• The resilience of irrigated areas at different spatial scales, mainly when water and soil are limiting factors.
• Estimation of crop transpiration/crop water requirement, even considering the possibility to apply regulated water deficit conditions.
• Coupling natural and human systems where ground and surface water and land are limiting resources for irrigation
• Safety in marginal water use in irrigated agriculture. Use of irrigation water from different non-conventional water sources
• Traditional, novel, and transitional technologies for irrigation management, control and practical application.
• Digital irrigation: application of available remote and proximal sensed data to tackle current and future irrigation problems.
• Improving the integration of climate change scenarios and weather forecasts into agro-hydrological models and decision support systems to improve decisions in irrigation management and safe surface water-groundwater interactions.
Posters and oral communications are available. Likewise, a Special Issue is foreseen.
Sustainable agricultural intensification necessitates precise deficit irrigation strategies to address global water scarcity. However, optimizing intra-seasonal scheduling under stochastic climatic conditions remains a complex control problem. While Deep Reinforcement Learning (DRL) offers a promising approach to flexible decision-making, existing applications often exhibit instability due to high-dimensional state spaces and an inability to enforce physical constraints. This study advances state-of-the-art irrigation control by proposing and benchmarking a tailored DRL framework based on Proximal Policy Optimization (PPO), coupled with the AquaCrop-OSPy simulation model. Moving beyond standard implementations, the research introduces specific enhancements to the learning agent: a reduced observation space limited to five causal biophysical variables, action masking to ensure strict adherence to seasonal water quotas, and a dense reward function based on transpiration efficiency. To rigorously quantify the value of information, the proposed approach is benchmarked against both a standard DRL baseline and a global Evolutionary Algorithm configured with perfect foresight of future weather events. This "oracle" defines the theoretical upper bound of achievable crop water productivity. Experimental validation on maize cultivation under deterministic and stochastic scenarios (Tunis and Nebraska) demonstrates that the proposed agent effectively navigates the trade-off between conservation and yield. The enhanced agent captures approximately 93.5% of the theoretical yield potential defined by the oracle, indicating a minimal performance penalty for the lack of future weather knowledge. Conversely, the standard reference implementation failed to converge under tight resource constraints. Economically, the proposed strategy not only stabilizes yields during extreme drought years but also increases mean net profits by up to 66% compared to the baseline. These findings confirm that integrating domain knowledge through action masking and feature selection transforms DRL into a robust tool for near-optimal irrigation scheduling without requiring extensive weather forecasting.
How to cite: Schuetze, N., Kunze, J. B., and Grové, B.: Closing the Gap to the Oracle: Benchmarking Domain-Informed Deep Reinforcement Learning for Deficit Irrigation against Perfect Foresight, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21783, https://doi.org/10.5194/egusphere-egu26-21783, 2026.
In Mediterranean and arid environments facing increasing hydro-climatic pressure, irrigated agriculture is central to food security while placing strong pressure on limited water resources. In response, on-farm irrigation optimization, particularly regulated deficit irrigation and efficiency-oriented technologies, has demonstrated benefits in yield stability, water productivity, and farmer income. However, these improvements often produce unintended basin-scale effects, as efficiency gains do not necessarily translate into collective water savings and can undermine long-term water management objectives. Drawing on evidence from four ACQUAOUNT pilot sites (Italy, Tunisia, Jordan, and Lebanon), this contribution examines the trade-offs between farm-level optimization and basin-scale water management objectives. Using a narrative-based Decision Support Tool that integrates climate projections, hydrological modeling, agricultural water demand, and socio-economic evaluation, we assess how deficit irrigation interacts with allocation rules, sectoral competition, and ecosystem requirements under current and future climate conditions. Our analysis shows that in water-scarce basins, economically accessible water resources and productivity-driven incentives often lead farmers to reinvest efficiency gains into expanded or intensified water use, offsetting potential basin-level savings. These dynamics highlight a structural disconnect between agricultural decision-making and sustainable water governance. By disentangling these processes across contrasting contexts, we derive actionable recommendations to better align irrigation practices with basin management interventions. The results support Integrated Water Resources Management (IWRM) approaches that balance agricultural resilience, ecosystem preservation, and climate adaptation, explicitly accounting for cross-scale feedbacks in irrigated systems.
How to cite: Rianna, G., Napolitano, L., Borgo, A., Delpiazzo, E., Debolini, M., Mereu, S., and Mazzoni, A.: Assessing Farm-Basin Feedback: Deficit Irrigation and Water Savings across the Mediterranean, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14173, https://doi.org/10.5194/egusphere-egu26-14173, 2026.
Improving crop water use efficiency may not necessarily alleviate water scarcity—a phenomenon that represents a specific manifestation of the Jevons Paradox in agricultural water resources. However, this paradox has not been quantified yet, which has limited our ability to understand the trade-off between efficiency gain and water loss and support informed irrigation decision-making in the face of climate change. Here, we introduce a novel index to quantify the crop water use efficiency paradox and explore its patterns and trends across China's 15 major crop types from 1991 to 2019. Our findings reveal clear evidence of the persistence and uneven threat of this paradox at both national and regional levels over the past three decades in China. Although the crop water efficiency in China has increased significantly by 48.78 kcal m-3 yr-1 since the 1990s, both the total blue water (+1.36 km3 yr-1) and water scarcity index (+0.003 yr-1) have also increased during the same period. The intensity of the paradox has, however, diminished since 2010, but climate change threatens to more than double its intensity in the mid-21st century if no proactive measures are implemented. Region-specific agricultural strategies are derived by quantifying water-use paradox intensity, scarcity, and efficiency trade-offs. These insights provide valuable scientific guidance for designing regional water-saving strategies in China and around the world, while also addressing the potential threats posed by the water use efficiency paradox to agricultural sustainability.
How to cite: Shi, W.: Agricultural water sustainability under threat from the water use efficiency paradox, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2349, https://doi.org/10.5194/egusphere-egu26-2349, 2026.
Coupled hydro-economic models are increasingly used to analyse how land-use decisions, water availability, and policy interventions interact in agricultural catchments. However, most existing couplings remain ad hoc and weakly reproducible, largely because hydrological and microeconomic models operate on fundamentally different spatial representations. Distributed and semi-distributed hydrological models require spatially explicit land-use representations (e.g. HRUs, grids, or sub-basins), whereas microeconomic models typically allocate land and water over aggregated administrative units or representative agents. As a result, land-use change is often imposed through static scenario maps or one-way translations, preventing fully operational two-way feedback between hydrology and economic decision-making.
We introduce the hydro-economic land-use interoperability method (HILAM), a spatial template designed to enable consistent, bidirectional coupling between distributed or semi-distributed hydrological models and microeconomic land-allocation models. HILAM is not tied to any specific economic or hydrological model: any land-use allocation model and any spatially explicit hydrological model can be plugged into the framework. HILAM uses a semi-distributed spatial layer that links detailed land-use, soil, and climate data into common spatial units. These units allow crop shares from the economic model to be passed to the hydrological model and hydrological outputs to be returned to the same locations, enabling spatially explicit two-way feedback.
As an illustration of the economic component, we use a positive mathematical programming (PMP) model, representing one member of a broader class of mathematical programming approaches (e.g. linear programming, multi-attribute models, PMP) that can be coupled through HILAM. PMP provides a microeconomic characterisation of farmers’ production choices under binding resource constraints, reproducing observed land-use allocations as the outcome of constrained optimisation while allowing consistent responses to changes in prices, yields, and water availability for irrigation.
As an illustration of the hydrological component, we use the Soil and Water Assessment Tool (SWAT) as one example of a semi-distributed, process-based hydrological model that can be coupled through HILAM. SWAT translates land-use configurations into water flows, soil moisture dynamics, irrigation requirements, and crop water stress, ensuring that economic decisions are grounded in physically meaningful hydrological constraints. Through HILAM, these outputs are returned to the economic model at consistent spatial units, closing the hydro-economic feedback loop. A key innovation of HILAM is that the spatial template explicitly controls the level of spatial detail, allowing modellers to regulate complexity, uncertainty, and computational cost in a transparent and reproducible manner, providing an intermediate alternative between fully gridded simulations and highly aggregated representations.
We demonstrate HILAM through a case study that couples SWAT with a PMP land-use model in an intensively irrigated aquifer system in central Spain. The coupled framework enables continuous feedback between crop choice, irrigation water availability, crop yields, and hydrological responses over multi-year simulations. Results show that spatially explicit hydro-economic coupling produces substantially different projections of water use, crop distribution, and aquifer stress than conventional aggregated approaches, highlighting the importance of spatial interoperability for water governance and climate-adaptation analysis.
How to cite: Rivas-Tabares, D., Bouzidi, N., and Pérez-Blanco, D.: HILAM: a spatially consistent framework for bidirectional hydro-economic land-use modelling , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20823, https://doi.org/10.5194/egusphere-egu26-20823, 2026.
Climate change is intensifying water scarcity and increasing uncertainty in water availability, placing growing pressure on irrigated agriculture, which accounts for nearly 70% of global freshwater withdrawals. In response to these challenges, many irrigation districts have transitioned from open channels to pressurized distribution systems to improve efficiency. However, despite substantial investments, operational performance often lags expectations due to hydraulic constraints, uneven pressure distribution, and limited capacity to diagnose system behavior under variable demand conditions. Addressing these challenges requires integrated approaches that combine field observations with hydraulic simulation to support targeted rehabilitation and enhance system resilience.
This study presents an integrated diagnostic–simulation framework for the performance assessment and rehabilitation of pressurized irrigation systems, applied to District 10 of the Capitanata irrigation scheme (southern Italy) as a representative case study. The district covers approximately 2,000 ha (317 hydrants) and is supplied by a storage reservoir. The proposed methodology couples extensive field-surveys with stochastic hydraulic modeling to capture system behavior under peak and uncertain demand conditions.
Field surveys including infrastructure layout, water delivery rules, design parameters, cropping patterns, and registered irrigation volumes, were utilized to parameterize the simulation. Hourly discharge data was analyzed to identify the critical 10-day peak demand period, representing the most demanding operating conditions. Upstream discharges were estimated using the Clément model and validated against empirical data to ensure consistency.
Hydraulic performance was assessed at two complementary levels using the ICARE and AKLA simulation models, based on 1,000 randomly generated operating configurations of hydrant demand (simultaneous operation and discharge), from which the resulting pressure conditions were computed to explicitly account for demand uncertainty. ICARE quantified network-scale performance through Indexed Characteristic Curves, expressing the percentage of configurations satisfying minimum pressure requirements. AKLA evaluated hydrant-level performance using relative pressure deficit (RPD) and reliability, defined as the frequency with which a hydrant meets the required pressure.
Diagnostic results under peak conditions revealed an overall network performance of only 62%, with significant localized pressure deficits. Based on these findings, we developed a targeted rehabilitation strategy that prioritized existing infrastructure, allowing pipe diameter to increase only where strictly necessary to minimize costs. Post-rehabilitation simulations demonstrated a critical performance shift: the operating set-point improved to 100% satisfaction, with all hydrants meeting minimum pressure requirements.
The proposed framework demonstrates that integrating field diagnostics with stochastic simulation can drive cost-effective rehabilitation, ensuring the resilience of pressurized irrigation networks against the growing threat of climatic uncertainty.
How to cite: Mohammedi, S., Gentile, F., and Lamaddalena, N.: A Decision-Support Framework for Performance Assessment and Rehabilitation of Pressurized Irrigation Systems under Water Scarcity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3997, https://doi.org/10.5194/egusphere-egu26-3997, 2026.
Soil texture is a fundamental physical property that strongly influences other soil properties critical to agricultural productivity. Comprehensive information on soil properties and their spatial variability is essential for the implementation of effective soil management strategies. Digital soil mapping typically relies on data collected from discrete sampling points. Therefore, estimating topsoil properties from satellite data at a high resolution remains a significant challenge. Nevertheless, estimative models can be developed by relating soil physical parameters to remotely sensed and topographic variables.
In this study, pedotransfer functions were developed using multiple linear regression (MLR) to derive soil texture maps for sand silt clay and organic matter (OM). Functions were based on PlanetScope imagery and topographic data derived from a digital elevation model (DEM) at 3 m resolution. The models established statistical relationships between soil properties and selected predictor variables. Model performance was assessed using the coefficient of determination R², showing satisfactory results: 0.64 for clay, 0.64 for OM, and 0.82 for sand.
Finally, management zones based on plant-available water derived from the updated European hydraulic pedotransfer functions (PTFs) were delineated using spatial fuzzy C-means clustering (SFCM), providing a practical framework for precision agriculture and sustainable land management.
Index Terms—precision agriculture, soil texture, multiple linear regression, spatial fuzzy C-means clustering
Acknowledgements: The authors acknowledge the support of SANTO, from Universidad Politécnica de Madrid (RP220220C024) and to NetLIFE-CODES from Agencia Estatal de Investigación (PID2024-157869NB-I00)
How to cite: Ksantini, F., Quemada, M., Borra-Serrano, I., Gabriel, J. L., and Tarquis, A. M.: Digital Mapping of Plant Available Water Using PlanetScope Imagery and Pedotransfer Functions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17964, https://doi.org/10.5194/egusphere-egu26-17964, 2026.
Freshwater resources have long supported gains in crop productivity, yet their sustainability is increasingly challenged by changes in both the climate system and land. As one of the most important cash crops in China, cotton has undergone notable changes in recent years, with a decline in harvest area and a shift toward dryland regions. Nevertheless, the impacts of such a transition on cotton water consumption and its underlying factors remain unclear. To address this knowledge gap, we employed the water footprint concept, spatial cluster, and decomposition analysis to depict the long-term dynamic of cotton water footprint over regions in China from 1990 to 2020, and to untangle the impacts from various aspects. Our results demonstrated that the total water footprint of cotton in China has substantially decreased, from 24.2 G m3 in 1990 to 11.9 G m3 in 2020, with a rapid decrease in the Yellow River Valley (YERV) and the Yangtze River Valley (YARV) cotton regions, while an increase in the Northwest Inland China (NIC) cotton region. These changes were accompanied by a proportion that varied from 23.5% to 7.8% for the green water footprint and from 70.1% to 87.7% for the blue water footprint, respectively. The geographical centroids of both cotton harvest area and water footprint exhibited a northwestward trend in the NIC and YERV cotton regions, while the YARV cotton region experienced a southwestward shift, along with heightened spatial coupling from the declining distance between such centroids. Changes in the climate system, harvest area, and fertiliser applications resulted in yearly variations in total water footprint over the region, with changes in harvest area driving nearly double the changes in cotton water footprint compared to climate change. Our findings underscore the importance of optimizing cropping patterns to promote sustainable water use and mitigate the adverse effects of climate change on cotton production.
How to cite: Liu, S., Zhang, C., Li, T., and Ma, X.: Harvest area exerts nearly twice the influence of climate change on long-term cotton water footprint changes in China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15634, https://doi.org/10.5194/egusphere-egu26-15634, 2026.
Peat-based casing is widely used in Agaricus bisporus production because it provides a stable structure and a reliable water reservoir for fruitbody development. As the sector moves toward peat-reduced and peat-free casing materials, the way water is stored and released within the casing layer can change substantially, making irrigation decisions less straightforward. We show how embedded sensors make casing water dynamics measurable and interpretable, relate these dynamics to yield and quality outcomes in peat-reduced casings, and are extending the same framework to peat-free systems at industry-representative scale.
The water status of casing treatments, comprising peat diluted in volume proportions with wood-fibre, composted and uncomposted bark, was monitored in real time, using embedded solid-state tensiometers measuring matric potential (Ψm), i.e. how tightly water is held in the casing (more negative Ψm indicates lower water availability). Rather than relying on single-point Ψm values, we focus on time-resolved Ψm behaviour (e.g., the duration of sustained low Ψm and rate of recovery following irrigation). In two repeated peat-reduced casing trials, wood-fibre amendments closely matched peat controls in in-crop Ψm evolution (a similar rate of decline during each flush; not exceeding −34.4 kPa) and produced mushroom yield and quality (colour) that was statistically indistinguishable from peat across both trials. By contrast, bark-amended casings diverged from peat controls: Ψm dynamics were more sensitive to changes in crop management, and yields were significantly lower than peat in the first trial, with uncomposted bark remaining significantly below peat across both trials. Importantly, high pin-set, more common in the looser-structured bark treatments, increased water demand early in the crop cycle and reduced water availability for subsequent flushes, highlighting the need to avoid or control over-pinning to protect against casing structural degradation and related losses in potential yield.
To link in-crop sensor signals to casing hydraulic behaviour, water-release characteristic curves were measured independently (Hyprop 2) for all peat-reduced materials. These curves describe each material’s inherent water-release response, independent of irrigation regime and uptake by developing mushroom, providing a physical basis for interpreting in-crop Ψm patterns and separating material-driven responses from management and demand effects.
We are extending this approach to peat-free casings at 1 m² scale using a balanced blocked design (16 trays per run, two runs) comparing standard irrigation with a sensor-guided watering strategy that applies small in-flush top-ups only when Ψm indicates a persistent deficit. Continuous Ψm monitoring across all trays (24 sensors; one per tray plus dual-sensor subsets) and irrigation event logging will link Ψm time-metrics to yield, grade and mushroom dry matter.
Overall, combining water-release characteristics with in-crop, multi-point Ψm monitoring offers a practical route to understand and manage water availability in peat-alternative and peat-free casings, supporting peat reduction while maintaining yield and quality.
How to cite: Corbett, E., McGuinness, B., Tumbure, A., and Gaffney, M.: Tracking Casing Water Dynamics to Support Peat-Alternative Mushroom Production, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18798, https://doi.org/10.5194/egusphere-egu26-18798, 2026.
Stem water potential (SWP) is a widely used, integrative indicator of vine water status because it reflects the combined effects of soil water availability, atmospheric demand and canopy conditions, and it provides a direct basis for irrigation decision making. However, SWP measurements with a pressure chamber are time-consuming and labor-intensive, which motivates the use of UAV-based thermal approaches as scalable alternatives for vineyard scale monitoring. This study aims to validate previously developed UAV thermal models for estimating SWP (Atencia et al., 2024), originally calibrated using data from a 2021–2022 irrigation trial, using an independent dataset collected in 2023 across three contrasting field trials (irrigation, pruning and soil type). UAV thermal acquisitions and concurrent SWP measurements were performed at two times of day (11:00 and 14:00) on multiple dates during the growing season to capture temporal variability in plant water status. Model performance and transferability across seasons and trials was evaluated by comparing CWSI-based estimates with simpler canopy–air temperature difference metrics, and by assessing how environmental and canopy conditions (e.g., atmospheric demand and seasonal progression) influence the robustness of SWP–thermal relationships. The SWP–CWSI relationship remained in 2023, but transferability of the 2021–2022 calibration decreased due to shifts in slope and intercept, likely linked to differences in atmospheric demand (including relatively low VPD during 2023 acquisitions) and the predominance of mild-to-moderate stress levels during most sampling dates. Overall, the results support the practical utility of UAV thermal indices for monitoring vine water status, while highlighting that robust conversion of CWSI to absolute SWP may require campaign-specific recalibration or models that account for key environmental and canopy covariates.
References
Atencia-Payares, L.K., Gomez, M., Tarquis, A.M., García, M., 2024. Thermal imaging from UAS for estimating crop water status in a Merlot vineyard in semi ‑ arid conditions. Irrig. Sci. https://doi.org/10.1007/s00271-024-00955-1
Jackson, R.D., Idso, S.B., Reginato, R.J., Pinter, P.J., 1981. Canopy temperature as a crop water stress indicator. 17, 1133–1138
How to cite: Atencia Payares, L. K., Garcia, M., Junquera, P., Gomez del Campo, M., and Tarquis, A.: Model Validation for Estimating Stem Water Potential in Merlot Grapevines Using Thermal UAV-Based Imagery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3042, https://doi.org/10.5194/egusphere-egu26-3042, 2026.
California almond production faces increasing pressure to improve irrigation efficiency under limited and variable water supplies. Variable-rate micro-irrigation offers a promising pathway to address spatial variability in orchard water demand; however, its effective implementation requires reliable, field-scale information on soil water availability, tree water status, irrigation inputs, and crop productivity response. This study developed and applied an integrated monitoring framework to support site-specific irrigation management in a commercial almond orchard over three growing seasons (2019–2021). Fourteen monitoring locations were established across contrasting soil and irrigation management zones. At each location, applied irrigation water was measured, soil water dynamics were monitored using neutron probes, and midday stem water potential was measured on three representative trees with a pressure chamber at weekly to biweekly intervals. Crop water use was evaluated using evapotranspiration estimates supported by in-field observations, including eddy covariance measurements, to provide context for irrigation scheduling decisions. Yield data were collected at all monitoring locations to quantify spatial and temporal variability in production and to evaluate crop responses to irrigation management informed by the integrated dataset.
Across the study period, average almond yield increased from approximately 1.93 t ha⁻¹ in 2019 to 2.58 t ha⁻¹ in 2021, while substantial spatial variability in both yield and water use persisted among monitoring locations. These findings reinforce the need for variable-rate irrigation approaches in heterogeneous orchard environments. The results demonstrate the value of combining soil, plant, irrigation, and atmospheric measurements within a coordinated monitoring framework capable of informing variable-rate micro-irrigation management. This work provides a foundation for the development of data-driven, site-specific irrigation strategies that move beyond uniform management and enhance water productivity in almond orchards under increasing water constraints.
How to cite: Peddinti, S. R. and Kisekka, I.: Integrated Monitoring Framework for Site-Specific Irrigation Management in California Almond Orchards , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3199, https://doi.org/10.5194/egusphere-egu26-3199, 2026.
Water scarcity in Mediterranean regions demands innovative irrigation strategies for perennial crops such as pomegranate (Punica granatum L.). Previous studies have demonstrated that deficit irrigation approaches—regulated deficit irrigation (RDI) and sustained deficit irrigation (SDI)— reduce water use while influencing agronomic performance and fruit quality. RDI, which applies water restrictions during specific phenological stages, achieved moderate water savings without compromising yield or fruit characteristics, whereas SDI provided substantial water savings (up to 50%) but significantly reduced commercial yield and increased physiological disorders such as fruit cracking. These findings highlight the potential of RDI as a sustainable solution under water-limited conditions.
However, traditional agronomic approaches in these studies assumed uniformity within experimental replications and did not account for variability among individual trees. Our new research addresses this limitation by incorporating precision agriculture techniques, specifically the use of drones equipped with multispectral sensors to capture high-resolution canopy data. This approach revealed considerable differences between trees within the same replication, underscoring the need for site-specific management. The results demonstrate that integrating remote sensing and data-driven strategies is essential to optimize water use efficiency and improve agricultural sustainability in Mediterranean fruit crops.
How to cite: Pascual-Seva, N., Porras-Jorge, R., Aguilar, J. M., Baixauli, C., Bartual, J., and Pascual, B.: Integrating Deficit Irrigation Strategies and Precision Agriculture for Sustainable Pomegranate Production in Mediterranean Conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5033, https://doi.org/10.5194/egusphere-egu26-5033, 2026.
Process-based crop models such as AquaCrop are widely used to assess crop responses to irrigation management and climate variability. However, large-scale scenario exploration and optimization are often constrained by the high computational cost of repeated model simulations. In this study, we develop a machine-learning surrogate model to emulate AquaCrop-OSPy (ACOSP) simulated wheat yields under a wide range of soil-moisture-triggered irrigation (SMT) thresholds and daily maximum irrigation limits (MaxIrr) across 26 growing seasons in Northwest India. A simulation ensemble of 21,840 ACOSP runs was generated by systematically varying SMT (0–100%) and MaxIrr (0–40 mm day⁻¹) for each season. The surrogate model was trained using season-wise climate variability, seasonal precipitation and irrigation strategy parameters (Season, SMT, MaxIrr) as predictors, with wheat yield (t ha⁻¹) as the target variable. We implemented and compared Random Forest and XGBoost regression models using a time-based train–test split to avoid information leakage across seasons. The best-performing XGBoost model explained ~87–90% of the inter-season and management-driven yield variability in the independent test period, while maintaining computational runtimes several orders of magnitude lower than ACOSP. Feature-importance analysis showed that SMT was the dominant explanatory factor, followed by climate-driven seasonal variability, whereas MaxIrr primarily influenced high-stress scenarios. The surrogate model successfully reproduced non-linear yield responses and threshold behaviour, suggesting strong potential for near-real-time decision support and large-scale scenario exploration. This work demonstrates that machine-learning surrogates can complement process-based crop models by enabling rapid evaluation of irrigation strategies, uncertainty assessment, and future climate scenario testing at regional scales. The developed framework is transferable to other regions, crops, and water-limited environments, offering a scalable pathway toward computationally efficient agricultural water-management assessment.
How to cite: Garg, D. and Kumar, H.: Developing a machine-learning Surrogate Model to rapidly predict wheat yields under Soil-Moisture-Triggered irrigation strategies in Northwest India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10703, https://doi.org/10.5194/egusphere-egu26-10703, 2026.
Increasing climate variability, more frequent droughts, and growing competition for freshwater resources amplify uncertainty in irrigation water availability and challenge sustainable agricultural water management. Addressing this uncertainty is essential for maintaining crop productivity under changing climatic conditions. This study presents a preliminary, climate-driven assessment of irrigation strategies using numerical irrigation experiments at more than ten climatically representative agricultural sites worldwide.
The site selection is based on the second level of the Köppen–Geiger climate classification, with each site representing a distinct agro-climatic zone characterized by contrasting precipitation regimes, evaporative demand, and seasonal variability. By prioritizing climatic representativeness over dense spatial coverage, the analysis enables systematic comparison of irrigation responses across a broad range of hydroclimatic conditions.
Crop growth and yield responses are simulated using the FAO AquaCrop model, which provides a robust representation of crop water productivity under water-limited conditions. AquaCrop simulations are coupled with multiple irrigation scheduling strategies implemented through the Deficit Irrigation Toolbox (DIT), an open-source probabilistic simulation–optimization framework developed in MATLAB. This integrated modeling approach explicitly accounts for climate-driven uncertainty by evaluating rainfed conditions, full irrigation, and optimized deficit irrigation strategies under stochastic weather forcing. Model outputs include irrigation water demand, yield response, and water productivity, enabling the assessment of trade-offs between production stability and water use efficiency.
The results highlight pronounced climate-dependent differences in irrigation requirements and sensitivity to water availability. In particular, the simulations indicate that deficit irrigation can reduce vulnerability to interannual water scarcity and improve water productivity in arid and semi-arid regions, while offering limited benefits in humid climates.
This preliminary analysis establishes a methodological foundation for a future global-scale assessment of irrigation strategies under uncertain water resources and supports the development of climate-adaptive irrigation management approaches.
How to cite: Dietz, A. and Schütze, N.: A Preliminary Analysis of Irrigation Strategies Across Global Climate Zones, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10897, https://doi.org/10.5194/egusphere-egu26-10897, 2026.
Limited rainfall makes irrigation critical for plant growth and optimal yield. In this context, soil moisture management plays a key role in optimizing irrigation practices, improving plant performance, and enhancing fruit quality. However, conventional monitoring systems, based on single or multiple sensors installed along the soil profile, often fail to deliver accurate and representative information on water availability within the root zone. The aim of this research was to evaluate the effectiveness of a real time system made of in situ probes, able to predict the moisture in the soil unit explored by root, for the development of an irrigation recommendation. In a two-year (2024-2025) research experiment carried out in northern Italy, on mature pear fruit trees cv. ‘Abbé Fétel’, grafted on seedlings and planted at a distance of 4 x 2,4 m apart, to evaluate the effectiveness of a smart irrigation system (SMARTER) compared to a traditional one (CONTROL). The main goal of the study was to use a probe system to detect the total amount of water in the first 100 cm of soil depth and wisely select the amount of water to irrigate the crop. Water management in the CONTROL treatment followed the advisory service guidelines, based on daily evapotranspiration, soil texture, and crop phenological stage. In contrast, the SMARTER system applied irrigation according to soil water content measured by potentiometric probes located according to the grid of nine sensors (placed ad different distance and depth from the emitters). Irrigation started when soil matric potential dropped below -0.1 MPa in more than 50% of the volume of soil explored by the root system and was aimed at replacing the optimal water level for the phenological stage. The first year (2024), two treatments were applied: SMARTER vs CONTROL, with an irrigation system made of a single pipeline. While in the second year (2025), a new treatment was added, consisting of the same irrigation system, however water application rate was reduced to maintain only 40% of the volume of soil at a matric potential > -0.1 MPa (SMARTER 2). During the growing season, stem water potential was evaluated as a measure of the plant water status and at harvest the total yield was compared to the control. In comparison to CONTROL, the SMARTER system decreased the volume of water used for irrigation of 36% and 19%, in 2024 and 2025, respectively; while in 2025 the SMARTER 2 showed a 52% of water saved compared to the CONTROL. Total yield and fruit quality were not affected by the treatments during the two-year trial. However, in 2025 growing season, fruit size was increased by SMARTER and SMARTER 2 compared to CONTROL. In conclusion, the real time, in situ smart system used in this experiment, showed an important potential to decrease the volume of water commonly used in the traditional irrigation system, without affecting the total production and fruit quality.
Keywords: soil moisture, water potential, soil matric potential, Pyrus communis, drip irrigation, evapotranspiration rate
How to cite: Chiarelli, G., Quartieri, M., Rinaldi, J., Mahmood, M. H., Larocca, G. N., Baldi, E., Pasini, M., Baiardi, A., Francia, M., Golfarelli, M., and Toselli, M.: SMARTER probe system for precision irrigation in pear tree orchards toward a higher water use efficiency in agricultural soil , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11977, https://doi.org/10.5194/egusphere-egu26-11977, 2026.
The sustainable management of agri-food ecosystems in Mediterranean regions requires an integrated approach that recognizes the interdependencies between Water, Energy, Food, and Ecosystems (WEFE Nexus). This is especially important in the face of climate change, rising energy costs, and socioeconomic pressure on irrigation systems. This work presents the main results of the participatory process developed at the Nexus Ecosystem Lab (NEL) in Duero, Spain, as part of the NEXUS-NESS project. The project aims to co-design strategies for transitioning toward fair, efficient, and sustainable allocation of natural resources.
The process is based on applying an Ecosystem Innovation approach to the Nexus through a series of multi-stakeholder workshops. These workshops involved irrigation communities, public administrations, the agricultural and energy sectors, academia, and environmental organizations. Using participatory methodologies, including World Café dynamics, Nexus mapping exercises, and a serious game, the main challenges, critical interactions, and territorial priorities in the Duero basin's irrigation systems were identified.
A central element of the process was the co-development of a set of WEFE indicators. These were co-designed in a participatory approach with the Duero basin stakeholders to assess the current status and future scenarios of water, energy, productive resources, and ecosystems, as well as the performance of the measures proposed in the WEFE transition Plan. This approach defined a comprehensible, operational, and relevant indicators that align with the interests, concerns, and actual capacities of local actors. The indicators are meaningful from a territorial perspective and facilitate decision-making.
The results showed that the main challenges lie on: efficiency of water and energy use, economic viability of irrigation, multilevel governance, and integrating ecosystem services into agricultural planning. The co-created indicators also reinforces social learning, transparency, and continuous monitoring of the WEFE transition. More over, they allow for the evaluation of impacts, adjustment of measures, and risks’ anticipation under climate change conditions.
This work demonstrates the effectiveness of Living Labs combined with co-designed of WEFE indicators for operationalizing the WEFE nexus approach in real contexts. This approach provides a solid basis for designing public policies and adaptive management strategies in Mediterranean agricultural basins.
How to cite: Rodriguez-Sinobas, L., Schneider, X., Sánchez-Revuelta, M., Nardi, F., and Segovia-Cardozo, D. A.: Co-designing meaningful WEFE Nexus Transition Pathways in Mediterranean Irrigation Systems with stakeholders: Implementing the WEFE Nexus in Agricultural Systems of the Duero Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13275, https://doi.org/10.5194/egusphere-egu26-13275, 2026.
Water scarcity has intensified in recent years, becoming an almost permanent constraint on competitiveness in Mediterranean irrigated agricultural systems. In this context, agronomic practices such as regulated deficit irrigation (RDI) and digitalization allow these systems to maintain productivity. Nevertheless, increasing pressure on water resources with climate change projections has driven the search for complementary solutions to alleviate crop water stress. Among these, reducing leaf area through pruning or using biostimulants based on seaweed has shown to be effective in increasing irrigation water productivity (WPi) in agriculture. For this reason, the aim of this study was to evaluate the impact of these combined practices over the agronomic response and WPi of adult almond trees in SE Spain under semiarid conditions. We established a completely randomized factorial experimental design with three factors (Irrigation × Pruning × Biostimulation) and two levels each. For the irrigation factor, we established two levels: precision irrigation (PI), in which irrigation was based on an irrigation threshold of soil water depletion of up to 20% of field capacity during the entire season (0-60 cm depth); and regulated deficit irrigation (RDI) in which the trees were irrigated like PI until phase IV, when the water applied was reduced by 25% during May and June and by 50% during July and August with respect to PI. For the pruning factor, we established two levels based on the intensity: normal pruning (NP) was performed according to the criteria for adult almond trees, removing around 42.8 kg fresh weight per tree, and severe pruning (SP) removing up 76.7 kg fresh weight per tree. Finally, the biostimulation factor was defined according to its application or not: trees without application (NB), while biostimulated trees (BIO) were treated with seaweed-based extracts through foliar spraying and drip irrigation during flowering and fruit set. The results obtained showed that yield were only affected by severe pruning, decreasing by 18.2% in comparison to NP. Even though irrigation water was reduced by 21.4% in trees under RDI with respect to PI (2870 m3 ha−1), yield was not reduced, increasing the WPi in 46.8%. Under PI conditions, WPi increased significantly in biostimulated (BIO) trees under NP. In contrast, SP tended to decrease WPi because of reduced yield compared with NP. The interaction between factors did not show significant differences in WPi. Biostimulation alleviated water stress, leading to an increase in almond size, particularly under severe pruning. The total amount of biomass removed in SP significantly reduced yield due to a lower number of fruits. Consequently, severe pruning may be considered under extreme water scarcity conditions, provided it is complemented with biostimulation, as seaweed-based extracts also enhanced the photosynthetic rate and reduced the intensity of water stress in the trees.
Funding: Laura Marín is thankful for her Research Staff Training contract funded by the Regional Agency for Science and Technology of the Region of Murcia (Fundación Séneca 22340/FPI/23) and co-funded by the company FMC through the FMC Agricultural Sciences Chair at the Technical University of Cartagena.
How to cite: Marín-Durán, L., Pérez-López, R., Talavera, J., Monllor, C., Temnani, A., Berrios, P., Zapata-García, S., and Pérez-Pastor, A.: Integrated agronomic management to alleviate water stress in adult almond trees under Mediterranean conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21764, https://doi.org/10.5194/egusphere-egu26-21764, 2026.
Under increasing water scarcity and rising production costs, the competitiveness of Mediterranean agriculture depends on the efficient and economically sustainable use of irrigation water. Beyond maximizing yield, irrigation strategies would be evaluated in terms of their capacity to generate economic returns. With the aim of relating economic water productivity to irrigation strategies in the production of adult mandarin trees in SE-Spain, three randomly distributed irrigation treatments were applied over three seasons: (i) control (CTL), irrigated at approximately 100% of crop evapotranspiration (ETc) throughout the entire crop cycle; (ii) Sustained Deficit Irrigation (SDI), irrigated at 70% of ETc during the entire season; and (iii) Regulated Deficit Irrigation (RDI), irrigated as SDI except during the initial phase of fruit growth stage II, when irrigation was reduced to 35% of ETc until fruits reached about 70% of their final size. The economic irrigation water productivity (EWPi) was calculated as the ratio between economic profit and irrigation water use. For this purpose, we considered the parameters reported by Martin-Gorriz et al. (2022). Accordingly, water and energy costs were set at 0.24 € m⁻³, the mandarin price at 0.33 € kg⁻¹, and a fixed subsidy of 325 € ha⁻¹ from the European Union’s Common Agricultural Policy was included. The profit was calculated as the difference between total revenue and the sum of variable, fixed, and opportunity costs. Total variable costs (TVC) included expenses related to machinery, raw materials, irrigation, and labour, while fixed costs (TFC) comprised depreciation, start-up costs, insurance, and taxes; and total opportunity costs (TOC) accounted for land rental value and the interest on both fixed and variable capital. Despite an average seasonal reduction of 1095 m³ ha⁻¹ in irrigation water under SDI compared with the CTL treatment (5387 m³ ha⁻¹), no differences were detected in yield. In contrast, the RDI treatment, which reduced applied water by an average of 2539 m³ ha⁻¹, resulted in a 29.3% yield reduction relative to CTL. The TFC and TOC remained relatively constant across treatments and seasons, at 1185 and 269 € ha⁻¹, respectively. In contrast, TVC showed a wider variation, ranging from 2875 to 3812 € ha⁻¹, and were mainly driven by water and energy for irrigation. On average, TVC accounted for approximately 70% of the total production costs. Over the study period, the irrigation scheduling of SDI increased the EWPi by 94.3% compared with the CTL (0.68 vs 0.35 € m⁻³), whereas the RDI strategy reduced EWPi a 22.9% due to the water stress intensity reached and its negative effect on crop yield. The relationship between yield and EWPi was linear (y₀=−0.97*** and a=7.49×10⁻⁵***; R2=0.87) and according to this relationship, a minimum yield of 12.9 t ha⁻¹ is required for irrigation water to generate positive economic value. Finally, SDI proved to be an effective and economically robust irrigation strategy, translating water savings into higher economic returns per unit of water and enhancing the competitiveness of semi-arid Mediterranean citrus production systems.
Funding: Biodiversity-Foundation: “Innovative agricultural practices to contribute to environmental improvement and biodiversity in the Mar Menor area (NEWAGROMARMENOR)”.
How to cite: Berríos, P., Temnani, A., and Pérez-Pastor, A.: Enhancing economic irrigation water productivity through sustained deficit irrigation in mandarin under semi-arid conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21649, https://doi.org/10.5194/egusphere-egu26-21649, 2026.
Water scarcity and high pressure over available water among productive sectors are one of the main limiting factors on the competitiveness of irrigated Mediterranean crop production. In addition to agronomic practices to improve water productivity, using non-conventional water sources, such as desalinated water (DW), could be an effective solution if its suitability for irrigation is improved, as it often presents constraints related to its mineral composition, particularly due to high concentrations of boron (B), which can reduce crop productivity. In this regard, treating DW with natural and cost-effective adsorbents, such as clay or carbon ash, offers a promising strategy to reduce water nutrient excess and enhance its suitability for irrigation. For this reason, the objective of our work was to evaluate the effectiveness of adsorbents on DW quality, and the response of winter lettuce grown in pots with coconut fiber irrigated with treated DW. Three randomized treatments (n = 5) were established according to the irrigation water source: (i) a control treatment (CTL), in which plants were irrigated with DW containing 1.2 mg L⁻¹ of B; (ii) T1, in which DW was treated by decantation with 20 g L⁻¹ of clay, mainly composed of kaolinite and sepiolite; and (iii) T2, in which DW was treated by filtration using 50 g L⁻¹ of carbon ash. In all treatments, irrigation was scheduled using a threshold corresponding to a maximum soil water depletion of 20% relative to field capacity. Crop physiological response was assessed as net photosynthesis (Pn) and leaf stomatal conductance (Lc), together with aerial fresh weight and the concentration of macro- and micro-nutrients in both leaves and roots. Prior to the ANOVA assumptions were tested and when differences were detected, means were separated by using LSD-Fisher test (p<0.05). Lettuce irrigated with treated water (T1 and T2) showed a significant increase of 4.13% in fresh weight compared with the DW treatment, whereas net Pn and Lc were not affected. Boron concentrations in leaves and roots were reduced by 37.0% and 25.9% for clay and ash treatments, respectively; while leaf Cu concentration decreased by 28.2% in T2. Leaf manganese also decreased by an average of 38.0% in T1 and T2, while no differences were detected for Na, K, Ca, Mg, and Cl. Finally, our results suggested that the use of natural adsorbents could effectively reduce the excess of some nutrients in desalinated water, reducing the negative effect on growth. Furthermore, the selection of an adsorbent should be based on the crop's tolerance to the most critical element, with clay being most effective for filtering B and ash for Cu. This approach offers a practical strategy for utilizing non-conventional water sources in agriculture and reducing pressure on water resources through cost-effective and scalable solutions.
Funding: Biodiversity Foundation: “Innovative agricultural practices to contribute to environmental improvement and biodiversity in the Mar Menor area (NEWAGROMARMENOR)”.
How to cite: Brahmi, J., Krouma, A., Temnani, A., Berrios, P., Talavera, J., and Pérez-Pastor, A.: Improving the suitability of desalinated water for irrigated agriculture using adsorbents based on clay and ash, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21802, https://doi.org/10.5194/egusphere-egu26-21802, 2026.
Mediterranean agroecosystems are increasingly exposed to environmental pressures driven by climate change, including reduced precipitation, rising temperatures, and growing competition for water resources. These trends intensify uncertainty in water availability and pose significant challenges to irrigation management and long-term agricultural sustainability. Improving irrigation efficiency has therefore become a key strategy to optimize water use while maintaining crop productivity under water-limited conditions.
This study presents the development and field application of a low-cost, automated, and smart irrigation system for horticultural crops, designed to enhance water management in Mediterranean agroecosystems. The system was tested during the 2025 growing season on two tomato crop varieties in north-western Sardinia. The area is characterized by intensive agriculture and increasing pressure on groundwater resources.
The experimental setup integrates an intelligent control unit with a local weather station and soil moisture sensors, enabling continuous monitoring of key environmental variables, including air temperature, relative humidity, wind speed, solar radiation, precipitation, and soil water content. Irrigation scheduling was based on real-time soil moisture measurements in relation to field capacity and meteorological conditions, allowing adaptive control of irrigation timing and applied volumes. Smart irrigation data collected over the entire growing season were analyzed and compared with a conventional irrigation management approach. Preliminary results indicate that real-time monitoring combined with adaptive irrigation control significantly improves water use efficiency, reducing irrigation inputs without compromising crop performance.
The study highlights the potential of smart irrigation systems to address uncertainty in water resources by supporting data-driven decision-making and adaptive management. Beyond agronomic benefits, such systems represent operational tools for precision agriculture approaches aimed at sustainable water use and climate adaptation in Mediterranean farming systems.
How to cite: Piga, A., Bortolu, S., Vagnoni, E., Duce, P., and Cesaraccio, C.: Real-time monitoring and adaptive irrigation control for improved water use efficiency., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12937, https://doi.org/10.5194/egusphere-egu26-12937, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot 2
EGU26-22150 | Posters virtual | VPS17
Basin-Scale Design of Irrigation Districts and Water Planning Strategies for Sustainable Agricultural IntensificationWed, 06 May, 14:33–14:36 (CEST) vPoster spot 2
Uruguay is facing increasing pressure on its water resources due to a strong dependence on agricultural production and a rising frequency of droughts. These trends intensify competition between agricultural water use and environmental water requirements, highlighting the need for adaptive strategies that ensure both ecosystem integrity and agricultural productivity.
Irrigated agriculture relies on on-farm, gravity-fed systems in which water is supplied from reservoirs and distributed through open channel networks. Although effective at the field scale, this traditional approach creates challenges for water allocation control, monitoring, and basin-scale planning due to the large number of small reservoirs and users. In addition, it largely overlooks land use planning, as irrigation development tends to follow water availability rather than optimising the use of high-quality soils or avoiding areas with a high risk of nutrient runoff.
In this context, the study examines the sustainable intensification of irrigated agriculture in the Arapey Basin (northern Uruguay). The basin covers approximately 11,400 km² and contains extensive agricultural lands with high potential for crops such as rice, maize, and improved pastures. The Soil and Water Assessment Tool (SWAT) model, calibrated and validated against long-term streamflow records (30 years), was implemented to represent current and future water management scenarios, including the design of irrigation districts, reservoir operations, and their impacts on streamflow, nutrient transport, and agricultural production. The analysis includes the potential expansion of the reservoir system by seven new reservoirs, increasing total basin storage from 50 hm3 to 280 hm3 across 13 reservoirs.
Simulation results indicate that coordinated reservoir development and controlled water releases could support the expansion of irrigated agriculture while mitigating the effects of drought in the main river. Additionally, regulated reservoir operations and strategically located irrigation districts may help dilute downstream nutrient concentrations. However, the results also highlight the need for good management practices at the field scale to prevent local nutrient accumulation and degradation of water quality. The findings suggest that a basin-scale approach to irrigation development, combining expanded reservoir storage with careful management, can enable sustainable agricultural intensification in northern Uruguay while simultaneously enhancing water governance and protecting environmental resources.
How to cite: Zubelzu, S., Gelos, M., Pais, J. I., Estrada, L., Medina, G., Rosas, J. F., Carriquirry, M., and Navas, R.: Basin-Scale Design of Irrigation Districts and Water Planning Strategies for Sustainable Agricultural Intensification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22150, https://doi.org/10.5194/egusphere-egu26-22150, 2026.
