Search Results (314)

To address freshwater scarcity and the underutilization of low-saline water in the North China Plain, a field study was conducted to evaluate the effects of alternating sprinkler irrigation using saline and fresh water on soil water–salt dynamics and corn growth. Two salinity levels (3 and 5 g·L⁻¹, representing S1 and S2, respectively) and three irrigation strategies—saline–fresh–saline–fresh (F1), saline–fresh (F2), and mixed saline–fresh (F3)—were tested, resulting in six treatments: S1F1, S1F2, S1F3, S2F1, S2F2, and S2F3. S1F1 significantly improved soil water retention at a 30–50 cm depth and reduced surface electrical conductivity (EC) and Na⁺ concentration (p < 0.05). S1F1 also promoted more uniform Mg²⁺ distribution and limited Ca²⁺ loss. Under high salinity (5 g·L⁻¹), surface salt accumulation and ion concentration (Na⁺, Mg²⁺, and Ca²⁺) increased, particularly in S2F3. Corn growth under alternating irrigation (F1/F2) outperformed the mixed mode (F3), with S1F1 achieving the highest plant height, leaf area, grain number, and 100-grain weight. The S1F1 yield surpassed others by 0.4–3.0% and maintained a better ion balance. These results suggest that alternating irrigation with low-salinity water (S1F1) effectively regulates root-zone salinity and improves crop productivity, offering a practical strategy for the sustainable use of low-saline water resources. Full article

To explore the feasibility of integrated deficit water-biogas slurry irrigation under indirect subsurface drip irrigation, three deficit irrigation levels (60%FC, 70%FC, and 80%FC; FC represents field capacity) were established during the three growth stages of tomatoes. The results indicated that biogas slurry irrigation treatments increased the soil organic matter content in the root zone and water use efficiency (WUE) and reduced soil pH. As the degree of deficit increased, the plant height and stem diameter of tomatoes decreased significantly (p < 0.05), particularly during the seedling and flowering-fruiting stages. A mild deficit during the seedling stage was beneficial for subsequent plant growth, yielding maximum leaf area (6871.42 cm² plant⁻¹). Moderate deficit treatment at the seedling stage maximized yield, which was 19.79% higher than the control treatment in 2020 and 19.22% higher in 2021. The WUE of severe deficit treatment at the maturity stage increased by 26.6% (2020) and 31.04% (2021) compared to the control treatment. Comprehensive evaluation using TOPSIS combined with the weighted method revealed that severe deficit treatment at the maturity stage provided the best comprehensive benefits for tomatoes. In summary, deficit irrigation at different growth stages positively influenced tomato growth, quality, and soil environment in response to water-biogas slurry irrigation. Full article

With increasing global challenges related to water scarcity and phosphorus depletion, the recovery and reuse of wastewater-derived nutrients offer a sustainable path forward. This study evaluates the dual role of lanthanides (Ce³⁺ and La³⁺) in recovering phosphorus from municipal wastewater and supporting corn (Zea mays) cultivation through lanthanide phosphate (Ln-P) and lanthanide-reclaimed wastewater (LRWW, wastewater spiked with lanthanide). High-purity precipitates of CePO₄ (98%) and LaPO₄ (92%) were successfully obtained without pH adjustment, as confirmed by X-ray photoelectron spectroscopy (XPS) and energy-dispersive spectroscopy (EDS). Germination assays revealed that lanthanides, even at concentrations up to 2000 mg/L, did not significantly alter germination rates compared to traditional coagulants, though root and shoot development declined above this threshold—likely due to reduced hydrogen peroxide (H₂O₂) production and elevated total dissolved solids (TDSs), which induced physiological drought. Greenhouse experiments using desert-like soil amended with Ln-P and irrigated with LRWW showed no statistically significant differences in corn growth parameters—including plant height, stem diameter, leaf number, leaf area, and biomass—when compared to control treatments. Photosynthetic performance, including stomatal conductance, quantum efficiency, and chlorophyll content, remained unaffected by lanthanide application. Metal uptake analysis indicated that lanthanides did not inhibit phosphorus absorption and even enhanced the uptake of calcium and magnesium. Minimal lanthanide accumulation was detected in plant tissues, with most retained in the root zone, highlighting their limited mobility. These findings suggest that lanthanides can be safely and effectively used for phosphorus recovery and agricultural reuse, contributing to sustainable nutrient cycling and aligning with the United Nations’ Sustainable Development Goals of zero hunger and sustainable cities. Full article

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions. Full article

The Subsurface self-regulating, Low-Energy, Clay-based Irrigation (SLECI) system is a recently developed irrigation method. The SLECI system supplies water directly to the crop root zone by utilising the potential difference established between its permeable interior and exterior radial walls. In this study, we investigated the effect of the SLECI emitter’s operating pressure head and burial depth on the water flow characteristics in sandy loam soil. The results show that the developed COMSOL-2D model accurately predicted water flow characteristic under SLECI. The operating pressure head significantly influenced the water flow characteristics. As the operating pressure head increased, emitter discharge increased, and the wetted soil area was extended. The burial depth had a minimal effect on the emitter discharge but notably affected the advancement and time at which wetting fronts reached the soil surface and bottom boundaries. Operating the SLECI emitter at a higher operating pressure head and shallower burial depth could degrade irrigation water application and water use efficiencies. Based on a multi-objective optimisation algorithm, we recommend that the SLECI emitter be operated at a 125 cm pressure head and buried at 40 cm for crops with a root zone depth of 100 cm. Our study is expected to provide a greater understanding of the SLECI system and offer some recommendations and guidelines for its efficient deployment in sandy loam for enhanced water use efficiency in crop production. Full article

Accurate measurement and understanding of the spatiotemporal distribution of soil water content (SWC) are crucial in various environmental and agricultural sectors. The present study implements a novel precision agriculture (PA) approach under sugarbeet field conditions of two moisture-irrigation treatments with two subfactors, clay loam (CL) and clay (C) soils, for geostatistics modeling (seven models’ evaluation) of time domain reflectometry (TDR) multisensor network measurements. Two different sensor calibration methods (M1 and M2) were trialed, as well as the results of laboratory soil analysis for geospatial two-dimensional (2D) imaging for accurate GIS maps of root zone moisture profiles, granular, and hydraulic profiles in multiple soil layers (0–75 cm depth). Modeling results revealed that the best-fitted semi-variogram models for the granular attributes were circular, exponential, pentaspherical, and spherical, while for hydraulic attributes were found to be exponential, circular, and spherical models. The results showed that kriging modeling, spatial and temporal imaging for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps, the exponential model was identified as the most appropriate with TDR sensors using calibration M1, and the exponential and spherical models were the most appropriate when using calibration M2. The resulting PA profile maps depict spatiotemporal soil water variability with very high resolutions at the centimeter scale. The best validation measures of PA profile SWC ${\theta v}_{TDR}$ maps obtained were Nash-Sutcliffe model efficiency NSE = 0.6657, MPE = 0.00013, RMSE = 0.0385, MSPE = −0.0022, RMSSE = 1.6907, ASE = 0.0418, and MSDR = 0.9695. The sensor results using calibration M2 were found to be more valuable in environmental irrigation decision-making for a more accurate and timely decision on actual crop irrigation, with the lowest statistical and geostatistical errors. The best validation measures for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps obtained for clay loam over clay soils. Visualizing the SWC results and their temporal changes via root zone profile geostatistical maps assists farmers and scientists in making informed and timely environmental irrigation decisions, optimizing energy, saving water, increasing water-use efficiency and crop production, reducing costs, and managing water–soil resources sustainably. Full article

In arid agricultural production, exploring suitable water-saving irrigation strategies and analyzing their water-saving mechanisms are of great significance. Alternating partial root-zone drying irrigation (APRI), a water-saving strategy, enhances the water use efficiency (WUE) of alfalfa (Medicago sativa L.) This paper aims to clarify the physiological mechanisms by which the APRI method enhances the physiological WUE of alfalfa, as well as the differences between this water-saving irrigation strategy, conventional irrigation (CI), and their water deficit adjustments, in order to seek higher water use efficiency for alfalfa production in arid regions. In this experiment, alfalfa was used as the research subject, and three irrigation methods, CI, fixed partial root-zone drying (FPRI), and APRI, were set up, each paired with three decreasing moisture supply gradients of 90% water holding capacity (WHC) (W1), 70% WHC (W2), and 50% WHC (W3). Samples were taken and observed once after every three complete irrigation cycles. Through a comparative analysis of the growth status, leaf water status, antioxidant enzyme activity, and osmotic adjustment capabilities of alfalfa under different water supplies for the three irrigation strategies, the following conclusions were drawn: First, the APRI method, through artificially created periodic wet–dry cycles in the rhizosphere soil, provides pseudo-drought stress that enhances the osmotic adjustment capabilities and antioxidant enzyme activity of alfalfa leaves during the early to middle phases of irrigation treatment compared to CI and FPRI methods, resulting in healthier leaf water conditions. Secondly, the stronger drought tolerance and superior growth conditions of alfalfa under the APRI method due to reduced water availability are key factors in enhancing the water use efficiency of alfalfa under this strategy. Full article

Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article

Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework that combines satellite-derived soil moisture estimates, ground-based observations, the HYDRUS-1D vadose zone model, and the ensemble Kalman filter (EnKF) data assimilation method to improve soil moisture simulations over saline-affected farmland in the Hetao irrigation district. Vegetation effects were first removed using the water cloud model; after correction, a cubic regression using the vertical transmit/vertical receive (VV) signal retrieved surface moisture with an R² value of 0.7964 and a root mean square error (RMSE) of 0.021 cm³·cm⁻³. HYDRUS-1D, calibrated against multi-depth field data (0–80 cm), reproduced soil moisture profiles at 17 sites with RMSEs of 0.017–0.056 cm³·cm⁻³. The EnKF assimilation of satellite and ground observations further reduced the errors to 0.008–0.017 cm³·cm⁻³, with the greatest improvement in the 0–20 cm layer; the accuracy declined slightly with depth but remained superior to either data source alone. Our study improves soil moisture simulation accuracy and closes the knowledge gaps in multi-source data integration. This framework supports sustainable land management and irrigation policy in vulnerable farming regions. Full article

Early detection of plant water status is crucial for efficient crop management. In this research, proximal sensing tools (i.e., hyperspectral imaging HSI and thermal IR camera) were used to monitor changes in spectral and thermal profiles of a citrus orchard in Sicily (Italy), managed under five irrigation systems. The irrigation systems differ in the amount of water distribution and allow four different strategies of deficit irrigation to be obtained. The physiological traits, stem water potential, net photosynthetic rate, stomatal conductance and the amount of leaf chlorophyll were measured over the crop’s growing season for each treatment. The proximal sensing data consisted of thermal and hyperspectral imagery acquired in June–September during the irrigation seasons 2023–2024 and 2024–2025. Significant variation in physiological traits was observed in relation to the different irrigation strategies, highlighting the highest plant water stress in July, in particular for the partial root-zone drying irrigation system. The water-use efficiency (WUE) values in subsurface drip irrigation were similar to the moderate deficit irrigation treatment and more efficient (up to 50%) as compared to control. Proximal sensing measures confirmed a different plant water status in relation to the five different irrigations strategies. Moreover, four spectral indices (Normalized Difference Vegetation Index NDVI; Water Index WI; Photochemical Reflectance Index PRI; Transformed Chlorophyll Absorption Ratio Index TCARI), calculated from HSI spectra, highlighted strong correlations with physiological traits, especially with stem water potential and the amount of leaf chlorophyll (coefficient of correlation ranged between −0.4 and −0.5). This study demonstrated the effectiveness of using proximal sensing tools in precision agriculture and ecosystem monitoring, helping to ensure optimal plant health and water use efficiency. Full article

This study examined groundwater dynamics under saline–alkali improvement measures in a 3.66 × 10⁷ m² study area in Wuyuan County, Hetao Irrigation District, where agricultural sustainability is constrained by soil salinization. This work investigated the spatiotemporal evolution patterns and influencing factors of the groundwater environment in the context of soil salinity–alkalinity improvement, as well as the impact of irrigation on the ionic characteristics of groundwater. Furthermore, based on this analysis, a groundwater numerical model and a prediction model for the study area were developed using Visual MODFLOW Flex 6.1 software to forecast the future groundwater levels in the study area and evaluate the effects of varying irrigation scenarios on these levels. The key findings are as follows: (1) The groundwater depth stabilized at 1.63 ± 0.15 m (0.4 m increase) post-improvement measures, maintaining equilibrium under current irrigation but increasing with reductions in water supply. The groundwater salinity increased by 0.59–1.2 g/L across the crop growth period. (2) Spring irrigation raised the groundwater total dissolved solids by 15.6%, as influenced by rock weathering (38.2%), evaporation (31.5%), and cation exchange (30.3%). (3) Maintaining current irrigation systems and planting structures could stabilize groundwater levels at 1.60–1.65 m over the next decade, confirming the sustainable hydrological effects of soil improvement measures. Reducing irrigation to 80% of the current water supply of the Yellow River enables groundwater level stabilization (2.05 ± 0.12 m burial depth) within 5–7 years. This approach decreases river water dependency by 20% while boosting crop water efficiency by 18.7% and reducing root zone salt stress by 32.4%. Full article

The effects of stomatal factors of plant leaves under partial root-zone drying (PRD) have been widely studied. However, the non-stomatal factors and the relationship between photosynthesis with soil moisture have not been explored. In this study, four treatments over-irrigation, full irrigation, moderate water deficit, and severe water deficit were investigated, aiming to evaluate the effects on the diurnal variation of alfalfa leaf photosynthesis under PRD and its relationship with stomatal and non-stomatal limitations, as well as soil moisture. The results showed that any levels of water deficit led to a decrease in the photosynthetic rate (P_n) of alfalfa leaves. Leaves under moderate and severe water deficit displayed a pronounced midday “photosynthetic lunch break,” while those under over- and full irrigation did not display this phenomenon. Before 11:30 a.m., the reduction in P_n was primarily due to stomatal limitations, as evidenced by reduced stomatal conductance (G_s) and decreased intercellular CO₂ concentration (C_i). After 11:30 a.m., non-stomatal limitations became the dominant factor, with both G_s and transpiration rate (T_r) continuing to decrease, while C_i increased, indicating a shift in the limiting factors. Under PRD with moderate water deficit, alfalfa experienced both stomatal and non-stomatal limitations within a single day, leading to a hay yield reduction of 18.6%. Additionally, over-irrigation helped to maintain higher P_n and T_r, increasing alfalfa yield and thus improving water productivity by 33.1%. The correlation coefficients between soil moisture content at 10 cm depths with alfalfa leaf P_n, T_r, and G_s on the photosynthetic measurement day were 0.9864, 0.8571, and 0.8462, respectively. At 20 cm, the correlation coefficients were 0.8820, 0.6943, and 0.6951, respectively. The study concluded that both stomatal and non-stomatal mechanisms contributed to reduced alfalfa P_n in water deficit of PRD. Furthermore, shallow soil moisture also played a crucial role in influencing photosynthetic performance. 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 salinization of cultivated soil in arid zones is a core obstacle restricting the sustainable development of agriculture, particularly in regions like Xinjiang, China, where extreme aridity and intensive irrigation practices exacerbate salt accumulation through evaporation–crystallization cycles. Conventional drip irrigation, while temporarily mitigating surface salinity, often leads to secondary salinization due to elevated water tables and inefficient leaching. Recent studies highlight the potential of integrating drip irrigation with subsurface drainage systems to address these challenges, yet the synergistic mechanisms governing ion transport dynamics, hydrochemical thresholds, and their interaction with crop physiology remain poorly understood. In this study, we analyzed the effects of spring irrigation during the non-fertile period, soil hydrochemistry variations, and salt ion dynamics across three arid agroecosystems in Xinjiang. By coupling drip irrigation with optimized subsurface drainage configurations (burial depths: 1.4–1.6 m; lateral spacing: 20–40 m), we reveal a layer-domain differentiation in salt migration, Cl⁻ and Na⁺ were leached to 40–60 cm depths, while SO₄²⁻ formed a “stagnant salt layer” at 20–40 cm due to soil colloid adsorption. Post-irrigation hydrochemical shifts included a 40% decline in conductivity, emphasizing the risk of adsorbed ion retention. Subsurface drainage systems suppressed capillary-driven salinity resurgence, maintaining salinity at 8–12 g·kg⁻¹ in root zones during critical growth stages. This study establishes a “surface suppression–middle blocking–deep leaching” three-dimensional salinity control model, providing actionable insights for mitigating secondary salinization in arid agroecosystems. Full article

Seasonal freeze–thaw irrigation areas face challenges of soil salinization and water scarcity, requiring a deep understanding of soil freeze–thaw dynamics under the interaction between irrigation and groundwater. An in situ lysimeter experiment was conducted in the winters of 2020–2021 and 2023–2024 to investigate the effects of autumn irrigation (AI) timing (late AI conducted in late November and icing AI conducted in early December) and quota (0, 35, 135, 270 mm) on soil water, heat, and salt transport under varying groundwater levels in the Hetao Irrigation District, Northwest China. Results showed that AI had a strong short-term effect on the groundwater depth and there was a significant negative correlation between groundwater depth and air temperature on a monthly scale. The quota and air temperature during AI were the key factors in utilizing the “refrigerator effect”—where irrigation water pre-cooled by frozen layer accelerates soil freezing—to regulate soil water and salt transport under freeze–thaw cycles. The drastic reduction in AI water consumption lowered the groundwater level, highlighting air temperature as the dominant driver of soil dynamics. Thus, icing AI with low quota (35 mm) can optimize water use (water saving of 77% compared to the traditional quota of 150 mm) while maintaining soil moisture (an increase of 17.4% in water storage) and salinity control (a decrease of 41.6% in salt storage) in the root zone (0–40 cm) through the “refrigerator effect”, demonstrating its potential for sustainable irrigation in water-scarce cold regions. Full article