Search Results (164)

A GIS-based Plant Coefficient Method (PCM), termed the Plant Coefficient Method Tool (PCMT), is presented and validated through this research. It is designed for sustainable irrigation management within arid urban environments, exemplified by Riyadh, Saudi Arabia. The study integrates remote sensing data, including Landsat 8 satellite imagery, vegetation indices (NDVI, LAI), and climatic parameters to estimate daily and seasonal plant water demand for diverse landscape species. Results demonstrate that plant-specific coefficients (${\mathrm{K}}_{\mathrm{p}\mathrm{l}}$) fluctuate seasonally, ranging from 0.1 to 1.4, with average water demand (${\mathrm{E}\mathrm{T}}_{\mathrm{p}\mathrm{l}}$) reaching up to 25 L per square meter during the summer months and decreasing to around 6 L in winter. It may be found by good management based on PCMT that average daily projected ${\mathrm{E}\mathrm{T}}_{\mathrm{p}\mathrm{l}}$ rates can be lowered to as low as 3 mm/day, resulting in a significant decrease in water needs, by around 70% to 50%, when compared to higher categories. Validation across three sites (urban trees, date palms, and turf grass), showed strong correlations (R² > 0.8) between satellite-derived vegetation indices and modeled water needs. The volumetric water demand estimates closely aligned with actual irrigation practices, albeit with some over- and under-irrigation episodes. Spatial analysis indicated that high-demand zones predominantly occur in summer, emphasizing the necessity of adaptive irrigation scheduling. Overall, the PCMT presents a scalable, accurate tool for optimizing water use, supporting sustainable landscape management aligned with Saudi Arabia’s green initiatives. Full article

Optimized water-saving irrigation (WSI) practices are critical for enhancing resource use efficiency and ensuring sustainable wheat production in water-scarce regions. This meta-analysis quantitatively assessed the effects of various WSI methods on wheat yield, water use efficiency (WUE), and partial factor productivity of nitrogen (PFPN) across China’s wheat regions. The results showed that optimized irrigation, particularly drip and micro-sprinkler systems, significantly reduced irrigation water and nitrogen inputs by 35.1% and 7.2%, respectively, without yield penalties. Drip and micro-sprinkler irrigation, which together accounted for over 97% of observations, improved WUE by 18.7% and 10.1%, respectively, and increased PFPN by 6.8% and 5.5%, highlighting their dominant role in current WSI practices. Moderate deficit irrigation (60–100% of full irrigation) optimized WUE and PFPN while maintaining stable yields, whereas severe deficit irrigation (<40%) caused substantial yield losses. Soil texture and bulk density strongly modulated WSI effectiveness. Climatic factors, particularly growing season precipitation, negatively correlated with WSI benefits, highlighting enhanced efficiency gains under drier conditions. These findings emphasize the need to prioritize drip and micro-sprinkler irrigation in national water-saving strategies and advocate for integrated approaches combining WSI with soil health management and site-specific irrigation scheduling to promote sustainable wheat intensification under variable agroecological conditions. Full article

Accurately partitioning actual evapotranspiration ET_{c act} into soil evaporation E_s and plant transpiration T_{c act} is crucial for improving water use efficiency and devising precise irrigation schedules. In water-saving irrigated rice fields, ET_{c act}, E_s and T_{c act} were estimated using a dual crop coefficient method based on three approaches: FAO56 adjusted, locally calibrated and leaf area index LAI-based coefficients. Continuous measurements of hourly and daily ET_{c act}, E_s and T_{c act} with weighing lysimeters were used to validate these coefficients. Results showed that hourly ET_{c act}, E_s and T_{c act} exhibited a distinct inverted “U” shape single-peak trend. Daily ET_{c act} and T_{c act}, along with the corresponding crop coefficients K_{c act} and basal crop coefficients K_{cb act}, initially increased and then decreased throughout the rice growth stages, while daily E_s and soil evaporation coefficient K_{e act} were high during the initial stage and gradually decreased as the development stage progressed. FAO56 adjusted coefficients consistently underestimated both hourly and daily ET_{c act}, E_s and T_{c act}. Locally calibrated basal crop coefficients K_{cb Cal} were determined as 0.28, 1.17 and 1.09 for the initial, mid-season and end-season stages, respectively, and locally calibrated turbulent transport coefficient of water vapor K_{cp Cal} (recommended as 1.2 by FAO) was determined to be 1.59. Based on these calibrated coefficients, estimates of hourly and daily evapotranspiration ET_{c Cal}, soil evaporation E_{s Cal} and plant transpiration T_{c Cal} performed poorly during the initial stage but showed improved accuracy during subsequent growth stages. Hourly and daily evapotranspiration and its components based on LAI-based coefficients exhibited similar performance in estimating measurements, albeit slightly inferior to FAO56 calibrated coefficients. Overall, both the FAO56 calibrated coefficients and LAI-based coefficients are recommended for estimating evapotranspiration and its components at daily and hourly scales. These research findings provide valuable insights for optimizing irrigation regimes and improving water use efficiency in rice cultivation. Full article

Crop aeroponic cultivation still faces issues such as insufficient precision in water supply control and scientifically-based irrigation scheduling. To address this challenge, the present study aims to establish a precision irrigation protocol adapted to the characteristics of crop aeroponic cultivation. Using coriander (Coriandrum sativum L.) as the experimental subject, crop water requirements were estimated utilizing both the FAO56 P-M equation and its revised form. The RMSE between the water requirement measured values and the calculated values using the P-M formula is 2.12 mm, the MAE is 2.0 mm, and the MAPE is 14.29%. The RMSE between the water requirement measured values and the calculated values using the revised P-M formula is 0.88 mm, the MAE is 0.82 mm, and the MAPE is 5.78%. The results indicate that the water requirement values calculated using the revised P-M formula are closer to the measured values. For model development, this study used coriander evapotranspiration as a basis. Major environmental variables influencing water requirement were selected as input features, and the daily reference water requirement served as the output. Three modeling approaches were implemented: Random Forest (RF), Bagging, and M5P Model Tree algorithms. The results indicate that, in comparing various input combinations (C1: air temperature, relative humidity, atmospheric pressure, wind speed, radiation, photoperiod; C2: air temperature, relative humidity, wind speed, radiation; C3: air temperature, relative humidity, radiation), the RF model based on C1 input demonstrated superior performance with RMSE = 0.121 mm/d, MAE = 0.134 mm/d, MAPE = 2.123%, and R² = 0.971. It significantly outperforms the RF models with other input combinations, as well as the Bagging and M5P models across all input scenarios, in terms of convergence rate, determination coefficient, and comprehensive performance. Its predictions aligned more closely with observed data, showing enhanced accuracy and adaptability. This optimized prediction model demonstrates particular suitability for forecasting water requirements in aeroponic coriander production and provides theoretical support for efficient, intelligent water-saving management in crop aeroponic cultivation. Full article

Seasonal drought and nitrogen loss through runoff are two critical problems in the sloping land with shallow soils in southwest China. Irrigation is an effective way to alleviate drought and increase crop yields. Although irrigation is a proven strategy to mitigate drought stress and enhance yields, increased soil moisture under irrigation may exacerbate water and nitrogen losses. Therefore, this study aimed to investigate the long-term effects of irrigation regimes on crop yield, surface runoff, leaching, and nitrogen loss in shallow soil systems. Three experimental treatments were implemented: rainfed control (RF), single irrigation at a flowering stage (SI), and full irrigation (FI). The annual crop yield under SI and FI treatments was 16.4% and 43.5% higher than treatment RF, respectively. The surface runoff in RF was 46.2% and 52.8% higher than the values in SI and FI, respectively. Conversely, the leaching water volume in RF was 13.7% and 13.6% lower than in SI and FI, respectively. The total runoff did not differ significantly, as reduced surface runoff offset elevated leaching. The annual nitrogen loss was 35.4, 30.5, and 22.0 kg N ha⁻¹ in RF, SI, and FI treatments, respectively. Irrigation can significantly decrease the nitrogen loss. Leaching accounted for 96% of the total nitrogen loss. Enhanced crop nitrogen uptake under irrigation reduced total nitrogen concentrations in both soil and leaching water solution, which was the main factor for the decrease in total nitrogen loss under irrigation. These results indicate that in sloping land with shallow soil layers, optimal irrigation scheduling can effectively enhance crop yield without elevating nitrogen leaching risks. The study provides a scientific basis for formulating irrigation strategies in the study region. Full article

In arid and semi-arid agricultural regions, the increasing frequency of extreme climatic events—particularly high temperatures and drought—has severely disrupted crop growth dynamics, leading to significant yield uncertainty and potential threats to the growing global food demand. Optimizing irrigation strategies by integrating dynamic crop growth monitoring and accurate yield estimation is essential for mitigating the adverse effects of extreme weather and promoting sustainable agricultural development. Therefore, this study conducted two consecutive years of field experiments in cotton fields to evaluate the effects of irrigation interval and drip irrigation frequency on cotton growth dynamics and yield, and to develop an optimized irrigation schedule based on the AquaCrop model assimilated with Particle Swarm Optimization (AquaCrop-PSO). The sensitivity analysis identified the canopy growth coefficient (CGC), maximum canopy cover (CCX), and canopy cover at 90% emergence (CCS) as the most influential parameters for canopy cover (CC) simulation, while the crop coefficient at full canopy (KCTRX), water productivity (WP), and CGC were most sensitive for aboveground biomass (AGB) simulation. Ridge regression models integrating multiple vegetation indices outperformed single-index models in estimating CC and AGB across different growth stages, achieving R² values of 0.73 and 0.87, respectively. Assimilating both CC and AGB as dual-state variables significantly improved the model’s predictive accuracy for cotton yield, with R² values of 0.96 and 0.95 in 2023 and 2024, respectively. Scenario simulations revealed that the optimal irrigation quotas for dry, normal, and wet years were 520 mm, 420 mm, and 420 mm, respectively, with a consistent irrigation interval of five days. This study provides theoretical insights and practical guidance for irrigation scheduling, yield prediction, and smart irrigation management in drip-irrigated cotton fields in Xinjiang, China. Full article

Wheat is a staple crop widely sown in Northwest China, and understanding and modelling evapotranspiration (ET) during the wheat-growing stage is important for irrigation scheduling and the efficient use of agricultural water resources. In this study, a four-year observation was conducted on a spring wheat field with border irrigation (BI) treatment and drip irrigation (DI) treatment, based on two Bowen ratio energy balance (BREB) systems. The results showed that the average ET across the whole growing stage scale was 512.0 mm for the BI treatment and 446.9 mm for the DI treatment, and the DI treatment reduced ET by 65.1 mm across the growing stage scale. The driving factors of the changes in ET in the two treatments were investigated using partial correlation analysis after understanding the changing pattern of ET. Net radiation (Rn), soil water content (SWC), and leaf area index (LAI) were the main meteorological, soil, and crop factors leading to the changes in ET in the two treatments. In terms of ET simulation, the SWAP model and different types of machine learning algorithms were used in this study to numerically simulate ET at a daily scale. The total ET values simulated by the SWAP model at the interannual scale were 11.0–14.2% lower than the observed values of ET, and the simulation accuracy varied at different growing stages. In terms of the machine learning simulation of ET, this study is the first to apply five machine learning algorithms to simulate a typical irrigated wheat field in the arid region of Northwest China. It was found that the Stacking algorithm as well as the SWAP model had the optimal simulation among all machine learning algorithms. These findings can provide a scientific basis for irrigation management and the efficient use of agricultural water resources in spring wheat fields in arid regions. Full article

Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydropower scheduling—while also mitigating flood and drought risks. To address this need, in this study, we propose a novel time-variant encoder–decoder (ED) model designed specifically to improve multi-step reservoir inflow forecasting, enabling accurate predictions of reservoir inflows up to seven days ahead. Unlike conventional ED-LSTM and recursive ED-LSTM models, which use fixed encoder parameters or recursively propagate predictions, our model incorporates an adaptive encoder structure that dynamically adjusts to evolving conditions at each forecast horizon. Additionally, we introduce the Expected Baseline Integrated Gradients (EB-IGs) method for variable importance analysis, enhancing interpretability of inflow by incorporating multiple baselines to capture a broader range of hydrometeorological conditions. The proposed methods are demonstrated at several diverse reservoirs across the United States. Our results show that they outperform traditional methods, particularly at longer lead times, while also offering insights into the key drivers of inflow forecasting. These advancements contribute to enhanced reservoir management through improved forecasting accuracy and practical decision-making insights under complex hydroclimatic conditions. Full article

Sediment accumulation in irrigation channels poses a significant challenge to water resource management, impacting hydraulic efficiency and agricultural sustainability. This study introduces an innovative multidisciplinary framework that integrates advanced image analysis (FIJI/ImageJ 1.54p), statistical validation (RStudio), and vector field modeling with a novel Sinusoidal Morphodynamic Bedload Transport Equation (SMBTE) to predict sediment deposition patterns with high precision. Conducted along the Malacatos River in La Tebaida Linear Park, Loja, Ecuador, the research captured a natural sediment transport event under controlled flow conditions, transitioning from pressurized pipe flow to free-surface flow. Observed sediment deposition reduced the hydraulic cross-section by approximately 5 cm, notably altering flow dynamics and water distribution. The final SMBTE model (Model 8) demonstrated exceptional predictive accuracy, achieving RMSE: 0.0108, R²: 0.8689, NSE: 0.8689, MAE: 0.0093, and a correlation coefficient exceeding 0.93. Complementary analyses, including heatmaps, histograms, and vector fields, revealed spatial heterogeneity, local gradients, and oscillatory trends in sediment distribution. These tools identified high-concentration sediment zones and quantified variability, providing actionable insights for optimizing canal design, maintenance schedules, and sediment control strategies. By leveraging open-source software and real-world validation, this methodology offers a scalable, replicable framework applicable to diverse water conveyance systems. The study advances understanding of sediment dynamics under subcritical (Fr ≈ 0.07) and turbulent flow conditions (Re ≈ 41,000), contributing to improved irrigation efficiency, system resilience, and sustainable water management. This research establishes a robust foundation for future advancements in sediment transport modeling and hydrological engineering, addressing critical challenges in agricultural water systems. Full article

This study investigates the impact of climate variability on wheat production in Algeria’s semi-arid interior plains from 2014 to 2024, aiming to curb the challenges of rainfed wheat cultivation, optimize irrigation, and improve water productivity. The Soil–Water–Atmosphere–Plant (SWAP) model-driven approach refined irrigation scheduling to mitigate climate-induced losses and improve resource efficiency. Using historical climate data, soil properties, and wheat growth observations from the experimental farm of the Technical Institute for Field Crops, the SWAP model was calibrated and validated using one-factor-at-a-time sensitivity analysis, achieving a coefficient of determination (R²) of 0.93 and a Normalized Root Mean Squared Error (NRMSE) of 17.75. Two drought-based irrigation indices, Soil Moisture Drought Index (SMDI) and Crop Water Stress Index (CWSI), guided adaptive irrigation strategies, showing a significant reduction in crop failure during drought periods. Results revealed a strong link between rainfall variability and wheat yield. Adopting a 9-day irrigation interval could increase water productivity to 18.91 kg ha⁻¹ mm⁻¹, enhancing yield stability under varying climatic conditions. The SMDI approach maintained soil moisture during extreme drought, while CWSI optimized water use in normal and wet years. This study integrates SMDI and CWSI into a validated irrigation framework, offering data-driven strategies to enhance wheat production resilience. Findings support sustainable water management and provide practical insights for policymakers and farmers to refine irrigation planning and climate adaptation, contributing to long-term agricultural sustainability. Full article

This study aimed to reveal the characteristics of returned water in paddy fields at different scales and the rules of its reuse in China’s Ganfu Plain Irrigation District through multiscale (field, lateral canal, main canal, small watershed) observations, thereby optimizing water resource management and improving water use efficiency. Subsequent investigations during the 2021–2022 double-cropping rice seasons revealed that the tillering stage emerged as a critical drainage period, with 49.5% and 52.2% of total drainage occurring during this phase in early and late rice, respectively. Multiscale drainage heterogeneity displayed distinct patterns, with early rice following a “decrease-increase” trend while late rice exhibited “decrease-peak-decline” dynamics. Smaller scales (field and lateral canal) produced 37.1% higher drainage than larger scales (main canal and small watershed) during the reviving stage. In contrast, post-jointing-booting stages showed 103.6% higher drainage at larger scales. Return flow utilization peaked at the field-lateral canal scales, while dynamic regulation of Fangxi Lake’s storage capacity achieved 60% reuse efficiency at the watershed scale. We propose an integrated optimization strategy combining tillering-stage irrigation/drainage control, multiscale hydraulic interception (control gates and pond weirs), and dynamic watershed storage scheduling. This framework provides theoretical and practical insights for enhancing water use efficiency and mitigating non-point source pollution in plain irrigation districts. 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

Monitoring the water status of fruit orchards is required to optimize crop water management and determine irrigation scheduling. For this purpose, capacitance probes are commonly used to measure soil water content (θ_s). However, when these probes are not calibrated, the estimates of θ_s are, therefore, unreliable. Our objective was to relate the measurements of capacitance probes, without a site-specific calibration, with a reliable indicator of the water status (stem water potential at solar noon (Ψ_stem)) of rain-fed grapevines grown under contrasting soil management strategies (tillage and spontaneous vegetation) and of irrigated peach and pear trees. During the 2023 growing season, θ_s was monitored in a peach and a pear orchard and in a vineyard in northeast Spain using capacitance sensors at three depths: 0.15, 0.30, and 0.45 m. Correlation coefficients ranged from 0.75 to 0.87 in peach trees, from 0.53 to 0.56 in pear trees, and from 0.56 to 0.90 in grapevines, depending on soil depth. These relationships were significant for both peach trees and grapevines but not for pear trees. Under the conditions of this study, uncalibrated capacitance measurements of θ_s could be useful to assess grapevine and peach tree water status in real time but were limited for pear trees. Full article

A detailed characterization of evapotranspiration (ET) patterns is of paramount importance for optimizing irrigation scheduling and enhancing water-use efficiency in the North China Plain. To delve into this, a two-season study was conducted at the National Experimental Station for Precise Agriculture in Beijing. Using 12 weighing lysimeters, the study compared two summer maize varieties with contrasting canopy sizes: Jingke 968 (JK), characterized by a large canopy, and CF 1002 (CF), with a small canopy. The comprehensive analysis yielded the following significant findings: (1) The daily average ET rates exhibited consistent trends across cultivars, yet with notable disparities in magnitude. JK consistently demonstrated higher water consumption throughout the growth seasons. In the first season, at the V13–R1 stage, the peak daily ET of JK and CF reached 5.91 mm/day and 5.52 mm/day, respectively. In the second season, during the R1–R3 stage, these values were 5.21 mm/day for JK and 5.22 mm/day for CF, highlighting the nuanced differences in water use between the varieties under varying growth conditions. (2) Regardless of canopy size, the hourly ET fluctuations across different growth stages followed similar temporal patterns. However, the most striking inter-varietal differences in ET emerged during the R1–R3 reproductive stages, when both cultivars had achieved peak canopy development (leaf area index, LAI > 4.5). Notably, the ET differences between JK and CF adhered to a characteristic diurnal “increase–decrease” pattern. These differences peaked during mid-morning (09:00–11:00) and early afternoon (13:00–15:00), while minimal divergence was observed at solar noon. This pattern suggests complex interactions between canopy structure, microclimate, and plant physiological processes that govern water loss over the course of a day. (3) Analysis of the pooled data pinpointed two critical time periods that significantly contributed to the cumulative ET differences between the varieties. The first period was from 12:00–17:00 during the R1–R3 (anthesis) stage, and the second was from 08:00–16:00 during the R3–R5 (grain filling) stage. JK maintained significantly higher transpiration rates (Tr) compared to CF, especially during the morning hours (09:00–12:00). On average, the Tr of JK exceeded that of CF by 5.3% during the pre-anthesis stage and by 16.0% during the post-anthesis stage. These observed Tr differentials strongly indicate that canopy architecture plays a pivotal role in modulating stomatal regulation patterns. Maize varieties with large canopies, such as JK, demonstrated enhanced morning photosynthetic activity, which likely contributed to increased transpiration. At the same time, both varieties seemed to employ similar midday water conservation strategies, possibly as an adaptive response to environmental stress. In summary, this study has comprehensively elucidated the intricate relationship between the leaf area index and the evapotranspiration of summer maize across multiple timescales, encompassing periodic, daily, and hourly variations. The findings provide invaluable data-driven insights that can underpin the development of precise and quantitative irrigation strategies, ultimately promoting sustainable and efficient maize production in the North China Plain. Full article