Search Results (11,663)

Selecting suitable agricultural land is critical for food security and sustainable development, particularly in arid regions facing resource scarcity and environmental degradation. This study assesses agricultural land suitability in Sohag governorate, Egypt, using a hybrid Geographic Information System (GIS), Fuzzy Analytical Hierarchy Process (FAHP) and geostatistical approach. Thirty-four representative soil profiles were morphologically described and analyzed for ten physical (slope, depth, erosion, texture, stoniness, drainage) and chemical (EC, pH, CaCO₃, organic matter) criteria, weighted using both the Analytical Hierarchy Process (AHP) and FAHP. Geostatistical analysis characterized the spatial variability in soil properties across the study area. The two suitability maps were validated using Receiver Operating Characteristic (ROC) curves and Kappa statistics: the FAHP achieved higher prediction accuracy (AUC = 0.83, Kappa = 0.91) than the AHP (AUC = 0.81, Kappa = 0.88). The AHP-based map classified 40% (1154.92 km²) as moderately suitable (S2), 33% (952.81 km²) as marginally suitable (S3), and 27% (779.57 km²) as unsuitable (N); the FAHP-based map classified 42% (1212.67 km²) as S2, 30% (866.19 km²) as S3, and 28% (808.44 km²) as N. The proposed framework may serve as a reference for similar arid environments and support progress toward Sustainable Development Goals 2, 6, 8, 9, 11, 13 and 15, conditional on local soil, water, climate and management conditions. Full article

Soil moisture is a key indicator of agricultural and hydrological drought, but its meteorological controls vary across temporal scales. Using ESA CCI soil moisture products from 2010 to 2022, this study investigated soil moisture variability in the Yangtze River Basin at weekly, monthly, and annual scales. The product was first validated using ground observations and ERA5 reanalysis data, and a generalized additive model (GAM) was then applied to quantify the relative contributions of precipitation, temperature, wind speed, and VPD under normal conditions and during three extreme drought events. The validation showed that ESA CCI soil moisture captured basin-scale variations well, with mean absolute deviations of 0.0460, 0.0434, and 0.0072 m³/m³ in the upper, middle, and lower reaches, respectively, and a basin-wide RMSE of 0.0127 m³/m³ against ERA5. The attribution results revealed a clear scale-dependent shift in soil moisture controls. At the weekly scale, VPD dominated soil moisture variability, with a basin-wide average contribution of 47.04% and a maximum contribution of 55.92% in the lower reaches, indicating that short-term soil drying is mainly driven by VPD. At the monthly scale, precipitation became the primary control, with a basin-wide average contribution of 36.62% and a maximum contribution of 44.65% in the upper reaches, reflecting the role of accumulated rainfall recharge in maintaining soil moisture storage. During extreme drought events, the monthly-scale dominance of precipitation weakened, and precipitation, VPD, temperature, and wind speed each contributed approximately 20–30%, suggesting that drought development results from the combined effects of reduced water input and enhanced atmospheric water loss. These findings indicate that precipitation-based drought monitoring may underestimate rapid soil drying risks, whereas incorporating atmospheric demand indicators such as VPD can improve drought early warning and water resource management under a warming climate. Full article

Desertification represents a critical environmental challenge in arid and semi-arid regions, driven by the combined impacts of climate change and unsustainable land-use practices. This review provides a comparative assessment of desertification dynamics, mitigation strategies, and ecological restoration approaches in the Ningxia Hui Autonomous Region (China) and Egypt. Both regions are characterized by severe water scarcity, increasing climatic variability, and fragile ecosystems; however, they differ in ecological conditions, institutional frameworks, and dominant land degradation processes. The study synthesizes major drivers of desertification, including rising temperatures, precipitation variability, recurrent droughts, soil salinization, overgrazing, wind erosion, and unsustainable agricultural expansion. It further evaluates key control measures implemented in both regions, such as afforestation and ecological engineering, sand dune stabilization, water-efficient irrigation systems, soil rehabilitation practices, and the integration of remote sensing and GIS-based monitoring technologies. The analysis highlights China’s large-scale, long-term ecological restoration programs, which have significantly improved vegetation cover and reduced land degradation, compared to Egypt’s emphasis on irrigation efficiency, land reclamation, and salinity management under extreme aridity constraints. Importantly, the comparison underscores institutional differences, with China’s state-led ecological engineering contrasting against Egypt’s multi-actor reclamation initiatives, offering novel insights into governance pathways for combating desertification. The comparative synthesis demonstrates that effective desertification control requires integrated strategies combining ecological restoration, sustainable water resource management, technological innovation, and strong policy support. Despite contextual differences, both regions offer complementary lessons for dryland management. The study emphasizes the potential for enhanced China–Egypt cooperation in climate-smart agriculture, digital environmental monitoring, and nature-based solutions, thereby advancing sustainable land restoration and contributing to global efforts toward land degradation neutrality under future climate change scenarios. Full article

Rapid conversion of agricultural land into low-density housing reshapes local water balances at a time when climate change is intensifying hydrological extremes in Central Europe. This study evaluates the rooftop rainwater harvesting (RWH) potential of a newly urbanized housing estate in Rokietnica (Greater Poland Voivodeship, Poland) using a publicly available true orthophoto (5 cm ground sampling distance) as a reliable geometric data source. Roof footprints of 41 single-family buildings (5305 m² in total) were manually vectorized in QGIS and combined with monthly precipitation recorded at the nearest meteorological station (Złotniki) in a simplified volumetric model (V = A × R × C, with C = 0.95). To place the single reference year (2021) in its climatic context, the full 72-year precipitation record (1952–2023) was analyzed using the Mann–Kendall test and Sen’s slope estimator. The results indicate a harvestable volume of approximately 2642 m³ in 2021 (53–73 m³ per building; mean 64 m³), with May and August jointly accounting for almost one-third of the annual total. Annual precipitation at Złotniki exhibits a statistically significant increasing trend (+1.29 mm yr⁻¹; p = 0.028) concentrated in winter and early spring, while summer totals remain trendless but highly variable. A monthly storage simulation driven by the recent 30-year record (1994–2023) shows that summer garden irrigation, the dominant practical application of harvested rainwater in Polish households, is considerably harder to meet from the roof alone than year-round indoor non-potable uses: a 5 m³ tank covers approximately 73% of the seasonal demand of a 100 m² garden plot, while a 3–5 m³ tank would theoretically secure 91–98% of toilet-flushing demand for a four-person household. Under Poland’s national rainwater co-financing scheme (formerly “Moja Woda,” active 2020–2024, succeeded by “Mikroretencja” from June 2026), simple payback periods of roughly 3–7 years make household installations financially defensible. The findings suggest that true orthophotos enable rapid, low-cost RWH assessments in dynamically developing suburbs, and that storage sizing should anticipate the ongoing seasonal redistribution of precipitation under climate change. To our knowledge, this is the first study to combine free national true-orthophoto imagery, a 72-year Mann–Kendall trend analysis, and a subsidy-linked storage-reliability simulation within a single suburban rainwater-harvesting assessment. Full article

The excessive use of conventional NPK fertilizers can reduce nutrient use efficiency due to nutrient losses, emphasizing the need for controlled-release fertilizer systems. This study aimed to prepare and evaluate olive pomace-derived biochar (BC) as a carrier for nitrogen–phosphorus–potassium (NPK) fertilizer and assess its effects on nutrient release and lettuce performance. Biochar was produced by pyrolysis at 400 °C and loaded with NPK fertilizer. The BC/NPK composite was characterized using Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD), nutrient-release behavior was evaluated in deionized water and soil. The cumulative nutrient release reached 64% in deionized water and 91% in soil. Under greenhouse conditions, BC/NPK applied at 100% and 75% NPK rates increased lettuce fresh weight, plant height and leaf number to 163.33 and 183.67 g, 23.00 and 23.67 cm, 34 and 36, respectively. Moreover, BC/NPK reduced nitrate accumulation in lettuce leaves, with nitrate concentrations decreasing in outer leaves to 10 and 0.73 mg g⁻¹ and in inner leaves to 2 and 1.33 mg g⁻¹ at the 100% and 75% rates, respectively. These findings demonstrate that olive pomace-derived BC/NPK is a promising slow-release fertilizer capable of improving crop performance while supporting sustainable nutrient management and agricultural waste valorization. Full article

Mediterranean agriculture is increasingly constrained by severe and recurrent water scarcity. Rising temperatures, declining precipitation, and more frequent droughts are driving higher crop water demand and reducing rainfall reliability. Combined with growing competition for limited freshwater resources, these climatic pressures pose a major challenge to the long-term sustainability and productivity of farming systems in the region. In this context, urban treated wastewater (UTWW) is increasingly recognized as a strategic alternative resource. Regulation (EU) 2020/741 establishes minimum quality requirements for agricultural water reuse based on the fit-for-purpose principle. The Italian regulatory framework requires regional authorities to plan water reuse by mapping reclamation facilities, water demand, and distribution networks. From this perspective, to support the planning process, this study describes the application of a Geographic Information System (GIS)-based methodology to assess the potential for UTWW reuse in Sicily by integrating national datasets (regularly updated) and comparing reclaimed water supply with irrigation demand from both qualitative and quantitative perspectives. UTWW supply was estimated by integrating data from the Italian National Institute of Statistics (ISTAT) Census of Water for Civil Use and the European Environment Agency (EEA) dataset, while irrigation demand was derived by combining Copernicus crop data, ISTAT irrigated-to-total UAA ratios and crop-specific irrigation demand (Seasonal Specific Volumes) derived from the SIGRIAN database. Two scenarios were analyzed for grasslands, vineyards, olive groves, and orchards. Scenario 1 included only wastewater treatment plants (WWTPs) located inside irrigation areas, whereas Scenario 2 also included selected WWTPs located outside irrigation areas where orographic and distance constraints allowed potentially feasible connections. Under Regulation (EU) 2020/741, potential compatibility with the required water quality classes was identified at the screening level for all crop categories considered, which require classes B, C or D. From a quantitative perspective, Scenario 1 identified 19 WWTPs serving 11 out of 38 irrigation areas with an aggregated uncapped demand coverage of 53%; only two areas reached full local demand satisfaction. Scenario 2 added 23 external WWTPs, increased potentially served areas to 20, and raised the aggregate uncapped coverage to 73%. When surplus volumes were capped within each irrigation area, effective demand satisfaction was 12% and 22% in Scenarios 1 and 2, respectively, highlighting the importance of storage, conveyance and inter-area transfer. The results indicate that treated wastewater reuse could contribute to agricultural water security and climate-resilient water management in Mediterranean regions. The framework supports the first-order identification of candidate reuse areas, but site-specific assessments of effluent quality, risk management, costs, and seasonal storage remain necessary before implementation. Full article

River ecosystems play a key role in both natural and urbanized environments, acting as essential links in ecological systems. However, they are increasingly exposed to the synergistic effects of anthropogenic pressure and hydrological instability. This study aims to identify and assess changes in land use and their impact on the phytosociological structure and functioning of ash-alder (Fraxino-Alnetum) patches in floodplains. Based on cartographic analyses (1941, 1961, 2025) and detailed geobotanical surveys (2024–2025) conducted at six representative sites, the spatio-temporal dynamics of land use and vegetation habitat conditions were evaluated using ecological indicator values. The results indicate a substantial landscape transformation: a transition from areas dominated by agricultural and forest ecosystems to zones heavily altered by urban expansion, transport infrastructure, and industrialization. Phytosociological analyses show that while species richness remains relatively stable, floristic composition is changing due to hornbeam succession and the emergence of mesophilous species. The presence of non-native species indicates ongoing anthropization in plant patches. Furthermore, hydromorphological modifications, including bank reinforcement and channel deepening in the vicinity of sports and railway infrastructure, have hindered natural river dynamics and accelerated floodplain drainage. The observed changes indicate that ash-alder forests in the Biała Przemsza valley are in a transitional phase between riparian and oak-hornbeam communities, with a clear oak-hornbeam transformation evident in many areas, driven by changes in water relations. This study highlights the urgent need to implement integrated restoration strategies that account for both hydrological catchment recovery and the mitigation of urban pressure to ensure the long-term resilience of riparian ecosystems. Full article

Water scarcity has a significant impact on global agriculture, particularly in semi-arid regions, hindering economic development. The use of recycled urban wastewater in agriculture is a sustainable practice; however, it is essential to assess its impact on soil carbon stocks and microbial activity. This study hypothesized that the use of wastewater in soil cultivated with forage cactus and amended with 8 or 12 tons of sorghum straw as soil cover could increase carbon stocks, microbial biomass, and microbial activity compared to bare soil, even after only 8 months. The experiment was conducted in a tropical semi-arid region of Brazil, based on a factorial design with different cactus intercropping systems and soil cover treatments under wastewater irrigation. Overall, soil carbon stocks did not increase significantly compared to the control, although they increased by approximately 21% over the study period. However, soil cover increased C-CO₂ emissions by 70% after 4 and 8 months. Microbial biomass carbon increased by 65% compared to the baseline (time 0), particularly in treatments with soil cover. Soil cover and consortium under wastewater irrigation improved microbial activity and biomass, even over a short experimental period, indicating a sustainable soil management strategy to enhance soil organic matter quality and microbial properties. Full article

This study aims to spatially analyze the potential soil loss rates of the Özkonak Watershed, located within Nevşehir Province, using the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information Systems (GIS) and Remote Sensing (RS) technologies. In the 149.9 km² watershed, the main parameters triggering erosion—rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), land cover and management (C), and support practices (P)—were modeled in a GIS environment. According to the spatial analysis results, 85.8% of the watershed area falls within the “very low” and “low” erosion susceptibility classes. Nevertheless, erosion increases markedly in the northern areas with high slope gradients and in areas where agricultural activities are concentrated. The mean soil loss across the watershed was calculated as 2.75 t ha⁻¹ yr⁻¹. The eroded and transported material was determined to constitute a threat to the dam. The findings indicate that conservation plans, including afforestation in the upper watershed, adjustment of land use to natural land capability, and construction of check dams, should be implemented to ensure sustainable management of the watershed. From a soil and sediment remediation perspective, the identification of erosion-source areas and sediment-transport pathways provides a scientific basis for source-control measures aimed at reducing sediment delivery and associated water-quality deterioration in the Bayramhacılı Dam reservoir. Full article

Agricultural management influences progress towards carbon neutrality through its effects on water consumption, energy use, and greenhouse gas (GHG) emissions. However, few studies have translated policies across sectors into management scenarios and evaluated their combined consequences for the agricultural carbon neutrality. We quantified the water, energy use, and carbon nexus for wheat, rice, and corn production across the North China Plain using 2018 as a baseline scenario. We then evaluated conditional management scenarios informed by China’s 14th Five-Year Plan. The three crop production generated net emissions of 1.8 × 10¹⁰ kg C yr⁻¹ in 2018, while cropland net ecosystem productivity offset 16.9% of GHG emissions related to crop production. Energy use was positively correlated with GHG emissions (r = 0.74, p < 0.01). The integrated scenario combining a 30% reduction in nitrogen fertilizer, more efficient nitrogen fertilizer production, sprinkler irrigation, and a 50% crop straw return rate reduced the water footprint, energy use, and GHG emissions by 4.9%, 27.6%, and 39.2%, respectively. By contrast, drip irrigation alone reduced the water footprint but increased energy use by 6.8% and GHG emissions by 12.9%. The results show that water saving measures do not necessarily improve the carbon neutrality when their energy requirements are overlooked. These findings also provide more enlightenment for local policy-makers. Full article

Vertical farming (VF) offers a smart way to grow crops in stacked layers inside controlled indoor environments. By doing so, it uses far less land and water than traditional open-field agriculture, making it a promising solution for cities with limited space and resources. In recent years, artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) technologies have begun to transform vertical farming. These tools are moving the industry away from rigid, rule-based systems toward more flexible, data-driven operations that can adapt in real time. This paper presents a systematic review of 208 peer-reviewed studies from 2015 to 2025. It explores how AI, ML, and IoT are applied across the VF ecosystem, focusing on key areas such as computer vision for disease detection, crop growth and yield prediction, smart climate control, and precision nutrient and irrigation management. This review examines the performance of different algorithms, including Convolutional Neural Networks (CNNs), Random Forest, XGBoost, and LSTMs across hydroponic, aeroponic, and aquaponic systems. The review also covers IoT setups with multi-sensor networks, edge-cloud computing, and automated control systems. Commercial farms have shown real gains in resource efficiency and shorter supply chains. However, challenges remain: high energy use (especially from LED lighting, which makes up 40–60% of costs), expensive setup, scattered datasets, and limited real-world testing. Many high-accuracy claims (>95%) come from lab conditions and need better validation in actual farms. Overall, AI-powered vertical farming has strong potential to support resilient urban food systems. Future work should focus on lightweight edge AI models, improved data standards, explainable AI, and robust life cycle assessments to ensure the benefits outweigh the environmental and economic costs. Full article

This study evaluates whether existing long-term monitoring data are sufficient to distinguish natural background concentrations from anthropogenic influences on chemical river water quality in Lake Sevan basin. A comprehensive dataset covering physicochemical parameters, nutrients, and trace metals was analyzed for nine major tributaries with different geological settings and land-use characteristics. Multivariate statistical analysis was applied to identify baseline conditions and deviations attributable to human activities. The results indicate that water chemistry is primarily controlled by lithology and hydrological regime, particularly in minimally impacted headwater regions. In contrast, elevated concentrations of nutrients (e.g., nitrate and phosphate) and selected trace elements were associated with agricultural runoff, urban discharge, and localized industrial inputs. Spatial patterns reveal clear gradients of increasing anthropogenic impact downstream and in densely populated sub-basins. The study also demonstrates that, while the current monitoring network is suitable for assessing the overall chemical status of rivers, it is less effective in defining natural background levels and quantifying individual pollution sources due to limited upstream reference conditions. Overall, this approach provides a scientific basis for improved water quality management and policy implementation in the Lake Sevan basin. The findings highlight the importance of integrating long-term monitoring data with statistical tools to support sustainable watershed management in vulnerable catchments. Full article

Accurate estimation of canopy water content (OWC) is essential for precision irrigation, crop growth monitoring, and yield prediction. Unmanned aerial vehicle (UAV)-based multispectral remote sensing provides a rapid and non-destructive approach for monitoring crop water status; however, the selection of effective spectral features and appropriate machine learning algorithms for robust OWC estimation remains insufficiently investigated, particularly across multiple growing seasons. This study evaluated the potential of UAV multispectral imagery for estimating oilseed rape canopy water content using two feature selection strategies and four representative machine learning algorithms. Field experiments were conducted during two consecutive growing seasons (2023–2024 and 2024–2025). Different sowing dates, nitrogen application rates, and planting densities were used to create a broad range of canopy water conditions. UAV multispectral images were acquired at ten representative growth stages during the reproductive period, from stem elongation to physiological maturity. Fourteen vegetation indices (VIs) were extracted from the multispectral imagery. Pearson correlation analysis and principal component analysis (PCA) were used to select informative features. These features were then used to develop multiple linear regression (MLR), partial least squares (PLS), support vector machine (SVM), and random forest (RF) models. Model performance was evaluated using each single-year dataset and the combined two-year dataset to assess robustness under different seasonal conditions. The RF model consistently achieved the highest prediction accuracy. The correlation-based RF model developed from the combined two-year dataset produced the best performance. It achieved an R² of 0.966, an RMSE of 1.734%, and an RRMSE of 2.360% for the training dataset. For the independent testing dataset, the corresponding values were 0.901, 2.794%, and 3.830%, respectively. The PCA-based models showed similar performance and effectively reduced feature redundancy. However, they did not consistently outperform the correlation-based models. These results indicate that combining UAV multispectral imagery with appropriate feature selection and machine learning algorithms can accurately estimate oilseed rape canopy water content under field conditions. Integrating data from multiple growing seasons further improves model robustness and provides a practical basis for UAV-assisted crop water monitoring and precision agricultural management. Full article

To address the limited synergy between integrated water–fertilizer technologies and intelligent application equipment, as well as the low water–nitrogen use efficiency in rapeseed production systems in southwestern China—which together constrain the large-scale adoption of smart fertigation equipment—this study conducted a two-season field experiment using an integrated water–fertilizer application system. The experiment included two irrigation regimes (W1: 60% ET_c; W2: 100% ET_c) and three nitrogen rates (F1: 220, F2: 300, F3: 360 kg N ha⁻¹), plus a rainfed control (CK), to quantify rapeseed responses to water–nitrogen interactions under an equipment-based fertigation framework. Results showed that water–nitrogen interactions significantly regulated rapeseed physiological processes, growth, yield formation, and resource-use efficiency. Compared with CK, appropriate water and nitrogen supply markedly enhanced PSII efficiency and overall energy conversion. In particular, W2F2 increased leaf photosynthetic rate by 51.2% and 50.8% across the two seasons. Water–nitrogen coupling also improved yield components such as branch number and thousand-seed weight, thereby increasing final yield. Although nitrogen partial factor productivity declined with increasing N rates, smart fertigation improved economic returns to varying degrees. EWM-TOPSIS results indicated that W2F2 consistently achieved the highest comprehensive performance across both seasons. By balancing yield, seed quality, resource efficiency, and economic benefit, this treatment represents the optimal strategy under intelligent fertigation systems. Overall, this study provides a decision-oriented optimization framework based on crop physiological responses under smart water–fertilizer equipment, offering practical guidance for intelligent fertigation deployment in rapeseed systems in southwestern China. Full article

Agriculture is increasingly challenged by water scarcity, climate change, and rising energy demands, requiring more efficient and sustainable irrigation solutions. Conventional irrigation systems often lack the capability to adapt their operation to changing soil conditions and renewable energy availability, resulting in inefficient water and energy use. This study proposes a digital twin-based framework for energy management and irrigation optimization in photovoltaic (PV)-powered smart agriculture systems using Internet of Things (IoT) soil monitoring. The proposed system integrates a physical irrigation infrastructure, an IoT monitoring network, a fuzzy logic control layer, and a digital twin environment that periodically synchronizes the virtual model with IoT measurements to support the system representation and decision-making. The digital twin models soil moisture, temperature, nutrient levels, PV energy generation, battery state of charge, and irrigation water consumption. The virtual representation was periodically aligned with the physical system using measurements transmitted through the long-range (LoRa)-based network. An energy-aware irrigation scheduling strategy was developed to optimize irrigation timing based on soil conditions, battery status, and solar energy availability. The framework was evaluated using field data collected in a real apple orchard through an ESP32-based IoT platform and a standalone PV-powered irrigation system; quantitative experimental validation was performed for the soil twin. The results demonstrate high soil twin synchronization accuracy, with an overall RMSE of 1.47 percentage points and R² of 0.981, based on experimental field measurements. The energy twin and irrigation twin were evaluated using experimentally acquired sensor data together with model-based performance assessment, demonstrating the potential of the proposed digital twin framework for integrated water–energy management in smart agriculture. Full article