foot of the Himalayas) is sensitive to climate change and its mountain ecosystem provides important services, we aim to evaluate its spatial and temporal variation patterns of vegetation, represented by the Normalized Difference Vegetation Index (NDVI), during 2000–2021 and identify the dominant driving factors of vegetation change. Based on the NDVI dataset (MOD13A1), we used the simple linear trend model, seasonal and trend decomposition using loess (STL) method, and Mann–Kendall test to investigate the spatiotemporal variation features of NDVI during 2000–2021 on multiple scales (annual, seasonal, monthly). We used the partial correlation coefficient (PCC) to quantify the response of the NDVI to land surface temperature (LST), precipitation, humidity, and soil moisture. The results indicate that the annual NDVI in 52.6% of the study area (with elevation of 1–3 km) increased significantly, while 0.9% of the study area (due to urbanization) degraded significantly during 2000–2021. Daytime LST dominates NDVI changes on spring, summer, and winter scales, while precipitation, soil moisture, and nighttime LST are the primary impact factors on annual NDVI changes. After removing the influence of soil moisture, the contributions of climate factors to NDVI change are enhanced. Precipitation shows a 3-month lag effect and a 5-month cumulative effect on the NDVI; both daytime LST and soil moisture have a 4-month lag effect on the NDVI; and humidity exhibits a 2-month cumulative effect on the NDVI. Overall, the study area turned green during 2000–2021. The dominant driving factors of NDVI change may vary on different time scales. The findings will be beneficial for climate change impact assessment on the regional eco-environment, and for integrated watershed management. Full article

There is a lack of systematic research on the comprehensive regulatory effects of urea and organic fertilizer application on soil quality and cotton yield in summer direct-seeded cotton fields in the Yangtze River Basin. Additionally, there is a redundancy of indicators in the cotton field soil quality evaluation system and a lack of reports on constructing a minimum dataset to evaluate the soil quality status of cotton fields. We aim to accurately and efficiently evaluate soil quality in cotton fields and screen nitrogen application measures that synergistically improve soil quality, cotton yield, and nitrogen fertilizer utilization efficiency. Taking the summer live broadcast cotton field in Jiangxi Province as the research object, four treatments, including CK without nitrogen application, CF with conventional nitrogen application, N1 with nitrogen reduction, and N2 with nitrogen reduction and organic fertilizer application, were set up for three consecutive years from 2022 to 2024. A total of 15 physical, chemical, and biological indicators of the 0–20 cm plow layer soil were measured in each treatment. A minimum dataset model was constructed to evaluate and verify the soil quality status of different nitrogen application treatments and to explore the physiological mechanisms of nitrogen application on yield performance and stability from the perspectives of cotton source–sink relationship, nitrogen use efficiency, and soil quality. The minimum dataset for soil quality evaluation in cotton fields consisted of five indicators: soil bulk density, moisture content, total nitrogen, organic carbon, and carbon-to-nitrogen ratio, with a simplification rate of 66.67% for the evaluation indicators. The soil quality index calculated based on the minimum dataset (MDS) was significantly positively correlated with the soil quality index of the total dataset (TDS) (R² = 0.904, p < 0.05). The model validation parameters RMSE was 0.0733, nRMSE was 13.8561%, and the d value was 0.9529, all indicating that the model simulation effect had reached a good level or above. The order of soil quality index based on MDS and TDS for CK, CF, N1, and N2 treatments was CK < N1 < CF < N2. The soil quality index of N2 treatment under MDS significantly increased by 16.70% and 26.16% compared to CF and N1 treatments, respectively. Compared with CF treatment, N2 treatment significantly increased nitrogen fertilizer partial productivity by 27.97%, 31.06%, and 21.77%, respectively, over a three-year period while maintaining the same biomass, yield level, yield stability, and yield sustainability. Meanwhile, N1 treatment had the risk of significantly reducing both boll density and seed cotton yield. Compared with N1 treatment, N2 treatment could significantly increase the biomass of reproductive organs during the flower and boll stage by 23.62~24.75% and the boll opening stage by 12.39~15.44%, respectively, laying a material foundation for the improvement in yield and yield stability. Under CF treatment, the cotton field soil showed a high degree of soil physical property barriers, while the N2 treatment reduced soil barriers in indicators such as bulk density, soil organic carbon content, and soil carbon-to-nitrogen ratio by 0.04, 0.04, 0.08, and 0.02, respectively, compared to CF treatment. In summary, the minimum dataset (MDS) retained only 33.3% of the original indicators while maintaining high accuracy, demonstrating the model’s efficiency. After reducing nitrogen by 20%, applying 10% total nitrogen organic fertilizer could substantially improve cotton biomass, cotton yield performance, yield stability, and nitrogen partial productivity while maintaining soil quality levels. This study also assessed yield stability and sustainability, not just productivity alone. The comprehensive nitrogen fertilizer management (reducing N + organic fertilizer) under the experimental conditions has high practical applicability in the intensive agricultural system in southern China. Full article

Forest drought monitoring tools are crucial for managing tree water stress and enhancing ecosystem resilience. The Forest Drought Response Index (ForDRI) was developed to monitor drought conditions in forested areas across the contiguous United States (CONUS), integrating vegetation health, climate data, groundwater, and soil moisture content. This study evaluated ForDRI using Pearson correlations with the Bowen Ratio (BR) at 24 AmeriFlux sites and Spearman correlations with the Tree-Ring Growth Index (TRSGI) at 135 sites, along with feedback from 58 stakeholders. CONUS was divided into four forest subgroups: (1) the West/Pacific Northwest, (2) Rocky Mountains/Southwest, (3) East/Northeast, and (4) South/Central/Southeast Forest regions. Strong positive ForDRI-TRSGI correlations (ρ > 0.7, p < 0.05) were observed in the western regions, where drought significantly impacts growth, while moderate alignment with BR (R = 0.35–0.65, p < 0.05) was noted. In contrast, correlations in Eastern and Southern forests were weak to moderate (ρ = 0.4–0.6 for TRSGI and R = 0.1–0.3 for BR). Stakeholders’ feedback indicated that ForDRI realistically maps historical drought years and recent trends, though suggestions for improvements, including trend maps and enhanced visualizations, were made. ForDRI is a valuable complementary tool for monitoring forest droughts and informing management decisions. Full article

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. Full article

Seasonal climate variability and agronomic management profoundly influence both the productivity and nutritive value of temperate hay meadows. We analyzed five years of data (2019, 2020, 2022–2024) from 15 meadows in the central Spanish Pyrenees to quantify how environmental variables (January–June minimum temperatures, rainfall), management variables (fertilization rates (N, P, K), livestock load, cutting date), and vegetation (plant biodiversity (Shannon index)) drive total biomass yield (kg ha⁻¹), protein content (%), and Relative Feed Value (RFV). Using Random Forest regression with rigorous cross-validation, our yield model achieved an R² of 0.802 (RMSE = 983.8 kg ha⁻¹), the protein model an R² of 0.786 (RMSE = 1.71%), and the RFV model an R² of 0.718 (RMSE = 13.86). Variable importance analyses revealed that March rainfall was the dominant predictor of yield (importance = 0.430), reflecting the critical role of early-spring moisture in tiller establishment and canopy development. In contrast, cutting date exerted the greatest influence on protein (importance = 0.366) and RFV (importance = 0.344), underscoring the sensitivity of forage quality to harvest timing. Lower minimum temperatures—particularly in March and May—and moderate livestock densities (up to 1 LU) were also positively associated with enhanced protein and RFV, whereas higher biodiversity (Shannon ≥ 3) produced modest gains in feed quality without substantial yield penalties. These findings suggest that adaptive management—prioritizing soil moisture conservation in early spring, timely harvesting, balanced grazing intensity, and maintenance of plant diversity—can optimize both the quantity and quality of hay meadow biomass under variable climatic conditions. Full article

Soil salinization severely limits global agricultural sustainability, particularly across the saline–alkaline landscapes of the Qinghai–Tibet Plateau. We examined how potassium fulvate (PF) modulates oat (Avena sativa L.) performance, soil chemistry, and rhizospheric microbiota in the saline–alkaline soils of the Qaidam Basin. PF markedly boosted shoot and root biomass, with the greatest response observed at 150 kg hm⁻². At the same time, it enhanced soil fertility by increasing organic matter, nitrate-N, ammonium-N, and available potassium, and improved ionic balance by lowering Na⁺ concentrations and the sodium adsorption ratio (SAR), while increasing Ca²⁺ levels and soil moisture content. Under the high-dose treatment (F2), endogenous fungal contributions declined sharply, exogenous replacements increased, and fungal α-diversity fell; multivariate ordinations confirmed that PF reshaped both bacterial and fungal communities, with fungi exhibiting the stronger response. We integrated three machine learning algorithms—least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—to minimize the bias inherent in any single method. We identified microbial β-diversity, organic matter, and Na⁺ and Ca²⁺ concentrations as the most robust predictors of the Soil Salinization and Alkalization Index (SSAI). Structural equation modeling further showed that PF mitigates salinity chiefly by improving soil physicochemical properties (path coefficient = −0.77; p < 0.001), with microbial assemblages acting as key intermediaries. These findings provide compelling theoretical and empirical support for deploying PF to rehabilitate saline–alkaline soils in alpine environments and offer practical guidance for sustainable land management in the Qaidam Basin. Full article

Climate change has intensified drought stress, threatening global food security by affecting sensitive crops like maize (Zea mays). This study evaluated the potential of Antarctic fungal endophytes (Penicillium chrysogenum and P. brevicompactum) to enhance maize drought tolerance under field conditions with different irrigation regimes. Drought stress reduced soil moisture to 59% of field capacity. UAV-based multispectral imagery monitored plant physiological status using vegetation indices (NDVI, NDRE, SIPI, GNDVI). Inoculated plants showed up to two-fold higher index values under drought, indicating improved stress resilience. Physiological analysis revealed increased photochemical efficiency (0.775), higher chlorophyll and carotenoid contents (45.54 mg/mL), and nearly 80% lower lipid peroxidation in inoculated plants. Lower proline accumulation suggested better water status and reduced osmotic stress. Secondary metabolites such as phenolics, flavonoids, and anthocyanins were elevated, particularly under well-watered conditions. Antioxidant enzyme activity shifted: SOD, CAT, and APX were suppressed, while POD activity increased, indicating reprogrammed oxidative stress responses. Yield components, including cob weight and length, improved significantly with inoculation under drought. These findings demonstrate the potential of Antarctic endophytes to enhance drought resilience in maize and underscore the value of integrating microbial biotechnology with UAV-based remote sensing for sustainable crop management under climate-induced water scarcity. Full article

Andean grassland ecosystems in Peru are characterized by diverse plant species adapted to environmental factors including weather, soil type, elevation, slope orientation, and soil moisture. This study evaluated the floristic composition, alpha diversity, indicator species, and suitable species for Andean livestock in the Sillapata micro-watershed, Junín region, Peru, across rainy and dry seasons. Data collection involved 100 m linear transects, and analyses included floristic composition and dissimilarity, Shannon-Wiener (H′) and Simpson (D) diversity indices, and the identification of indicator and suitable species using QGIS vs 3.28.14 and R software vs 4.3.2. Results revealed a total of 130 species classified into 74 genera and 23 families, with Asteraceae and Poaceae as the dominant families, exhibiting variations in richness and dissimilarity between control points and seasonal periods. Alpha diversity (H′) ranged from 2.07 to 3.1867, while Simpson’s index (D) ranged from 0.7644 to 0.9234. Six indicator species were identified, along with 11 families containing suitable species, predominantly Poaceae (38–60%), Cyperaceae (11–15%), and Asteraceae (3–9%). The findings indicate that the studied ecosystem exhibits a heterogeneous floristic composition with medium to low and variable diversity, influenced by seasonal climatic changes and the current grassland management regime, which involves rotational grazing with cattle adapted to high-altitude conditions. Full article

Globally, ecosystems are affected by climate change, human activities, and natural disasters, which impact ecosystem quality and stability. Vegetation plays a crucial role in ecosystem material cycle and energy transformation, making it important to monitor its resilience under disturbance stress. The Critical Slowing Down (CSD) indicates that as ecosystems near collapse, the autocorrelation of lag temporal increases and resilience decreases. We used the lag Temporal Autocorrelation (TAC) of long-term remote sensing Leaf Area Index (LAI) to monitor vegetation resilience in the Three Gorges Reservoir Area (TGRA). The Disturbance Event Model (DEM) was used to validate the CSD. The results showed the following: (1) The eastern TGRA exhibited high and increasing vegetation resilience, while most areas showed a decline. (2) Among the various vegetation types, forests demonstrated higher resilience than other vegetation types. (3) Precipitation, temperature, and soil moisture significantly influenced vegetation resilience dynamics within the TGRA. (4) For model accuracy, the CSD’s results were consistent with the DEM, confirming its applicability in the TGRA. Overall, the CSD when applied to long-term remote sensing data, provided valuable quantitative indicators for vegetation resilience. Furthermore, more CSD-based indicators are needed to analyze vegetation resilience dynamics and better understand the biological processes determining vegetation degradation and restoration. Full article

Anthropogenic revegetation allows effective and timely soil development in mine restoration areas. The evaluation of soil quality is one of the most important criteria for measuring reclamation effectiveness, providing scientific reference for the subsequent management of ecological restoration projects. The aim of this research was to further investigate the influence of revegetation on mine-reclaimed soils in a semi-arid region. Thus, a coal-gangue dump within the afforestation chronosequence of 1 and 19 years in Shanxi Province, China, was selected as the study area. We assessed the physicochemical properties and nutrient stock of topsoils under four revegetation species, i.e., Pinus tabuliformis (PT), Medicago sativa (MS), Styphnolobium japonicum (SJ), and Robinia pseudoacacia ‘Idaho’ (RP). A two-way ANOVA revealed that reclamation age significantly affected SOC, TN, EC, moisture, and BD (p < 0.05), while the interaction effects of revegetation type and age were also significant for TN and moisture. In addition, SOC and TN stocks at 0–30 cm topsoil at the RP site performed the best among 19-year reclaimed sites, with an accumulation of 62.09 t ha⁻¹ and 4.23 t ha⁻¹, respectively. After one year of restoration, the MS site showed the highest level of SOC and TN accumulation, which increased by 186.8% and 88.5%, respectively, compared to bare soil in the 0–30 cm interval, but exhibited declining stocks during the 19-year restoration, possibly due to species invasion and water stress. In addition, an integrated soil quality index (ISQI) and the partial least squares structural equation model (PLS-SEM) were used to estimate comprehensive soil quality along with the interrelationship among influencing factors. The reclaimed sites with an ISQI value > 0 were 19-RP (3.906) and 19-SJ (0.165). In conclusion, the restoration effect of the PR site after 19 years of remediation was the most pronounced, with soil quality approaching that of the undisturbed site, especially in terms of soil carbon and nitrogen accumulation. These findings clearly revealed the soil dynamics after afforestation, further providing a scientific basis for choosing mining reclamation species in the semi-arid regions. Full article

The detection of archeological sites in satellite imagery is often hindered by environmental constraints such as vegetation cover and variability in meteorological conditions, which affect the visibility of subsurface structures. This study aimed to develop predictive models for assessing archeological site visibility in satellite imagery by integrating vegetation indices and meteorological data using machine learning techniques. The research focused on megalithic tombs associated with the Funnel Beaker culture in Poland. The primary objective was to create models capable of detecting archeological features under varying environmental conditions, thereby enhancing the efficiency of field surveys and reducing associated costs. To this end, a combination of vegetation indices and meteorological parameters was employed. Key indices—including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Archeological Index (NAI)—were analyzed alongside meteorological variables such as wind speed, temperature, humidity, and total precipitation. By integrating these datasets, the study evaluated how environmental conditions influence the visibility of archeological sites in satellite imagery. The machine learning models, including logistic regression and decision tree-based algorithms, demonstrated strong potential for predicting site visibility. The highest predictive accuracy was achieved during periods of high soil moisture variability and fluctuating weather conditions. These findings enabled the development of visibility prediction maps, guiding the optimal timing of aerial surveys and minimizing the risk of unsuccessful data acquisition. The results underscore the effectiveness of integrating meteorological data with satellite imagery in archeological research. The proposed approach not only improves site detection but also reduces operational costs by concentrating resources on optimal survey conditions. Furthermore, the methodology is applicable to diverse archeological contexts, enhancing the capacity to locate and document heritage sites across varying environmental settings. Full article

Occupational exposure to commercial formic and acetic acids through dermal contact and inhalation during rubber sheet processing poses significant health risks to workers. Additionally, the use of these acids contributes to environmental pollution by contaminating water sources and soil. This study investigates the potential of three types of wood vinegar—derived from para-rubber wood, bamboo, and eucalyptus—obtained through biomass pyrolysis under anaerobic conditions, as sustainable alternatives to formic and acetic acids in the production of ribbed smoked sheets (RSSs). The organic constituents of each wood vinegar were characterized using gas chromatography and subsequently mixed with fresh natural latex to produce coagulated rubber sheets. The physical and chemical properties, equilibrium moisture content, and drying kinetics of the resulting sheets were then evaluated. The results indicated that wood vinegar derived from para-rubber wood contained a higher concentration of acetic acid compared to that obtained from bamboo and eucalyptus. As a result, rubber sheets coagulated with para-rubber wood and bamboo vinegars exhibited moisture sorption isotherms comparable to those of sheets coagulated with acetic acid, best described by the modified Henderson model. In contrast, sheets coagulated with eucalyptus-derived vinegar and formic acid followed the Oswin model. In terms of physical and chemical properties, extended drying times led to improved tensile strength in all samples. No statistically significant differences in tensile strength were observed between the experimental and reference samples. The concentration of acid was found to influence Mooney viscosity, the plasticity retention index (PRI), the thermogravimetric curve, and the overall coagulation process more significantly than the acid type. The drying kinetics of all five rubber sheet samples displayed similar trends, with the drying time decreasing in response to increases in drying temperature and airflow velocity. Full article