Search Results (4,877)

The agro-pastoral ecotone in Yinshanbeilu is the main potato-producing region. In recent years, the shift from rainfed to irrigated agriculture has created challenges in understanding potato water consumption patterns, water use efficiency, and irrigation optimization. This study utilized the DSSAT model to simulate soil moisture, leaf area index, and potato yield based on a two-year in situ observational experiment. The study showed that simulated values of the soil water moisture, leaf area index, and yield, with Absolute Relative Error (ARE) of 4.18–5.27%, Normalized Root Mean Square Error (nRMSE) of 5.64–8.65%, and Coefficient of Determination (R²) values of 0.86–0.921, exhibited acceptable accuracy. Simulated results pointed out that potato water consumption ranged between 375.2 and 414.2 mm, with 50–52% occurring during tuber formation to bulking stages, and the average water consumption intensity was 2.62~2.81 mm/d. Based on DSSAT model simulation, this study found that water use efficiency (WUE) reached 162.17–166.20 kg/(hm²·mm), while irrigation water use efficiency (IWUE) varied between 86.1 and 108.1 kg/(hm²·mm). With the highest yield as the target, the recommended irrigation amounts for potato in normal year and dry year were 180 mm and 240 mm. With the highest utilization rate of groundwater resources as the target, the recommended irrigation amounts in normal year and dry year were 162 mm and 192 mm. These findings offer valuable insights for promoting sustainable groundwater use and enhancing water conservation practices in the Yinshanbeilu agro-pastoral ecotone. Full article

Recycling agricultural residues is a promising strategy to enhance soil organic carbon (SOC) and improve soil quality. This study investigated the effects of exogenous organic carbon (EOC) amendments—straw and hydrochar—on SOC, its labile fractions, and the carbon pool management index (CPMI, an indicator of soil carbon quality and management efficiency) under flooding (FI) and controlled irrigation (CI) in a two-year pot experiment using paddy soil under field conditions. CI improved the soil average readily oxidizable organic carbon (ROC), dissolved organic carbon (DOC), and microbial biomass carbon (MBC) by 6.37–12.19%, 18.70–26.00% (p < 0.05), and 11.95–17.97% (p < 0.05), compared to FI. Similarly, EOC addition increased average ROC, DOC, and MBC during the entire rice growth period by 12.33–22.95%, 4.50–24.35%, and 6.24–21.51%, respectively, compared to the unamended controls. Additionally, CI increased soil carbon lability (L), carbon pool activity index (LI), carbon pool index (CPI), and CPMI by 3.39–14.01%, 3.65–8.84%, 1.75–2.58%, and 6.19–16.01%, respectively, although some of these increases were not statistically significant. Notably, the combination of CI and EOC application significantly increased CPMI by 19.45–20.29% (p < 0.05), with the highest values observed in CI treatments amended with either straw or hydrochar. Hydrochar application had a smaller effect on increasing soil active OC fractions compared to straw incorporation, but demonstrated a greater potential for long-term SOC sequestration. These findings demonstrate the potential of hydrochar as a waste-derived amendment for long-term carbon sequestration and provide insights for optimizing water–carbon management strategies in sustainable rice cultivation. Full article

Excess fertiliser and sub-optimal irrigation threaten soil health in greenhouse vegetable systems on black soils. This study explored how water–fertilizer regimes shape soil aggregate structure, stability, and soil organic carbon (SOC) sequestration in a meadow black soil eggplant system in Heilongjiang, China. Using a randomized block design with drip irrigation, three treatments were tested: conventional water and fertilizer (WF), conventional water with 20% fertilizer reduction (W80%F), and 20% water reduction with conventional fertilizer (80%WF). Results showed that 80%WF significantly increased macro-aggregate proportion, improved stability (mean weight diameter, MWD; geometric mean diameter, GMD), enhanced total organic carbon (TOC) content, and strengthened carbon sequestration, whereas W80%F weakened aggregate stability and reduced SOC in deeper layers. Water availability was the dominant factor for aggregate formation and SOC in surface and middle layers, while nutrients were more influential at depth. These findings demonstrate that moderate water reduction is more effective than fertilizer reduction in improving soil structure and carbon sink capacity, providing a scientific basis for precision water–fertilizer management and sustainable greenhouse agriculture in black soil regions. Full article

Agroforestry, an integrated land-use practice combining trees and woody shrubs with crop and animal farming, offers significant ecological and agricultural benefits, including enhanced biodiversity, improved soil fertility, and increased resilience to environmental pressures. Despite its advantages, agroforestry faces challenges such as high initial investments, long maturation periods for trees, land tenure issues and a high level of complexity in technical management. Digital agriculture introduces advanced technologies and sensors, which provide precise data on soil moisture, nutrient levels, and plant health, enabling more efficient resource use and better farm management. Integrating these sensing technologies into agroforestry can address key challenges, optimize irrigation and nutrient management, and enhance overall system productivity and sustainability. This review explores the interaction between agroforestry and digitalization, highlighting case studies, and discusses the potential for these technologies to support sustainable agriculture and climate change mitigation. Increased investment in research and development, along with supportive policies, is essential for advancing the adoption of these innovative practices in agroforestry. Full article

Fog water collection, as a sustainable approach to alleviating water scarcity, has attracted considerable attention due to its low energy consumption and environmental friendliness. Various organisms in nature have evolved unique biological structures that efficiently capture and direct fog water. The fog water collection structures (FWCSs) and physical mechanisms of these organisms provide valuable inspiration for innovations in fog water collection technologies. This review systematically summarizes biomimetic structures designed for fog water collection, with a focus on representative natural examples such as the Namib desert beetle, cactus spines, spider silk, and Nepenthes mirabilis, highlighting how they achieve efficient fog water capture, coalescence, and transport through special surface textures, wettability regulation, and structural design. The underlying physical mechanisms are discussed in depth, including droplet behavior on micro/nanostructured surfaces, surface energy gradients, and Laplace pressure gradients in directional droplet transport. On this basis, the current challenges in bioinspired FWCSs design are outlined, and future perspectives are proposed. Future research may focus on the multiscale structural optimization of bioinspired FWCSs, the development of dynamically tunable designs, and the use of efficient and sustainable materials to further enhance fog water collection efficiency and ensure the long-term stability of FWCSs. Ultimately, by integrating modern manufacturing technologies and stimuli-responsive materials, bioinspired FWCSs hold great potential for applications in extreme environments, agricultural irrigation, and energy-efficient architecture, offering innovative solutions to the global water crisis. Full article

Climate change and increasing water scarcity are driving the need for resilient and fit-for-purpose urban water management. This study presents a case from Lisbon, Portugal, where twenty-one groundwater sources were evaluated as potential alternative supplies for emergency drinking and non-potable uses. Between 2018 and 2022, 127 samples were analyzed for microbiological (Escherichia coli, enterococci, fecal coliforms, heterotrophic plate count, Pseudomonas aeruginosa and Legionella pneumophila, physicochemical and fungal parameters (filamentous and yeast), alongside with microbial source tracking (MST) to determine contamination origins. Most sites showed exceedances of fecal indicators and heterotrophic bacteria, making water unsuitable for direct consumption without treatment, while fungi were ubiquitous and often above proposed guidance levels, highlighting a major regulatory gap. MST results indicated that human-derived contamination was rare and highly localized. Physicochemical parameters generally met legal thresholds, although occasional nitrate or salinity elevations reflected agricultural or coastal influences. Several sources were considered suitable for irrigation (EF, CC, AB, VF, and BS) whilst a subset met the criteria for potable supply with minimal treatment for risk management (CG, MM, CC, QC, EB, GR, PO, and MS). The findings of this study demonstrate that systematic, multiparametric assessment supports adaptive water allocation and emergency planning, aligning with EU regulations and advancing Sustainable Development Goal 6. The study argues for reconsideration of current microbiological standards, to improve public health protection in urban water reuse and emergency supply strategies. Full article

Water scarcity has become one of the most pressing challenges to agricultural sustainability, particularly in arid and semi-arid regions where climate change, dam construction, and rapid population growth have intensified the pressure on water and food resources. Groundwater adjacent to rivers represents a potential supplementary resource that can reduce reliance on restricted surface water supplies. This study assessed the hydrochemical characteristics and agricultural suitability of shallow groundwater located near the Tigris River, Iraq. Fieldwork involved monitoring four active wells and collecting samples over six periods from October 2022 to May 2023, combined with twelve soil samples from surrounding agricultural fields. Laboratory analyses determined key water and soil properties, including pH, electrical conductivity, major cations and anions, and a range of salinity and sodicity indices such as total dissolved solids (TDS), sodium adsorption ratio (SAR), residual sodium carbonate (RSC), potential salinity (PS), magnesium ratio, Simpson ratio (SR), Jones ratio (JR), and sodium percentage (Na%). Results indicated that groundwater levels fluctuated seasonally in tandem with the Tigris River, which directly influenced salinity levels. SI values were positive, TDS values were in the high salinity class, RSC values were consistently negative, PS values were in the medium to poor category, Na% values and MR values were within acceptable limits for irrigation, and SR values were moderately to highly contaminated. Groundwater quality, according to the U.S. Salinity Laboratory classification, was categorized between the C4S1 class (very high salinity, low sodium) and the C3S1 (high salinity, low sodium). Soil analyses showed predominantly light-textured soils with moderate Ec and SAR values below sodicity thresholds. The combination of soil permeability and groundwater characteristics suggests that irrigation is feasible under specific management practices. The study concludes that groundwater adjacent to rivers can serve as a valuable supplementary source for agriculture in semi-arid regions. Its use is most effective when applied to salt-tolerant crops, supported by leaching requirements, or blended with fresh water. These findings emphasize the importance of integrated groundwater management for enhancing agricultural resilience and sustainable land use under water-scarce conditions. Excessive extraction of groundwater near rivers can also pose long-term sustainability challenges. Full article

In arid regions, the soil degradation from salinization, low organic matter content, and compaction severely limits agricultural productivity. Leguminous perennials such as alfalfa (Medicago sativa L.) have the potential to restore soil quality, but their long-term effects remain underexplored in North African drylands. This study aimed to evaluate the impacts of long-term (7–8 years) alfalfa cultivation on soil fertility and salinity in the Ziban region of Algeria. Ninety topsoil samples (0–30 cm) from cultivated and adjacent uncultivated plots were collected and analyzed, determining organic matter (OM), soil organic carbon (SOC), soil nitrogen stock (SNS), electrical conductivity (EC), sodium adsorption ratio (SAR), pH, major cations (Ca²⁺, Mg²⁺, Na⁺), sulfate (SO₄²⁻), bulk density (BD), and texture. Compared with uncultivated soils, alfalfa cultivation increased OM by 82.26%, SOC by 78.38%, and SNS by 102.99%, while reducing EC by 40.36%, SAR by 28.94% and BD by 6.16% (p < 0.05), indicating significant improvements in fertility, structure and reductions in sodicity. PCA revealed distinct gradients separating fertility–salinity parameters from compaction–sodicity in cultivated and uncultivated soils. These results confirm that alfalfa systems enhance nutrient cycling, reduce salt stress, and improve structural stability in arid agroecosystems through reduced bulk density and increased organic matter in arid agroecosystems. Integrating alfalfa into land management strategies could promote sustainable restoration of degraded soils in drylands. Further research should optimize irrigation and organic inputs to maximize these benefits under climate-stress conditions. Full article

Enhancing sustainability in agriculture has become a significant challenge today where in the current context of climate change, particularly in countries of the Mediterranean area, the amount of water available for irrigation is becoming increasingly limited. Automating irrigation processes using affordable sensors can help save irrigation water and produce almonds more sustainably. This work presents an IoT-enabled edge computing model for smart irrigation systems focused on precision agriculture. This model combines IoT sensors, hybrid machine learning algorithms, and edge computing to predict soil moisture and manage Controlled Deficit Irrigation (CDI) strategies in high density almond tree fields applying reductions of 35% ETc (crop evapotranspiration). By gathering and analyzing meteorological, humidity soil, and crop data, a soft ML (Machine Learning) model has been developed to enhance irrigation practices and identify crop anomalies in real-time without cloud computing. This methodology has the potential to transform agricultural practices by enabling precise and efficient water management, even in remote locations with lack of internet access. This study represents an initial step toward implementing ML algorithms for irrigation CDI strategies. Full article

Given the current climate change scenario, it is essential to find strategies to reduce environmental risks and obtain economically sustainable agricultural productions. This study investigated the impact of various agronomic treatments on an almond orchard in northeastern Portugal, focusing on their relationships with crop growth/vigour and yield. The experiment was conducted using a factorial design that combined three variables: almond cultivar (Constantí and Vairo), irrigation regime (full and regulated deficit irrigation), and kaolin application (with or without application). These combinations resulted in eight distinct treatments, each replicated across two experimental plots. To monitor the crop physiological status, two drone flights equipped with a multispectral camera were flown during the kernel-filling stage (3 and 30 August 2021). Vegetation indices (VI) derived from the multispectral images were used to assess the crop vigour. In relation to the production data, including kernel and in-shell almond weights, these were collected in 14 representative trees of each treatment. Lastly, parametric and nonparametric regression analyses were performed to better understand relationships between VI and crop yields and derive predictive models. The main results can be summarised as follows: (a) cv. Vairo was more vulnerable to the regulated deficit irrigation strategy with striking repercussions on almond production, translating into an average reduction per tree of 22% and 16% in almond kernel and in-shell almonds compared to full irrigation, respectively; (b) kaolin application did not reflect statistically significant differences in the mean crop yield, as Tukey’s pairwise comparisons involving kaolin as a differentiating factor (e.g., C100+k—C100, V100+K—V100) showed confidence intervals with central value close to zero; and (c) regression analysis using the nonparametric random forest model and individualised treatments demonstrated a better agreement with the observed data (R² > 0.7). This research provided valuable insights into how cultivar selection, irrigation strategy, and kaolin application can influence the almond crop performance. When integrating multispectral aerial monitoring and advanced statistical modelling, it enables an effective assessment of both crop vigour and expected yield, supporting the development of more informed and adaptive management practices to face emerging environmental challenges. Full article

Traditional water–fertilizer control systems often suffer from poor precision and slow response, limiting precision agriculture development. This study developed an electrical conductivity (EC) control system for water–fertilizer integration using a fuzzy Proportional-Integral-Derivative (PID) controller optimized by particle swarm optimization (PSO) and integrated with IoT technology. MATLAB/Simulink simulations showed the proposed controller achieved the smallest overshoot (7.64–8.15%), with average settling time reduced by 62.48 s and 20.38 s compared to conventional PID and fuzzy PID controllers, respectively (p < 0.001). Field experiments on winter wheat demonstrated a mean absolute EC deviation of 0.01125 ms/cm, with root-mean-square error (RMSE) of 0.0217 ms/cm, indicating high precision under field conditions. The system also maintained soil moisture in the optimal range (19–25%) with high irrigation uniformity (Christiansen’s coefficient Cu = 97.6%). The system maintained soil moisture in the optimal range (19–25%) while supporting stable soil nutrient levels and crop growth parameters. This study provides a validated technical solution for precision EC control while establishing a foundation for future fully integrated water–fertilizer management systems. Full article

Precision spraying is a crucial goal for modern agriculture to achieve water and fertilizer conservation, reduced pesticide use, high yield, and green and sustainable development. This relies on the accurate identification of crop positions, high-precision path planning, and the positioning and control of intelligent agricultural machinery. For the precision production of corn, this paper proposes a new row detection method based on histogram peak detection and sliding window search, avoiding the issues of deep learning methods that are not conducive to lightweight deployment and large-scale promotion. Firstly, green channel segmentation and morphological operations are performed on high-resolution drone images to extract regions of interest (ROIs). Then, the ROIs are converted to a top-view image using perspective transformation, and a histogram analysis is performed using the find_peaks function to detect multiple peaks corresponding to row positions. Furthermore, a sliding window centered around the peak is constructed to search for complete single-row crop pixels in the vertical direction. Finally, the least squares method is used to fit the row curve, estimating the average row spacing (RowGap) and plant spacing (PlantGap) separately. The experimental results show that the accuracy of row detection reaches 93.8% ± 2.1% (n = 60), with a recall rate of 91.5% ± 1.8% and an F1 score of 0.925 ± 0.018. Under different growth stages, row numbers (6–8 rows), and weed interference conditions, the average row spacing measurement error is better than ±2.5 cm, and the plant spacing error is less than ±3.0 cm. Through field verification, this method reduces pesticide use by 23.6% and water consumption by 21.4% compared to traditional uniform spraying, providing important parameter support for field precision planting quality assessment and the dynamic monitoring of planting density, achieving variable irrigation and fertilization and water resource conservation. Full article

The Yellow River Diversion Irrigation District is a critical area for food security within the river basin; however, a significant contradiction exists between water supply and demand. The lag process of irrigation return flow is crucial for effective water resource management, yet this aspect has been overlooked in existing studies. This research focuses on the east-ern part of the Jingdian Irrigation District, where data related to agricultural hydrology was collected through monitoring efforts. The unit hydrograph method was introduced to construct a model, and numerical simulations were developed using Hydrus-2D to investigate the lag characteristics of irrigation return flow. The findings indicate that the lag time of return flow in response to precipitation and irrigation in the Hongbiliang Basin ranges from 0 to 2.3 months, while in the Nanshahe Basin, it spans from 0 to 5 months. The unit hydrograph model demonstrated high predictive accuracy, with a coefficient of determination (R²) exceeding 0.72 and a mean relative error (MRE) below 11.6% in both basins. The peak lag times recorded were 60 days and 110 days, respectively. The formation of return flow occurs in three stages: soil water infiltration, groundwater recharge, and channel drainage. Additionally, the unit hydrograph exhibited a strong fitting effect on silt loam and other soil types, confirming the validity of the “proportion and superposition” principle. This study contributes to the optimization of the water cycle model and the establishment of a comprehensive system within the irrigation district, thereby aiding in alleviating the pressure on water resources. Full article

Salinization is one of the main environmental challenges affecting agriculture in semi-arid regions. We evaluated the feasibility of foliar magnesium and its effects at different doses on the acclimation of cowpea varieties under salt stress. The experiment occurred in a greenhouse using a randomized block design in a 2 × 3 × 4 factorial scheme, with five replicates. Two cowpea varieties—‘Pingo de Ouro’ and ‘Costela de Vaca’—were subjected to three salinity levels in irrigation water (0.54, 3.50, and 5.00 dS m⁻¹) and four foliar magnesium (Mg) doses (0, 1, 2, and 3 mL L⁻¹). Under 3.50 dS m⁻¹ salinity, the 1 mL L⁻¹ dose resulted in the highest yield per plant (18.29 g). CO₂ assimilation was highest with 2 mL L⁻¹ Mg at 3.50 dS m⁻¹ for ‘Costela de Vaca’, and with 1 mL L⁻¹ Mg at 5.00 dS m⁻¹ for ‘Pingo de Ouro’. The ‘Pingo de Ouro’ variety was more tolerant to ‘Costela de Vaca’. Foliar Mg fertilization proved to be a promising strategy to mitigate the effects of salt stress in cowpea, especially for ‘Pingo de Ouro’. Magnesium effectively reduces salt stress, but its effect varies by plant variety and irrigation salinity, necessitating customized dose adjustments. Full article

This study evaluates groundwater quality dynamics in the Harran Plain (∼1500 km²), a key agricultural zone within Türkiye’s Southeastern Anatolia Project (GAP). Satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used to assess land-use changes over the years 1990, 2000, 2010, and 2020, with the GIS employed for classification and analysis. In this study, groundwater samples collected from twenty different locations in 2005, 2015 and 2025 were analyzed. For each sample, pH, EC, and various ion concentrations (Na, K, Cl, SO₄, NO₃, Ca, Mg, HCO₃) were measured. All analyses were performed using standard hydrogeochemical methods. Data from 20 wells (2005–2015) revealed significant reductions in EC (8235 to 2510 µS/cm) and NO₃⁻ (720 to 327 mg/L), due to drainage systems, improved irrigation, and fertilizer management. Nonetheless, localized pollution persisted. Land-use shifts toward high-value crops improved water efficiency, while urban and industrial expansion introduced new pressures. Results emphasize integrated water–land policies for sustainable groundwater management in arid agroecosystems. Full article