Search Results (150)

Efficient irrigation planning in data-scarce regions remains challenging due to limited access to localized meteorological data, reliance on complex computer-based models, and the technical knowledge required to deploy them at the field scale. Hence, the need for accessible, smartphone-based tools that simplify soil water balance calculations using public data to support practical decision-making in resource-limited contexts. This smartphone-based application estimates Net and Gross Irrigation Requirements using a Soil Water Balance (SWB) framework. The app combines region-specific empirical formulations for Effective Rainfall (Pe) calculation. The application utilizes user-supplied crop and irrigation parameters and meteorological data available in the public domain and operates at multiple temporal scales (daily, 10-day, weekly, and monthly), thereby supporting flexible irrigation schedules. The performance of app was evaluated through simulation-based benchmarking against FAO-CROPWAT 8.0 using harmonized inputs across five representatives agro-climatic region: Central India, Southern Vietnam, Northern Thailand, Western Bangladesh, and Central Sri Lanka. Quantitative comparison showed deviations within ±5% for Effective Rainfall, crop evapotranspiration, Net Irrigation, and Gross Irrigation, and low mean bias values (−2.8% to +3.3%) show the absence of systematic over- or under-estimation compared to CROPWAT model. The application also demonstrated responsiveness to climatic variability. Although the validation is limited to few representative locations and assumed minimal runoff conditions, the results suggest that the proposed method is technically consistent and feasible in practice. This study demonstrates smartphone-based application as a decision support for field-level irrigation planning and water resource management, particularly in data-limited agricultural contexts. Full article

The article presents a method for determining the irrigation requirements of crops based on soil moisture. The proposed approach enables scheduling irrigation at the most appropriate time of day by combining current soil moisture measurements with forecasts of moisture levels for the following day. A narrow Artificial Intelligence (AI) model is developed and applied to the task of 24 h-ahead soil moisture forecasting. Water loss due to excessive irrigation is minimized through precise soil moisture monitoring, postponement or reduction of irrigation in response to measured precipitation, temperature, and wind speed, as well as meteorological forecasts of future rainfall. The proposed irrigation system is suitable for both drip irrigation and central pivot systems. It is built using cost-effective components and incorporates LoRa connectivity, which facilitates integration in remote areas without the need for internet access. Furthermore, the addition of new irrigation zones does not require physical modifications to the central server. Experimental tests demonstrated that the system effectively controls irrigation timing and achieves the desired soil moisture levels with high accuracy, while accounting for additional external factors that influence soil moisture. Full article

Dynamic canal-system scheduling faces the fundamental challenge of determining the optimal reduction in the current period’s water allocation to reserve sufficient water for remaining periods, thereby hedging against potentially greater future water shortages. Although forecast information has been widely incorporated to address this hedging problem, its effectiveness is heavily dependent on forecast accuracy. Integrating abundant historical canal scheduling data with forecast information provides a promising pathway to improve scheduling performance, yet relevant studies remain limited. This study introduces the concept of Target Residual Lump-Sum Water Quota (TRLSWQ) for each time interval and develops a novel “Bi-level, Two-stage” (BT) model for dynamically updated canal-system scheduling that jointly leverages TRLSWQ and forecast information. The model defines clear canal scheduling rules and effectively adapts to the hierarchical structure in canal system scheduling. The model is applied to the summer–autumn irrigation scheduling of the Yongji main canal and six associated sub-canals in the Hetao Irrigation Area, Inner Mongolia, China. The results indicate that compared with the conventional model, the BT model reduces the total water shortage index of sub-canals from 40.81 to 31.44 (a decrease of 22.9%) and increases the utilization rate of the water quota from 89.3% to 92.9% (an increase of 3.9%). Furthermore, this study clarifies the mechanism of canal scheduling deviations caused by forecast errors: early-stage rainfall under-forecasting induces excessive early-stage allocation, leaving no water for later periods, whereas early-stage over-forecasting leads to withheld early allocation and unused residual lump-sum quota in later stages. The BT model effectively balances shortage risks between current and future periods and offers a practical and robust strategy for improving dynamic canal scheduling in irrigation districts. Full article

Land surface temperature (LST) is a critical parameter in agricultural drought monitoring, crop growth analysis, and climate change research. However, the challenge of acquiring high-resolution LST data with both fine spatial and temporal scales remains a significant obstacle in remote sensing applications. Despite the high temporal resolution afforded by daily MODIS LST observations, the coarse (1 km) spatial scale of these data restricts their applicability for studies demanding finer spatial resolution. To address this challenge, a novel deep learning-based approach is proposed for LST downscaling: the spatial resolution downscaling attention network (SRDA-Net). The model is designed to upscale the resolution of MODIS LST from 1000 m to 250 m, overcoming the shortcomings of traditional interpolation techniques in reconstructing spatial details, as well as reducing the reliance on linear models and multi-source high-temporal LST data typical of conventional fusion approaches. SRDA-Net captures the feature interaction between MODIS LST and auxiliary data through global resolution attention to address spatial heterogeneity. It further enhances the feature representation ability under heterogeneous surface conditions by optimizing multi-source features to handle heterogeneous data. Additionally, it strengthens the model of spatial dependency relationships through a multi-level feature refinement module. Moreover, this study constructs a composite loss function system that integrates physical mechanisms and data characteristics, ensuring the improvement of reconstruction details while maintaining numerical accuracy and model interpret-ability through a triple collaborative constraint mechanism. Experimental results show that the proposed model performs excellently in the simulation experiment (from 2000 m to 1000 m), with an MAE of 0.928 K and an R² of 0.95. In farmland areas, the model performs particularly well (MAE = 0.615 K, R² = 0.96, RMSE = 0.823 K), effectively supporting irrigation scheduling and crop health monitoring. It also maintains good vegetation heterogeneity expression ability in grassland areas, making it suitable for drought monitoring tasks. In the target downscaling experiment (from 1000 m to 500 m and 250 m), the model achieved an RMSE of 1.804 K, an MAE of 1.587 K, and an R² of 0.915, confirming its stable generalization ability across multiple scales. This study supports agricultural drought warning and precise irrigation and provides data support for interdisciplinary applications such as climate change research and ecological monitoring, while offering a new approach to generating high spatio-temporal resolution LST. Full article

Extensive practice has demonstrated that the continuous pursuit of high yields in the black soil region of Northeast China resulted in imbalances in soil nutrients and declines in both soil quality and water use efficiency. Alternate wetting and drying (AWD) irrigation offers a promising solution for increasing rice yield and maintaining soil fertility. However, the success of this irrigation method largely depends on its scheduling. This study examined the threshold effects of AWD on rice growth, yield, and soil nutrient availability in the Sanjiang Plain, a representative black soil region in Northeast China. A two-year trial was conducted from 2023 to 2024 at the Qixing National Agricultural Science and Technology Park. “Longjing 31”, a local cultivar, was selected as the experimental material. The lower limit of soil water content under AWD was set as the experimental factor, with three levels: −10 kPa (LA), −20 kPa (MA), and −30 kPa (SA). The local traditional irrigation practice, continuous flooding, served as the control treatment (CK). Indicators of rice growth and soil nutrient content were measured and analyzed at five growth stages: tillering, jointing, heading, milk ripening, and yellow ripening. The results showed that, compared to CK, AWD had minimal impact on rice plant height and tiller number, with no significant differences (p > 0.05). However, AWD affected leaf area index (LAI), shoot dry matter (SDM), yield, and soil nutrient availability. In 2023, control had little effect on rice plant height and tiller number among the different irrigation treatments. The LAI of LA was 11.1% and 22.5% higher than that of MA and SA, respectively, while SDM in LA was 10.5% and 17.2% higher than in MA and SA. Significant differences were found between LA and MA, as well as between LA and SA, whereas no significant differences were observed between MA and SA. The light treatment is beneficial to the growth and development of rice, while the harsh growth environment caused by the moderate and severe treatments is unfavorable to rice growth. The average contents of nitrate nitrogen (NO₃⁻-N), available phosphorus (AP), and available potassium (AK) in LA were 11.4%, 8.4%, and 9.3% higher than in MA, and 16.7%, 11.5%, and 15.0% higher than in SA, respectively. Significant differences were observed between LA and SA. This is because the light treatment facilitates the release of available nutrients in the soil, while the moderate and severe treatments hinder this process. Although panicle number per unit area and grain number per panicle in LA were 7.5% and 2.3% higher than in MA, and 10.8% and 2.2% higher than in SA, these differences were not statistically significant. Seed setting rate and thousand-grain weight showed little variation across irrigation treatments. The yield of LA was 10,233.3 kg hm⁻², 9.1% and 14.1% higher than that of MA and SA, respectively, with significant differences observed. Compared with the moderate and severe treatments, the light treatment increases indicators such as the number of panicles per unit area, grains per panicle, thousand-grain weight, and seed setting rate, resulting in significant differences among the treatments. Water use efficiency (WUE) decreased as the control level increased. The WUE of all AWD irrigation treatments was significantly higher than that of the control treatment (CK). Compared with CK, AWD reduces evaporation, percolation, and other water losses, leading to a significant decrease in water consumption. Meanwhile, the yield remains basically unchanged or even slightly increases, thus resulting in a higher WUE than CK. The trends in rice growth, soil nutrient indicators, and WUE in 2024 were generally consistent with those observed in 2023. In 2024, the yield of LA was 9832.7 kg hm⁻², 14.9% and 17.3% higher than that of MA and SA, respectively, with significant differences observed. Based on the results, the following conclusions are drawn: (1) AWD irrigation can affect the growth of rice, alter the status of available nutrients in the soil, and thereby cause changes in yield and WUE; (2) LA is the optimal treatment for increasing rice yield, improving the availability of soil available nutrients, and improving WUE; (3) Both MA and SA enhanced WUE; however, these practices negatively impacted rice growth and the concentration of soil available nutrients, leading to a concurrent decline in yield. To increase rice yield and maintain soil fertility, LA, with an irrigation upper limit of 30 mm and a soil water potential threshold of −10 kPa, is recommended for the Sanjiang Plain region. Full article

The increasing pressure on water resources caused by agricultural intensification, the rising food demand and climate change requires new irrigation strategies that improve the sustainability and efficiency of agricultural production. The objective of this study is to evaluate the performance of the digital twin (DT), Irri_DesK, in a 15-hectare commercial processing tomatoes plot in Extremadura (Spain) over two growing seasons (2023 and 2024). Three irrigation strategies were compared: conventional farmer management, management based on a remote-sensing platform (Smart4Crops) and automated scheduling using Irri_DesK DT-integrated soil moisture sensors, climate data and simulation models to adjust irrigation doses daily. Results showed that the DT-based strategy allowed for the application of regulated deficit irrigation strategies while maintaining productivity or fruit quality. In 2023, it achieved an economic water efficiency of 284.81 EUR/mm with a yield of 140 t/ha using 413 mm of water. In 2024, it maintained high production levels (126 t/ha) under more challenging conditions of spatial variability. These results support the potential of DTs for improving irrigation management in water-limited environments. Full article

Evapotranspiration (ET) variation is typically influenced by climatic factors, which are considered the primary drivers of agricultural water requirements. Any changes in ET rates directly affect crop water demands. In this study, temporal trends and magnitudes of key climatic variables, and their impacts on reference evapotranspiration (ETo) during 1981–2020, were evaluated across 36 districts of Punjab, Pakistan. Positive serial correlations, ranging from 0.29 to 0.48, were identified and removed using the pre-whitening technique. Increasing trends in maximum temperature (Tmax) and wind speed (WS) across Punjab and its subregions were observed, while relative humidity (RH) exhibited both increasing and decreasing trends. No significant trends were detected for the minimum temperature (Tmin). On a monthly scale, in the Southern Punjab (SP) region, Sen’s slope estimated an increase in ETo, ranging from 0.239 mm/year in November to 0.636 mm/year in May, at a significance level of α = 0.05 (5%). At the provincial scale, significant upward trends in ETo were observed for the annual, Kharif, and autumn seasons, with Z-values of 2.04, 2.16, and 3.13, respectively, at α = 0.05 and 0.01. It was determined that, on an annual scale in Punjab, ETo sensitivity to climatic parameters followed the following order: Tmax > wind speed (WS) > Tmin > RH. The best-fitted models for Tmax, Tmin, WS, and RH were Gaussian, exponential, and spherical. ETo was found to increase spatially from North to South Punjab, with an approximate rise of 70–80 mm/decade. The results provide a scientific basis for understanding hydroclimatic drivers of ETo in semi-arid regions and contribute to improving climate impact assessments on agricultural water use. The observed ETo increases, particularly in South Punjab and lower Central Punjab, highlight the need for region-specific irrigation scheduling and water allocation. These findings can guide cropping calendars, improve irrigation efficiency, and increase canal water supplies to high-ETo areas, supporting adaptive strategies against climate variability in Punjab. Full article

Water scarcity and greenhouse gas (GHG) emissions pose significant challenges to sustainable wheat production in tropical regions such as the Brazilian Cerrado. This study evaluated the effects of different soil water depletion levels, denoted as f (20%, 40%, 60%, and 80% of available water capacity—AWC), on no-tillage winter wheat irrigated after rainfed soybean cultivation. Grain yield decreased significantly at depletion levels ≥ 60%, with the highest yields observed at f = 20% (6933 kg ha⁻¹) and f = 40% (6814 kg ha⁻¹). Water use efficiency (WUE) ranged from 12.4 to 14.0 kg ha⁻¹ mm⁻¹, with no significant differences among treatments. Nitrous oxide (N₂O) emissions peaked at f = 60% (4.55 kg ha⁻¹), resulting in the highest average global warming potential (GWP = 1.185.78 kg CO₂ eq ha⁻¹) and greenhouse gas intensity (GHGI = 192.66 kg CO₂ eq Mg⁻¹ grain). Methane (CH₄) acted as a net sink across all irrigation levels. Soil enzymatic activities (β-glucosidase and arylsulfatase) were not significantly affected by irrigation management. Overall, irrigation scheduling based on f = 40% soil water depletion provided the best balance between productivity and environmental sustainability, representing a climate-smart and resource-efficient strategy for wheat production in tropical agroecosystems. These findings provide promising insights for tropical agriculture by showing that sustainable irrigation can balance productivity and climate mitigation in the Cerrado. Maintaining soil water depletion below 60% significantly reduces N₂O emissions and environmental impact, emphasizing the importance of conservation practices. Additionally, preserving soil biological quality supports the long-term viability of these practices and offers valuable guidance for policies promoting efficient irrigation in climate-vulnerable regions. Full article

Soil moisture prediction is a key parameter for effective irrigation scheduling and water use efficiency. However, accurate long-term prediction remains challenging, as most existing models excel in short- to medium-term prediction but struggle to capture the complex temporal dependencies and non-linear interactions of soil moisture variables over extended horizons. This study proposes a hybrid soil moisture prediction method, integrating a long short-term memory (LSTM) network and extreme gradient boosting (XGBoost) model for multistep soil moisture prediction at 24 h, 72 h, and 168 h horizons. The LSTM captures temporal dependencies and extracts high-level features from the dataset, which are then used by XGBoost for final predictions. The study uses real-world data from the D.A.T.A (Demonstrating Applied Technology in Agriculture) research farm at ABAC (Abraham Baldwin Agricultural College) Tifton, GA, USA, utilizing watermark soil moisture sensors and weather station’s data installed on the farm. Results show that the proposed method outperforms other hybrid models, achieving R² values of 98.67%, 98.54%, and 98.56% for 24, 72, and 168 h predictions, respectively. The study findings highlight that LSTM-XGBoost offers a precise long-term soil moisture prediction, making it a practical tool for real-time irrigation scheduling, enhancing water use efficiency in precision agriculture. Full article

The objective of this study was to evaluate the effects of different water levels on yield, morphological, and quality parameters, as well as the crop water stress index (CWSI), for pepper plants under a high tunnel greenhouse in a semi-arid region. For this purpose, the irrigation schedule used in this study includes 120%, 100%, 80%, and 60% (I120, I100, I80, and I60) of evaporation monitored gravimetrically. In this study, increasing irrigation levels (I100 and I120) resulted in increased stem diameter, plant height, fruit number, leaf number, and leaf area values. However, these values decreased as the water level dropped (I60 and I80). At the same time, increased irrigation resulted in improvements in fruit width, length, and weight, as well as a decrease in TSS values. While total yield and marketable yield values increased at the I120 water level, TWUE and MWUE were the highest at the I100 water level. I80 and I120 water levels were statistically in the same group. It was found that the application of I100 water level in the high tunnel greenhouse is the appropriate irrigation level in terms of morphology and quality parameters. However, in places with water scarcity, a moderate water deficit (I80) can be adopted instead of full (I100) or excessive irrigation (I120) in pepper cultivation in terms of water conservation. The experimental results reveal significant correlations between the parameters of green pepper yield and the CWSI. Therefore, a mean CWSI of 0.16 is recommended for irrigation level I100 for higher-quality yields. A mean CWSI of 0.22 is recommended for irrigation level I80 in areas where water is scarce. While increasing the CWSI values decreased the values of crop water consumption, leaf area index, total yield, marketable yield, total water use efficiency, and marketable water use efficiency, decreasing the CWSI increased these values. This study concluded that the CWSI can be effectively utilised in irrigation time planning under semi-arid climate conditions. With the advancement of technology, determining the CWSI using remote sensing-based methods and integrating it into greenhouse automation systems will become increasingly important in determining irrigation times. Full article

Optimizing irrigation and nitrogen (N) management in saline soils is critical for sustainable cotton production in arid regions that have been subjected to climate change. In this study, a two-year factorial field experiment (3 salinity levels × 3 N rates × 3 irrigation quotas) is integrated with the RZWQM2 model to (1) identify water–N–salinity thresholds for cotton yield and (2) to project climate change impacts under SSP2.4-5 and SSP5.8-5 scenarios (2031–2090) in Xinjiang, China, a global cotton production hub. The results demonstrated that a moderate salinity (6 dS/m) combined with a reduced irrigation (3600 m³/hm²) and N input (210 kg/hm²) achieved a near-maximum yield (6918 kg/hm²), saving 20% more water and 33% more fertilizer compared to conventional practices. The model exhibited a robust performance (NRMSE: 5.94–12.88% for soil–crop variables) and revealed that warming shortened the cotton growing season by 1.2–9.5 days per decade. However, elevated CO₂ (832 ppm by 2090) levels under SSP5.8-5 increased yields by 22.6–42.1%, offsetting heat-induced declines through enhanced water use efficiency (WUE↑27.5%) and biomass accumulation. Critically, high-salinity soils (9 dS/m) required 25% additional irrigation (4500 m³/hm²) and a full N input (315 kg/hm²) to maintain yield stability. These findings provide actionable strategies for farmers to optimize irrigation schedules and nitrogen application, balancing water conservation with yield stability in saline-affected arid agroecosystems that have been subjected to climate change. Full article

To optimize water–nitrogen management for mulched drip-irrigated peanuts in Xinjiang, a three-season field experiment was conducted to assess the impacts of drip irrigation rates and water–nitrogen coupling on peanut growth, yield, quality, and water–nitrogen use efficiency. Two irrigation accounts (30 and 37.5 mm, denoted as W1 and W2), three nitrogen application levels (half nitrogen application and conventional nitrogen application, denoted as N1 and N2), and a control treatment (CK) without nitrogen application, and two drip discharge rates (3.0 and 6.0 L h⁻¹, denoted as Q1 and Q2) were utilized for a total of five treatments per year, and the experiment was repeated three times. The results demonstrated that the irrigation and fertilization parameters of the W2N1Q2 treatment could significantly improve peanut growth, yield, quality, and water–nitrogen use efficiency, achieving optimal values for all measured indicators. Compared with the control (W2N0Q1), the main stem height increased by 9.59% and 13.13%, the aboveground biomass increased by 6.32% and 34.67%, the yield increased by 26.69% and 20.97% (p < 0.01), the water use efficiency increased by 27.08% and 16.33%, the nitrogen partial factor productivity values were 47.39 and 77.00 kg kg⁻¹, the protein content increased by 3.99% and 4.63%, and the oil content increased by 1.68% and 8.53%, respectively. A PCA was performed using five key performance indicators (yield, protein content, oil content, water use efficiency, and nitrogen partial factor productivity) to evaluate different treatment combinations. The W2N1Q2 treatment obtained the highest composite score, indicating its overall superior performance among all treatments. Therefore, under the conditions of this experiment, the irrigation and nitrogen application parameters for achieving both a high yield and quality of peanuts under mulched drip irrigation in Xinjiang were determined to be W2N1Q2 treatment (irrigation account of 37.5 mm, nitrogen application of 118 kg ha⁻¹, and drip discharge of 6.0 L h⁻¹). This optimized combination brings three key advantages to water-scarce regions: (1) maximizing yield water use efficiency through precise irrigation scheduling; (2) balanced nutrient management to prevent nitrogen wastage; and (3) providing a key technological reference for agricultural production in Xinjiang and other similar ecological zones. Full article

In citriculture, inputs like water and fertilizer are applied through traditional basin methods, thereby incurring reduced use-efficiency. The response of conventional crop coefficient-based fertigation scheduling continues to be inconsistent and complex in its field implementation, thereby necessitating the intervention of sensor-based (Internet of Things; IoT) technology for fertigation scheduling on a real-time basis. The study aimed to investigate fertigation scheduling involving four levels of irrigation, viz., I₁ (100% evapotranspiration (ET) as the conventional practice), I₂ (15% volumetric moisture content (VMC)), I₃ (20% VMC), and I₄ (25% VMC), as the main treatments and three levels of recommended doses of fertigation, achieved by reappropriating different nutrients across phenologically defined critical growth stages, viz., F₁, F₂, and F₃ (conventional fertilization practice), as sub-treatments, which were evaluated through a split-plot design over two harvesting seasons in 2021–2023. Nagpur mandarin (Citrus reticulata Blanco) was used as the test crop, which was raised on Indian Vertisol facing multiple nutrient constraints. Maximum values for physiological growth parameters (plant height, canopy area, canopy volume, and relative leaf water content (RLWC)) and fruit yield (characterized by 9% and 5%, respectively, higher A-grade-sized fruits with the I₄ and F₁ treatments over corresponding conventional practices, viz., I₁ and F₃) were observed with the I₄ irrigation treatment in combination with the F₁ fertilizer treatment (I₄F₁). Likewise, fruit quality parameters, viz., juice content, TSS, TSS: acid ratio, and fruit diameter, registered significantly higher with the I₄F₁ treatment, featuring the application of B at the new-leaf initiation stage (NLI) and Zn across the crop development (CD), color break (CB), and crop harvesting (CH) growth stages, which resulted in a higher leaf nutrient composition. Treatment I₄F₁ conserved 20–30% more water and 65–87% more nutrients than the I₁F₃ treatment (conventional practice) by reducing the rate of evaporation loss of water, thereby elevating the plant’s available nutrient supply within the root zone. Our study suggests that I₄F₁ is the best combination of sensor-based (IoT) irrigation and fertilization for optimizing the quality production of Nagpur mandarin, ensuring higher water productivity (WP) and nutrient-use-efficiency (NUE) coupled with the improved nutritional quality of the fruit. Full article

The inefficient irrigation strategy is an important factor affecting the yield and water productivity of tomatoes in greenhouses, seriously hindering the development of the cultivation industry. While the impact of irrigation level on tomato growth and yield has been extensively studied, irrigation interval, another crucial component of irrigation schedule, as well as their interaction, remain poorly explored. There were four irrigation levels (W1: 125% ET_c, W2: 100% ET_c, W3: 75% ET_c, and W4: 50% ET_c; ET_c represented crop evapotranspiration) and three irrigation intervals (D1: 4-day interval, D2: 7-day interval, and D3: 10-day interval), aiming to explore the effects of different irrigation intervals and levels on the performance of tomatoes. Here, we showed that the moderate increases in irrigation level and interval promoted root growth, improved nitrogen uptake and distribution, and enhanced plant height, stem diameter, leaf area index, and aboveground biomass, thereby promoting the net photosynthetic rate of plants and fruit yield. The fruit quality indicators of total soluble solids, vitamin C, and soluble sugar decreased with increasing irrigation level but increased with decreasing irrigation interval. Higher irrigation levels increased tomato water consumption and resulted in lower water-nitrogen use efficiency. Overall, compared with W2D2 and W2D3, the yield of W2D1 increased by 8.0% and 26.1%, respectively, and the water productivity increased by 5.7% and 19.3%, respectively, and the soluble sugar increased by 7.1% and 17.5%, respectively. In addition, nitrogen uptake in tomato organs increased and then decreased with the increase of irrigation level, while it consistently increased with decreasing irrigation interval. At the harvest period, the nitrogen uptake in plant organs followed the order of fruit > leaf > stem. Taken together, W2D1 (100% ET_c and 4-day interval) is the recommended irrigation strategy for this experiment, which can provide a theoretical basis and technical support for the sustainable production strategy of greenhouse drip irrigation tomatoes. Full article

The agricultural sector faces growing environmental and societal pressures to balance natural resource use with food security, particularly within the Water-Energy-Food-Ecosystems Nexus (WEFE). Increasing water demand, competition, and challenges like droughts and desertification are driving the need for innovative irrigation practices. Pressurized irrigation systems, such as sprinkler and micro-irrigation, are gaining prominence due to their automation, labor savings, and increased water application efficiency. To support farmers in designing and managing these systems, the R&D project AGIR developed a computational tool that simulates water application efficiency under site-specific conditions. The tool integrates key parameters, including system design, scheduling, soil properties, topography, meteorological data, and vegetation cover, providing a robust methodological framework with classification criteria for evaluating irrigation options. Validated using data from six case studies, the tool achieved simulated irrigation efficiencies of 73% to 90%, which are consistent with field observations. By simplifying complex irrigation requirement calculations, the model offers a user-friendly alternative while maintaining accuracy at the farm level. This innovative tool enables stakeholders to optimize irrigation systems, reduce water losses, and establish standardized recommendations for design, management, performance, and socio-economic considerations. It represents a significant step forward in supporting sustainable water management and advancing the goals of Agriculture 4.0. Full article