Search Results (1,339)

To promote sustainable development and guide the responsible use of natural ecosystems, the United Nations introduced the concept of ecosystem accounting. Ecosystem services are key components of ecosystem accounting. Water-related ecosystem services (ES) are of primary importance for Armenia due to relatively dry climate, and dependence on irrigation water for agriculture. This study aims to conduct a pilot-level quantitative scoping assessment and mapping of key water-related regulating ES in accordance with the SEEA-EA guidelines, and to offer recommendations to initiate their accounting in Armenia. We used three Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models—Seasonal Water Yield, Sediment Delivery Ratio, and Urban Flood Risk Mitigation. Input data for these models were sourced from global and national databases, as well as ESRI land cover datasets for 2017 and 2023. Government-reported data on river flow and water consumption were used to assess the ES supply–use balance. The results show that natural ecosystems contribute between 11% and 96% of the modeled ES, with the strongest impact on baseflow supply and erosion prevention. The average current erosion is estimated at 2.3 t/ha/year, and avoided erosion at 46.4 t/ha/year. Ecosystems provide 93% of baseflow, with an average baseflow index of 34%, while on bare ground it is only 3%. Changes in land cover from 2017 to 2023 have resulted in alterations across all assessed ES. Comparison of total water flow and baseflow with water consumption revealed water-deficient provinces. InVEST models show their general operability at the scoping phase of ecosystem accounting planning. Advancing ES accounting in Armenia requires model calibration and validation using local data, along with the integration of InVEST and hydrological and meteorological models to account for the high diversity of natural conditions in Armenia, including terrain, geological structure, soil types, and regional climatic differences. Full article

The transition toward renewable-powered greenhouse agriculture offers opportunities for reducing operational costs and environmental impacts, yet challenges remain in managing fluctuating energy loads and optimizing agricultural inputs. While second-life lithium-ion batteries provide a cost-effective energy storage option, their thermal and electrical characteristics under real-world greenhouse conditions are poorly documented. Similarly, although plasma-activated water (PAW) shows potential to reduce chemical fertilizer usage, its integration with renewable-powered systems requires further investigation. This study develops an adaptive monitoring and modeling framework to estimate the thermal resistances (R_u, R_c) and internal resistance (R_int) of second-life lithium-ion batteries using operational data from greenhouse applications, alongside a field trial assessing PAW effects on beefsteak tomato cultivation. The adaptive control algorithm accurately estimated surface temperature (T_s) and core temperature (T_c), achieving a root mean square error (RMSE) of 0.31 °C, a mean absolute error (MAE) of 0.25 °C, and a percentage error of 0.31%. Thermal resistance values stabilized at R_u ≈ 3.00 °C/W (surface to ambient) and R_c ≈ 2.00 °C/W (core to surface), indicating stable thermal regulation under load variations. Internal resistance (R_int) maintained a baseline of ~1.0–1.2 Ω, with peaks up to 12 Ω during load transitions, confirming the importance of continuous monitoring for performance and degradation prevention in second-life applications. The PAW treatment reduced chemical nitrogen fertilizer use by 31.2% without decreasing total nitrogen availability (69.5 mg/L). The NO₃⁻-N concentration in PAW reached 134 mg/L, with an initial pH of 3.04 neutralized before application, ensuring no adverse effects on germination or growth. Leaf nutrient analysis showed lower nitrogen (1.83% vs. 2.28%) and potassium (1.66% vs. 2.17%) compared to the control, but higher magnesium content (0.59% vs. 0.37%), meeting Japanese adequacy standards. The total yield was 7.8 kg/m², with fruit quality comparable between the PAW and control groups. The integration of adaptive battery monitoring with PAW irrigation demonstrates a practical pathway toward energy efficient and sustainable greenhouse operations. Full article

Cold stress severely limits legume productivity, threatening global food security, particularly in climate-vulnerable regions. This review synthesizes advances in understanding and enhancing cold tolerance in key legumes (chickpea, soybean, lentil, and cowpea), addressing three core questions: (1) molecular/physiological foundations of cold tolerance; (2) how emerging technologies accelerate stress dissection and breeding; and (3) integration strategies and deployment challenges. Legume cold tolerance involves conserved pathways (e.g., ICE-CBF-COR, Inducer of CBF Expression, C-repeat Binding Factor, Cold-Responsive genes) and species-specific mechanisms like soybean’s GmTCF1a-mediated pathway. Multi-omics have identified critical genes (e.g., CaDREB1E in chickpea, NFR5 in pea) underlying adaptive traits (membrane stabilization, osmolyte accumulation) that reduce yield losses by 30–50% in tolerant genotypes. Technologically, AI and high-throughput phenotyping achieve >95% accuracy in early cold detection (3–7 days pre-symptoms) via hyperspectral/thermal imaging; deep learning (e.g., CNN-LSTM hybrids) improves trait prediction by 23% over linear models. Genomic selection cuts breeding cycles by 30–50% (to 3–5 years) using GEBVs (Genomic estimated breeding values) from hundreds of thousands of SNPs (Single-nucleotide polymorphisms). Advanced sensors (LIG-based, LoRaWAN) enable real-time monitoring (±0.1 °C precision, <30 s response), supporting precision irrigation that saves 15–40% water while maintaining yields. Key barriers include multi-omics data standardization and cost constraints in resource-limited regions. Integrating molecular insights with AI-driven phenomics and multi-omics is revolutionizing cold-tolerance breeding, accelerating climate-resilient variety development, and offering a blueprint for sustainable agricultural adaptation. Full article

Despite advances in precision irrigation, no systematic review has yet integrated the roles of remote sensing, agro-meteorological data, and wireless sensor networks in high-value, water-sensitive crops such as mango, avocado, and vineyards. Existing research often isolates technologies or crop types, overlooking their convergence and joint performance in the field. This review fills that gap by examining how these tools estimate crop water demand and support sustainable, site-specific irrigation under variable climate conditions. A structured search across major databases yielded 365 articles, of which 92 met the inclusion criteria. Studies were grouped into four categories: remote sensing, agro-meteorology, wireless sensor networks, and integrated approaches. Remote sensing techniques, including multispectral and thermal imaging, enable the spatial monitoring of vegetation indices and stress indicators, such as the Crop Water Stress Index. Agro-meteorological data feed evapotranspiration models using temperature, humidity, wind, and radiation inputs. Wireless sensor networks provide continuous, localized data on soil moisture and canopy temperature. Integrated approaches combine these sources to improve irrigation recommendations. Findings suggest that combining remote sensing, wireless sensor networks, and agro-meteorological inputs can reduce water use by up to 30% without yield loss. Challenges include sensor calibration, data integration complexity, and limited scalability. This review also compares methodologies and highlights future directions, including artificial intelligence systems, digital twins, and affordable Internet of Things platforms for irrigation optimization. Full article

Rice is a staple crop for over half the world’s population, and accurate, timely information on its planted area and production is crucial for food security and agricultural policy, particularly in developing nations like Sri Lanka. However, reliable rice monitoring in regions like Sri Lanka faces significant challenges due to frequent cloud cover and the fragmented nature of smallholder farms. This research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data. The rice-planted fields were identified and mapped using a phenologically tuned image classification algorithm that highlights rice presence by observing water occurrence during transplanting and vegetation activity during subsequent crop growth. To estimate yields, a random forest regression model was trained at the district level by incorporating a satellite-derived chlorophyll index and environmental variables and subsequently applied at the field level. The approach has enabled the creation of two decades (2000–2022) of reliable, field-scale rice area and yield estimates, achieving map accuracies between 70% and over 90% and yield estimates with less than 20% error. These highly granular results, which are not available through traditional surveys, show a strong correlation with government statistics. They also demonstrate the advantages of a rule-based, phenology-driven classification over purely statistical machine learning models for long-term consistency in dynamic agricultural environments. This work highlights the significant potential of remote sensing to provide accurate and detailed insights into rice cultivation, supporting policy decisions and enhancing food security in Sri Lanka and other cloud-prone regions. Full article

This study develops integrated bioeconomic management strategies for the common squid (Todarodes pacificus) fishery in Korea’s coastal waters, addressing both biological conservation and economic sustainability amid severe stock depletion and declining fishery profitability. Drawing on recent catch data and cost structures for six Total allowable Catch (TAC)-managed fishery types, common squid-specific economic indicators were estimated using a stepwise cost allocation method. Based on previous research using the Catch—Maximum Sustainable Yield (CMSY) model with limited Catch Per Unit Effort (CPUE) data, the biomass in 2020 was estimated at approximately 56% of Biomass at Maximum Sustainable Yield (BMSY), indicating an overfished state. Scenario-based simulations identified TAC allocation thresholds at which net profits reach zero, providing a benchmark for adaptive quota redistribution. Results show variation in economic sensitivity and common squid dependency among fishery types: common squid-dependent gears such as offshore jigging and East Sea trawl exhibit high vulnerability, while multi-species fisheries such as purse seine remain resilient. These results provide a basis for developing tailored management strategies for each fishery, thereby enhancing the effectiveness of interventions. Accordingly, policy recommendations include dynamic TAC adjustments, expanded monitoring, introduction of an Individual Transferable Quota system, and coordinated stock assessments with China and Japan. These findings contribute to refining Korea’s TAC system by aligning stock recovery goals with the economic viability of fishing operations. Full article

The population dynamics and exploitation ratios of the narrow-barred Spanish mackerel (Scomberomorus commerson, Lacépède, 1800) were investigated from August 2020 to February 2023, with data collected from three landing sites (Bushehr, Bandar Abbas, and Chabahar) of Iran waters. During the study period, only length was measured for 6504 specimens and both the length and weight were measured for 504 specimens. The mean fork length of the samples was 86 ± 20 cm, and the mean weight was 6230 ± 3742 g. The relationship between length and weight for the total samples was described by the equation W = 0.022 × CL^2.76 (n = 504, R² = 0.90, 95% C.I. for b = 2.52–2.91). The population dynamics indices for S. commerson were as follows: infinite length (Linf) = 173 cm, natural mortality (M) = 0.47 per year, growth coefficient (K) = 0.52 per year, total mortality (Z) = 1.42 ± 0.06 (95% C.I. = 1.36–1.48), fishing mortality (F) = 0.95 per year, and exploitation coefficient (E) = 0.67. The exploitation rate (U) and total stock at the beginning of the year (B₀) were 0.6 and 48,333 tons, respectively. The annual average standing stock (B_t) was estimated at 30,526 tons. The exploitation ratio for maximum sustainable yield (E_MSY) was 0.50, and fishing mortality at maximum sustainable yield (F_MSY) was 1.5. The estimated range for maximum sustainable yield (MSY, in 1000 tons), the B/B_MSY ratio, F/F_MSY ratio, and saturation (S) ratio of S. commerson in the Iranian part of the Persian Gulf and the Sea of Oman was 20 (17–25), 1.55 (1.25–1.73), 0.90 (0.8–1.12), and 0.45, respectively. The stock of S. commerson is approaching overfishing in Iran waters, imposing immediate management actions to reduce catch and fishing effort. Full article

Ports with Mediterranean-like traffic profiles combine dense passenger, cargo, touristic, and local operations in confined waters where many small craft sail without AIS, increasing collision risk. Nature of such traffic in often unpredictable, due to often and sudden course corrections or changes. In such situations, it is possible that larger ships cannot manoeuvre to avoid collisions with small vessels. Hence, it is important to the port authority to develop a fast and adoptable mean to reduce collision risks. We present an end-to-end shore-based framework that detects and tracks vessels from fixed cameras (YOLOv9 + DeepSORT), estimates speed from monocular lateral video with an artificial neural network (ANN), and visualises collision risk in augmented reality (AR) for VTS/port operators. Validation in the Port of Split using laser rangefinder/GPS ground truth yields MAE 1.98 km/h and RMSE 2.18 km/h (0.605 m/s), with relative errors 2.83–21.97% across vessel classes. We discuss limitations (sample size, weather), failure modes, and deployment pathways. The application uses stationary port camera as an input. The core calculations are performed at user’s computer in the building. Mobile application uses wireless communication to show risk assessment at augmented reality smart phone. For training of ANN, we used The Split Port Ship Classification Dataset. Full article

The spatial distribution traits of microtopography exert a profound influence on the generation of runoff and sediment. Nevertheless, the underlying mechanism through which microtopography alterations, triggered by diverse factors, impact soil erosion remains largely elusive. In light of that, this study simulated conventional farming practices on the Loess Plateau: artificial backhoe, artificial digging, and contour tillage (CT), with no tillage (CK) designated as the control group. The objective was to meticulously investigate the variations in microtopography, runoff, and sediment yield under disparate treatment conditions, rainfall intensities (60 mm/h and 90 mm/h), and slope gradients (5°, 10°, and 20°). The principal findings were as follows: With the amplification of rainfall intensity, the elevation change rate and fractal dimension of various treatments generally exhibited an upward trend, whereas the structural ratio showed a downward tendency. As the slope gradient increased, the elevation change rate and structural ratio of different treatments typically increased. However, the fractal dimension displayed no conspicuous alteration at a rainfall intensity of 60 mm/h and a decreasing trend at 90 mm/h. Under different rainfall intensity scenarios, a robust linear correlation existed between the fractal dimension and both runoff and sediment yield (R² > 0.73), rendering it an outstanding parameter for estimating these variables within the scope of this research. Path analysis revealed that the indirect effect of microtopography on sediment yield, which was mediated by runoff, constituted 77.80–96.47% of the direct effect. Moreover, under different rainfall intensities, the alterations in runoff and sediment yield ensuing from unit-scale changes in the fractal dimension varied significantly. Specifically, at a rainfall intensity of 90 mm/h, these changes were 1.70-fold and 3.75-fold those at 60 mm/h, respectively. Overall, the CT treatment engendered the lowest runoff and sediment yield, along with the highest fractal dimension, thereby emerging as the most efficacious measure for soil and water conservation in this study. The research outcomes offer valuable perspectives for further elucidating the mechanisms through which tillage practices impinge upon soil erosion. Full article

Water distribution networks (WDNs) are critical infrastructure yet vulnerable to contamination, thereby threatening public health. Rapid contaminant detection through sensor systems is essential for water safety. This study compares topological and optimization-based methods for sensor placement under intentional and accidental contamination scenarios triggered by low-pressure events. A novel approach is introduced to model pipe break events that generate low-pressure zones, creating pathways for contamination. Unlike traditional models, this method dynamically estimates contaminant intrusion volume based on the available node pressure. The study reveals that while optimization-based sensor placement yields better outcomes than the topological approach, the performance gap narrows as the number of sensors increases or when the system is tested against scenarios different from those used for optimization. The findings highlight a major issue in sensor detection when water quality is considered. For E. coli contamination in a chlorinated system, two conclusions emerge: rapid inactivation of E. coli makes it an unreliable indicator, even with optimized sensors, and sensor type and detection thresholds significantly affect performance, requiring careful assessment before implementation. This study provides a framework for evaluating sensor systems in WDNs, emphasizing tailored strategies that consider hydraulic conditions and water quality dynamics to improve contamination detection and public safety. Full article

In arid and semi-arid agricultural regions, the increasing frequency of extreme climatic events—particularly high temperatures and drought—has severely disrupted crop growth dynamics, leading to significant yield uncertainty and potential threats to the growing global food demand. Optimizing irrigation strategies by integrating dynamic crop growth monitoring and accurate yield estimation is essential for mitigating the adverse effects of extreme weather and promoting sustainable agricultural development. Therefore, this study conducted two consecutive years of field experiments in cotton fields to evaluate the effects of irrigation interval and drip irrigation frequency on cotton growth dynamics and yield, and to develop an optimized irrigation schedule based on the AquaCrop model assimilated with Particle Swarm Optimization (AquaCrop-PSO). The sensitivity analysis identified the canopy growth coefficient (CGC), maximum canopy cover (CCX), and canopy cover at 90% emergence (CCS) as the most influential parameters for canopy cover (CC) simulation, while the crop coefficient at full canopy (KCTRX), water productivity (WP), and CGC were most sensitive for aboveground biomass (AGB) simulation. Ridge regression models integrating multiple vegetation indices outperformed single-index models in estimating CC and AGB across different growth stages, achieving R² values of 0.73 and 0.87, respectively. Assimilating both CC and AGB as dual-state variables significantly improved the model’s predictive accuracy for cotton yield, with R² values of 0.96 and 0.95 in 2023 and 2024, respectively. Scenario simulations revealed that the optimal irrigation quotas for dry, normal, and wet years were 520 mm, 420 mm, and 420 mm, respectively, with a consistent irrigation interval of five days. This study provides theoretical insights and practical guidance for irrigation scheduling, yield prediction, and smart irrigation management in drip-irrigated cotton fields in Xinjiang, China. Full article

Soil water content (SWC) critically governs the physical and mechanical behavior of soils. However, conventional methods such as oven drying are laborious, time-consuming, and difficult to replicate in the field. To overcome these limitations, we developed an image-based interpolation framework that leverages histogram statistics from 12 soil surface photographs spanning 3.83% to 19.75% SWC under controlled lighting. For each image, pixel-level values of red, green, blue (RGB) channels and hue, saturation, value (HSV) channels were extracted to compute per-channel histograms, whose empirical means and standard deviations were used to parameterize Gaussian probability density functions. Linear interpolation of these parameters yielded synthetic histograms and corresponding images at 1% SWC increments across the 4–19% range. Validation against the original dataset, using dice score (DS), Bhattacharyya distance (BD), and Earth Mover’s Distance (EMD) metrics, demonstrated that the interpolated images closely matched observed color distributions. Average BD was below 0.014, DS above 0.885, and EMD below 0.015 for RGB channels. For HSV channels, average BD was below 0.074, DS above 0.746, and EMD below 0.022. These results indicate that the proposed method reliably generates intermediate SWC data without additional direct measurements, especially with RGB. By reducing reliance on exhaustive sampling and offering a cost-effective dataset augmentation, this approach facilitates large-scale, noninvasive soil moisture estimation and supports machine learning applications where field data are scarce. Full article

The increasing use of reclaimed wastewater in agriculture raises growing concerns about the accumulation of priority organic micropollutants in edible crops. In this study, we developed and validated a novel QuEChERS–SPME–GC/MS method for the simultaneous determination of 15 polycyclic aromatic hydrocarbons (PAHs), 3 nitro-PAHs, and 14 polychlorinated biphenyls congeners in Eruca vesicaria (rocket) leaves. The method was optimized to address the matrix complexity of leafy vegetables and included a two-step dispersive solid-phase extraction (d-SPE) cleanup and aqueous dilution prior to SPME. Validation showed excellent performance, with MDLs between 0.1 and 6.7 µg/kg, recoveries generally between 70 and 120%, and precision (RSD%) below 20%. The greenness of the protocol was assessed using the AGREE metric, yielding a score of 0.60. Application to rocket samples irrigated with treated wastewater revealed no significant accumulation of target pollutants compared to commercial samples. All PCB and N-PAH congeners were below detection limits, and PAH concentrations were low and mostly limited to lighter compounds. Human health risk assessment based on toxic equivalent concentrations confirmed that estimated cancer risk (CR) values 10⁻⁹–10⁻⁸ were well below accepted safety thresholds. These findings support the safe use of reclaimed water for leafy crop irrigation under proper treatment conditions and highlight the suitability of the method for trace-level food safety monitoring. Full article

Mid–long-term streamflow prediction (MLSP) plays a critical role in water resource planning amid growing hydroclimatic and anthropogenic uncertainties. Although AI-based models have demonstrated strong performance in MLSP, their capacity to quantify predictive uncertainty remains limited. To address this challenge, a DeepAR-based probabilistic modeling framework is developed, enabling direct estimation of streamflow distribution parameters and flexible selection of output distributions. The framework is applied to two case studies with distinct hydrological characteristics, where combinations of recurrent model structures (GRU and LSTM) and output distributions (Normal, Student’s t, and Gamma) are systematically evaluated. The results indicate that the choice of output distribution is the most critical factor for predictive performance. The Gamma distribution consistently outperformed those using Normal and Student’s t distributions, due to its ability to better capture the skewed, non-negative nature of streamflow data. Notably, the magnitude of performance gain from using the Gamma distribution is itself region-dependent, proving more significant in the basin with higher streamflow skewness. For instance, in the more skewed Upper Wudongde Reservoir area, the model using LSTM structure and Gamma distribution reduces RMSE by over 27% compared to its Normal-distribution counterpart (from 1407.77 m³/s to 1016.54 m³/s). Furthermore, the Gamma-based models yield superior probabilistic forecasts, achieving not only lower CRPS values but also a more effective balance between high reliability (PICP) and forecast sharpness (MPIW). In contrast, the relative performance between GRU and LSTM architectures was found to be less significant and inconsistent across the different basins. These findings highlight that the DeepAR-based framework delivers consistent enhancement in forecasting accuracy by prioritizing the selection of a physically plausible output distribution, thereby providing stronger and more reliable support for practical applications. Full article

This study developed an efficient cultivation strategy for cabbage production in paddy fields. To address poor drainage, discarded coir substrates (CS) were reused and compared with conventional paddy soil (PS). Four irrigation levels (ETc140, ETc100, ETc60, and ETc0) were applied to both CS and PS to evaluate their interactive effects. An automated irrigation system was deployed, integrating a weather sensor and solenoid valves via a LoRa-based IoT network. Hourly ET₀ was calculated based on Penman–Monteith in real time, and an irrigation event was triggered when cumulative ET₀ reached 1 mm (CS) or 3 mm (PS). The automated irrigation system showed stable performance. Hourly ET₀ estimates were 97% consistent with Korea Meteorological Administration data. The actual total irrigation depth (ID_actual) remained within 2% of the calculated depth (ID). Under moderate irrigation depths (ETc60 and ETc100), the reuse of CS significantly improved cabbage photosynthetic efficiency. Both CS-ETc60 and CS-ETc100 treatments maintained superior yield performance compared with other treatments. This integrated strategy not only offers a practical solution for improving water use efficiency but also enhances the multifunctional utilization of paddy fields, supporting the transition toward more sustainable agricultural practices. Full article