Search Results (787)

The Solar Agro Savior (SAS) is an innovative solution that is assisted by drones for the sustainable utilization of water and plant disease observation in the agriculture sector. This system integrates an alerting mechanism for humidity, moisture, and temperature variations, which affect the plants’ health and optimization in water utilization, which enhances plant yield productivity. A significant feature of the system is the efficient monitoring system in a larger region through drones’ high-resolution cameras, which enables real-time, efficient response and alerting for environmental fluctuations to the authorities. The machine learning algorithm, particularly recurrent neural networks, which is a pioneer with agriculture and pest control, is incorporated for intelligent monitoring systems. The proposed system incorporates a specialized form of a recurrent neural network, Long Short-Term Memory (LSTM), which effectively addresses the vanishing gradient problem. It also utilizes an attention-based mechanism that enables the model to assign meaningful weights to the most important parts of the data sequence. This algorithm not only enhances water utilization efficiency but also boosts plant yield and strengthens pest control mechanisms. This system also provides sustainability through the re-utilization of water and the elimination of electric energy through solar panel systems for powering the inbuilt irrigation system. A comparative analysis of variant algorithms in the agriculture sector with a machine learning approach was also illustrated, and the proposed system yielded 99% yield accuracy, a 97.8% precision value, 98.4% recall, and a 98.4% F1 score value. By encompassing solar irrigation and artificial intelligence-driven analysis, the proposed algorithm, Solar Argo Savior, established a sustainable framework in the latest agricultural sectors and promoted sustainability to protect our environment and community. Full article

The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. Full article

Traditional intensive agricultural system impedes ecological functions, such as nutrient cycling and biodiversity conservation, resulting in excessive nitrogen discharge, CH₄ emission, and ecosystem service losses. To enhance critical ecosystem services and mitigate environmental externalities in paddy fields, we developed a multi-objective agricultural system (MIA system), which combines two eco-functional units: paddy wetlands and Beitang (irrigation water collection pond). Pilot study results demonstrated that the MIA system enhanced biodiversity and inhibited pest outbreak, with only a marginal reduction in rice production compared with the control. Additionally, the paddy wetland effectively removed nitrogen, with removal rates of total nitrogen and dissolved inorganic nitrogen ranging from 0.06 to 0.65 g N m⁻² d⁻¹ and from 0.02 to 0.22 g N m⁻² d⁻¹, respectively. Continuous water flow in the paddy wetland reduced the CH₄ emission by 84.4% compared with the static water conditions. Furthermore, a simulation experiment indicated that tide flow was more effective in mitigating CH₄ emission, with a 68.3% reduction compared with the drying–wetting cycle treatment. The emergy evaluation demonstrated that the MIA system outperformed the ordinary paddy field when considering both critical ecosystem services and environmental externalities. The MIA system exhibited higher emergy self-sufficiency ratio, emergy yield ratio, and emergy sustainable index, along with a lower environmental load ratio. Additionally, the system required minimal transformation, thus a modest investment. By presenting the case of the MIA system, we provide a theoretical foundation for comprehensive management and assessment of agricultural ecosystems, highlighting its significant potential for widespread application. Full article

Elucidating the trade-offs and synergies among ecosystem services is crucial for effective ecosystem management and the promotion of sustainable development in specific regions. The Hexi Corridor, a vital agricultural hub in Northwest China, is instrumental in both ecological conservation and socioeconomic advancement throughout the area. Utilizing an integrated “water–soil–carbon–grain” framework, this study conducted a quantitative assessment of four essential ecosystem services within the Hexi Corridor from 2000 to 2020: water yield, soil conservation, vegetation carbon sequestration, and grain production. Our research thoroughly explores the equilibrium and synergistic interactions between grain production and other ecosystem services, while also exploring potential strategies to boost grain yields through the precise management of these services. The insights garnered are invaluable for strategic regional development and will contribute to the revitalization efforts in Northwest China. Key findings include the following: (1) between 2000 and 2020, grain production exhibited a steady increase, alongside rising trends in water yields, soil conservation, and carbon sequestration, all of which demonstrated significant synergies with agricultural productivity; (2) in areas identified as grain production hotspots, there were stronger positive correlations between grain output and carbon sequestration services, soil conservation, and water yields than the regional averages, suggesting more pronounced mutual benefits; (3) the implementation of strategic initiatives such as controlling soil erosion, expanding afforestation efforts, and enhancing water-saving irrigation infrastructure could simultaneously boost ecological services and agricultural productivity. These results significantly enhance our comprehension of the interplay between ecosystem services in the Hexi Corridor and present practical approaches for the optimization of regional agricultural systems. Full article

The western Shanxi Loess region, as a typical semi-arid ecologically fragile zone, faces severe soil and water resource constraints. The apple–peanut intercropping system can significantly improve water productivity and economic benefits through complementary resource utilization, representing an effective approach for sustainable agricultural development in the region. This study took the apple–peanut intercropping system as the research object (apple variety: ‘Yanfu 8’; peanut variety: ‘Huayu 38’), setting three peanut planting densities (D1: 27,500 plants/ha; D2: 18,333 plants/ha; D3: 10,833 plants/ha) and two water regulation measures—W1 (irrigation upper limit at 85% of field capacity, FC) and W2 (65% FC), with non-irrigated controls (CK) at different planting densities for comparison. This study systematically analyzed the synergistic regulation effects of intercropping density and water management on system water use and comprehensive benefits. Results showed that medium planting density combined with medium irrigation (W2D2 treatment) could maximize intercropping advantages, effectively improving the intercropping system’s soil water content (SWC), yield (GY), and water use efficiency (WUE). This research provides a theoretical basis for precision irrigation in fruit–crop intercropping systems in semi-arid regions. However, based on the significant water-saving and yield-increasing effects observed in the current experimental year, follow-up studies should verify its stability through multi-year fixed-position observation data. Full article

Rizhao Reservoir, Shandong Province, China, as a key regional water supply hub, provides water for domestic, industrial, and agricultural uses in and around Rizhao City by intercepting runoff, which plays a central role in guaranteeing water supply security and supporting regional development. This study systematically collected 66 surface water samples to elucidate the hydrochemical characteristics within the reservoir area, identify the principal influencing factors, and clarify the sources of dissolved ions, aiming to enhance the understanding of the prevailing water quality conditions. A systematic analysis of hydrochemical facies, solute provenance, and governing processes in the study area’s surface water was conducted, employing an integrated mathematical and statistical approach, comprising Piper trilinear diagrams, correlation analysis, and ionic ratios. Meanwhile, the entropy weight-based water quality index (EWQI) and irrigation water quality evaluation methods were employed to assess the surface water quality in the study area quantitatively. Analytical results demonstrate that the surface water system within the study area is classified as freshwater with circumneutral to slightly alkaline properties, predominantly characterized by Ca-HCO₃ and Ca-Mg-SO₄-Cl hydrochemical facies. The evolution of solute composition is principally governed by rock–water interactions, whereas anthropogenic influences and cation exchange processes exert comparatively minor control. Dissolved ions mostly originate from silicate rock weathering, carbonate rock dissolution, and sulfate mineral dissolution processes. Potability assessment via the entropy-weighted water quality index (EWQI) classifies surface waters in the study area as Grade I (Excellent), indicating compliance with drinking water criteria under defined boundary conditions. Irrigation suitability analysis confirms minimal secondary soil salinization risk during controlled agricultural application, with all samples meeting standards for direct irrigation use. Full article

A circular tri-trophic system integrating aquaponics, i.e., combined cultivation of crops and fish, with insect rearing is presented for lettuce cultivation. The nutrition cycle among crops, insects and fish turns waste into resource, thereby increasing the sustainability of this food production system. A comprehensive evaluation of the system’s efficiency was performed, including the growth, functional and resource use efficiency traits of lettuce, the dynamics of which were followed in a pilot-scale aquaponics greenhouse, under three treatments: conventional hydroponics (HP) as the control, coupled aquaponics (CAP) with crops irrigated with fish-derived water, and decoupled aquaponics (DCAP), where fish-derived water was amended with fertilizers to reach the HP target. The main findings indicate comparable physiological performance between DCAP and HP, despite the slightly lower yield observed in the former. The CAP treatment exhibited a significant decrease in biomass accumulation and functional impairments, which were attributed to reduced nutrient levels in lettuce leaves. The DCAP treatment exhibited a 180% increase in fertilizer use efficiency compared to the HP treatment. We conclude that the tri-trophic cropping system with the implementation of DCAP variant is an effective system that enables the combined production of crops and fish, the latter being fed with sustainably derived insect protein. The tri-trophic system improves the environmental impact and sustainability of lettuce production, while making circularity feasible. Full article

The rice-wheat rotation is the main agricultural cropping system in Jiangsu Province, playing a vital role in ensuring food security and promoting economic development. However, current research on rice-wheat systems mainly focuses on in-situ controlled experiments at the point scale, with limited studies addressing carbon footprint (CF) and energy balance (EB) at the regional scale and long time series. Therefore, we analyzed the evolution patterns of the CF and EB of the rice-wheat system in Jiangsu Province from 1980 to 2022, as well as their influencing factors. The results showed that the sown area and total yield of rice and wheat exhibited an increasing–decreasing–increasing trend during 1980–2022, while the yield per unit area increased continuously. The CF of rice and wheat increased by 4172.27 kg CO₂ eq ha⁻¹ and 2729.18 kg CO₂ eq ha⁻¹, respectively, with the greenhouse gas emissions intensity (GHGI) showing a fluctuating upward trend. Furthermore, CH₄ emission, nitrogen (N) fertilizer, and irrigation were the main factors affecting the CF of rice, with proportions of 36%, 20.26%, and 17.34%, respectively. For wheat, N fertilizer, agricultural diesel, compound fertilizer, and total N₂O emission were the primary contributors, accounting for 42.39%, 22.54%, 13.65%, and 13.14%, respectively. Among energy balances, the net energy (NE) of rice exhibited an increasing and then fluctuating trend, while that of wheat remained relatively stable. The energy utilization efficiency (EUE), energy productivity (EPD), and energy profitability (EPF) of rice showed an increasing and then decreasing trend, while wheat decreased by 46.31%, 46.31%, and 60.62% during 43 years, respectively. Additionally, N fertilizer, agricultural diesel, and compound fertilizer accounted for 43.91–45.37%, 21.63–25.81%, and 12.46–20.37% of energy input for rice and wheat, respectively. Moreover, emission factors and energy coefficients may vary over time, which is an important consideration in the analysis of long-term time series. This study analyzes the ecological and environmental effects of the rice-wheat system in Jiangsu Province, which helps to promote the development of agriculture in a green, low-carbon, and high-efficiency direction. It also offers a theoretical basis for constructing a low-carbon sustainable agricultural production system. Full article

Micro- and Nanoplastic (MNP) pollution is an emerging challenge globally, posing a significant threat to both aquatic and terrestrial ecosystems worldwide. This review critically examines the sources, exposure routes, and impact of plastics, with particular focus on implications for the livestock sector. MNPs enter animals’ bodies primarily through ingestion of contaminated feed and water, inhalation, and dermal exposure, subsequently accumulating in various organs, disrupting physiological functions. Notably, MNPs facilitate the horizontal transfer of antimicrobial resistance genes (ARGs), exacerbating the global challenge of antimicrobial resistance (AMR). In agricultural environments, sources such as organic fertilizers, wastewater irrigation systems, surface runoff, and littering contribute to soil contamination, adversely affecting plant growth and soil health, which in turn compromises feed quality and ultimately animals’ productivity. This review synthesizes current evidence demonstrating how MNP exposure impairs animal production, reproduction, and survival, and highlights the interconnected risks to food safety and ecosystem health. The findings call for the urgent need for comprehensive research under controlled conditions to underscore the fine details regarding mechanisms of MNP toxicity and to inform effective mitigation strategies. Addressing MNP pollution is crucial for safeguarding animal health, ensuring sustainable livestock production, and promoting environmental sustainability and integrity. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

The integration of water resources, water environment, and water ecology (hereinafter “three-water”) is essential not only for addressing the current water crisis but also for achieving sustainable development. Chaohu Lake is an important water resource and ecological barrier in the middle and lower reaches of the Yangtze River, undertaking such functions as agricultural irrigation, urban water supply, and flood control and storage. Studying the performance of “three-water” in the Chaohu Lake Basin will help to understand the pollution mechanism and governance dilemma in the lake basin. It also provides practical experience and policy references for the ecological protection and high-quality development of the Yangtze River Basin. We used the DPSIR-TOPSIS model to analyze the performance of the river–lake system in the Chaohu Lake Basin and employed an obstacle model to identify factors influencing “three-water.” The results indicated that overall governance and performance of the “three-water” in the Chaohu Lake Basin exhibited an upward trend from 2011 to 2022. Specifically, the obstacle degree of driving force decreased by 19.6%, suggesting that economic development enhanced governance efforts. Conversely, the obstacle degree of pressure increased by 34.4%, indicating continued environmental stress. The obstacle degree of state fluctuated, showing a decrease of 13.2% followed by an increase of 3.8%, demonstrating variability in the effectiveness of water resource, environmental, and ecological management. Additionally, the obstacle degree of impact declined by 12.8%, implying the reduced efficacy of governmental measures in later stages. Response barriers decreased by 5.8%. Variations in the obstacle degree of response reflected differences in response capacities. Spatially, counties and districts at the origins of major rivers and their lake outlets showed lower performance levels in “three-water” management compared to other regions in the basin. Notably, Wuwei City and Feidong County exhibited better governance performance, while Feixi County and Chaohu City showed lower performance levels. Despite significant progress in water resource management, environmental improvement, and ecological restoration, further policy support and targeted countermeasures remain necessary. Counties and districts should pursue coordinated development, leverage the radiative influence of high-performing areas, deepen regional collaboration, and optimize, governance strategies to promote sustainable development. Full article

This review examines water pressure regulation technologies in irrigation systems tailored for hilly and mountainous terrains. In such areas, effective water management is crucial due to the terrain’s complexity and variability, which can greatly affect water distribution and resource efficiency. This text analyzes various types of pressure-regulating devices, including direct-acting and pilot-operated regulators, delving into their working principles, performance characteristics, and practical advantages and disadvantages. This summary also addresses the current research trends in these technologies, focusing on design optimization and performance enhancements. By summarizing existing studies and highlighting areas for future research, this review aims to provide a solid foundation for technological advancements in agricultural irrigation systems suited to challenging landscapes. Full article

A field experiment in growing spring wheat (Triticum aestivum L.—cv. ‘Monsun’) under organic, integrated and conventional farming systems was conducted over the period of 2020–2022 at the Czesławice Experimental Farm (Lubelskie Voivodeship, Poland). The first experimental factor analyzed was the farming system: A. organic system (control)—without the use of chemical plant protection products and NPK mineral fertilization; B. conventional system—the use of plant protection products and NPK fertilization in the range and doses recommended for spring wheat; C. integrated system—use of plant protection products and NPK fertilization in an “economical” way—doses reduced by 50%. The second experimental factor was irrigation strategy: 1. no irrigation—control; 2. double irrigation; 3. multiple irrigation The aim of the research was to determine the physical, chemical, and enzymatic properties of loess soil under spring wheat crops as influenced by the factors listed above. The highest organic C content of the soil (1.11%) was determined in the integrated system with multiple irrigation of spring wheat, whereas the lowest one (0.77%)—in the conventional system without irrigation. In the conventional system, the highest contents of total N (0.15%), P (131.4 mg kg⁻¹), and K (269.6 mg kg⁻¹) in the soil were determined under conditions of multiple irrigation. In turn, the organic system facilitated the highest contents of Mg, B, Cu, Mn, and Zn in the soil, especially upon multiple irrigation of crops. It also had the most beneficial effect on the evaluated physical parameters of the soil. In each farming system, the multiple irrigation of spring wheat significantly increased moisture content, density, and compaction of the soil and also improved its total sorption capacity (particularly in the integrated system). The highest count of beneficial fungi, the lowest population number of pathogenic fungi, and the highest count of actinobacteria were recorded in the soil from the organic system. Activity of soil enzymes was the highest in the integrated system, followed by the organic system—particularly upon multiple irrigation of crops. Summing up, the present study results demonstrate varied effects of the farming systems on the quality and health of loess soil. From a scientific point of view, the integrated farming system ensures the most stable and balanced physicochemical and biological parameters of the soil due to the sufficient amount of nutrients supplied to the soil and the minimized impact of chemical plant protection products on the soil. The multiple irrigation of crops resulting from indications of soil moisture sensors mounted on plots (indicating the real need for irrigation) contributed to the improvement of almost all analyzed soil quality indices. Multiple irrigation generated high costs, but in combination with fertilization and chemical crop protection (conventional and integrated system), it influenced the high productivity of spring wheat and compensated for the incurred costs (the greatest profit). Full article

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

Rainwater harvesting (abbreviation: RWH) presents a valuable alternative water source for agriculture, particularly in regions facing water scarcity. However, contaminants leaching from roofing materials, such as asbestos cement (abbreviation: AC), may compromise water quality and affect plant physiological responses. This paper aimed to assess how simulated rainwater, reflecting the different levels of contamination (1, 2, 5, 10, and 20 mg/L), influences leaf temperature in tomato plants (Solanum lycopersicum), a known non-invasive indicator of plant water stress. The treatments were applied over a four-week period under controlled greenhouse conditions. Leaf temperature was monitored using infrared thermography. Results showed that higher treatment concentrations led to a significant increase in leaf temperature, indicating elevated water stress. These findings suggest that even low levels of contaminants originating from roofing materials can induce detectable physiological stress in plants. Monitoring leaf temperature offers a rapid and non-destructive method for assessing environmental water quality impacts on crops. The outcomes of this research have direct applicability in the safer design of RWH systems and in evaluating the suitability of collected rainwater for irrigation use. Full article