Search Results (233)

This work aims to demonstrate the successful, long-term human use of an automatic scheduling-manual operation (AS-MO) precision irrigation tool by farmers on a medium-scale Jordanian farm. Innovation in low-cost, accessible, and water-efficient irrigation technologies is critical as water resources become scarce, especially on resource-constrained farms in the drought-prone Middle East and North Africa (MENA) region. Prior work has shown that a proposed AS-MO decision support tool could bridge the gap between fully manual irrigation—a common practice on many MENA farms—and existing precision agriculture solutions, which are often too expensive or complex for medium-scale farmers to adopt. Recent developments have also demonstrated that the scheduling theory behind the proposed AS-MO tool uses up to 44% less water compared to fully manual irrigation. However, a functional design of the AS-MO tool has not been realized nor has it been demonstrated on a farm with farmer users. This work documents the detailed design of an AS-MO tool’s human–machine interaction (HMI) and validates the human execution of the tool in context. Through an 11-week case study conducted on a Jordanian farm, we show that farmers used a functional prototype of the AS-MO tool as intended. The functional tool prototype was designed to deliver a long-term AS-MO user experience to study participants. The prototype monitored local weather conditions, generated water-efficient schedules using an existing scheduling theory, and notified users’ phones when they should manually open or close valves. The irrigation practices of participants using the AS-MO prototype were measured, and participants demonstrated successful use of the tool. Users correctly confirmed 93% of the scheduled events using the tool’s HMI. Despite manual operation, a majority of confirmed irrigation event durations fell within 15% of the automatically scheduled durations; relative to the length of scheduled irrigation event durations, the medians of confirmed and scheduled durations were 102% and 88%, respectively. These results demonstrate the success of the tool’s decision support ability. Feedback from study participants can support the AS-MO tool’s next design iteration and can inform the development of other decision support systems designed for resource-constrained, medium-scale farms. This work presents an important step towards developing a precision irrigation tool that, if adopted at scale, could increase the adoption of water-efficient irrigation practices on resource-constrained farms that are not served by existing technology, improving sustainable agriculture in MENA. Full article

The introduction of Super-High-Density (SHD) olive orchards represents a crucial innovation in modern olive growing, enhancing sustainability. However, the long-term success of these planting systems depends strongly on cultivar selection, combining suitable vegetative and reproductive traits. This three-year field study investigated key floral biology parameters—flowering phenograms, gynosterility, and self-compatibility—of ten olive cultivars grown under irrigated conditions in southern Italy: ‘Arbequina’, ‘Arbosana’, ‘Cima di Bitonto’, ‘Coratina’, ‘Don Carlo’, ‘Frantoio’, ‘Favolosa’ (=‘Fs-17’), ‘I-77’, ‘Koroneiki’, and ‘Urano’ (=‘Tosca’). Flowering phenograms varied significantly across years and cultivars, showing temporal shifts related to chilling accumulation and yield of the previous year. Early blooming cultivars (‘Arbequina’, ‘Arbosana’, and ‘Coratina’) exhibited partial flowering overlap with mid-season ones, enhancing cross-pollination opportunities. Quantitative analysis of flowering overlap revealed that most cultivar combinations exceeded the 70% threshold required for effective pollination, although specific genotypes (‘Coratina’, ‘Fs-17’, and especially ‘I-77’) showed critical mismatches, while ‘Frantoio’ and ‘Arbequina’ emerged as the most reliable pollinizers. Gynosterility exhibited statistical differences among cultivars and canopy positions: ‘I-77’ showed the highest values (71.4%), while ‘Coratina’ and ‘Cima di Bitonto’ showed the lowest ones (7.3 and 8.4%, respectively). The median portions of the canopies generally displayed a greater number of sterile flowers (29.4%), reflecting the combined effect of genetic and environmental factors such as light exposure. In the inflorescence, the majority of gynosterile flowers were concentrated in the lower part, for all canopy portions (modal value). Self-compatibility tests were performed considering a fruit set of 1% as a threshold to discriminate. For open pollination, the fruit set was highly variable among cultivars, ranging from 0.5% in ‘I-77’ to 4.7% in ‘Arbosana’. Apart from ‘I77’, all varieties achieved a fruit set greater than 1%. Instead, for the self-pollination, only ‘Arbequina’, ‘Koroneiki’, ‘Frantoio’, and ‘Cima di Bitonto’ could be identified as pseudo-self-compatible, whereas ‘Coratina’, ‘Fs-17’, and the others were clearly self-incompatible and therefore unsuitable for monovarietal orchards in areas with limited availability of pollen. By integrating self-compatibility and gynosterility data, the cultivars were ranked according to reproductive aptitude, identifying ‘Cima di Bitonto’ and ‘Frantoio’ as the most fertile genotypes, whereas ‘Don Carlo’ and particularly ‘I-77’ showed severe genetic sterility constraints. The findings underline the critical role of floral biology in defining reproductive efficiency and varietal adaptability in SHD systems. This research provides valuable insights for optimizing cultivar selection, orchard design, and management practices, contributing to the development of sustainable, climate-resilient olive production models for Mediterranean environments. Full article

Solar-driven irrigation, a promising clean technology for agricultural water conservation, is constrained by mismatched photovoltaic (PV) pump outflow and irrigation demand, alongside unstable PV output. While compressed-air energy storage (CAES) shows mitigation potential, existing studies lack systematic explorations of pump water-lifting characteristics and supply capacity under coupled meteorological and air pressure effects, limiting its practical promotion. This study focuses on a solar-coupled compressed-air energy storage regulated sprinkler irrigation system (CAES-SPSI). Integrating experimental and theoretical methods, it establishes dynamic flow models for three DC diaphragm pumps considering combined PV output and outlet back pressure, introduces pressure loss and drop coefficients to construct a nozzle pressure dynamic model via calibration and iteration, and conducts a 1-hectare corn field case study. The results indicate the following: pump flow increases with PV power and decreases with outlet pressure (model deviation < 9.24%); nozzle pressure in pulse spraying shows logarithmic decline; CAES-SPSI operates 10 h/d, with hourly water-lifting capacity of 0.317–1.01 m³/h and daily cumulation of 6.71 m³; and the low-intensity and long-duration mode extends irrigation time, maintaining total volume and optimal soil moisture. This study innovatively incorporates dynamic air pressure potential energy into meteorological-PV coupling analysis, providing a universal method for quantifying pump flow changes, clarifying CAES-SPSI’s water–energy coupling mechanism, and offering a design basis for its agricultural application feasibility. Full article

Groundwater sustains more than 60% of irrigation in Pakistan’s Indus Basin, yet accelerating depletion, rising salinity and fragmented governance threaten agricultural productivity and rural livelihoods. Although new monitoring technologies and provincial water laws have emerged, a persistent gap remains between scientific evidence, policy frameworks and farmer practices. This study applies the Science–Policy–Practice Interface (SPPI) to examine these disconnects, drawing on qualitative data from multi-stakeholder focus groups and interviews with farmers, scientists and policymakers in Punjab, Sindh and federal agencies. The analysis identifies five governance challenges: weak knowledge integration, fragmented institutions, political resistance to regulation, limited adaptive capacity and under-recognition of farmer-led innovations. While depletion is well documented, it rarely informs enforceable rules and informal practices often outweigh formal regulation. At the same time, farmers contribute adaptive strategies, such as recharge initiatives and water-sharing arrangements, that remain invisible to policy. The findings highlight both the potential and the limits of SPPI. It provides a valuable lens for aligning science, policy and practice but cannot overcome entrenched political economy barriers such as subsidies and elite capture. The study contributes theoretically by extending SPPI to irrigation-dependent aquifers and practically by identifying opportunities for hybrid knowledge systems to support adaptive and equitable groundwater governance in Pakistan and other LMICs. Full article

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

Background/Objectives: The objective of this study was to investigate current endodontic practice patterns and the adoption of newer technologies among dentists, endodontic specialists, and postgraduate students in Spain and Latin America. Methods: A cross-sectional survey was conducted using a structured 30-item questionnaire covering demographics, training, technology adoption (NiTi instrumentation, magnification, CBCT, irrigation adjuncts, bioceramic sealers), obturation techniques, irrigant selection, and clinical procedures. The survey was distributed through a professional Instagram account and WhatsApp groups of dentists, specialists, and postgraduate students. Participation was voluntary, anonymous, and restricted to qualified professionals. Data were collected via Google Forms, cleaned, and grouped into Spain, Mexico, Venezuela, Colombia, Southern Cone & Andes (Argentina, Chile, Peru, Ecuador, Bolivia), and other countries. Descriptive statistics were calculated, and intergroup comparisons were performed using Chi-square or Fisher’s exact tests with Bonferroni correction (p < 0.05). Multiple regression analyses were performed. Results: A total of 1358 valid responses were analyzed, distributed as follows: Spain (219), Mexico (353), Venezuela (162), Colombia (108), Southern Cone & Andes (260), and other countries (256). Most respondents (62.8%) had ≤10 years of experience, and 61.2% reported postgraduate training. Loupes (55.4%) were the most frequent magnification system, followed by microscopes (18.6%). Sodium hypochlorite (98.3%) was the irrigant of choice, commonly used with EDTA (83.5%) and, to a lesser extent, chlorhexidine (33.4%). Sonic (83.2%) and ultrasonic (52.9%) activation were frequent. Bioceramic sealers were used by 18.9%, while calcium hydroxide medication was applied by 37.4%. Specialists and master-level clinicians showed greater use of rotary NiTi systems, CBCT, magnification, and bioceramic sealers, whereas general practitioners relied more on manual instrumentation and single-cone obturation. Success was mainly verified by combined clinical and radiographic evaluation (86.7%). Spain demonstrated higher adoption of microscopes, warm vertical compaction, and CBCT. Conclusions: Core practices such as sodium hypochlorite irrigation and rubber dam use were widespread, while advanced technologies and irrigant protocols varied with training level and region. Continuous education remains essential to promote evidence-based practice and reduce disparities in endodontic innovation. Full article

Agroforestry, an integrated land-use practice combining trees and woody shrubs with crop and animal farming, offers significant ecological and agricultural benefits, including enhanced biodiversity, improved soil fertility, and increased resilience to environmental pressures. Despite its advantages, agroforestry faces challenges such as high initial investments, long maturation periods for trees, land tenure issues and a high level of complexity in technical management. Digital agriculture introduces advanced technologies and sensors, which provide precise data on soil moisture, nutrient levels, and plant health, enabling more efficient resource use and better farm management. Integrating these sensing technologies into agroforestry can address key challenges, optimize irrigation and nutrient management, and enhance overall system productivity and sustainability. This review explores the interaction between agroforestry and digitalization, highlighting case studies, and discusses the potential for these technologies to support sustainable agriculture and climate change mitigation. Increased investment in research and development, along with supportive policies, is essential for advancing the adoption of these innovative practices in agroforestry. 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

Rhizosphere hypoxia, caused by soil compaction and waterlogging, is a major constraint on agricultural productivity. It severely impairs crop growth and yield by inhibiting root aerobic respiration, disrupting energy metabolism, and altering the rhizosphere microecology. Micro-nano bubbles (MNBs) show significant potential for alleviating rhizosphere hypoxia due to their unique physicochemical properties, including large specific surface area, high oxygen dissolution efficiency, prolonged retention time, and negative surface charge. This paper systematically reviews the key characteristics of MNBs, particularly their enhanced mass transfer capacity and system stability, and outlines mainstream preparation methods such as cavitation, electrolysis, and membrane dispersion. And the multiple alleviation mechanisms of MNBs—including continuous oxygen release, improvement of soil pore structure, and regulation of rhizosphere microbial communities—are clarified. The combination of MNBs aeration and subsurface drip irrigation can increase soil aeration by 5%. When applied in soilless cultivation and conventional irrigation systems, MNBs enhance crop yield and nutrient use efficiency. For example, tomato yield can be increased by 12–44%. Furthermore, the integration of MNBs with water–fertilizer integration technology enables the synchronized supply of oxygen and nutrients, thereby optimizing the rhizosphere environment efficiently. This paper sorts out the empirical effects of MNBs in soilless cultivation and conventional irrigation, and provides directions for solving problems such as “insufficient oxygen supply to deep roots” and “reactive oxygen species (ROS) stress in sensitive crops”. Despite these significant advantages, the industrialization of MNBs still needs to overcome challenges including high equipment costs and insufficient precision in parameter control, so as to promote large-scale agricultural application and provide an innovative strategy for the management of rhizosphere hypoxia. Full article

Greenhouse horticulture is a cornerstone of year-round vegetable production. However, escalating climate change is intensifying abiotic stressors (i.e., elevated temperatures, increased vapor pressure deficits, water shortage, and modified solar radiation), threatening both crop productivity and postharvest performance. This review synthesizes current knowledge on how these climatic shifts impact greenhouse microclimate, pest and disease patterns, energy and water requirements, as well as crop development in the Mediterranean region. This study focuses on three major crops (tomato, cucumber, and sweet pepper), which prevail in the regional protected cultivation sector. Among the climate-induced stressors examined, elevated temperature emerges as the primary environmental constraint on greenhouse productivity. In reality, however, a combination of climate-induced stressors is at play, acting simultaneously and often synergistically. Among crops, cucumber generally displays the highest sensitivity to climate-induced shifts, whereas sweet pepper tends to be the most resilient. Next, adaptive strategies are explored, including precision irrigation, structural retrofitting measures, renewable energy integration, Decision Support Systems, and climate-resilient cultivars. Regional case studies revealed diverse country-specific counteractive innovations. As key elements of inclusive climate adaptation, supportive policy frameworks and a practical agenda of targeted research priorities are outlined. In conclusion, the sustainability of greenhouse horticulture under a changing climate demands integrated, technology-driven, and region-focused approaches. Full article

Elite capture, a power structure problem involving rent-seeking, hinders sustainable water resources management. Governments play crucial roles in instilling public legitimacy in water governance, a common-pool resource that benefits from cooperative solutions such as pilot competitions, co-monitoring, and inter-agency coordination. A study of South-to-North Water Diversion Projects in China showed how, when governments outsource small projects to local sub-contractors, a method named moderate supervision (ruo jiandu) can enable effective oversight, which is superior to a bidding model with strict supervision (qiang jiandu). The concept of moderate supervision was initiated in 2023, before which most small projects had been left in a risky state with no supervision (ling jiandu). Analysis of a case in Shandong Yellow River Water Diversion Irrigation Area involved semi-structured in-depth interviews. Findings revealed that an elite-government-villagers tripartite spiral was composed of 3 dimensions reshaping a positive elite culture: first, a whitelist of qualified local contractors; second, co-monitoring of multiple stakeholders with influence exerted by a three-tier mobilization system; third, inter-agency coordination innovatively enabling smooth functioning between policy entrepreneurs of formal institutions and local social governance of informal ones. Policy implications to underscore real-world applicability are provided. Full article

This study develops an innovative unmanned aerial vehicle (UAV)-based intelligent system for winter wheat yield prediction, addressing the inefficiencies of traditional manual counting methods (with approximately 15% error rate) and enabling quantitative analysis of water–fertilizer interactions. By integrating an enhanced Faster Region-Based Convolutional Neural Network (Faster R-CNN) architecture with multi-source data fusion and machine learning, the system significantly improves both spike detection accuracy and yield forecasting performance. Field experiments during the 2022–2023 growing season captured high-resolution multispectral imagery for varied irrigation regimes and fertilization treatments. The optimized detection model incorporates ResNet-50 as the backbone feature extraction network, with residual connections and channel attention mechanisms, achieving a mean average precision (mAP) of 91.2% (calculated at IoU threshold 0.5) and 88.72% recall while reducing computational complexity. The model outperformed YOLOv8 by a statistically significant 2.1% margin (p < 0.05). Using model-generated spike counts as input, the random forest (RF) model regressor demonstrated superior yield prediction performance (R² = 0.82, RMSE = 324.42 kg·ha⁻¹), exceeding the Partial Least Squares Regression (PLSR) (R² +46%, RMSE-44.3%), Least Squares Support Vector Machine (LSSVM) (R² + 32.3%, RMSE-32.4%), Support Vector Regression (SVR) (R² + 30.2%, RMSE-29.6%), and Backpropagation (BP) Neural Network (R²+22.4%, RMSE-24.4%) models. Analysis of different water–fertilizer treatments revealed that while organic fertilizer under full irrigation (750 m³ ha⁻¹) conditions achieved maximum yield benefit (13,679.26 CNY·ha⁻¹), it showed relatively low water productivity (WP = 7.43 kg·m⁻³). Conversely, under deficit irrigation (450 m³ ha⁻¹) conditions, the 3:7 organic/inorganic fertilizer treatment achieved optimal WP (11.65 kg m⁻³) and WUE (20.16 kg∙ha⁻¹∙mm⁻¹) while increasing yield benefit by 25.46% compared to organic fertilizer alone. This research establishes an integrated technical framework for high-throughput spike monitoring and yield estimation, providing actionable insights for synergistic water–fertilizer management strategies in sustainable precision agriculture. Full article

The escalating pressure on water resources in agricultural regions has become a catalyst for water conflicts. The adoption of innovative approaches to estimate actual evapotranspiration (ETa) offers potential solutions to mitigate conflicts related to water usage. This research presents the application of a remote sensing-based methodology for estimating actual evapotranspiration (ETa) based on a two-source energy balance model (TSEB) for riparian vegetation in Nebraska, US using the Spatial EvapoTranspiration Modeling Interface (SETMI). Estimated results through SETMI and field data using the eddy covariance system (EC) considering the period 2008–2013 were used to validate the energy balance components and ETa. Modeled energy balance components showed a strong correlation to the ground data from EC, with ET presenting R2 equal to 0.96 and RMSE of 0.73 mm.d⁻¹. In 2012, the lowest adjusted crop coefficient (Kcadj) values were observed across all land covers, with a mean value of 0.49. The years 2013 and 2012, due to the dry conditions, recorded the highest accumulated ETa values (706 mm and 664 mm, respectively). Soybeans and corn exhibited the highest ETa values, recording 699 mm and 773 mm, respectively. Corn and soybeans, together accounting for a substantial portion of the land cover at 15% and 3%, respectively, play a significant role. Given that most fields cultivating these crops are irrigated, both pumped groundwater and surface water directly impact the water source of the Republican River. The SETMI model has generated appropriate estimated daily ETa values, thereby affirming the model’s utility as a tool for assisting water management and decision-makers in riparian zones. Full article

The primary objective of this study is to assess the techno-economic feasibility of an innovative solar energy generation system with a rainwater collection feature to generate electrical energy and meet irrigation needs in agriculture. The proposed system is designed for an agricultural area (Gonyeli, North Cyprus) with high solar potential and limited rainfall. In the present study, global rainfall datasets are utilized to assess the potential of rainwater harvesting at the selected site. Due to the lack of the measured rainfall data at the selected site, the accuracy of rainfall of nine global reanalysis and analysis datasets (CHIRPS, CFSR, ERA5-LAND, ERA5, ERA5-AG, MERRA2, NOAA CPC CMORPH, NOAA CPC DAILY GLOBAL, and TerraClimate) are evaluated by using data from ground-based observations collected from the Meteorological Department located in Lefkoşa, Northern Cyprus from 1981 to 2023. The results demonstrate that ERA5 outperformed the other datasets, yielding a high R-squared value along with a low mean absolute error (MAE) and root mean square error (RMSE). Based on the best dataset, the potential of the rainwater harvesting system is estimated by analyzing the monthly and seasonal rainfall patterns utilizing 65 different probability distribution functions for the first time. Three goodness-of-fit tests are utilized to identify the best-fit probability distribution. The results show that the Johnson and Wakeby SB distributions outperform the other models in terms of fitting accuracy. Additionally, the results indicate that the rainwater harvesting system could supply between 31% and 38% of the building’s annual irrigation water demand (204 m³/year) based on average daily rainfall and between 285% and 346% based on maximum daily rainfall. Accordingly, the system might be able to collect a lot more water than is needed for irrigation, possibly producing an excess that could be stored for non-potable uses during periods of heavy rainfall. Furthermore, the techno-economic feasibility of the proposed system is evaluated using RETScreen software (version 9.1, 2023). The results show that household energy needs can be met by the proposed photovoltaic system, and the excess energy is transferred to the grid. Furthermore, the cash flow indicates that the investor can expect a return on investment from the proposed PV system within 2.4 years. Consequently, the findings demonstrate the significance of this system for promoting resource sustainability and climate change adaptation. Besides, the developed system can also help reduce environmental impact and enhance resilience in areas that rely on water and electricity. Full article

Climate change is expected to reduce water availability during cropping season, while growing populations and rising living standards will increase the global water demand. This creates an urgent need for national water management tools to optimize water allocation. In particular, agriculture requires targeted approaches to improve efficiency. Alongside field measurements and remote sensing, agro-hydrological models have emerged as a particularly valuable resource for assessing and managing agricultural water demand. This study introduces WaterCROPv2, a state-of-the-art agro-hydrological model designed to estimate national-scale irrigation water demand while effectively balancing accuracy with practical data requirements. WaterCROPv2 incorporates innovative features such as hourly time-step computations, advanced rainwater canopy interception modeling, detailed soil-dependent leakage dynamics, and localized daily evapotranspiration patterns based on meteorological data. Through comprehensive analyses, WaterCROPv2 demonstrates significantly enhanced reliability in estimating irrigation water needs across various climatic regions, particularly under contrasting dry and wet conditions. Validation against independent data from the Italian National Institute of Statistics (ISTAT) for maize cultivation in Italy in 2010 confirms the model’s accuracy and underscores its potential for broader international applications. A spatial analysis further reveals that the estimation errors align closely with regional precipitation patterns: the model tends to slightly underestimate irrigation needs in the wetter northern regions, whereas it somewhat overestimates demand in the drier southern areas. WaterCROPv2 has also been used to analyze irrigation water requirements for maize cultivation in Italy from 2005 to 2015, highlighting its significant potential as a strategic decision-support tool. The model identifies optimal cultivation areas, such as the Pianura Padana, where the irrigation requirements do not exceed 200 mm for the entire maize growing period, and unsuitable regions, such as Salentino, where over 500 mm per season are required due to the local climatic conditions. In addition, estimates of the water volumes required for the current extent of maize cultivation show that the Pianura Padana region demands nearly three times the amount of water used in the Salentino area. The model has also been used to identify regions where adopting efficient irrigation technologies could lead to substantial water savings. With micro-irrigation currently covering less than 18% of irrigated land, simulations suggest that a complete transition to this system could reduce the national water demand by 21%. Savings could reach 30–40% in traditionally water-rich regions that rely on inefficient irrigation practices but are expected to be increasingly exposed to temperature increases and precipitation shifts. The analysis shows that those regions currently lacking adequate irrigation infrastructure stand to gain the most from targeted irrigation system investments but also highlights how incentives where micro-irrigation is already widespread can provide further 5–10% savings. Full article