Search Results (4,563)

Competition for water resources makes it necessary to advance research focused on estimating the water needs of row crops, such as vineyards. Following the FAO-56 methodology and the A&P approach, the soil water balance model was applied to a vineyard with continuous vegetation cover in temperate climate conditions (Galicia, Spain). Basal crop coefficients adjusted to local conditions were obtained for both the vineyard and the active vegetation. After SIMDualKc model adjustment, r² values greater than 0.86 were obtained, along with goodness-of-fit indicators that demonstrate the model’s ability to predict soil water content (PBIAS_avg = 1.16; EF_avg = 0.89; dIA_avg = 0.97). A correction factor is proposed that improves the partitioning of the transpiration component in row crops with active cover. The transpiration demand of the vineyard increased by 35% in four study cases (northern Portugal, northwestern Spain, and Italy). The proposed correction factor is shown to be in line with the actual conditions and complex behaviour of a vineyard with active vegetation cover, which opens the way for improved water requirement prediction in complex management situations such as the one studied here. The proposed methodology is expected to improve the efficiency of irrigation management through more accurate determination of the real water amount required by orchards. Full article

Agroforestry and perennial tree crop production systems, particularly in Mediterranean regions, exhibit a high degree of integration among trees, herbaceous, and soil components. They provide essential services including provisioning, regulation, support, and cultural services, which enhance human health, well-being, and economic stability. However, guaranteeing their long-term resilience in the face of environmental challenges, including drought and soil degradation, is essential for the sustainable management of these systems. We examine the impact of microsite conditions (soil and herbaceous layer) and their management on olive trees (Olea europaea L.) under varying levels of drought stress. A fully factorial design was implemented in a Spanish agroforestry system, combining two irrigation regimes (rainfed vs. summer irrigation) and two soil management practices (customary plowing vs. herbaceous layer conservation) across four independent and replicated zones. Twelve olive trees per zone were individually monitored, treating each tree as the experimental unit, with one 50 × 50 cm sampling plot per tree in which microsite conditions were characterized for each tree. Plowed areas (shallow tillage) showed lower industrial extraction yield (%), fat yield based on dry matter (%), olive maturity and phytosanitary status compared to areas conserving their herbaceous layer cover (0.81, 0.96, 0.92, and 0.65-fold lower, respectively). Rainfed areas (i.e., those without supplemental water supply) showed a reduction in both industrial extraction yield (%), olive yield (kg tree⁻¹) and oil yield (kg ha⁻¹) (0.77, 0.86 and 0.67-fold lower, respectively). Under combined tillage and water-deficit conditions, oil yield (kg ha⁻¹), industrial extraction yield (%), and total phenolic content (ppm) were considerably lower (0.50, 0.60, and 0.67-fold lower, respectively). Furthermore, low quality of the herbaceous layer dominated by nitrophilous invasive species were associated with decreased leaf nutrient content, lower industrial extraction yield, reduced olive maturity and poorer phytosanitary status of olives. These findings suggest that maintaining a spontaneous herbaceous layer with a high-quality species (legume incorporation) and well-managed herbaceous cover, i.e., repeated mowing of the herbaceous layer instead of customary plowing, can enhance sustainable olive production by improving soil resilience, reducing water stress, and optimizing nutrient use, thereby supporting long-term ecosystem stability and agricultural productivity. Full article

Gaseous nitrogen losses and residual soil nitrogen accumulation are primary drivers of low nitrogen use efficiency in alkaline irrigated cropping systems. A two-year field experiment (2019–2020) in the Hetao Irrigation District under alkaline flood-irrigated maize evaluated the effects of nitrogen rate, fertilizer formulation, and enhanced-efficiency fertilizers—urea with urease inhibitor NBPT and ammonium sulfate with nitrification inhibitor DMPP—on NH₃ volatilization, N₂O emissions, post-harvest soil mineral nitrogen, and grain yield. A soil pH manipulation sub-experiment (±0.5 units, ambient pH ~8.8) was conducted to quantify the direct effect of alkalinity on volatilization. NH₃ volatilization was insensitive to fertilizer formulation and inhibitor inclusion but strongly responsive to soil pH; a 0.5-unit increase in soil pH elevated volatilization efficiency by up to 25% relative to ambient conditions. N₂O emissions were around 18% higher under ammonium sulfate than under urea and were reduced by 21–32% with inhibitor treatments, without increasing NH₃ volatilization. Inhibitor-assisted optimized management (urea + NBPT and ammonium sulfate + DMPP) achieved higher yields and lower emission intensity than urea alone. These results confirm that NH₃ and N₂O losses are governed by distinct controls, and that concurrent mitigation of both pathways requires interventions that independently target each loss driver, beyond rate optimization and inhibitor application alone. Full article

Accurate precipitation forecasting is essential for water resource management, flood early-warning systems, and agriculture, but remains difficult because of the nonlinear and highly variable spatiotemporal nature of rainfall. This paper compares four deep learning architectures—a standalone LSTM, a standalone CNN, a hybrid CNN–LSTM, and a Transformer encoder—against three classical baselines (persistence, day-of-year climatology, and per-grid-point ARIMA) for daily precipitation forecasting over Washington State at lead times of one to four days. A 40-year ERA5 dataset (1985–2024) of near-surface air temperature, mean sea-level pressure, and total precipitation is split into training (1985–2012), validation (2013–2015), and test (2016–2024) periods, with the test years held out completely. Each (model, horizon) is trained with three random seeds and evaluated in physical units (mm/day). On the held-out test period, the hybrid CNN–LSTM achieves the lowest RMSE at every horizon $h\ge 2$, with $R^2=0.576\pm 0.007$ and RMSE $=15.08\pm 0.07$ mm/day at $h=4$. Diebold–Mariano tests, paired t-tests, and bootstrap 95% confidence intervals confirm that the CNN–LSTM advantage over the LSTM is statistically significant at horizons 2–4 (but not at $h=1$), while CNN–LSTM is significantly better than every classical baseline and the Transformer at every horizon. The headline result is reproduced under a rolling-origin temporal cross-validation across three non-overlapping splits ($R^2\in [0.576,0.590]$). Practically, the sub-millisecond inference cost of the CNN–LSTM makes it directly deployable in operational forecasting pipelines used for flood early-warning, irrigation scheduling, and reservoir management, where even modest improvements in 3–4-day-ahead RMSE translate into measurable risk reduction and improved decision lead time for water managers and emergency planners. Full article

Reliable estimates of volumetric water content at field capacity (θFC) are important inputs for irrigation scheduling because θFC contributes to the estimation of plant-available water, depletion thresholds, and refill targets. In irrigated systems, θFC is therefore an operational decision variable rather than a fixed soil property. However, θFC varies systematically across estimation methods, introducing uncertainty into irrigation management. This study evaluated method-dependent differences in θFC for irrigated tropical soils in the Roldanillo–Unión–Toro agricultural irrigation district (Valle del Cauca, Colombia). Field capacity was estimated at 42 sampling points (0–0.10 m depth) using four methods: Mariotte bottle (MB), filter paper (FP), a pedotransfer function (PTF), and the Richards pressure plate method (RPP). The RPP method was used as an operational reference for comparative purposes, not as an absolute representation of true FC. Agreement and bias were assessed using descriptive statistics, error metrics, regression, Bland–Altman analysis, and texture-stratified comparisons. RPP θFC averaged 39.37% (range: 29.85–46.41%), whereas MB, FP, and PTF produced higher mean values of 42.66%, 44.26%, and 46.38%, respectively. Relative to RPP, mean error and root mean square error increased from MB (3.29% and 5.21%) to FP (4.89% and 8.16%) and PTF (7.01% and 10.82%). Disagreement also varied with soil texture. These results show that low-cost θFC methods are not directly interchangeable with RPP measurements in the evaluated surface layer. Because θFC is commonly used in irrigation calculations, the observed method-dependent differences may affect the estimation of depletion thresholds and refill targets if surface-layer values are extrapolated without local validation. Overall, surface-layer θFC in the Roldanillo–Unión–Toro irrigation district was strongly method-dependent, highlighting the need to account for method-related uncertainty before using alternative θFC estimates as inputs for irrigation decision support. Full article

Water balance calculations at the watershed scale are fundamental to water resource planning and the sustainable management of limited water supplies. These calculations rely on stream and canal gaging networks operated by local, state and federal entities, whose availability has varied over time due to cost, staffing constraints, and limitations on suitable gaging locations. The Green River Basin (GRB) above Fontenelle Dam in Wyoming illustrates this trend, where the number of operational stream gaging sites has varied over time and the majority of locations have less than 15 years of streamflow records. Recent advancements in the ability to perform streamflow reconstruction and estimate agricultural water use offer a new avenue for estimating the water balance for watersheds with discontinuous gage observations. But the use of these datasets and approaches has not been tested. Therefore, this paper proposes and tests a novel framework that combines discontinuous streamflow observations with new datasets (OpenET, ET-Demands, and GEOGLOWS) to calculate monthly water balances in the GRB from water year 1991 to 2023. Focusing on two main test basins, the Green River and the New Fork River, the integration of modern datasets enables the successful calculation of the water balance in the GRB with good agreement with downstream gaging records, achieving a Nash–Sutcliffe efficiency (NSE) of 0.88 for the New Fork River and 0.80 for the Green River. By improving the ability to quantify water balance components in data-limited basins, this framework supports more transparent water accounting and informed decision-making for sustainable water management, including irrigation planning, drought response, and long-term resource allocation in semi-arid river systems. Full article

The increasing pressure on water resources has stimulated the use of treated wastewater in agricultural irrigation, although its effects on plant development remain uncertain. This study evaluated the effects of wastewater treatments and irrigation depths on the morphophysiological development of lettuce (Lactuca sativa L.). A split-plot experiment was conducted with crop cycles in the main plots and a factorial arrangement in the subplots, consisting of five water sources and five irrigation depths (50% to 150% ETc), with three replications. Seven variables were evaluated, including growth traits and water productivity. Irrigation depth significantly affected all variables (p ≤ 0.01) and was the main driver of vegetative growth, increasing shoot fresh mass, stem diameter, and plant height. In contrast, water sources showed smaller effects. Water productivity decreased with increasing irrigation depth and showed weak correlations with other variables (r ≤ 0.468). Machine learning models achieved moderate accuracy for irrigation depth prediction (≈55%), with confusion among adjacent classes, indicating detection of a gradient rather than precise classification. Prediction of water sources was low (<30%), confirming limited morphological differentiation. Plant height and stem diameter were the most informative variables. These results indicate that irrigation management has a stronger influence on lettuce growth than water source. Full article

Soil apparent electrical conductivity (EC_a), volumetric water content (VWC), and temperature are important parameters for evaluating soil condition and supporting irrigation and crop management practices. This study presents the design and experimental evaluation of a ultra-low-hardware-cost soil sensing system capable of estimating these three parameters through impedance-based measurements at different frequency ranges. The proposed system uses sinusoidal excitation in the kHz range for EC_α estimation and in the MHz range for VWC estimation, while temperature is also considered as a relevant factor affecting the electrical behavior of soil. The sensor was experimentally tested on three soil types under two moisture conditions, namely water addition with and without mixing, and the results were compared with those obtained from a commercial instrument (5TE Meter Group). The overall mean error of the developed system, without calibration, was 20.2%, with mean errors of 16.3% for EC_a and 24.2% for VWC. Although the accuracy achieved is lower than that of commercial instruments, the results demonstrate that the proposed system can provide a satisfactory preliminary assessment of soil conditions in applications where low cost, simplicity and ease of implementation are important. The results can be significantly improved if calibration is made initially for the soil type of the field to be measured. Electrode geometry, lack of calibration with a larger set of soil samples and PCB implementation issues are the main limitations affecting performance. Overall, the proposed approach shows potential as a supportive tool for low-cost agricultural monitoring and decision-making applications. The implementation of a system that measures soil conductivity and moisture in two frequency ranges measurement (kHz for EC_α/MHz for VWC), with synchronous soil temperature measurement, at a particularly low cost, is the innovation of the sensor system. Full article

Water structures play a vital role in regulating irrigation water within open-channel networks by controlling discharge, water levels, flow direction, and velocity. Despite their importance, these structures act as hydraulic obstructions that induce flow disturbances, which may reduce hydraulic efficiency and threaten structural integrity. One of the most critical consequences is localized erosion downstream, posing serious risks to structural safety and long-term performance. From a sustainability perspective, maintaining structural stability and hydraulic efficiency is essential to ensure reliable water delivery, minimize maintenance costs, and extend the service life of irrigation structures. Therefore, mitigating such adverse hydraulic effects is a key component of sustainable water resources management. This study aims to investigate the mechanisms responsible for this phenomenon and propose engineering solutions to reduce its impacts. The geometry of upstream wing walls significantly influences flow behavior both through and downstream of the structure. Additionally, irrigation canals are constructed with varying side slopes depending on soil conditions, which further affect flow characteristics. However, the combined effect of different upstream wing wall configurations and canal inside slopes has not been sufficiently addressed. Accordingly, this research evaluates their integrated impact to support the development of more efficient, resilient, and sustainable irrigation structures. A total of 435 laboratory experiments were conducted using a physical model under varying discharge conditions. Common canal inside slopes were tested with four widely used wing wall types. Scour hole geometry, including depth, length, and shape, was measured and analyzed. Results indicate that the splayed wing wall configuration outperforms the box type, reducing maximum scour depth and length by approximately 22.74% and 23.61%, respectively, when combined with a 1:1 canal inside slope. Additionally, new dimensionless empirical equations were developed to predict downstream scour behavior, providing practical tools for selecting optimal wing wall configurations under different canal conditions. Full article

The Souss-Massa basin, one of Morocco’s major agricultural regions, is increasingly affected by water scarcity and climatic stress. However, the long-term interactions between hydro-climatic change and farmers’ cropping system adjustments remain insufficiently documented. This study analyzes hydro-climatic trends and agricultural transformations over the period 1995–2021. The methodology combines statistical trend analysis of meteorological data (Mann–Kendall test and Sen’s slope estimator), diachronic land use/land cover mapping using Google Earth Engine, Crop Water Stress Index (CWSI) assessment, and groundwater piezometric analysis. Results reveal declining and highly variable precipitation, together with a significant warming trend reaching +0.116 °C/year. In parallel, cultivated cereal areas (rainfed and irrigated) declined, while irrigated forage crops expanded, particularly Berseem/Maize. Despite increasing aridity, CWSI results indicate maintained crop vigor in irrigated areas, suggesting growing dependence on groundwater extraction. These findings highlight an ongoing agricultural transition that increases pressure on already vulnerable water resources and underscores the need for integrated climate adaptation and groundwater management strategies in the basin. Full article

Reclaiming saline–alkaline soil is critical for food security and land expansion. While paddy rice is the key pioneer crop for remediation, the soil fertility–yield relationship remains poorly understood. To optimize remediation strategies, this study evaluated soil fertility under 16 agronomic treatments—integrating irrigation quality, fertilizer regimes, and soil amendments—across three rice growth stages (tillering, heading, and maturity) in the Yellow River Delta using the minimum data set (MDS), integrated soil fertility index (SFI), and random forest models. Saline water irrigation increased soil salinity by 24.6%, while straw returning and desulfurization gypsum reduced salinity by 18.3% and 22.7%, respectively. Straw, biochar, and desulfurization gypsum significantly influenced soil organic carbon (SOC), total nitrogen (TN), inorganic nitrogen (NH₄⁺-N, NO₃⁻-N), and available phosphorus (AP), with effects varying across growth stages. Growth-stage-specific MDS indicators were significantly correlated with SFI based on the total data set (R² = 0.70, 0.65, and 0.81, p < 0.01), and stage-specific SFI was significantly positively related to rice yield. Notably, heading-stage SFI, although relatively low, explained the highest yield variance (R² = 0.51, p < 0.01) and prediction accuracy (%IncMSE = 25.22), especially under conventional NPK combined with full straw incorporation and desulfurization gypsum. These findings highlight the critical role of heading-stage soil fertility in regulating rice production, providing a targeted nutrient management blueprint for saline–alkaline paddy fields in the Yellow River Delta. Overall, this study offers a reliable scientific template to enhance yield and promote sustainable agriculture in comparable saline–alkaline paddy fields globally. Full article

The Agricultural Energy Internet (AEI) represents an emerging systemic paradigm driven by the convergence of intelligent agriculture and rural energy transformation. It is not a simple extension of agricultural informatization or rural electrification; rather, it redefines agricultural processes—such as irrigation, greenhouse environmental control, supplementary lighting, cold-chain logistics, and agricultural machinery—as perceptible, computable, and schedulable energy-related processes, thereby enabling the deep integration of agriculture, energy, environmental management, and intelligent decision-making. This review systematically examines the evolutionary trajectory of AEI, from early agricultural digitalization and Internet of Things (IoT)-based monitoring to edge intelligence and digital twin technologies, and ultimately to the coordinated optimization of agriculture–energy–environment systems. A comprehensive technical framework is established, encompassing physical energy coupling, multi-source sensing and actuation, interconnection and interoperability, edge–cloud collaborative control, data governance, digital twin modeling, artificial intelligence-enabled optimization, and application-oriented decision-making. The review further highlights that high-quality data governance, edge–cloud collaboration, and digital twin calibration are critical enablers of the transition from visualization-oriented management to closed-loop intelligent operation. In addition, this study clarifies the complementary relationship between agricultural informatization and electrification: the former provides capabilities for perception, prediction, optimization, and coordination, whereas the latter provides a controllable execution chain. Together, they constitute the foundation of a cyber-physical agricultural energy system. Finally, frontier research directions are identified, including high-temperature solid oxide electrolysis for hydrogen production, edge AI–IoT-enabled closed-loop agricultural operation, and privacy, security, and trust mechanisms in federated edge intelligence. The findings suggest that AEI can serve as a strategic technological framework for supporting the next generation of smart agriculture toward low-carbon, resilient, and collaborative operation. Full article

This study developed and evaluated a cloud-based smart irrigation platform (DSmart Farming) integrating low-cost sensors and IoT technology for automated irrigation control in community greenhouses of Puen Jai Insee, organic group in Sa Kaeo Province. The system combined soil moisture, air temperature, and relative humidity sensors, with a LoRa32-based control unit in each greenhouse and a central web-based management application linked to a MariaDB database on a cloud server. Five vegetable crops, including cherry tomato, broccoli, cabbage, Chinese kale, and kale, were grown over two distinct seasons under four irrigation strategies in a completely randomized design with three replications: three smart irrigation treatments based on soil moisture thresholds (on/off at 40/50%, 45/55%, and 50/60%) and a farmer-managed conventional irrigation control. The smart irrigation system maintained root-zone moisture within the target range (approximately 50–60%) and moderated greenhouse microclimate, preventing daytime temperatures from exceeding 40 °C, in contrast to 40–45 °C peaks in the conventional greenhouses. Across crops, smart irrigation increased yields by 20–29% while reducing water use by 41–60% compared to conventional practice, leading to income increases of 20–56%, depending on the crop. Bacterial soft rot caused by Pectobacterium carotovorum subsp. carotovorum occurred only under conventional irrigation, whereas no soft rot or other major diseases were detected in smart-irrigated greenhouses. These results demonstrate that the DSmart Farming system can enhance water use efficiency, avoid disease incidence, and improve the productivity and profitability of organic greenhouse vegetable production in water-limited smallholder systems. Full article

To investigate the effects of water-saving irrigation and different straw retention methods on soil CH₄ and N₂O emissions from paddy fields in cold regions and their potential underlying mechanisms, a field experiment was conducted in Qing’an City, Heilongjiang Province. Two water management regimes were set, combined with four straw retention treatments. The static chamber-gas chromatography method was used to monitor CH₄ and N₂O emission fluxes during the entire rice growth period. Meanwhile, soil pH, oxidation–reduction potential (Eh), dissolved oxygen (DO), and dynamic changes in carbon and nitrogen substrates were measured, and the global warming potential (GWP) and greenhouse gas emission intensity (GHGI) were comprehensively evaluated. The results showed that controlled irrigation significantly increased soil dissolved oxygen content and oxidation–reduction potential. Compared with conventional flooding irrigation, total CH₄ emission decreased by more than 50%, while N₂O emission increased by 1.5–2.5 times, exhibiting an obvious divergent correlation with the two gas emission fluxes. Among different straw retention methods, organic fertilizer returning and direct straw returning significantly promoted CH₄ emission by supplying easily decomposable organic carbon. In contrast, biochar, due to its stable carbon structure and favorable pore properties, inhibited CH₄ emission without significantly stimulating N₂O emission. The treatment of controlled irrigation combined with biochar returning (CB) achieved the lowest global warming potential and greenhouse gas emission intensity at 7230.82 kg CO₂-eq/hm² and 0.8054 kg CO₂-eq/kg, respectively, while maintaining high rice yield. Path analysis based on soil physicochemical properties and emission fluxes further revealed that Eh and DO were significantly negatively correlated with CH₄ emission but positively correlated with N₂O emission. Path inference from flux and substrate data indicated that carbon and nitrogen availability were the key factors limiting the denitrification process. In conclusion, the combined application of controlled irrigation and biochar returning can realize the synergistic effect of stable yield and emission reduction in cold-region paddy fields by improving soil aeration and regulating the transformation of carbon and nitrogen substrates, providing a scientific basis for establishing a green and low-carbon rice production technology system for black soil in cold regions. Full article

This study aims to quantify the response of sunflower root water uptake to stratified soil water and salinity stress. Based on field observations, the root water uptake function in the existing model was improved by developing a new equation for the root water uptake rate that accounts for spatial differences in root response. Field experiments were conducted in 2021 and 2022 using irrigation water with four salinity levels: CK (0.87 g/L), S1 (1.0 g/L), S2 (1.5 g/L), and S3 (2.0 g/L). Soil moisture and salinity in five soil layers (0–100 cm) were continuously monitored using sensors. The actual crop water requirement (ET_a) was estimated using the soil water balance method, while the actual (T_a) and potential (T_p) plant transpiration rates were calculated based on the canopy-scale water consumption principle. Results indicated that with increasing irrigation water salinity, both soil moisture content and electrical conductivity exhibited an overall increasing trend. Significant differences were observed in the combined soil moisture and salinity conditions across soil depths. In particular, salt accumulation in the surface layer reduced root water uptake in the upper soil profile. Based on the differential root response to soil water and salinity stratification, the root water uptake function was further optimized, and the parameters representing water and salinity conditions in each soil layer were calibrated using the least squares method. Model validation with 2021 and 2022 data demonstrated good agreement between simulated and observed T_a values, with RMSE = 11.41 mm and MRE = 0.32%, R² ranging from 0.66 to 0.98, NSE between 0.52 and 0.96, and regression slope b between 0.90 and 1.10. This enhancement in the root water uptake rate formulation significantly improves model simulation accuracy and provides a robust basis for optimizing irrigation management in saline–alkali environments. Full article