Search Results (2,021)

Under the combined pressures of climate change and irrigated cropland expansion, groundwater tables are declining rapidly across arid regions, thereby intensifying water limitation in riparian ecosystems. However, the mechanisms by which dominant riparian tree species coordinate multiple functional traits to maintain carbon–water balance remains poorly understood. This study investigated coordinated ecophysiological trait shifts of Populus euphratica Oliv. along a groundwater-depth gradient (2.19, 4.88, and 7.45 m) in the middle reaches of the Tarim River (China), hereafter referred to as shallow, middle, and deep groundwater depths, respectively. We quantified photosynthetic, hydraulic, stomatal, leaf anatomical and nutrient traits, and estimated long-term intrinsic water-use efficiency (WUE_i) from foliar δ¹³C. As the groundwater table declined, (1) photosynthetic capacity and photochemical performance decreased, whereas WUE_i increased markedly from 38.5 ± 2.9 to 54.2 ± 1.0 μmol mmol⁻¹, accompanied by the lowest transpiration rate at the deep groundwater depth (4.6 ± 0.5 mmol m⁻² s⁻¹); (2) stomatal and anatomical adjustments consistent with water-loss reduction were observed, including a significant decline in stomatal density from 93.5 ± 14.5 to 79.3 ± 17.4 pores mm⁻², and reduced stomatal size and stomatal area fraction (−20.3% and −32.7%, respectively); (3) the percentage loss of hydraulic conductivity increased, whereas sapwood-specific hydraulic conductivity declined, accompanied by greater sapwood investment relative to leaf area, with Huber value rising from 0.06 ± 0.02 to 0.11 ± 0.04 mm² cm⁻² at deep water depth; and (4) chlorophyll concentrations and leaf water content declined, whereas structural investment increased, as reflected by higher specific leaf mass and leaf dry matter content, and leaf nutrients were enriched, with total nitrogen and total phosphorus increasing by 67.1% and 42.0%, respectively. Trait-WUE_i relationships further indicated that WUE_i covaried most strongly with leaf anatomical and nutrient traits. These results demonstrate that increasing groundwater depth was associated with coordinated shifts in carbon assimilation, water-use regulation, hydraulic function, and nutrient allocation in P. euphratica. Such trait coordination may help explain how this species persists under chronic water limitation in arid riparian forests. Full article

The northern part of the state of Mato Grosso is located at the intersection of large-scale agricultural production and the Amazon, a tropical biome of great importance for ecosystem services and biodiversity. Agricultural production activities interact with natural capital, among other factors, in land use and in biogeochemical cycles of water and carbon. In this study, we sought to use remote sensing at the regional level to diagnose and spatialize the contribution of agricultural activity to dry areas. Using carbon dioxide orbital models, land use classification techniques, the Standardized Precipitation Index (SPI), and Pettitt and Mann–Kendall statistics, the variables were compared spatially for the biogeographic boundary of the Amazon in Mato Grosso in two distinct time frames: (i) over the crop years of the CO₂ efflux model (2020 to 2023), and (ii) over the years 2008 to 2023, with consolidated data from the MODIS sensor system. The hot and cold spots analysis reinforces the correlation of carbon variables to land use; the drought index suggests a spatial correlation to forest loss, where more intense agricultural activity favors drought and inhibits moderate rainfall, and in turn is linked to the amount of forest in the context of intense continentality. Temporally, the statistical diagnosis highlights abrupt changes in 2011, 2013, and 2019, restate the complex relation of tropical forest and biogeochemical cycles, above all with carbon dioxide. Full article

Early-age shrinkage is a critical factor governing the dimensional stability and cracking susceptibility of ultra-high-performance concrete (UHPC). However, accurate prediction of UHPC shrinkage remains challenging due to the strong nonlinear interactions among mixture parameters, curing conditions, and hydration-induced internal moisture evolution, particularly when only limited experimental data are available. In this study, a systematic experimental program was conducted to investigate the influence of the binder-to-sand ratio, water-to-binder ratio, polypropylene fiber dosage, and curing environment on both early drying shrinkage and autogenous shrinkage of UHPC. Based on the experimental results, a structured dataset covering all shrinkage test data was constructed to support data-driven modeling. To improve prediction reliability under small-sample conditions, a Bayesian-optimized Extreme Gradient Boosting (BO-XGBoost) framework was developed and benchmarked against several conventional machine learning models, including Backpropagation Neural Networks (BPNNs), Random Forest (RF), and Support Vector Machines (SVMs). Shrinkage test data from other literature validated the prediction accuracy of this model, demonstrating its rationality and practicality. In addition, the Shapley Additive Explanations (SHAP) method was employed to quantitatively interpret the contribution and interaction mechanisms of key variables affecting shrinkage behavior. The results show that the BO-XGBoost model achieves the highest prediction accuracy and stability among the evaluated algorithms. SHAP analysis further reveals that curing age and curing environment dominate drying shrinkage, whereas autogenous shrinkage is primarily governed by the curing age and water-to-binder ratio. The interaction analysis also identifies the coupled effects between low water-to-binder ratio and extended curing age. The proposed framework not only improves prediction robustness for UHPC shrinkage under limited data conditions but also provides interpretable insights into the mechanisms governing early-age deformation. These findings offer a data-driven basis for optimizing UHPC mixture design and mitigating early-age cracking risks in engineering applications. Full article

Conserving biodiversity requires identifying and prioritizing critical habitats at a fine scale, as coarse-scale approaches often fail to address the needs of specialized and threatened species. This study applies a fine-scale prioritization approach across temperate and tropical regions of North America and the Caribbean using a detailed map of 636 ecosystem types and high-resolution Area of Habitat (AOH) data. We then evaluated the current protection status and risk of future land use changes for each habitat type and prioritized them for conservation. Our results revealed that 38% of the area was identified in the top quartile of high-priority habitats, with 56 (33%) of identified IUCN threatened ecosystem types captured within these areas. Top priority habitats include the Meso-American Premontane Semi-deciduous Forest, Central American Caribbean Evergreen Lowland Forest, and Guerreran Dry Deciduous Forest, all characterized by low protection, high projected land-use conversion, and large numbers of threatened and habitat-specialist species, highlighting their urgent conservation importance in Meso-American and Caribbean tropical forests. Our findings emphasize the need for targeted conservation strategies that consider finer-scale habitat classifications and species requirements to improve the precision of conservation planning, especially where already at-risk species and ecosystems are located, and human land use intensities are high. Full article

Deforestation in unprotected areas (UPAs) within the Brazilian Amazon affects environmental sustainability and regional climate. This study quantifies shifts in near-surface air temperature, precipitation, and evapotranspiration (ET) during the dry season resulting from UPA loss. Utilizing a five-year ensemble (2015–2019) to isolate the climatic response from interannual variability, simulations indicate a warmer (+1.0 ± 0.4 °C) and drier climate, characterized by a basin-wide 12 ± 8% reduction in precipitation and a 12 ± 4% reduction in ET following UPA removal. This shifted climate state extends to Rondônia, a southwestern state where detailed risk mapping was developed by integrating changes in climate variables with socio-economic, agricultural, and demographic. UPA deforestation, largely external to Rondônia, is associated with a simulated decrease in precipitation by 20 ± 7% and ET by 11 ± 9% coupled with an increase in air temperature by 1.2 ± 0.4 °C. These shifts indicate increased vulnerability for municipalities, including the capital, potentially affecting agricultural productivity. Findings suggest that to protect remaining forests these biophysical risks must be mitigated. This study establishes a spatial framework for identifying municipalities most suceptible to the climatic shifts triggered by UPA loss. Full article

Alternate Wetting and Drying (AWD) is a proven water-saving irrigation technique that reduces irrigation water use and methane emissions from rice cultivation. The emission reduction achievable through AWD irrigation practices represents a significant opportunity for credits generation, particularly for the major rice-producing countries. To capitalize on this opportunity, a scalable, reliable, and cost-effective information system for AWD irrigation monitoring, reporting, and verification (MRV) is urgently needed. However, most existing MRV systems depend on manual data collection or software systems driven by field-based observation. Satellite remote sensing, derived from different tools and techniques, has achieved considerable traction in agriculture monitoring. This study attempts to develop a remote sensing and Internet of Things (IoT)-based system for large-scale AWD irrigation detection and monitoring as a potential tool for the MRV system. IoT sensor-based water level measurement, L-band PALSAR-2 full polarimetric data, and intensive field survey data were integrated and analyzed. Three study sites in the Naogaon District of Bangladesh, one of the major rice-growing regions, were selected as the study area. The PALSAR-2 full-polarimetric data were collected, radiometrically and geometrically corrected, and converted into the backscattered coefficient (Sigma-naught) value. Using the full-polarimetric channel of VV, VH, HH, and HV, the Freeman–Durden three-component decomposition, surface scattering, double-bounce, and volume scattering were constructed to assess the irrigation water condition of the rice paddy field. IoT sensors data, field survey data, and three-component data on 8 different dates and a total of 704 fields during the rice growing period were subsequently analyzed and cross-calibrated. The results showed that surface scattering and double bounce are more sensitive to irrigation water status, while volume scattering primarily responds to plant height changes. By leveraging the backscatter characteristics of these three components, a Random Forest classifier was applied to classify AWD and non-AWD irrigated paddy fields. Classification accuracy achieve 94% in early crop growth stages and declined to 80% during dense canopy stages. These findings offer a reliable and scalable approach to documenting water regime management with direct applicability to carbon emissions reduction verification and carbon credits claims. Full article

Agricultural ecosystems in northeastern Bangladesh are increasingly vulnerable to climate-induced stressors, particularly rising temperatures and seasonal droughts. While previous research has examined the climate’s impact on agriculture in broader contexts, no study has specifically investigated long-term seasonal vegetation and thermal dynamics in Sylhet. This study addresses this gap by assessing spatio-temporal variations in vegetation health under climate stress in the Sylhet region from 2005 to 2025 using remote sensing techniques. To investigate this problem, the study derived the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) from Landsat satellite imagery and evaluated their seasonal behavior across the major cropping periods Rabi, Kharif I, and Kharif II. The relationship between vegetation health and surface temperature was examined using Pearson’s correlation matrix along with a statistical comparison to identify change patterns, transitions among vegetation and thermal stress classes, and the seasonal intensity of climate stress. The findings indicate that increased LST generally corresponds with reduced vegetation cover in lowland agricultural zones, whereas elevated areas with forest or tree covers show an opposite response. Distinct spatial hotspots of thermal stress and drought-prone zones were also identified, particularly during the dry Rabi season. These results highlight the idea that rising LST corresponds with declining NDVI values, indicating that increasing thermal stress and potential reductions in agricultural vegetation productivity and climate stress across Sylhet’s agricultural landscape have intensified markedly from 2005 to 2025, with clear seasonal differences in vulnerability. NDVI analysis reveals a consistent decline in vegetation health, while LST patterns show widespread transitions from moderate to high and severe thermal stress, particularly during the Kharif seasons. The observed NDVI decline under elevated LST conditions indicates reduced vegetation vigor and potential productivity within agricultural lands, rather than a direct reduction in cultivated areas, since NDVI primarily captures vegetation density and physiological condition. The strongest NDVI–LST inverse relationship occurs in Rabi and Kharif I, indicating vegetation’s cooling role, whereas this linkage weakens in Kharif II due to dominant monsoon-driven atmospheric controls. Full article

As wildfires intensify globally due to climate change, accurate wildfire danger forecasting systems have become essential for effective disaster management and early warning. Fuel Moisture Content (FMC), defined as the ratio of water mass to dry fuel mass, plays a critical role in determining ignition probability and fire spread dynamics. This study conducts a comparative analysis of five major national wildfire danger rating systems: the National Fire Danger Rating System (NFDRS, USA), Canadian Forest Fire Danger Rating System (CFFDRS), European Forest Fire Information System (EFFIS), Australian Fire Danger Rating System (AFDRS), and the Korean Forest Fire Danger Rating System (KFDRS). Using a multi-criteria comparative framework, the systems were evaluated based on fuel classification structure, input variables, modeling approach, and spatiotemporal prediction resolution. The results reveal substantial disparities in spatial resolution (100 m to district-level), temporal resolution (hourly vs. daily), and fuel moisture modeling approaches (physics-based, index-based, and hybrid systems). Specifically, NFDRS and AFDRS provide high-frequency forecasting with hourly temporal resolution, operating at spatial resolutions of 1 km and 100 m, respectively, and incorporating dynamic fuel moisture modeling. In contrast, CFFDRS and KFDRS primarily rely on daily index-based predictions. Furthermore, while many global systems increasingly leverage remote sensing and machine learning for real-time FMC estimation, South Korea’s KFDRS remains predominantly empirical and weather-driven. The analysis identifies critical limitations in the KFDRS, including coarse spatial resolution (district-level), limited integration of Live Fuel Moisture Content (LFMC) modeling, and the lack of AI-augmented hybrid approaches. Accordingly, this study proposes a phased three-stage policy roadmap (2026–2035), emphasizing sensor-network expansion, AI–physics fusion modeling, and high-resolution (10 m) FMC mapping to enhance forecasting accuracy in complex terrains. These findings provide strategic insights for improving wildfire risk management and supporting the transition from reactive response to predictive wildfire forecasting under increasing climate variability. Full article

Compound Drought and Heatwave (CDH) events increasingly threaten terrestrial carbon uptake, yet the spatiotemporal heterogeneity of Gross Primary Productivity (GPP) responses in urban agglomerations remains unclear. This study analyzed CDH impacts in China’s three major urban agglomerations, namely the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions, using ERA5 and satellite GPP data (GOSIF and FluxSat) for representative CDH years (2007 for BTH; 2022 for YRD and PRD). CDH conditions exhibited a coherent hot–dry coupling, with temperature anomalies of 0.46–1.26 K and soil moisture deficits of −0.042 to −0.169 m³ m⁻³, accompanied by enhanced atmospheric dryness. Pronounced spatial heterogeneity in GPP responses aligned with regional climatic regimes and ecosystem types. The water-limited BTH region exhibited significant GPP deficits, with anomalies of −1.13 Standard Deviations (STD) and −0.96 STD for GPP_FluxSat and GPP_GOSIF, respectively. Conversely, the energy-limited regions showed positive anomalies: the YRD recorded +0.32 and +1.79 STD, while the PRD reached +1.86 and +1.06 STD for GPP_FluxSat and GPP_GOSIF, respectively. Mechanistically, the north–south contrast suggests a transition from water-limited vulnerability to energy-limited resilience, with vegetation traits and management (e.g., potential irrigation buffering in croplands and deeper water access in forests) modulating sensitivity to atmospheric dryness. These findings provide quantitative benchmarks for improving regional carbon-cycle assessments and adaptation planning under increasing compound extremes. Full article

The Yeongnam wildfires in March 2025 destroyed over 40 temple halls across five Buddhist monasteries in South Korea, exposing a critical gap in wildfire management for mountain-sited cultural heritage: the existing approaches rely on static hazard maps and reactive suppression, lacking real-time terrain-aware prediction and proactive resource deployment. This study proposes a Digital Twin framework coupling high-resolution wildfire simulation with adaptive water-mist optimization to address this gap. Bogwangsa Temple (est. 949 CE, ~315 m elevation, Cheonmasan Mountain, Namyangju) serves as the case study, selected for its representative vulnerability—dense Pinus densiflora forests on steep western slopes forming a continuous fire corridor, limited vehicular access, and proximity to recent large-scale fire events. A modified Rothermel model on a 1 m cellular-automata grid, driven by a 1 m DEM, Korea Forest Service fuel data, and local weather records, simulates five scenarios from normal spring to extreme dry-wind conditions through Monte Carlo ensembles. Binary integer optimization selects the minimum-cost nozzle configuration, keeping the fire-arrival probability at four heritage structures below a safety threshold via pre-emptive activation. The adaptive deployment reduces the mean fire-arrival probability by approximately 80% compared with static sprinklers while substantially lowering water consumption. Sensitivity analyses confirm that 1 m DEM resolution captures micro-terrain features that are critical to accurate spread prediction that are lost at coarser resolutions. The modular, transferable framework contributes to SDG 11 (Sustainable Cities and Communities, Target 11.4) and SDG 13 (Climate Action). Full article

The energy utilization of residual woody biomass is a relevant strategy for the decentralized energy transition and local waste management in rural areas. The objective of this study was to characterize (physically, chemically, and energetically) five types of residual biomass: pine branches, huinumo (this material refers to the long, thin pine needles that, after drying and falling, form a layer on the forest floor), cherry branches and leaves, and grass waste generated in the community of San Francisco Pichátaro, Michoacán, Mexico, in order to evaluate its viability for the production of densified solid biofuels. A comprehensive analysis was conducted, including moisture content, higher heating value, proximate characterization, structural chemical analysis (using the Van Soest method), elemental CHONS analysis, ash microanalysis (by ICP-OES), and a multicriteria analysis with normalized energy and compositional indicators. The results showed that huinumo and cherry leaves were the most outstanding biomasses, presenting the highest heating values (20.7 MJ/kg) and low moisture and ash contents. Pine branches obtained the most balanced results, characterized by their equilibrium in fixed carbon and lignin, as well as their low potassium content. The multicriteria analysis showed that there is no absolute optimal biomass; however, it indicates that pine branches and huinumo are the most robust feedstocks for the production of briquettes or pellets. The results confirm the significant technical and environmental potential of local lignocellulosic residues for the production of solid biofuels and for contributing to sustainable energy solutions at the local scale. Full article

Plant diversity plays a fundamental role in ecosystem functioning and is essential for sustaining ecosystem services. National forest inventories are key instruments for assessing floristic diversity. However, their measurement protocols may introduce bias by omitting smaller individuals because of the stem diameter criterion used or the minimum plant size threshold applied. Such bias is exacerbated in dryland ecosystems where small-statured plants with low-branching stems are particularly abundant. In this study, we evaluated the effects of using basal diameter (BD) instead of diameter at breast height, and of sampling small individuals (BD ≥ 2.5 cm), on the estimation of abundance, alpha and gamma diversity and community composition in different vegetation types in NW Mexico. We found substantial underestimation due to the omission of smaller individuals in xeric shrubland and tropical dry forest, where gamma diversity may be underestimated by up to 209% and 139%, respectively. Broadleaf forest also showed strong underestimation (133%), whereas mixed conifer–broadleaf forests were unaffected. We discuss these differential effects and propose a methodology to attenuate this underestimation and achieve more accurate floristic diversity estimates from national forest inventories in dryland vegetation, which encompasses roughly one-third of the Earth’s surface and more than half of Mexico’s territory. Full article

This study presents a nanosensor network system for autonomous microclimate optimization in precision horticulture, leveraging a field-programmable gate array (FPGA)-based control architecture that is integrated with an edge-level machine learning inference. Unlike the conventional greenhouse automation systems, which exhibit thermal and hygroscopic hysteresis often exceeding 32 °C and 78% relative humidity, the proposed framework embeds a random forest regression (RFR) model directly within the Altera DE2-115 FPGA fabric to enable predictive environmental regulation. The model achieved an R² of 0.985 and root mean square error (RMSE) of 0.28 °C, allowing proactive compensation for the thermodynamic disturbances from the high-intensity light-emitting diode (LED) lighting with a 120 s predictive horizon. The real-time monitoring and remote supervision were supported via a NodeMCU-based IoT gateway, achieving a 140 ms mean communication latency and a 99.8% packet delivery reliability. The preliminary validation using lettuce (Lactuca sativa) optimized the environmental parameters, while the subsequent experiments with pepper (Capsicum annuum), a commercially important and environmentally sensitive crop, demonstrated system performance under real-world conditions. The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption. These results highlight the efficacy of FPGA-integrated edge intelligence combined with low-latency IoT telemetry as a scalable, energy-efficient, and high-fidelity solution for sub-degree environmental control in next-generation, controlled-environment, and vertical farming systems. Full article

This study explored the feasibility of near-infrared (NIR) spectroscopy for detecting total water, free water and bound water in carrot slices during freeze-drying, with low-field nuclear magnetic resonance (LF-NMR) characterizing water state distribution and oven-drying determining moisture content (MC). NIR spectra (10,000–4000 cm⁻¹) were processed via optimized sample partitioning, preprocessing and feature extraction; partial least squares regression (PLSR), support vector regression (SVR), back-propagation artificial neural network (BPANN), extreme gradient boosting (XGBoost) and particle swarm optimization–random forest (PSO-RF) models were established and evaluated. Results showed that SVR and BPANN performed robustly, with CARS being the optimal feature extraction method. The full-moisture system achieved high total/free water prediction accuracy ($R_p^2$ = 0.9902/0.9740), while the low-moisture system improved bound water prediction ($R_p^2$ = 0.9709). The established NIR models exhibited excellent fitting and generalization ability, enabling rapid and non-destructive quantitative prediction of moisture content during carrot freeze-drying. Full article

Accurate prediction of winter oilseed rape yield is essential for optimising crop management and improving production efficiency. However, the reliability of commonly reported model performance remains uncertain due to the widespread use of random validation strategies. This study evaluated the predictive potential of multi-temporal Normalised Difference Vegetation Index (NDVI) metrics collected between September 2023 and May 2024 for yield estimation across multiple Lithuanian fields, while explicitly addressing spatial generalisation. The analytical dataset comprised dry yield (t ha⁻¹), monthly NDVI, and field identifiers, and underwent quality control, including outlier removal. Four modelling approaches were compared: ordinary least squares (OLS) regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a Deep Neural Network (DNN). Model performance was assessed using both random (80/20) and a spatially independent field-wise (GroupSplit) validation schemes designed to assess model transferability to previously unseen fields, further extended by repeated group-based resampling to quantify variability in model generalisation. Under random sampling, RF and XGBoost achieved the highest accuracy (RMSE ≈ 0.85 t ha⁻¹, R² ≈ 0.55). However, under spatially independent validation, predictive performance declined markedly for all models, with tree-based ensembles showing near-zero R² values, indicating limited transferability to unseen fields. In contrast, the DNN demonstrated more consistent generalisation (RMSE = 1.09 t ha⁻¹, R² = 0.28). Repeated field-wise validation confirmed that performance estimates based on random splits substantially overestimate true predictive capability. Feature importance analyses consistently identified spring NDVI, particularly from March to May, as the dominant predictor of yield, whereas autumn NDVI showed weaker and less consistent relationships with yield. These findings demonstrate that a large portion of the predictive skill reported in NDVI-based yield modelling may arise from spatial information leakage rather than transferable crop-environment relationships. By explicitly quantifying the gap between random and spatial validation, this study provides a more realistic benchmark for model performance and highlights the necessity of spatially robust evaluation frameworks for operational yield prediction in precision agriculture. Full article