Search Results (477)

In regional traditional landscape studies where continuous literature and physical relics are scarce, image-based materials serve as a crucial medium for reconstructing historical spatial structures. This study focuses on the sandbar polder landscapes in the Yangzhong area, located in the lower Yangtze River. By integrating historical maps, military cartographic surveys, CORONA satellite imagery, and modern remote sensing data, this study developed a multi-source image interpretation framework to reconstruct the traditional dike–water–field–settlement spatial structure. Employing image recognition and morphological analysis, the study extracted features such as dikes, water systems, and settlements, revealing their adaptation mechanisms to microtopography and associated ecological functions, including multi-level irrigation and drainage, hydrological buffering, and flood prevention. The results demonstrate that traditional sandbar polder landscapes exhibit a high degree of experiential adaptation, and their spatial organization offers valuable insights for future green infrastructure planning. The study confirms the applicability of image-based interpretation methods for historical landscape reconstruction and provides a practical path for the activation and translation of traditional landscape units in contemporary urban–rural governance. Full article

The article presents an analysis of current (during the period 1985–2022) and projected (during the period 2025–2099) changes in the hydrological regime of the Buktyrma, Yesil, and Zhaiyk river basins in Kazakhstan under the conditions of global climate change. This study is based on the integration of data from General Circulation Models (GCMs) of the sixth phase of the CMIP6 project, socio-economic development scenarios SSP2-4.5 and SSP5-8.5, as well as the results of hydrological modelling using the SWIM model. The studies were carried out with an integrated approach to hydrological change assessment, taking into account scenario modelling, uncertainty analysis and the use of bias correction methods for climate data. A calculation method was used to analyse the intra-annual distribution of runoff, taking into account climate change. Detailed forecasts of changes in runoff and intra-annual water distribution up to the end of the 21st century for key water bodies in Kazakhstan were obtained. While the projections of river flow and hydrological parameters under CMIP6 scenarios are actively pursued worldwide, few studies have explicitly focused on forecasting intra-annual flow distribution in Central Asia, calculated using a methodology appropriate for this region and using CMIP6 ensemble scenarios. There have been studies on changes in the intra-annual distribution of runoff for individual river basins or local areas, but for the historical period, there have also been studies on modelling runoff forecasts using CMIP6 climate models, but have been very few systematic publications on the distribution of predicted intra-annual runoff in Central Asia, and this issue has not been fully studied. The projections suggest an intensification of flow seasonality (1), earlier flood peaks (2), reduced summer discharges (3) and an increased likelihood of extreme hydrological events under future climatic conditions. Changes in the seasonal structure of river flow in Central Asia are caused by both climatic factors—temperature, precipitation and glacier degradation—and significant anthropogenic influences, including irrigation and water management structures. These changes directly affect the risks of flooding and water shortages, as well as the adaptive capacity of water management systems. Given the high level of water management challenges and interregional conflicts over water use, the intra-annual distribution of runoff is important for long-term planning, the development of adaptation measures, and the formulation of public policy on sustainable water management in the face of growing climate challenges. This is critically important for water, agricultural, energy, and environmental planning in a region that already faces annual water management challenges and conflicts due to the uneven seasonal distribution of resources. Full article

The Elfeija Dune Field (EDF) is a continental aeolian system in an arid region of southeastern Morocco. Studying this system is critical for understanding the effects of mounting climatic and anthropogenic pressures. This study provides a comprehensive characterization of the EDF’s morphology, sedimentology, aeolian dynamics, genesis, and recent evolution. A multi-scale, multidisciplinary approach was adopted, integrating field observations, sedimentological analyses, MERRA-2 reanalysis wind data, cartographic analysis, digital terrain modeling, and morphometric measurements. The results reveal an active 30 km² dune field, elongated WSW-ENE, which is divisible into three morphodynamic zones with a high dune density (80–90 dunes/km²). The wind regime is predominantly from the W to WSW, driving a net ENE sand transport and creating conditions conducive to barchan formation (RDP/DP > 0.78). Sediments are quartz dominated, with significant calcite and various clay minerals (illite, kaolinite, and smectite). Dune sands are primarily fine- to medium-grained and well sorted, in contrast to the more poorly sorted interdune deposits. The landscape is dominated by barchans (mean height H = 2.5 m; mean length L = 50 m) and their coalescent forms, indicating sustained aeolian activity. The potential sand flux was estimated at 1.7 kg/m/s, with a dune collision probability of 32%. The field’s genesis is hypothesized to be controlled by a topographically induced Venturi effect, with an initiation approximately 1000 years ago, potentially linked to the Medieval Climatic Optimum. Significant anthropogenic impacts from expanding irrigated agriculture are observed at the dune field margins. By providing a detailed characterization of the EDF and its sensitivity to natural and anthropogenic forcings, this study establishes a critical baseline for the sustainable management of arid environments. Full article

This paper investigates the optimization of an irrigation system distributed over an agricultural area discretized into unit cells, using evolutionary algorithms for the control of water irrigation points (taps). The model simulates the distribution of water through strategically placed irrigation points, considering the individual requirements of each cell. The main objective is to minimize the difference between the amount of water needed and delivered, while reducing the total consumption. The dynamics of fitness over generations are analyzed, as well as the average behavior of deficit, surplus, and relative humidity. The results highlight a relatively uniform distribution of delivered water and a stable convergence of the fitness function, demonstrating the efficiency of the proposed method in managing water resources in a sustainable way. In this matter, compared to the full-activation scenario, the presented model reduced total water use by more than 50%, achieving zero deficit, minimal surplus, and a 46% improvement in overall fitness. Although the approach demonstrates promising results in simulated scenarios, it does not currently incorporate real-time sensor data or field validation, which are planned for future development. The study provides a solid basis for the development of smart irrigation systems, adaptable to the variability of soil and climatic conditions. Full article

Agricultural activities are widely recognized as major sources of water pollution, primarily due to the introduction of heavy metals (HMs) through fertilizers, pesticides, manures, sewage sludge, and irrigation water. Owing to their persistence and non-biodegradability, these metals pose substantial risks to ecosystems and public health. While certain HMs such as cobalt, copper, and zinc are essential micronutrients for crops at low concentrations, others—like arsenic, cadmium, lead, and mercury—enter agricultural systems as contaminants and serve no biological function in plants. This paper explores the complex issue of HM contamination in water resulting from agricultural practices. It reviews the primary sources and pathways through which HMs enter aquatic systems, discusses their ecological and health impacts, and examines analytical methods used for HM detection and monitoring. In response to this challenge, several mitigation strategies are highlighted, including the optimized use of agrochemicals, adoption of sustainable farming practices, and implementation of phytoremediation and bioremediation techniques. Additionally, the importance of community education and regulatory enforcement is emphasized as part of an integrated approach to pollution control. Ultimately, this paper underscores the need for balanced solutions that safeguard water resources while maintaining agricultural productivity. Full article

Investigating the physiological responses and resistance mechanisms in plants under drought stress provides critical insights for optimizing irrigation water utilization efficiency and promoting the development of irrigation science. In this study, cotton seedlings were cultivated in a light incubator. Three drought stress levels were applied: mild (M1, 50–55% field moisture), moderate (M2, 45–50%), and severe (M3, 40–45%). Transcriptome analysis was performed under mild and severe stress. The results revealed that differentially expressed genes (DEGs) related to proline degradation were down-regulated and proline content increased in cotton. Under different stress treatments, cotton exhibited a stress-intensity-dependent regulation of carbohydrate metabolism and soluble sugar content decreased and then increased. And the malondialdehyde content analysis revealed a dose-dependent relationship between stress intensity and membrane lipid peroxidation. Stress activated the antioxidant system, leading to the down-regulation of DEGs for reactive oxygen species production in the mitogen-activated protein kinase (MAPK) signaling pathway. Concurrently, superoxide dismutase activity and peroxidase content increased to mitigate oxidative damage. Meanwhile, the photosynthetic performance of cotton seedlings was inhibited. Chlorophyll content, stomatal conductance, the net photosynthetic rate, the transpiration rate and water use efficiency were significantly reduced; intercellular carbon dioxide concentration and leaf stomatal limitation value increased. But photosynthesis genes (e.g., PSBO (oxygen-evolving enhancer protein 1), RBCS (ribulose bisphosphate carboxylase small chain), and FBA2 (fructose-bisphosphate aldolase 1)) in cotton were up-regulated to coordinate the photosynthetic process. Furthermore, cotton seedlings differentially regulated key biosynthesis and signaling components of phytohormonal pathways including abscisic acid, indoleacetic acid and gibberellin. This study elucidates the significant gene expression of drought-responsive transcriptional networks and relevant physiological response in cotton seedlings and offers a theoretical basis for developing water-saving irrigation strategies. Full article

Cyanotoxins in agricultural waters pose a human and animal health risk. These toxins can be transported to nearby crops and soil during irrigation practices; they can remain in the soil for extended periods and be adsorbed by root systems. Additionally, in livestock watering ponds, cyanotoxins pose a direct ingestion risk. This work evaluated the performance of the random forest algorithm in estimating microcystin concentrations using eight in situ water quality measurements at one active livestock water pond and two working irrigation ponds in Georgia and Maryland, USA. Measurements of microcystin along with eight in situ-sensed water quality parameters were used to train and test the machine learning model. The models performed better at the Georgia ponds compared to the Maryland pond, and interior models performed better than nearshore or whole-pond models. The most important variables for microcystin prediction were water temperature and phytoplankton pigments. Overall, the random forest algorithm(RF), augmented with a ‘trainControl’ function to perform repeated cross validations, was able to explain 40% to 70% of the microcystin concentration variation in the three agricultural ponds. Water quality measurements showed potential to aid water monitoring/sampling design by predicting the microcystin concentrations in the studied ponds by using readily available and easy to collect in situ data. Full article

This study examines diurnal surface temperature dynamics across major Moroccan cities during the growing season and explores the interaction between urban and vegetated surfaces. We also introduce the Urban Thermal Impact Ratio (UTIR), a novel metric designed to quantify urban thermal comfort as a function of the surface urban heat island (SUHI) intensity. The analysis is based on outputs from a land surface model (LSM) for the year 2010, integrating high-resolution Landsat and MODIS data to characterize land cover and biophysical parameters across twelve land cover types. Our findings reveal moderate urban–vegetation temperature differences in coastal cities like Tangier (1.8 °C) and Rabat (1.0 °C), where winter vegetation remains active. In inland areas, urban morphology plays a more dominant role: Fes, with a 20% impervious surface area (ISA), exhibits a smaller SUHI than Meknes (5% ISA), due to higher urban heating in the latter. The Atlantic desert city of Dakhla shows a distinct pattern, with a nighttime SUHI of 2.1 °C and a daytime urban cooling of −0.7 °C, driven by irrigated parks and lawns enhancing evapotranspiration and shading. At the regional scale, summer UTIR values remain below one in Tangier-Tetouan-Al Hoceima, Rabat-Sale-Kenitra, and Casablanca-Settat, suggesting that urban conditions generally stay within thermal comfort thresholds. In contrast, higher UTIR values in Marrakech-Safi, Beni Mellal-Khénifra, and Guelmim-Oued Noun indicate elevated heat discomfort. At the city scale, the UTIR in Tangier, Rabat, and Casablanca demonstrates a clear diurnal pattern: it emerges around 11:00 a.m., peaks at 1:00 p.m., and fades by 3:00 p.m. This study highlights the critical role of vegetation in regulating urban surface temperatures and modulating urban–rural thermal contrasts. The UTIR provides a practical, scalable indicator of urban heat stress, particularly valuable in data-scarce settings. These findings carry significant implications for climate-resilient urban planning, optimized energy use, and the design of public health early warning systems in the context of climate change. Full article

A field experiment in growing spring wheat (Triticum aestivum L.—cv. ‘Monsun’) under organic, integrated and conventional farming systems was conducted over the period of 2020–2022 at the Czesławice Experimental Farm (Lubelskie Voivodeship, Poland). The first experimental factor analyzed was the farming system: A. organic system (control)—without the use of chemical plant protection products and NPK mineral fertilization; B. conventional system—the use of plant protection products and NPK fertilization in the range and doses recommended for spring wheat; C. integrated system—use of plant protection products and NPK fertilization in an “economical” way—doses reduced by 50%. The second experimental factor was irrigation strategy: 1. no irrigation—control; 2. double irrigation; 3. multiple irrigation The aim of the research was to determine the physical, chemical, and enzymatic properties of loess soil under spring wheat crops as influenced by the factors listed above. The highest organic C content of the soil (1.11%) was determined in the integrated system with multiple irrigation of spring wheat, whereas the lowest one (0.77%)—in the conventional system without irrigation. In the conventional system, the highest contents of total N (0.15%), P (131.4 mg kg⁻¹), and K (269.6 mg kg⁻¹) in the soil were determined under conditions of multiple irrigation. In turn, the organic system facilitated the highest contents of Mg, B, Cu, Mn, and Zn in the soil, especially upon multiple irrigation of crops. It also had the most beneficial effect on the evaluated physical parameters of the soil. In each farming system, the multiple irrigation of spring wheat significantly increased moisture content, density, and compaction of the soil and also improved its total sorption capacity (particularly in the integrated system). The highest count of beneficial fungi, the lowest population number of pathogenic fungi, and the highest count of actinobacteria were recorded in the soil from the organic system. Activity of soil enzymes was the highest in the integrated system, followed by the organic system—particularly upon multiple irrigation of crops. Summing up, the present study results demonstrate varied effects of the farming systems on the quality and health of loess soil. From a scientific point of view, the integrated farming system ensures the most stable and balanced physicochemical and biological parameters of the soil due to the sufficient amount of nutrients supplied to the soil and the minimized impact of chemical plant protection products on the soil. The multiple irrigation of crops resulting from indications of soil moisture sensors mounted on plots (indicating the real need for irrigation) contributed to the improvement of almost all analyzed soil quality indices. Multiple irrigation generated high costs, but in combination with fertilization and chemical crop protection (conventional and integrated system), it influenced the high productivity of spring wheat and compensated for the incurred costs (the greatest profit). Full article

Effective detection of center-pivot irrigation systems is crucial in understanding agricultural activity and managing groundwater resources for sustainable uses, especially in semi-arid regions such as North Dakota, where irrigation primarily depends on groundwater resources. In this study, we have adopted YOLOv11 to detect the center-pivot irrigation systems using multiple remote sensing datasets, including Landsat 8, Sentinel-2, and NAIP (National Agriculture Imagery Program). We developed an ArcGIS custom tool to facilitate data preparation and large-scale model execution for YOLOv11, which was not included in the ArcGIS Pro deep learning package. YOLOv11 was compared against other popular deep learning model architectures such as U-Net, Faster R-CNN, and Mask R-CNN. YOLOv11, using Landsat 8 panchromatic data, achieved the highest detection accuracy (precision: 0.98; recall: 0.91; and F1-score: 0.94) among all tested datasets and models. Spatial autocorrelation and hotspot analysis revealed systematic prediction errors, suggesting a need to adjust training data regionally. Our research demonstrates the potential of deep learning in combination with GIS-based workflows for large-scale irrigation system analysis, adopting precision agricultural technologies for sustainable water resource management. Full article

Introduction: Successful root canal therapy relies on thorough cleaning and disinfection to eliminate microorganisms and residual pulp tissue. Advanced irrigation activation techniques, including Sonic, Ultrasonic, and Diode Laser activation, have improved cleaning efficacy, bacterial reduction, smear layer removal, and irrigant hydrodynamics. On the other hand, these irrigation activation techniques may lead to a temperature rise that may risk the surrounding periodontal tissue. Thus, this study aimed to investigate the temperature rise during different irrigation activation techniques at various time intervals and evaluate the efficacy of these techniques in removing biofilm-mimicking hydrogel BMH of a simulated root canal system in 3D-printed tooth models. Methods: Ten extracted human mandibular premolars, prepared to size 40/0.04 taper, and a hundred 3D-printed resin premolars with simulated main (0.25 mm) and lateral canals (0.15 mm at 3, 7, 11 mm from apex) were used; 50 of them were filled with biofilm-mimicking hydrogel (BMH). Five irrigation activation techniques were evaluated: Diode Laser, Ultrasonic, Sonic, XP-Finisher, and Control (n = 10). Temperature rises were measured on the extracted premolars after 30 and 60 s of activation using a thermographic camera in a controlled environment (23 ± 2 °C). Irrigant penetration, with and without BMH, was assessed in 3D-printed premolars using a 2.5% sodium hypochlorite-contrast medium mixture, visualized with a CMOS radiographic sensor. Penetration was scored (main canal: 3 points; lateral canals: 0–2 points) and analyzed with non-parametric tests. Results: Diode Laser activation technique resulted in the highest temperature rise on the external root surface, followed by the Ultrasonic, with no statistically significant difference observed among the remaining groups. In terms of efficacy, Ultrasonic and Sonic activation achieved significantly greater irrigant penetration in samples without BMH, and greater BMH removal in samples with BMH, compared to Diode Laser, XP-Finisher, and Control groups. Conclusions: In this in vitro study, Diode Laser caused the highest temperature rise, followed by Ultrasonic, with significant increases from 30 to 60 s. Temperature rise did not significantly affect penetration or BMH removal. Ultrasonic and Sonic irrigation techniques achieved the highest depth of penetration (without BMH) and biofilm-mimicking Hydrogel removal (with BMH) compared to Diode Laser, XP-Finisher, and Control. Full article

The interactions between iron oxides and organic carbon within the particulate organic matter (POM) and mineral-associated organic matter (MAOM) fractions in paddy soils remain insufficiently understood, yet they are likely crucial for unlocking the carbon sequestration potential of these systems. In this study, we investigated the distribution of soil iron oxides and organic carbon within POM and MAOM fractions following 10 years of continuous irrigation and organic amendment management. We also examined the relationship between iron oxide transformation and these two SOC (soil organic carbon) fractions. Our results demonstrated that, under both flooded irrigation and controlled irrigation regimes, straw return or manure application effectively enhanced soil carbon sequestration, as evidenced by increases in both POM-C (POM-associated organic carbon) and MAOM-C (MAOM-associated organic carbon) contents. Meanwhile, exogenous carbon inputs promoted the transformation of crystalline iron oxides into short-range ordered iron oxides and iron oxide colloids, thereby enhancing the activation and complexation degree of soil iron oxides and facilitating the formation of Fe-bound organic carbon. Further regression analysis revealed that the activation degree of iron oxides had a stronger influence on POM-C, whereas the complexation degree had a greater effect on MAOM-C. This implies that exogenous carbon inputs are effective in promoting soil carbon sequestration in both flooded and water-saving irrigated rice paddies and that iron oxide transformation plays a key role in mediating this effect. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

Agricultural water resource management is increasingly challenged by climate variability, land degradation, and socio-economic pressures, particularly in the Mediterranean region. This study, conducted in 2023–2024 within the REACT4MED project (PRIMA initiative), addresses sustainable water use through a comparative analysis of organic and conventional farms in the Stornara and Tara area (Puglia, Italy). The research aimed to identify critical indicators for sustainable water management and develop ecosystem restoration strategies that can be replicated across similar Mediterranean agro-ecosystems. An interdisciplinary, participatory approach was adopted, combining technical analyses and stakeholder engagement through three workshops involving 30 participants from diverse sectors. Fieldwork and laboratory assessments included soil sampling and analysis of parameters such as pH, electrical conductivity, soil organic carbon, nutrients, and salinity. Cartographic studies of vegetation, land use, and pedological characterization supplemented the dataset. The key challenges identified were water loss in distribution systems, seawater intrusion, water pumping from unauthorized wells, and inadequate public policies. Soil quality was significantly influenced by salt stress, hence affecting crop productivity, while socio-economic factors affected farm income. Restoration strategies emphasized the need for water-efficient irrigation, less water-intensive crops, and green vegetation in infrastructure channels while incorporating also the native flora. Enhancing plant biodiversity through weed management in drainage channels proved beneficial for pathogen control. Proposed socio-economic measures include increased inclusion of women and youth in agricultural management activities. Integrated technical and participatory approaches are essential for effective water resource governance in Mediterranean agriculture. This study offers scalable, context-specific indicators and solutions for sustainable land and water management in the face of ongoing desertification and climate stress. Full article

Background: With the inclusion of evidence-based dentistry, numerous systematic reviews (SRs) and meta-analyses (MAs) have been conducted in endodontics with the best available scientific evidence to improve diagnosis and treatment. Objective: To synthesize the scientific evidence on the effectiveness of irrigation protocols in endodontic therapy. Methods: Following the umbrella review methodology (UR), a comprehensive literature search was conducted using scientific and grey literature databases. A quality evaluation and a descriptive analysis of the included SRs and MAs were conducted. Quantitative comparability between MAs was carried out. Results: Four descriptive SRs and nine MAs were included. Eight articles evidenced high methodological quality. Studies showed the effectiveness and efficacy depending on the study design, the findings of primary clinical trials, and factors related to the type of irrigant, concentration, volume, and irrigation systems. Variability between irrigants and protocols was observed. Follow-up periods extend from hours to years, and there were different study samples. SRs and MAs evidenced limitations regarding methodological aspects. Low overlap of the primary studies was found. Quantitative analyses indicated greater efficacy in microbial reduction and apical healing in favor of passive ultrasonic irrigation (PUI; RD −0.15; 95% CI −0.28, −0.01; p = 0.03; I2 = 60%; RD −0.09; 95% CI −0.16, −0.02; p = 0.01; I2 = 0%, respectively). Conclusions: This UR highlights the importance of root canal disinfection, emphasizing sodium hypochlorite (NaOCl) as the primary irrigant. Enhanced activation methods, such as PUI and lasers, improve irrigant efficiency, while alternatives like chlorhexidine (CHX) offer better biocompatibility. Standardized protocols and evidence-based clinical guidelines are needed. PROSPERO register: CRD42023409044. Full article