Search Results (166)

Morocco, located at the southern margin of the Mediterranean climate-change hotspot, is exposed to a rapidly evolving precipitation regime whose national-scale characterization remains incomplete. This study delivers an integrated assessment of the spatio-temporal variability and trends of precipitation and its extremes over the country during the most recent World Meteorological Organization (WMO) climate-normal period (1991–2020), based on daily observations from 31 synoptic stations operated by the Direction Générale de la Météorologie (DGM). Trends in annual, seasonal and monthly precipitation were quantified using the non-parametric Mann–Kendall test combined with Sen’s slope estimator, while the structural transformation of the rainfall regime was characterized through three indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI): the Consecutive Dry Days (CDDs), the Simple Daily Intensity Index (SDII) and the amount of precipitation from very wet days (R95pTOT). The results reveal an apparent tendency toward a negative trend, with a predominance of negative precipitation trends in winter and early spring, most pronounced in February, that reach statistical significance at only a limited number of stations, partly offset by a spatially coherent wetting in November over central and eastern Morocco. The joint analysis of the three ETCCDI indices indicates a north–south contrasted reorganization: northern stations exhibit longer dry spells coexisting with intensified extreme rainfall, whereas southern stations show a generalized weakening of both intensity and extremes. These findings point to a structural shift toward more episodic and contrasted precipitation regimes, with the wet season starting later, ending earlier and concentrating rainfall into fewer but more intense events. The analysis provides an updated observational baseline for the validation of CMIP6 based regional projections and for the design of climate-resilient water and agricultural strategies in Morocco. Full article

Modern agriculture faces critical challenges, including climate change, soil degradation, and reliance on synthetic inputs, necessitating sustainable alternatives. A promising approach is applying microbial solutions. This study investigated the effects of a commercially available powder-formulated seed coating product containing Rhizoglomus irregularis and Azospirillum spp. on high-linoleic sunflower (Helianthus annuus L.) under field conditions at three Hungarian sites with contrasting soil properties. Plant growth parameters, yield, and the rhizosphere’s microbial carbon-source utilisation potential were assessed to evaluate treatment effects. Seed-applied inoculation increased plant height, stem diameter, and biomass, although responses varied with site conditions and sampling time. The agronomic response was highly site-dependent: the strongest effect was observed at a low-pH, phosphorus-limited site, where seed inoculation achieved a significant yield increase of 18–21%, while effects at sites with higher pH and sufficient phosphorus resulted only in minor, statistically non-significant numerical trends. Distinct shifts in culture-dependent carbon-source utilisation potential were detected by Biolog EcoPlate™ analysis, showing temporal and site-specific variability. These results indicate that seed-applied formulations offer a logistically feasible, targeted delivery method for plant-beneficial microbes, with their agronomic efficacy fundamentally governed by site-specific soil chemical profiles and baseline nutrient availability. Overall, the agronomic benefits of inoculation appear to be context-dependent and more pronounced under nutrient-limited conditions, highlighting the critical importance of site-specific application strategies and the need for further long-term studies. Full article

The changing climate raises the level of hydroclimatic non-stationarity and export of pollutants at the event scale in agricultural, mixed-land-use, and urbanizing watersheds. In this review, there is an emphasis on nitrogen, phosphorus, and sediment; however, selective references are made to pesticides, pathogens, microplastics, and wet-weather mixed-source processes when characteristics similar to event-driven transport, threshold exceedance, and adaptive control are identified. Drawing on a structured literature search of studies published from 2000 to December 2025, this narrative review synthesizes evidence from 138 selected references on how extreme rainfall, drought–rewetting, warming, and freeze–thaw processes alter source activation, hydrological connectivity, biogeochemical processing, and receiving-water hazards. Our resilience assessment is based on resistance, recovery, robustness, and persistence, which we interpret using exposure, sensitivity, and adaptive capacity. It is shown that standard average-load and fixed-baseline measurements may not detect short pollution pulses, cross-scenario failure, and long-term drift; operational measurement must thus involve event thresholds, recovery trajectories, tail-risk measures, and propagation of uncertainty. Extrapolation, interpretability, data demand, and applicability for data-sparse basins are used to compare process-based, data-driven, and hybrid models. Adaptation options are associated with measurable triggers as part of a monitoring–trigger–action cycle with location-specific instructions for monsoon-agricultural, cold-region, semi-arid and urban systems. The novel aspect of this framework is the integration of mechanism-based evidence, quantitative resilience indicators, model uncertainty, and adaptive governance into one decision-focused workflow. This sustainability-oriented framework advances long-term watershed management by linking water-quality protection and resilient development. Full article

This article presents the results of a study assessing changes in climatic resources in various natural zones of the Zhetysu region, Republic of Kazakhstan, conducted based on long-term climate data for the period 1966 to 2024 (from 12 meteorological stations). The study examines current trends in climatic indicators in spatial and temporal aspects that influence agricultural land use within the region. The first part of this study examines current trends in climate indicators from both spatial and temporal perspectives within the Zhetysu Region of the Republic of Kazakhstan; the second part focuses on studying trends in climate indicators using the non-parametric Mann–Kendall test and the Sen’s slope test, as well as Fisher’s t-test. The authors identified divergent trends in relative air humidity and precipitation and detected a steady trend toward an increase in the average annual air temperature across the region. Based on the analysis of time series of climate-forming and climate–environment-forming indicators, a persistent increasing trend in mean annual air temperature was identified, while relative humidity, precipitation, and evaporation exhibited divergent (both positive and negative) trends across the territory of the region. The developed climate–resource-forming models and a series of estimated applied maps of climate indicators for 1966–1975 and 2016–2024 serve as the scientific basis for climate change forecasting and can be used by administrative bodies to improve agricultural land use strategies in the region. Full article

With the occurrence of global climate change and the depletion of agricultural water resources, there is a growing need to develop rapid, non-destructive, and autonomous plant health monitoring systems. As an economically valuable crop, Ocimum basilicum L. (basil) is sensitive to changes in water availability and may exhibit stress-related morphological variations under drought and over-irrigation conditions. However, due to the visual similarity of leaf symptoms under drought stress, waterlogging stress, and optimal irrigation conditions, accurately distinguishing these conditions remains challenging in practical applications. To address this challenge, this paper presents an attention-based dual-branch deep learning framework designed to extract both subtle leaf details and channel-related features from high-resolution plant images. By combining the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) mechanism in a parallel structure, the proposed network improves the analysis of high-resolution images with an input size of 720 × 720 pixels. Under controlled environmental conditions, with ground-truth labels obtained using soil moisture sensor measurements, the proposed model was compared with eight deep learning architectures, including DenseNet121, InceptionV3, and VGG16. The proposed model achieved a hold-out evaluation accuracy of 99.54%, outperforming the second-best model, DenseNet121, which achieved 96.43%. In addition, the proposed model reached a class-specific precision value of 100% for the Drought Stress category and achieved an area under the receiver operating characteristic curve of 1.00 under the controlled experimental setting. Taylor Diagram analysis also indicated that the model closely preserved the variability pattern of the reference data. These results suggest that the proposed application-specific framework may support non-destructive basil water-stress detection under controlled conditions. After further validation with larger datasets, different cultivars, variable environmental conditions, and real-world agricultural scenarios, the proposed approach may contribute to precision irrigation management and sustainable agricultural production. The contribution of this study should be interpreted as an application-specific implementation and evaluation of complementary attention mechanisms for controlled-environment basil water-stress classification, rather than as the introduction of a fundamentally new deep learning methodology. Full article

With increasing constraints on extensive farming—including soil degradation, salinisation and more frequent climatic anomalies—the development of ‘smart’ agriculture requires the integration of affordable, non-invasive methods for monitoring the physiological state of plants. A key indicator for assessing productivity and the early detection of stress is the rate of photosynthetic CO₂ assimilation (A); however, widely available commercial gas analysers are characterised by high cost, technical complexity and considerable weight, which limits their use in large-scale field studies. Here, a new handheld system for measuring assimilation was developed and tested, based on the accumulative principle of recording changes in CO₂ concentration using simple infrared sensors and without maintaining a constant air flow around the leaf. A comparison was carried out between a prototype of the developed system and a commercial gas analyser when measuring leaf assimilation under irrigation and simulated drought conditions. The results demonstrated the consistency of the readings from the two systems. The developed system is characterised by its compact size, low cost, and the absence of moving parts and consumables. The proposed system has the potential to be effective for large-scale screening tasks and rapid diagnosis of stress-induced changes; it represents a promising, affordable tool for addressing applied tasks in precision agriculture, environmental monitoring and physiological research. Full article

Mediterranean agricultural systems are highly vulnerable to increased climatic variability, which threatens soil water availability and the functionality of the soil carbon (C) cycle. Soil management practices strongly influence water dynamics and C-substrate quality, thus potentially affecting the temperature sensitivity of soil respiration. We evaluated the combined effects of tillage (traditional tillage, TT; reduced tillage, RT), fertilization (mineral, MF; addition of biosolid compost, BC), and rainfall inputs (ambient conditions, C; reduction of 30% rainfall inputs, EX) on soil water content (SWC) and storage (SWS), and in situ soil respiration (Resp) dynamics over three agricultural seasons in a Mediterranean legume–wheat rotation, using a factorial field experiment. We also evaluated how the sensitivity of soil respiration to temperature could be affected by tillage and fertilization types in a complementary laboratory experiment under controlled moisture and temperature conditions. RT was effective in improving SWS and mitigating surface desiccation, although this advantage was attenuated in wet years due to homogenization of moisture along the soil profile. Soil Resp was primarily controlled by SWC. BC stimulated soil respiration mainly during the first crop season, with a residual non-significant trend in the third season. This effect appeared constrained under dry periods, although no significant fertilization × rainfall exclusion interaction was detected. The diurnal cycle of Resp showed a clear decoupling from diurnal soil temperature. Crucially, the intrinsic thermal sensitivity of respiration (Q₁₀) remained stable across all tillage and fertilization treatments, suggesting that field variability is driven by water dynamics and crop phenology and not by microbial responses to changes in substrate availability. Our results confirmed the hierarchical role of climate on C-cycling processes. Full article

Drought is a widespread natural hazard in China that can sequentially trigger meteorological, hydrological, agricultural, and socio-economic drought types, yet traditional drought indices typically focus on a single hydrologic component and cannot capture integrated water deficits across multiple compartments. This study aims to systematically characterize the spatiotemporal evolution of comprehensive drought across China’s ten major river basins and to identify and quantify the main natural and anthropogenic factors associated with drought dynamics. We utilized the Gravity Recovery and Climate Experiment (GRACE) Mascon dataset spanning the entire mission period (April 2002–June 2017), which provides a continuous 15-year observation window suitable for detecting decadal-scale trends and inter-annual variability. Given the documented asynchrony between precipitation and terrestrial water storage changes, a zoned index framework was applied: the Combined Climatologic Deviation Index (CCDI) for arid basins and the Drought Severity Index (DSI) for humid basins. The Theil–Sen estimator and Mann–Kendall test, both non-parametric and robust to outliers, were employed for trend detection, and Pearson correlation analysis was used to evaluate statistical associations between drought indices and potential influencing factors. The results reveal a clear “dry gets drier, wet gets wetter” pattern during 2002–2017: severe drought episodes in humid basins (e.g., the Yangtze) were concentrated in 2002–2006, whereas those in arid basins (e.g., the Haihe) occurred mainly in 2013–2017. Groundwater storage anomaly (GWSA) constituted the primary component of total water storage changes in most basins, with the most rapid depletion rate of −45 mm yr⁻¹ in the northern arid basins. Land use/cover change, especially urban expansion, showed a significant statistical association with drought intensification in arid regions, with its standardized contribution being comparable to that of natural factors such as runoff. This study provides a systematic cross-basin assessment and offers scientific insights for differentiated drought mitigation strategies and water resources management. Full article

The detection and quantification of reactive oxygen and nitrogen species (RONS) in plasma-treated water (PTW) are essential for advancing plasma applications in biomedical and agricultural fields. However, RONS characterization remains challenging, as conventional techniques often require chemical reagents that can alter the sample. Electrochemical impedance spectroscopy (EIS) offers a non-destructive alternative by probing the electrical response of aqueous systems and providing information on ionic concentration, charge transfer, and diffusion processes. This study investigates the feasibility of EIS as a diagnostic tool for characterizing physicochemical changes in PTW. Calibration experiments were performed using saline solutions with different ionic concentrations to evaluate the sensitivity of impedance measurements. Impedance spectra were recorded over a frequency range of 0.1 Hz to 10 kHz and analyzed using Nyquist and Bode plots with equivalent circuit modeling. Deionized water was treated with cold atmospheric plasma at different discharge powers (3.53–10.15 W) and treatment times (5–30 min) to generate RONS. The results show that EIS can monitor plasma-induced changes in conductivity and interfacial properties associated with variations in ionic content. In particular, systematic changes in solution resistance and admittance were observed and were correlated with plasma-induced changes in ionic composition. These findings demonstrate that EIS is a sensitive and non-invasive diagnostic method for PTW analysis. Full article

Intelligent perception systems are essential for precision agriculture, yet their deployment in agricultural field environments is significantly challenged by dense target occlusion and strict resource constraints on edge devices. To address this issue, this paper reviews lightweight object detection technologies from a problem-driven perspective, focusing on the interaction between occlusion-induced feature degradation and limited model capacity under real-world conditions. Unlike existing surveys that mainly summarize model evolution or application scenarios, this work presents a systematic review based on a unified analytical framework to examine how lightweight models compensate for feature loss caused by complex physical factors. Specifically, we analyze the mechanisms underlying feature degradation arising from morphological similarity, extreme scale variation, and dynamic environmental disturbances such as illumination changes and non-rigid deformation. Based on this analysis, recent advances in lightweight detection architectures are comparatively reviewed, including the YOLO series, real-time Transformers, and State Space Models, with an emphasis on their design trade-offs between computational efficiency and representation capability. In addition, key optimization strategies are discussed, such as multi-scale attention mechanisms and dynamic routing for adaptive computation allocation, as well as distribution-aware loss functions for improving localization robustness in densely occluded scenarios. The role of large vision models is also explored, highlighting their lightweight adaptation through knowledge distillation and parameter-efficient fine-tuning. Overall, by synthesizing empirical findings and comparative evidence from the recent literature, this review provides a structured understanding of collaborative optimization pathways and offers evidence-based strategic insights into achieving an effective balance between detection accuracy and computational efficiency for agricultural edge deployment. Full article

Using our terahertz sensor, we addressed the agricultural challenge of nondestructively and cost-effectively detecting internal plant moisture. For plant health assessment, we developed a low-cost nanobolometer imaging sensor array. The proposed terahertz imaging system can detect changes in leaf moisture content under stress, even at low moisture levels. The system enables terahertz imaging of living plant tissues to assess moisture and nutrient distribution in leaves. Because terahertz radiation is non-ionizing and strongly interacts with water molecules, it can reveal internal plant processes. Plant development can also be monitored using time-series imaging. In addition, specialized software was used to enhance the quality of terahertz images and to fuse them with conventional images. This feature enables a more comprehensive assessment of plant health. Such an approach may support future applications, such as disease detection and evaluation of fertilizer effects. Full article

Children living in agricultural regions are exposed to pesticides through multiple environmental and occupational exposure processes; however, the relative contribution of these processes remains insufficiently characterised in many rural contexts of the Global South. This study assessed pesticide exposure among children residing in an agricultural community in western Mexico characterised by close spatial proximity between residential areas and intensively cultivated fields. Urine samples were collected from children at two points in the agricultural cycle (March and December 2018). Pesticide concentrations were determined using liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). Paired longitudinal analyses were conducted to evaluate intra-individual changes in detection frequencies and urinary concentrations across sampling periods. Multiple pesticides were detected, including compounds with near-universal presence across both sampling periods. Significant increases in urinary concentrations were observed between March and December for several pesticides, consistent with seasonal agricultural dynamics, while no systematic differences were identified between children from agricultural and non-agricultural households. These findings indicate that pesticide exposure in this setting operates as a community-level exposure regime that is both structurally produced and territorially embedded. Exposure patterns reflect the convergence of agricultural practices, environmental dispersion processes, and spatial configurations that extend beyond occupational boundaries. The results highlight the limitations of risk models focused exclusively on individual or occupational exposure and underscore the need for public health strategies that address pesticide exposure as a structurally produced and territorially embedded condition. 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

In the ecologically fragile West Liao River Basin, characterizing groundwater dynamics is crucial for sustainable water management. Using 2000–2016 groundwater level data, this study applies Seasonal-Trend decomposition using Loess (STL) and change-point detection to analyse trends. Driving factors are quantified via random forest combined with SHapley Additive exPlanations (SHAP) analysis, and a novel STL–Long Short-Term Memory (STL-LSTM) hybrid model is developed for forecasting. Key findings include: (1) Groundwater levels declined persistently, with a significant change point in 2009. The post-2009 decline rate accelerated to −0.749 m/yr, a 55.7% increase. (2) Statistical attribution reveals that soil moisture (43.5%) and climatic factors (29.0%) are the primary predictors of groundwater variability. The dominance of soil moisture highlights the key role of agricultural irrigation, which strongly modifies soil water dynamics during the growing season. (3) The STL-LSTM model achieves optimal predictive performance (R² = 0.8805, RMSE = 0.7081 m), demonstrating enhanced accuracy for non-stationary sequences. This integrated framework combines trend diagnosis, driver interpretation, and hybrid modelling, offering scientific support for precise groundwater management in semi-arid agricultural basins. Full article

The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and enable consistent tracking of people and objects across non-overlapping views, thereby improving robustness against occlusions and viewpoint changes. This article presents a comprehensive review of multi-camera vision systems published between 2020 and 2025, covering application domains including public security and biometrics, intelligent transportation, smart cities and IoT, healthcare monitoring, precision agriculture, industry and robotics, pan–tilt–zoom (PTZ) camera networks, and emerging areas such as retail and forensic analysis. The review synthesizes predominant technical approaches, including deep-learning-based detection, multi-target multi-camera tracking (MTMCT), re-identification (Re-ID), spatiotemporal fusion, and edge computing architectures. Persistent challenges are identified, particularly in inter-camera data association, scalability, computational efficiency, privacy preservation, and dataset availability. Emerging trends such as distributed edge AI, cooperative camera networks, and active perception are discussed to outline future research directions toward scalable, privacy-aware, and intelligent multi-camera infrastructures. Full article