Accurate simulation of water and nutrient transport in macroporous loess soils remains a key challenge for process-based crop models, as preferential flow is typically represented only implicitly. In the Environmental Policy Integrated Climate (EPIC) model, the assumption of a fully connected pore system biases simulated water and nutrient losses in structurally complex soils. To address this limitation, this study introduces a macroporosity (MACR) parameter into EPIC, defined as the fraction of total soil porosity actively participating in preferential bypass flow, providing a physically interpretable control on effective flow connectivity. MACR modifies the governing equations of key hydrological process modules—surface runoff, percolation, lateral flow, and evapotranspiration—thus enabling a transition from an implicit single-domain to a conceptually constrained dual-domain water partitioning scheme. In an apple orchard in Dali County, Shaanxi Province, China, a Sobol-based global sensitivity analysis was conducted to evaluate the influences of parameters and interactions on the simulated annual apple yield, which was the sole variable against which the model was calibrated and validated. Model calibration (2005–2012) and validation (2013–2022) demonstrated substantially improved yield simulation performance (R² = 0.93 and 0.92; RMSE = 1.12 and 1.47 t ha⁻¹, respectively) relative to the original EPIC formulation. Compared with the baseline single-domain configuration, the modified model framework yielded a 30–60
This study evaluated the combined effects of manure application and deficit irrigation on soil health, spinach plant health, and water productivity. A two-year field experiment compared cow manure (FM) and mineral (M) fertilization at three irrigation levels (W1, full irrigation; W2, 85
Agricultural water optimization is critical for improving water security for the arid Western USA and other water-scarce regions. Optimization includes assessment of agricultural water diversions within conveyance systems managed by irrigation canal companies (ICC), yet few water balance approaches exist at this level. Satellite data provides insight into historical agricultural water use and the potential for optimization. In this study, historical remote sensing data from OpenET and gridMET were coupled with Geographic Information Systems data (fields/canals distribution and agricultural diversion records) to implement a water balance model for three ICCs in Northern Utah. The monthly water balance model determined the change of water depletion at the root zone (∆Dr) with precipitation, irrigation and capillary rise from groundwater (CR) as inflows; and actual crop evapotranspiration (ETc act), runoff and deep percolation (DP) as outflows. The water balance residual (WBR) was calculated as the unresolved portion of the water balance and reflect runoff, DP and other unquantified hydrologic exchanges (CR was considered only where supported). The spatial distribution of ∆Dr was captured by a Random Forest model using local observed data (ETc act and soil water content). Across the study area, WBRs varied spatially and temporally (2020–2023) from 0.01 to 2.7 cubic hectometers (12 − 2,210 acre-feet) per month, with the highest values occurring during peak irrigation season (July-August). Seasonally (Apr-Oct), WBR represented 5–29
Food security remains a critical challenge in densely populated regions such as China, where intensive irrigation has increased crop yields but imposed substantial environmental costs. Sustainable strategies that maintain production while reducing resource use are urgently needed. This study evaluated five cropping systems—winter wheat–summer maize (WW–M), winter wheat–summer maize–spring maize (WW–M–sM), spring maize–winter fallow (sM–F), ryegrass–spring maize (R–sM), and winter wheat–summer fallow (WW–F)—under four irrigation regimes: rainfed (RF), minimum irrigation (MI), critical irrigation (CI), and normal irrigation (NI). APSIM simulations using 1980–2020 climate data quantified energy yield, evapotranspiration (ET), nitrate leaching, water use efficiency (WUE), and nitrogen use efficiency (NUE) across a precipitation gradient in the North China Plain. The conventional WW–M system achieved the highest energy yield, reaching 245 × 10³ MJ ha⁻¹, but also showed the greatest nitrate leaching, reaching 92 kg N ha⁻¹. At the 437-mm site, when compared under the same NI regime, sM–F and WW–M–sM reduced nitrate leaching by 95.8
Maize is a crop that plays a fundamental role in global food security, particularly in tropical and subtropical regions. However, drought stress remains one of the major constraints that limit maize production worldwide. Maize sensitivity to water deficit is especially critical during the flowering stage, often resulting in substantial yield losses. This study aimed to investigate a sustainable strategy to mitigate drought stress in sweet corn through the combined application of silicon (Si) and inoculation with arbuscular mycorrhizal fungi (AMF) in non-sterilized native soil. We hypothesized that the association between Si and AMF could enhance drought tolerance by improving the physiological and agronomic performance of the plants. Our results demonstrated that the combined treatment increased mycorrhizal colonization by 50
The detection and management of crop water stress is essential for optimizing irrigation practices and this becomes more critical with the increasing water scarcity problem due to climate change. The Crop Water Stress Index (CWSI) is widely accepted as a useful tool for measuring water stress, but the computation of the index is quite complicated. The direct automation of CWSI calculation from thermal and multispectral bands without any manual and intermediate processing steps is not well explored. This paper aims to investigate the use of an Unmanned Aerial Vehicle (UAV) with thermal and multispectral cameras to monitor water stress in sweet orange trees. Two UAV missions were used to obtain ortho-images and calculate the required indices such as the normalized difference vegetation index (NDVI), land Surface Temperature (LST) and CWSI. Two Deep Learning (DL) models, U-Net and DeepLabV3+, were used to automate the pixel wise segmentation of CWSI derived water stress classes. U-Net outperformed DeepLabv3 + in this task, achieving an accuracy of 83.24
Super-high-density olive groves have expanded globally, relying on cultivars with specific traits and optimized irrigation to ensure productivity and quality. This study investigated the effects of soil water availability on vegetative growth, yield, and fruit quality of three olive cultivars (Arbequina, Lecciana, and Oliana) grown in super-high-density planting systems in central Italy over two consecutive seasons. Three irrigation regimes were applied: rainfed (T0), sustained deficit irrigation (T1), and full irrigation (T2), corresponding to approximately 27–47
The Hetao Irrigation District (HID), a crucial grain-producing area, is currently affected by continuous climate change. Quantifying how future climate change may alter crop production water footprints (WF) is essential for improving irrigation water allocation and enhancing water use efficiency. In this study, the SDSM model was calibrated and validated to predict future meteorological factors under RCP4.5 and RCP8.5 scenarios from 2021 to 2060. The spatiotemporal distributions of the WF, crop production blue water footprint (WFb), and crop production green water footprint (WFg) for spring wheat, maize, and sunflower were simulated and analyzed by coupling the SDSM with the SWAP-WOFOST model. The adjustment of the crop planting structure of the three crops was further explored. The results indicated that temperature and precipitation were projected to increase under the RCP4.5 and RCP8.5 scenarios from 2021 to 2060. The WFg of the three crops increased significantly in two projected scenarios, reflecting enhanced reliance on rainfall and soil water. In contrast, WFb generally decreased in sub-regions with higher projected effective precipitation, implying reduced irrigation water consumption for maintaining production. Under the adjusted crop planting structure in RCP4.5 scenario, the WF was reduced by 3.59
The widespread adoption of dome greenhouses has been constrained by challenges in structural modeling, high design specificity, dense grid configurations, and elevated construction costs. To address these limitations, this study proposes a bionic design approach informed by the fractal characteristics of Euryale ferox leaf veins. The box-counting dimension of the leaf vein network was determined to be 2.01, which guided the development of a primary beam system with fractal dimensions ranging from 2.12 to 2.39, achieving a balance between mechanical efficiency and agricultural spatial suitability. Experimental validation using metal and resin models showed agreement between measured and simulated axial stresses, with mean relative errors of 4.85
Water scarcity poses a critical threat to tomato (Solanum lycopersicum L.) production in arid and semi-arid regions. In this study, the local fresh-market tomato cultivar "X-13" was used as the experimental material. Five irrigation treatments were tested, including CK (70
Water scarcity is a critical challenge in West Texas, where agriculture and the oil and gas industry compete for limited water resources. The oil and gas industry generates approximately 32
Climate change poses a critical challenge to agricultural sustainability due to alterations in water demand and crop yields. This study evaluates the projected agroclimatic, hydrological, productive, and economic impacts on maize, sugar beet, and wheat crops in the Itata River basin for the period 2035–2065, using simulations with the AquaCrop model under the RCP8.5 climate scenario. In addition, the effect of an adaptation strategy based on earlier sowing dates was analyzed. The results revealed marked spatial heterogeneity in net irrigation requirements (NIR), with higher demands for sugar beet (735–917 mm), followed by maize (566–733 mm) and wheat (503–636 mm). Compared to the historical period (1980–2010), projected reductions in precipitation and the duration of the growing cycle limited the accumulation of both potential and actual evapotranspiration. Yields increased for sugar beet (16.2
Rising irrigation demand and declining groundwater resources threaten the sustainability of irrigated maize production in Nebraska. Benchmarking farmers’ applied irrigation against optimal amounts offers insight into management performance and highlights opportunities for improvement. However, the few benchmarking studies conducted in Nebraska have often relied on simplified modeling assumptions to estimate optimal irrigation, potentially overestimating the magnitude of differences from actual practices and overlooking factors that influence farmers’ decisions. This study applied the DSSAT CERES-Maize model to determine optimal irrigation using multi-year data from the University of Nebraska-Lincoln’s Testing Ag Performance Solutions (UNL-TAPS) program. The model, calibrated for cultivar traits and site-specific conditions, demonstrated strong agreement between simulated and observed phenology and yield during validation (NRMSE < 5
Accurate estimates of actual evapotranspiration (ETa) are essential for irrigation management in vineyards. This study locally calibrated and validated the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model for California vineyard systems using Landsat 8 imagery and multi-year eddy covariance (EC) measurements collected between 2013 and 2016 in the Central Valley of California. Two calibration approaches were implemented: k-fold cross-validation and a split-sample (two-year) strategy. SAFER performance was evaluated using both closed and unclosed energy balance EC fluxes to assess the influence of energy balance closure on model calibration and validation. Energy balance closure ratios ranged from 0.67 to 0.95, with moderate interannual variability and lower closure observed during some periods. Local calibration of SAFER resulted in regression coefficients that differed substantially from the original parametrization. Compared to the original coefficients, root mean square error (RMSE) was reduced by approximately 71–77
Soil salinization is a major constraint on agricultural productivity worldwide. In the Yellow River irrigation district of Ningxia, salinization severely depresses maize yields. Existing crop–water–salt models lack daily two-way coupling between salt transport and crop growth. To address this gap, a fully coupled WOFOST–HYDRUS-1D model was developed that dynamically links soil water–salt transport and crop growth, with an improved representation of salt stress. The model was calibrated for maize using field observations from Huinong District in 2024 and validated against an independent dataset in 2025. Maize grain yield was reproduced well in both years (R² = 0.883 and 0.824 for calibration and validation), confirming strong inter-annual parameter stability. It also captured the substantial yield reduction observed under high-salinity conditions. With parameters fixed after the 2024 calibration, the coupled model outperformed standard WOFOST in 2025, indicating that the gain arose mainly from coupling rather than recalibration. These findings support the framework as a practical tool for yield prediction, irrigation scheduling, and water–salt management in salinized farmland.
Light intensity and substrate moisture content are critical environmental factors influencing the productivity of greenhouse cherry tomatoes (Lycopersicon esculentum var. cerasiforme A. Gray). This study investigated their combined effects on cherry tomato growth, yield, and quality. Experiments were conducted in the springs of 2022 and 2023, utilizing two light intensity levels (natural light, L1; 70
Water scarcity increasingly challenges sustainable walnut production, yet the influence of irrigation management on walnut oil quality remains insufficiently understood. This study evaluated the effects of different irrigation regimes and irrigation levels on fruit quality and oil composition of Chandler walnut (Juglans regia L.) over three consecutive years (2021–2023) in Tekirdağ, Türkiye. Two irrigation strategies—Class A pan evaporation and soil water depletion—were applied at three irrigation levels (75
Accurate modeling of filter head losses is essential for the optimal design and management of microirrigation systems, particularly when treating reclaimed water. In this study, Gene Expression Programming (GEP) was applied—for the first time in the literature—to develop predictive models for head losses in disc, screen, and sand filters operating with effluents. A k-fold cross-validation procedure was adopted to rigorously evaluate model performance under both direct learning (DL) and transfer learning (TL) scenarios. GEP models achieved Scatter Index (SI) values ranging from 0.049 to 0.071 for direct learning, consistently outperforming existing empirical equations when a fair comparison using independent test sets was conducted. Under transfer learning, screen filter-derived models demonstrated superior transferability to both sand and disc filters (SI 0.058–0.101), while combining disc and sand filter data improved predictions for screen filters (SI 0.064–0.069). These results provide a novel, data-driven framework for filter head loss estimation that can support microirrigation system design when filter-specific training data are unavailable.
Improving water–nutrient management to raise yield without compromising quality is crucial for Coffea arabica. Therefore, this study evaluated split drip fertigation regimes across three growth stages: flowering and fruit-setting (Stage I), fruit expansion (Stage II), and fruit maturation (Stage III). Five treatments (F90,270,90, F120,360,120, F150,450,150, F180,540,180, and F210,630,210) were compared with the control (CK, F0,900,0), where subscripts denote fertilizer rates (kg ha− 1 year− 1) at each growth stage. Responses of soil quality, plant physiology, yield, bean nutrients, and cup quality were quantified to develop a comprehensive evaluation. The soil quality index (SQI) improved by 12.73–58.18
Understanding within-field and season variability of soil water supply and crop water stress is critical for successful variable-rate irrigation (VRI) management. The objectives of this study were to (1) assess within-field variability of field capacity (FC), wilting point (WP), total available water (TAW), and initial soil volumetric water content (VWCinit); (2) validate a water balance model based on the American Society of Civil Engineers (ASCE) standardized Penman-Monteith estimation of reference evapotranspiration (ET0) using measured within-field VWC; and (3) evaluate the model sensitivity to FC, WP, VWCinit, and crop coefficients. A 22-ha field of winter wheat (Triticum aestivum L.) near Grace, Idaho, United Sates of America was delineated into zones and managed with VRI. Spatial variability of soil water characteristics among 102 sites was measured in the field: FC (355–488 mm), WP (103–153 mm), TAW (230–361 mm), and VWCinit (325–464 mm) in a 1.2 m soil profile. Model validation against measured VWC resulted in root mean square error (RMSE) values of 23.2–61.3 mm for different sampling dates. These RMSE values showed the ability to model within-field spatially variable soil water dynamics and crop water stress. The model showed high sensitivity of predicted ET and soil water depletion output values to FC and VWCinit inputs, but lower sensitivity to WP. The sensitivity analysis also suggested spatially variable crop coefficients improve prediction of spatially variable ET rates. The model validation highlights opportunities for a spatially variable modelling approach, while the model’s sensitivity to soil and crop parameters highlights practical limitations to scheduling VRI.
