Search Results (1,465)

Jordan is among the most water-stressed countries globally, with renewable freshwater availability falling below 100 m³ per capita per year. The Jordan Valley (JV), the country’s primary irrigated agricultural corridor, faces interconnected pressures across water, energy, food, and ecosystem (WEFE) systems under intensifying climatic and demographic stressors. This study evaluates the integrated performance of the WEFE nexus in the Jordan Valley using updated evidence (2018–2023) to quantify cross-sector interactions, performance gaps, and intervention priorities. A mixed-methods empirical assessment integrated quantitative sectoral data on water supply–demand and quality, electricity supply–demand and renewable deployment, agricultural productivity, and ecosystem pressure indicators, complemented by Living Lab–based stakeholder interviews. Sectoral indices were calculated based on supply–demand adequacy and aggregated into an overall WEFE Nexus Index. Results indicate persistent water scarcity, with a domestic supply of 23.48 MCM yr⁻¹ versus demand of 26.00 MCM yr⁻¹ (deficit −2.52 MCM yr⁻¹) and irrigation supply of 206 MCM yr⁻¹ relative to approximately 400 MCM yr⁻¹ demand (deficit −194 MCM yr⁻¹). Water services account for 14% of national electricity consumption, while solar pumping provides approximately 40% of daytime irrigation energy. Agricultural productivity is constrained by salinity and water quality, resulting in yield gaps (e.g., greenhouse vegetables: 4.7 vs. 10.0 t/dunum). Sectoral performance is uneven (Water 0.71; Energy 1.00; Food 0.45; Ecosystem 0.50), yielding an overall WEFE Nexus Index of 0.63 (0.50 after efficiency adjustment). Climate projections indicate continued warming (+1.8 °C) and declining precipitation (−11%) by 2060. Water harvesting, integrated renewable-powered water services, wastewater reuse, salinity management, climate-smart agriculture, and ecosystem restoration are critical to enhancing climate-resilient resource security in the Jordan Valley. The WEFE index developed here offers a tool for integrated planning and underscores that achieving climate-resilient resource security in the Jordan Valley will require strategic, cross-sector interventions and adaptive governance rather than sector-specific fixes. Full article

This study evaluated the performance of a smart farming technology (SFT) and a climate-smart agriculture (CSA) approach for improving irrigation management in pecan (Carya illinoinensis) orchards in México through soil moisture monitoring, evapotranspiration estimation, and real-time data integration. Continuous monitoring allowed irrigation to be maintained at field capacity, preventing plant stress while avoiding total soil saturation or permanent wilting point. Calibration of soil moisture sensors showed a very strong correlation (R² = 0.99) between sensor reverse voltage and volumetric soil water content in predominant sandy loam soils, confirming the reliability of the monitoring system for irrigation scheduling. Seasonal analysis of reference evapotranspiration (ETo) and crop evapotranspiration (ETc) revealed increasing atmospheric water demand during summer months, with crop coefficient (Kc) values ranging from approximately 0.3 during dormancy to 1.0–1.3 during peak vegetative growth. After five years of field implementation of the technology, results showed water savings exceeding 50% compared with traditional flood irrigation practices. The optimized irrigation schedule reduced total seasonal irrigation depth from 216 cm to 128 cm, representing a 59% reduction in applied water while maintaining adequate soil moisture conditions for crop development at field capacity (FC). These results highlight the potential of integrating sensor-based monitoring, evapotranspiration modeling, and IoT platforms to enhance water-use efficiency and support sustainable pecan production under increasing climate variability. Full article

Wildfires represent a significant global environmental challenge, necessitating advanced monitoring and assessment techniques. This study explores the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data within a Google Earth Engine (GEE) framework to enhance wildfire detection, burned area estimation, and severity assessment. By leveraging SAR’s capability to penetrate atmospheric obstructions and optical data’s spectral sensitivity to vegetation changes, the proposed methodology addresses limitations of single-sensor approaches. The results demonstrate strong correlations between SAR-based indices, such as the Radar Vegetation Index (RVI) and Dual-Polarized SAR Vegetation Index (DPSVI), and traditional optical indices, including the Normalized Burn Ratio (NBR) and differenced NBR (ΔNBR). Despite challenges related to terrain influence, sensor resolution differences, and computational demands, the integration of multi-sensor data in a cloud-based environment offers a scalable and efficient solution for wildfire monitoring. During the peak of the fire events, significant atmospheric obstruction was technically verified using Sentinel-2 metadata and the QA60 cloud mask band, which confirmed persistent cloud cover and thick smoke plumes over the study areas. This interference limited the reliability of purely optical monitoring, further justifying the integration of SAR data. Future research should focus on refining data fusion techniques, incorporating additional datasets such as thermal infrared imagery and meteorological variables, and enhancing automation through artificial intelligence (AI). This study underscores the potential of remote sensing advancements in improving fire management strategies and global wildfire mitigation efforts. Full article

China is one of the world’s leading producers of celery. In recent years, the market price of celery has often experienced rollercoaster-like fluctuations. Such volatility has become a significant factor affecting the income of vegetable farmers, market stability, and household consumption. The key to addressing this issue lies in understanding the threshold of the celery planting area at which supply and demand are balanced. However, relevant research has been rarely conducted on this topic to date. Shandong Province is a major vegetable-producing region in China, and its celery output and pricing have a crucial impact on the national market. Therefore, this study takes Yucheng City, Shandong Province, as a case study. By leveraging the land vacancy characteristics before the celery planting period, the NDVI data was calculated, and the object-based supervised classification was used to extract the celery planting area from remote sensing imagery. Based on a comprehensive statistical analysis of collected annual celery wholesale prices and break-even prices over the past decade, it was found that when the autumn celery planting area in the study region exceeds 12,000 hectares, oversupply occurs, leading to losses for celery farmers. Moreover, this situation recurs approximately every four years. To prevent celery oversupply, the government should estimate the prospective celery planting area using remote sensing imagery during the one-month land vacancy period before celery transplantation. Once the estimated data reach or exceed the supply–demand balance threshold, proactive guidance should be provided to encourage celery farmers to switch to other vegetables, thereby reducing potential losses for farmers. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices. This study could also maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices and could enable farmers to achieve sustained profitability. The sustainable profit could maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. Full article

The assessment of landscape ecological risk and the exploration of its driving factors is a critical approach to alleviating the conflict between the growing demand of human activities and ecological environment conservation, and the Five Lakes Area in Central Yunnan serves as a typical representative of landscape ecological risk issues in plateau lake regions. Therefore, this study, based on the land use transfer change characteristics of the Five Lakes Area in Central Yunnan across four periods (1995–2024), employed the landscape pattern index method to calculate the spatiotemporal variation characteristics of the landscape ecological risk index; additionally, 10 driving factors (including natural and socio-economic factors) were selected, and the XGBoost-SHAP model and Random Forest model were applied to explore the driving factors, with the results showing that: (1) In terms of land use transfer, farmland, forest, and Grass land were transferred among each other, the inflow of Construction land increased, and Grass land had the largest outflow area; (2) regarding landscape ecological risk, the landscape pattern was unstable, the loss degree increased, and the moderate and moderately high-risk areas expanded; and (3) for driving factors, the dominance shifted from natural factors to socio-economic factors; among these, Precipitation, NDVI (Normalized Difference Vegetation Index), Land use intensity, and Night-time light index were significant influencing factors. Based on the above results, a zoning management and control strategy for landscape ecological risk was proposed, aiming to provide a scientific reference for policy formulation to reduce risks and alleviate human–land conflicts. Full article

The pursuit of carbon neutrality demands advanced low-carbon energy processes and their effective integration into building systems. Ground-source heat pumps (GSHPs) offer a key pathway for decarbonizing heating, yet their cold-climate application is compromised by soil thermal imbalance, which degrades their long-term efficiency. This study proposes and evaluates an innovative air-assisted GSHP system that integrates a vegetable greenhouse with a zoned borehole configuration for seasonal thermal storage to achieve carbon neutrality. The system segregates boreholes into core and peripheral zones to establish a controlled soil temperature gradient, enabling cascaded heat storage and thermal optimization. A comprehensive year-long field test was conducted on a residential building in Harbin, China. The results demonstrate that the system reliably maintains comfortable indoor conditions during severe winters, achieving average seasonal COPs of 3.82 for the heat pump unit and 2.85 for the overall system. The zoned operation strategy successfully generated a significant intra-field soil temperature gradient, with a maximum differential of 5.9 °C between the core and peripheral boreholes during charging. The measured heat extraction-to-storage ratio was 0.598, confirming effective cascaded utilization. From an environmental perspective aligned with low-carbon energy technologies, the system achieves annual savings of 8.66 tons of standard coal and a net CO₂ reduction of 1.3 tons when accounting for regional grid carbon intensity. This research provides empirical validation and practical design guidance for implementing efficient GSHP systems in severely cold regions, thereby contributing substantively to building sector decarbonization. Full article

Although greenhouse vegetable production is rapidly shifting toward innovative soilless systems, soil-based conventional cultivation still dominates globally. This production system faces growing pressure to transition to sustainable practices. However, introducing biofertilisers into intensive systems often yields inconsistent results. Specifically, their effects on different lettuce traits vary due to complex relationships between genotype, biofertiliser, environmental conditions, and market demands. Single-parameter evaluations fail to balance conflicting criteria, necessitating multi-criteria decision-making (MCDM) methods for selecting optimal choices. This study aims to overcome these inconsistencies through an integrated fuzzy MCDM-based optimisation model. Three lettuce cultivars (‘Carmesi’, ‘Aquino’, and ‘Gaugin’) were grown in an unheated Surčin (Serbia) greenhouse during a 58-day autumn experiment using a complete block design. Four treatments were applied: a control (without fertilisation), effective microorganisms, a Trichoderma-based fertiliser, and their combination. Biofertilisers were applied before transplanting and four times foliarly during the vegetation period via battery sprayer. This defined 12 production models (cultivar–fertiliser pairs), evaluated across 10 criteria: agronomic (core ratio, number of leaves), quality (nitrate content, total antioxidant capacity, total soluble solids, and chlorogenic acid), sensory (overall taste, overall quality), and economic (total variable costs, total income). Four decision-making experts from the Faculty of Agriculture and the ready-to-eat salad industry assessed weighting coefficients using the fuzzy PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method. The fuzzy MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) method was used to rank the alternatives. To confirm the stability of the obtained ranking with the fuzzy MARCOS method, we performed sensitivity analysis through 20 different scenarios. Applied fuzzy methods identified alternative A11—‘Aquino’ cultivar with combined biofertilisers—as the best-ranked option, followed by A6 and A7. This study validates fuzzy PIPRECIA and fuzzy MARCOS as effective tools for optimising lettuce production models. They support farmers in selecting the most favourable solution based on multiple criteria, aiding the shift from mineral fertilisers to sustainable biofertiliser-based systems in intensive production—especially helpful for producers making this transition. Full article

The Loess Plateau has experienced persistent vegetation greening over the past two decades, yet this recovery has occurred under a concurrent intensification of atmospheric evaporative demand and drying. This raises a key land–atmosphere question: which hydroclimatic processes most strongly constrain greening, and where do vegetation responses shift across environmental regimes? To address this issue, we integrated spatiotemporal trend analysis, Geographical Convergent Cross Mapping (GCCM)-based directional attribution, and an interpretable machine-learning framework combining Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to diagnose the dominant controls and threshold-like response patterns of vegetation activity. Using 1 km kernel Normalized Difference Vegetation Index (kNDVI) and eight hydroclimatic variables during 2000–2019, we found that regionally averaged kNDVI increased from 0.099 in 2000 to 0.164 in 2019, with a significant trend of 0.003 year⁻¹, and greening trends covered 65.503% of the Loess Plateau. Over the same period, Vapor Pressure Deficit (VPD) increased from 0.142 to 0.275 kPa (+0.133 kPa), indicating that vegetation recovery did not occur under a more humid atmospheric background. GCCM results consistently showed stronger directional influence from hydroclimatic drivers to kNDVI than the reverse, with evaporation and thermal conditions, especially Tmin, emerging as the dominant constraints, followed by Tmax, VPD, and wind speed, whereas precipitation showed comparatively weaker recoverable influence. The tuned XGBoost model achieved strong out-of-sample performance (R² = 0.9611, RMSE = 0.0188, MAE = 0.0131), and SHAP revealed clear nonlinear thresholds: evaporation and Tmin shifted into persistently positive contribution regimes beyond 302 mm and −17.6 °C, respectively; Tmax became predominantly inhibitory beyond −1.9 °C, and Palmer Drought Severity Index (PDSI) exhibited a multi-stage non-monotonic transition around −0.7. These results provide a coherent evidence chain linking directional influence, relative contribution, and threshold boundaries, offering quantitative support for identifying climate-sensitive zones and restoration risk regimes under continued warming and rising atmospheric dryness. Full article

The increasing demand for energy and the continued reliance on fossil fuels pose important environmental and social challenges, particularly for rural and isolated communities in developing countries that lack reliable access to the grid. Gravitational water vortex turbines (GWVT) are a run-of-river technology for low-head and moderate-flow sites that can provide decentralized electricity without the construction of large reservoirs. The expected environmental impacts are lower; nevertheless, to increase acceptance by the community, there is a necessity to identify and analyze the potential environmental impacts of GWVT in all its life-cycle phases (installation, operation, maintenance, and dismantling). The present study applies the Conesa cause–effect matrix to identify, classify, and analyze the potential environmental impacts associated with GWVT phases. Key identified impacts include removal of vegetation coverage and site disturbance (−32), sediment dynamics alterations (−39), formation of a depleted stretch (−45), accidental releases of hazardous maintenance products (−42), and remobilization of retained sediments (−46). These impacts can produce habitat alteration and fragmentation and loss of ecological connectivity. The relevant significance of energy generation that can have multiple benefits in the local communities was also identified. Primary mitigation measures include the incorporation of environmental flows in the design, sediment management, and strict protocols for hazardous materials. The findings underscore the necessity to conduct site-specific baseline surveys to preserve environmental, socio-economic, and cultural conditions in the local ecosystem and communities. Full article

Rice undergoes rapid growth and exhibits a high demand for nutrients during the tillering and booting stages. SPAD readings, which reflect relative leaf chlorophyll status, and leaf nitrogen content (LNC) are key indicators of plant nutritional status, directly influencing photosynthetic efficiency and biomass accumulation, while plant height (PH) reflects canopy structure and nutrient availability. Establishing quantitative relationships among these traits at key growth stages is essential for stage-specific precision rice management. In this study, Unmanned Aerial Vehicle (UAV) hyperspectral imagery and ground-truth measurements of SPAD, LNC, and PH were collected from rice fields in Qingbaijiang District, Chengdu, China. Twelve vegetation indices (VIs) were calculated, and three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR)—were employed to develop stage-specific retrieval models. A stage-specific modeling framework integrating PH with hyperspectral data was developed to statistically enhance estimation accuracy at the tillering and booting stages. The optimal models for SPAD readings and LNC achieved R² values of 0.916 and 0.936, respectively. The results indicate that integrating canopy structural information with hyperspectral features can improve the estimation accuracy of SPAD-related chlorophyll indicators and nitrogen status in rice. Under the controlled field conditions of this study, the proposed framework provides a plot-scale proof-of-concept demonstration for UAV-based stage-specific nitrogen monitoring. Full article

Urban vegetation is widely promoted as a nature-based solution for mitigating outdoor thermal stress in hot-arid cities, but aggregated or static indicators obscure species-specific behavior, diurnal variability, and the linkage between outdoor comfort and building energy demand in courtyard environments. This study addresses these constraints by integrating outdoor thermal comfort mitigation and cooling energy performance using a reference-based, species-sensitive analytical methodology. The Vegetation Cooling Efficiency Index (VCEI) quantifies vegetation-induced reductions in Physiologically Equivalent Temperature (PET) relative to a non-vegetated reference scenario and is normalized by vegetation coverage. The PET–Energy Sensitivity Index (PESI) characterizes building cooling energy demand’s responsiveness to outdoor thermal comfort. A hybrid approach integrating calibrated field measurements, hourly microclimatic simulations, and dynamic building energy modeling is applied to a university courtyard in Aswan City, Egypt, reflecting extreme hot-arid conditions. The canopy features of Cassia leptophylla (CL), Cassia nodosa (CN), and Ficus nitida (FN) are assessed across varied vegetation coverage ratios. The results show that vegetation covering alone cannot predict thermal mitigation outcomes. PET reduction is influenced by species-specific canopy structure, with peak-hour reductions surpassing 40 °C in dense-canopy species and significantly lower ΔPET values across vegetation coverage levels. The nonlinear relationship between outdoor thermal mitigation and indoor cooling energy demand underscores the necessity for a comprehensive comfort-energy assessment. The proposed indices allow for comprehensive, reference-based vegetation strategy comparison and transferable performance measurements for climate-responsive courtyard and campus design in hot-arid environments. Full article

Pondweeds, an important component of macrophyte vegetation, are influenced by various ecological factors of the aquatic ecosystem. In turn, pondweeds affect the nutrient and sediment dynamics and provide food and shelter for other organisms. As different species have specific environmental preferences and tolerances, they can serve as indicators of the ecological status of water bodies. Here, the ecological preference of the seven most frequent pondweeds in Croatia (Potamogeton berchtoldii, P. crispus, P. lucens, P. natans, P. nodosus, P. perfoliatus and Stuckenia pectinata) for chemical and physical water parameters was studied using 218 vegetation relevés and the accompanying water parameters. CCA revealed the main environmental gradients described by six parameters (chemical oxygen demand, total nitrogen, total phosphorus, electrical conductivity, dissolved oxygen and pH), while ecological responses of the species were further explored by GAMs. Potamogeton berchtoldii, P. lucens, P. natans and P. perfoliatus prefer clean, oxygenated, oligo- to mesotrophic water, and P. crispus and S. pectinata thrived in eutrophic water with low oxygen levels, while P. nodosus is a widespread generalist. The results of this study explain the distribution patterns of Potamogeton and Stuckenia species in Croatia, and add to the general knowledge on their role as bioindicators. Full article

Stormwater runoff represents a significant vector for the transport of organic pollutants and pathogens into aquatic ecosystems, posing serious environmental and public health risks. Although extensively employed for bank stabilization, traditional gabion structures demonstrate constrained efficacy in pollutant removal. In this study, an enhanced ecological gabion (EG) system was developed by integrating a stratified configuration of functional fillers (ceramsite, maifanite, and biochar) with vegetation (Iris germanica). This design leverages synergistic effects to enhance the concurrent removal of dissolved organic matter (DOM), particulate organic matter (POM), and fecal indicator bacteria (FIB) from simulated stormwater. The system was evaluated in continuous flow experiments through comparison with a traditional gravel gabion (TG). Results showed that, compared with the TG, the EG exhibited markedly enhanced removal performance, with chemical oxygen demand (COD), NH₄⁺–N, and TN removal efficiencies being approximately 2.48, 3.68, and 3.56 times those of the TG, respectively. In addition, the EG exhibited significantly higher removal efficiencies for both particulate organic carbon (POC) and dissolved organic carbon (DOC) than the TG, with increases of 329% and 137%, respectively. Fluorescence spectroscopy and particle size distribution analyses revealed that the EG effectively transformed and removed diverse DOM components and fine particulates. The stratified filler media synergistically enhanced pollutant retention, with biochar serving as the primary agent for nutrient and pathogen adsorption. These findings demonstrate the viability of the EG as an integrated, eco-friendly solution for enhanced stormwater purification in riparian zones, providing a compact and multifunctional alternative to conventional end-of-pipe systems. Full article

Land is the fundamental basis for maintaining agricultural production and ensuring food security. The task of managing and sustainably utilizing land resources has always been a priority for every country in the world. The study used GIS-MEC technology to integrate data from seven types of single-factor maps to construct a soil quality map with 47 land units (including eight land units with an area >100 ha, 29 land units with an area from 10 to 100 ha, and 10 land units with an area <10 ha). In addition, by combining soil quality maps and the nutritional needs of different crops, an assessment of land suitability for six major crops was conducted, and three key crops were selected for focused development: rice, vegetables, and flowers. The application of GIS in soil quality management is in line with the current trends of digital transformation and integrated data management in Vietnam and around the world. However, this method has several limitations that need to be considered when applying it, such as dependence on expert expertise, high demands on input data and verification of output results, and limitations in analyzing trends and analyzing social, non-linear factors. Full article

Precise timing of rice harvesting is critical for ensuring grain yield and quality. Traditional manual evaluation methods are highly subjective and time-consuming, highlighting the critical demand for rapid, non-destructive approaches to estimate rice maturity. This study focused on cold-region japonica rice grown in Heilongjiang Province, aiming to develop and validate dual-scale (pot and field) maturity estimation models. For model development, canopy spectral data were collected using two complementary acquisition tools: a ground-based active sensor (CGMD402) and UAV-borne multispectral imagery. Four modeling algorithms—Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM)—were utilized, with input variables comprising single spectral indices (Normalized Difference Vegetation Index, NDVI; Ratio Vegetation Index, RVI) and composite spectral indices (Normalized Difference Maturity Ratio Vegetation Index, NDMRVI; Normalized Difference Pigment Ratio Vegetation Index, NDPRVI). At the pot scale, composite spectral indices showed stronger correlations with rice maturity than single indices. Among the four algorithms, the DT model with combined NDVI + RVI input yielded the optimal comprehensive performance, with a coefficient of determination (R²) of 0.957, a root mean square error (RMSE) of 0.064, and a relative error (RE) of 4.8% in the test set. At the field scale, NDVI and RVI both exhibited strong negative correlations with maturity (Spearman’s correlation coefficients of −0.76 and −0.79, respectively). While the RF model performed best in the training set (R² = 0.752), it was prone to overfitting; in contrast, Multiple Linear Regression (MLR, Ridge Regression) with NDVI + RVI combination demonstrated greater stability in the test set (R² = 0.515, RMSE = 0.116). Notably, composite spectral indices consistently outperformed single indices across all modeling algorithms, but their accuracy was comparable to the optimal single index combination model. To tackle the challenge of scaling models from pot to field conditions, this research developed a “modeling–validation–evaluation–scaling” framework and a four-indicator combined judgment criterion (ΔR²–ΔRMSE–ΔRE–SF). Quantitative analysis showed that the optimal pot-scale model suffered significant accuracy loss during cross-scale transfer: ΔR² = 0.447, ΔRMSE = 0.120, ΔRE = 22.84%, and Scale Transfer Factor (SF) = 2.875. A “regional calibration + residual correction” scheme was proposed, which is expected to reduce the transferred RMSE to below 0.12 and SF to 1.8–2.0. Overall, this research offers a reliable technical method for large-scale, non-destructive monitoring of rice maturity, which can facilitate data-driven precision harvesting decisions. Full article