Search Results (365)

Efficient soil moisture monitoring is fundamental to precision agriculture, enabling improved irrigation management, enhanced crop productivity, and sustainable water use. This review comprehensively evaluates soil moisture sensing technologies, classifying them into invasive and non-invasive approaches. The underlying operating principles, strengths, and limitations, as well as documented practical applications, are critically discussed for each technology. Invasive methods, including dielectric sensors, matric potential devices, heat-pulse sensors, and microstructured optical fibres, offer high-resolution data but require careful installation and calibration to account for environmental and soil-specific variables such as texture, salinity, and temperature. Non-invasive technologies—such as microwave remote sensing, electromagnetic induction, and ground-penetrating radar—enable large-scale monitoring without disturbing the soil profile; however, they face challenges in terms of resolution, cost, and data interpretation. Key performance factors across all sensor types include installation methodology, environmental sensitivity, spatial representativeness, and integration with decision-support systems. The review also addresses recent innovations such as biodegradable and Micro–Electro–Mechanical Systems sensors, the incorporation of Internet of Things platforms, and the application of artificial intelligence for enhanced data analytics and sensor calibration. While sensor deployment has demonstrated tangible benefits for irrigation efficiency and yield improvement, widespread adoption remains constrained by technical, economic, and infrastructural barriers, particularly for smallholder farmers. The analysis concludes by identifying research gaps and recommending strategies to facilitate the broader uptake of soil moisture sensors, with a focus on cost reduction, calibration standardisation, and integration into climate-resilient agricultural frameworks. Full article

To address the pressing challenges facing global agriculture—including resource constraints, structural labour shortages, and climate change adaptation—exploring pathways for digital transformation is crucial for safeguarding regional food security and advancing sustainable agricultural development. Taking China’s Yangtze River Economic Belt as a case study, this research aims to dissect the interplay between the digital economy, new-quality agricultural productivity, and agricultural modernisation. Utilising panel data from 11 provinces and municipalities spanning 2013–2023, the study employs an entropy-weighted approach to construct a composite indicator system for these three core variables. Panel data analysis comprehensively employs random effects models, mediation effect tests, robustness checks, and heterogeneity analyses. Empirical results indicate that the digital economy exerts a significant positive driving effect on new-quality agricultural productivity. Mediation tests further reveal that agricultural modernisation plays a crucial mediating role in this relationship. Heterogeneity analysis finds that the promotional effect of the digital economy exhibits distinct regional gradient characteristics, being most pronounced in growth zones, followed by leading zones, and weakest in starting zones. These findings support the formulation of differentiated agricultural digitalization policies: Leading areas should focus on deep integration of AI and agricultural big data; growth zones require investments in scaling intelligent irrigation and UAV plant protection; and start-up areas should prioritize digital infrastructure and large-scale farmer digital literacy training to establish transformation foundations. Full article

The wisdom embedded within traditional human settlements offers profound insights for addressing contemporary ecological challenges. This study systematically investigates the spatial management strategies and ecological wisdom of ancient settlements in the Yiluo River Basin (Luoyang Section), a cradle of Chinese civilization. A mixed-methods approach combined with historical document analysis was utilized, and the results reveal how these settlements achieved harmonious coexistence between human activities and the natural environment over millennia. The research uncovers a sophisticated system of ecological wisdom, primarily manifested across four key dimensions: (1) Macro-Topography-Responsive Siting Strategy: Settlement locations adhered to the principle of “nestling against mountains and facing water,” utilizing natural barriers and resources to mitigate flood risks and optimize microclimates. (2) Context-Adaptive Spatial Layout: The internal layout of settlements was attuned to local topography, water systems, and wind corridors, enhancing living comfort and aesthetic appeal. (3) Gray–Green–Blue infrastructure Synergy: Ancient water management systems were integrated with farmland and transportation routes, forming a synergistic network for irrigation, drainage, flood control, and transportation. (4) Culture–Nature Symbiosis: Cultural practices integrated human life cycles with natural landscapes, fostering regional identity and cultural sustainability. This study argues that the ecological wisdom of ancient Yiluo settlements—marked by its systematic and adaptive nature—provides a valuable historical paradigm for enhancing ecosystem services, building climate resilience, and achieving human–nature harmony in contemporary watershed management and urban–rural development. Full article

In the São Brás de Alportel municipality, water scarcity poses a significant constraint on agricultural activities. This study utilises Remote Sensing (RS) and Geographical Information Systems (GISs) to identify existing irrigated areas, delineate catchment basins, and select the most suitable sites for the installation of new surface water reservoirs. First, the principal territorial components were characterised, including physical elements (climate, geology, soils, and hydrography) and anthropogenic infrastructure (road network and high-voltage power lines). Summer Sentinel-2 satellite imagery was then analysed to calculate the Normalised Difference Vegetation Index (NDVI), enabling the identification and classification of irrigated agricultural parcels. Flow directions and accumulations derived from Digital Elevation Models (DEMs) facilitated the characterisation of 38 micro-catchments and the extraction of 758 km of the drainage network. The siting criteria required a minimum setback of 100 m from roads and high-voltage lines, excluded farmland currently in use, and favoured mountainous areas with low permeability. Only 18.65% (2854 ha) of the municipality is agricultural land, of which just 4% (112 ha) currently benefits from irrigation. The NDVI-based classification achieved a Kappa coefficient of 0.88, indicating high reliability. Three sites demonstrated adequate storage capacity, with embankments measuring 8 m, 10 m, and 12 m in height. At one of these sites, two reservoirs arranged in a cascade were selected as an alternative to a single structure exceeding 12 m in height, thereby reducing environmental and landscape impact. The reservoirs fill between October and November in an average rainfall year and between October and January in a dry year, maintaining a positive annual water balance and allowing downstream plots to be irrigated by gravity. The methodology proved to be objective, replicable, and essential for the sustainable expansion of irrigation within the municipality. Full article

Climate change poses increasing challenges to Reclamation Consortia, which must ensure equitable and sustainable water distribution under conditions of growing scarcity. This study evaluates supplemental irrigation management strategies adopted by three Reclamation Consortia in the Venetian Plain (Northeast Italy): Piave, Veneto Orientale and Acque Risorgive. The Consortia were selected based on their territorial and structural characteristics, as well as their different approach to managing water resources. This study fills a critical gap by integrating FAO AquaCrop-based estimates of irrigation needs for the 2022 and 2023 irrigation seasons in maize, grapevine and radicchio with an institutional analysis of Reclamation Consortia, offering an innovative framework that links technical and governance aspects of sustainable water management. Results reveal considerable variability among Consortia in terms of organizational structure, technological adoption, and resilience to drought. The 2022 season, characterized by extreme drought, required substantially higher irrigation volumes across all crops and soil types with significant differences compared to 2023 (p < 0.001), particularly for maize and grapevine (73% more irrigation water in maize). Well-drained soils and sprinkler irrigated crops showed the highest water demand (+45 mm compared to drip irrigation, p = 0.058), while loamy soils and drip systems proved more efficient. The Piave Consortium demonstrated the most advanced management system, supported by digital tools and structured rotation schedules. Nevertheless, structural factors, such as geographic location and infrastructure capacity, play a critical role in shaping resilience, leading to higher vulnerability in Consortia like Veneto Orientale and robustness in Acque Risorgive during drought conditions (i.e., 2022). Overall, the findings highlight the need to strengthen the main pillars of adaptation in irrigated agriculture, i.e., technology (decision support systems), governance (inter-Consortium coordination), and infrastructure (storage facilities), to promote flexible irrigation planning, enhance adaptive capacity, and ensure long-term sustainability under changing climatic conditions. These strategies also contribute directly to the achievement of Sustainable Development Goals 2, 6, and 13 (Zero Hunger, Clean Water and Sanitation, and Climate Action) by improving water use efficiency, securing crop production, and enhancing resilience to climate change. Full article

The parameterization of vegetation indices (VIs) is crucial for sustainable irrigation and horticulture management, specifically for urban green infrastructure (GI) management. However, the constraints of roadside traffic, motor and industrially related pollution, and potential public vandalism compromise the efficacy of conventional in situ monitoring systems. The shortcomings of prevalent satellites, UAVs, and manual/automated sensor measurements and monitoring systems have already been reviewed. This research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms applied to data acquired from three novel sources: (1) Integrated gas sensor data using nine different volatile organic compounds using an electronic nose (E-nose), designed on a PCB for stable performance under variable environmental conditions; (2) Plant growth parameters including effective leaf area index (LAIe), infrared index (Ig), canopy temperature depression (CTD) and tree water stress index (TWSI); (3) Meteorological data for all measurement campaigns based on wind velocity, air temperature, rainfall, air pressure, and air humidity conditions. To account for spatial and temporal data acquisition variability, the integrated cameras and the E-nose were mounted on a vehicle roof to acquire information from 172 Elm trees planted across the Royal Parade, Melbourne. Results showed strong correlations among air contaminants, ambient conditions, and plant growth status, which can be modelled and optimized for better smart irrigation and environmental monitoring based on real-time data. Full article

Agriculture uses 80% of global water resources, driving several water management challenges across the world. These challenges require the exchange of effective practices. We found that California’s Tulare Lake Basin (TLB) and Mexico’s Nazas–Aguanaval watershed share key features, leading us to propose the TLB as a model of climate resilience. After contrasting the policies for TLB with those for Nazas–Aguanaval, we found that no constrained pricing policy proposal exists for the Nazas–Aguanaval watershed. We apply a hydro-economic model using Positive Mathematical Programming to support an incentive structure for reducing water use in agriculture while maximizing profits. The optimal crop policy could reduce water demand by 20%, with a trade-off of an 11% reduction in profits. This would save 185.4 hm³/year, which represents 90% of the volume required for an ongoing infrastructure project for urban water supply in the watershed. Additionally, implementing a price of 14 USD/dam³ could increase the irrigation district’s revenue, boosting farmers’ profits by up to 16% and district revenue by up to 134%. Our results demonstrate the benefits of applying Positive Mathematical Programming in a semiarid watershed to support water and agriculture policy. This research is a starting point for increasing the climate resilience of watersheds under water and financial stress. Full article

While many studies have investigated ecosystem services, the cooling potential of green and blue infrastructures (GBIs) for alleviating extreme heat in arid regions has been studied less frequently. The aim of this study is to measure GBI cooling potential for mitigating extreme heat in arid and semi-arid regions, using Ahvaz City (south-west Iran) as a case study. Multiple data sources were used with the InVEST urban cooling model to estimate cooling ecosystem services (CESs) by evaluating the cumulative effects of shade, evapotranspiration, and albedo. Results show: (a) spatial heterogeneity in GBI cooling effects; (b) the highest cooling capacity (Cooling Capacity Index = 0.75) is achieved along the Karun River corridor and adjacent irrigated agriculture, where land surface temperature is reduced by 2–6 °C relative to built-up areas; and (c) interconnected GBIs and high vegetation density enhance cooling. High cooling capacity (>0.6) covers only 8.3% of the city (14.2 km²), predominantly the Karun River (4.2 km²) and adjacent agriculture (10.0 km²). In contrast, built-up areas (76% of the city) exhibit low cooling capacity (<0.3). Therefore, improving GBI connectivity and integrating passive cooling strategies are essential to enhance thermal resilience and should be prioritized in urban planning to maximize CES effectiveness and reduce heat-related risks. Full article

This study explores the potential connections between the digital economy and water conservation technologies in the context of China’s water resource consumption from 2008 to 2021. The research employs a state-of-the-art M-MQR technique, including the PCA index, and yields several significant findings. Empirical results reveal that digital technologies play a crucial role in reducing water consumption: Mobile technology decreases water use by −0.00001 to −0.00002 across quantiles, while internet access cuts consumption by −0.0000306 at lower quantiles and −0.0000167 at higher quantiles. The digital economy index shows an overall reduction in water consumption of −0.0537 at lower quantiles and −0.0292 at higher quantiles. Water conservation technologies, such as sprinkler irrigation, also contribute significantly, with reductions of −0.005 at the 10th quantile. Furthermore, water-saving investments show a positive effect on reducing water consumption, with reductions of −0.0105 at the 95th quantile. The study emphasizes that digitalization moderates the impact of water-saving technologies, reducing consumption by −0.0124 to −0.0118 at lower quantiles and −0.00812 to −0.00761 at middle quantiles. These results highlight the potential of digital infrastructure and water-saving investments to improve water use efficiency and address China’s water resource challenges. This study proposes that digital water supply and distribution system devices can help develop smart water infrastructure, reduce waste, and improve efficiency. Full article

Effective investments in rural irrigation infrastructure are critical for sustainable agricultural development and rural revitalization. This study investigates how social capital influences farmers’ investment in public infrastructure in terms of management and maintenance of irrigation facilities in rural Xinjiang, China. By using field survey data from 700 farmers in southern Xinjiang, we distinguished traditional social capital from newly emerged social capital (accumulated aid assigned to Xinjiang governors by upper authorities) and found that (1) both social capitals significantly facilitate farmers’ investment in managing and maintaining irrigation maintenance, particularly the latter; (2) the main influencing mechanism is twofold, directly promoting irrigation investments and indirectly stimulating participation in non-agricultural employment. These findings suggest that policy interventions should simultaneously strengthen rural social networks and improve non-agricultural employment services to foster collective action in investing in public irrigation system management and maintenance. Full article

Anthropogenic activities are profoundly altering the terrestrial water cycle, yet a comprehensive understanding of their impact on surface soil moisture (SSM) at regional scales remains limited. This study investigates the spatiotemporal dynamics of SSM and its relationship with anthropogenic modification (OAM) across Southwest China from 2000 to 2017. We employed multi-year geospatial and statistical analyses, including kernel density estimation and boxplots, to examine the impacts of human activities on regional soil moisture patterns. The results revealed that SSM exhibited a slight long-term declining trend (Sen’s slope = −0.0009 m³/m³/year) but showed a notable recovery after 2011, while overall anthropogenic modification (OAM) intensified until 2010 before declining sharply by 2015. A statistically significant and systematic relationship was observed, with increasing OAM intensity corresponding to higher median SSM and reduced spatial variability, indicating a homogenizing effect of human activities. Critically, the impacts of detailed anthropogenic stressors were highly divergent: agricultural modification correlated with elevated SSM, whereas transportation infrastructure and energy-related activities exhibited a suppressive effect. These findings highlight the necessity of integrating high-resolution SSM and anthropogenic data into land-use planning and implementing stressor-specific management strategies, such as improving irrigation efficiency and developing infrastructure designs that minimize SSM suppression, to achieve sustainable water resource management in rapidly developing regions. Full article

The swift integration of AI, robotics, and advanced sensing technologies has revolutionized agriculture into a data-centric, autonomous, and sustainable sector. This systematic study examines the interplay between artificial intelligence and agricultural robotics in intelligent farming systems. Artificial intelligence, machine learning, computer vision, swarm robotics, and generative AI are analyzed for crop monitoring, precision irrigation, autonomous harvesting, and post-harvest processing. Employing PRISMA to categorize more than 10,000 high-impact publications from Scopus, WoS, and IEEE. Drones and vision-based models predominate the industry, while IoT integration, digital twins, and generative AI are on the rise. Insufficient field validation rates, inadequate crop and regional representation, and the implementation of explainable AI continue to pose significant challenges. Inadequate model generalization, energy limitations, and infrastructural restrictions impede scalability. We identify solutions in federated learning, swarm robotics, and climate-smart agricultural artificial intelligence. This paper presents a framework for inclusive, resilient, and feasible AI-robotic agricultural systems. Full article

Arid regions in Central Asia face persistent and increasing water scarcity, with groundwater serving as the primary source for drinking water, irrigation, and industry. The effective exploration and management of groundwater resources are critical, but are constrained by limited monitoring infrastructure and complex hydrogeological settings. This study investigates the Akbakay aquifer, a representative area within Central Asia with challenging hydrogeological conditions, to delineate potential zones for fresh groundwater exploration. A multi-criteria decision analysis was conducted by integrating the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS), supported by remote sensing datasets. To address the subjectivity of weight assignment, the AHP results were further validated using Monte Carlo simulations and fuzzy logic aggregation (Fuzzy Gamma). The integrated approach revealed stable high-suitability groundwater zones that consistently stand out across deterministic, probabilistic, and fuzzy assessments, thereby improving the reliability of the groundwater potential mapping. The findings demonstrate the applicability of combined AHP–GIS methods enhanced with uncertainty analysis for sustainable groundwater resource management in data-scarce arid regions of Central Asia. Full article

This study examines the effects of climate change on maize production in Romania between 2003 and 2024, focusing on yield dynamics, regional disparities, and economic losses. Maize, a key crop in Romanian agriculture, has become increasingly vulnerable to extreme weather events, particularly droughts, which remain the most frequent risk. The analysis highlights a marked decline in maize yields and cultivated area in recent years, strongly correlated with severe droughts in 2020, 2022, and 2024. The results show that western and northern counties display greater resilience, while southeastern regions face significant yield losses. The economic impact is substantial, with losses exceeding EUR 1 billion. These findings underscore the systemic nature of climate-related risks and call for region-specific adaptation strategies, expanded irrigation infrastructure, and index-based insurance schemes to strengthen resilience and ensure sustainable maize production under changing climatic conditions. Full article

This research investigates the integration of green cores as central biophilic elements in residential architecture, proposing a climate-responsive design methodology grounded in architectural optimization. The study begins with the full-scale refurbishment of a compact urban apartment, wherein interior partitions, fenestration and material systems were reconfigured to embed vegetated zones within the architectural core. Light exposure, ventilation potential and spatial coherence were maximized through data-driven design strategies and structural modifications. Integrated planting modules equipped with PAR-specific LED systems ensure sustained vegetation growth, while embedded environmental infrastructure supports automated irrigation and continuous microclimate monitoring. This plant-centered spatial model is evaluated using quantifiable performance metrics, establishing a replicable framework for optimized indoor ecosystems. Photosynthetically active radiation (PAR)-specific LED systems and embedded environmental infrastructure were incorporated to maintain vegetation viability and enable microclimate regulation. A programmable irrigation system linked to environmental sensors allows automated resource management, ensuring efficient plant sustenance. The configuration is assessed using measurable indicators such as daylight factor, solar exposure, passive thermal behavior and similar elements. Additionally, a post-occupancy expert assessment was conducted with several architects evaluating different aspects confirming the architectural and spatial improvements achieved through the refurbishment. This study not only demonstrates a viable architectural prototype but also opens future avenues for the development of metabolically active buildings, integration with decentralized energy and water systems, and the computational optimization of living infrastructure across varying climatic zones. Full article