Agricultural Water Research Articles

In response to the significant waste of agricultural irrigation resources and the inaccuracy of water demand predictions, this study aims to develop an automated irrigation system that can reduce fluctuations in water volume and enable precise control. Against the backdrop of current water scarcity and low agricultural water efficiency, improving irrigation precision is of great significance for ensuring food security and promoting sustainable agricultural development. This study combines particle swarm optimization algorithm with extreme learning machine and integrates it into a microcontroller to construct a new intelligent irrigation system. This technology can solve the problem of inaccurate crop water demand predictions in existing technologies and promote the transformation of intelligent agriculture from empirical to data-driven. This technology uses a LoRa based wireless sensor network to collect data and is controlled by a microcontroller. The particle swarm algorithm optimizes the initial parameters of the extreme learning machine, improving the accuracy with which it predicts farmland water demand. The results showed that the proposed method had the lowest root mean square error value, with an average of only 0.1025, indicating that the algorithm had the most accurate irrigation prediction effect. The automatic water-saving irrigation technology proposed in this study required less water compared to traditional irrigation techniques, with a minimum water consumption of 3015 m 3 /hm 2 and a maximum water consumption of only 5268.3 m 3 /hm 2 . The system’s accuracy in predicting crop irrigation water demand could reach over 98%. The method proposed in this study can accurately control irrigation water. It can also maximize irrigation water conservation. This brings new research directions for the knowledge system of automated water-saving irrigation technology in farmland. It also provides new technical ideas for the development of intelligent agricultural irrigation technology.

Water scarcity has become a significant constraint to agricultural development in China. In this study, we employed bibliometric methods to systematically review the current research on crop water use efficiency (WUE) and the development trends in the North China Plain (NCP) and Northwest China (NWC). We analyzed 1569 articles (NCP = 788; NWC = 781) from the Web of Science Core Collection (1995–2025) using visualization tools such as CiteSpace and VOSviewer to investigate annual numbers of publications, leading scholars and research institutions, and then to map keyword co-occurrence and co-citation structures. Our results showed that keyword clustering exhibited high structural quality (NCP: Q = 0.7345, S = 0.8634; NWC: Q = 0.758, S = 0.8912), supporting reliable thematic interpretation. The bibliometric analysis indicates a steady growth in annual publications since 1995, with the Chinese Academy of Sciences and China Agricultural University as leading contributors. From 1995 to 2005, studies centered on irrigation, yield and field-scale WUE, emphasizing the optimization of irrigation strategies and crop productivity. During 2006–2015, the thematic focus has broadened to encompass nitrogen use efficiency, crop quality and eco-environmental performance, thereby moving toward integrated evaluation frameworks that capture ecological synergies. Since 2016, the literature now emphasizes system integration, regional adaptability, climate-response mechanisms and the ecological co-benefits of agricultural practices. Future studies are expected to incorporate indicators such as crop quality, water footprint and carbon isotope indicators to support the sustainable development of agricultural water use. This study offers insights and recommendations for developing a comprehensive crop WUE evaluation framework in China, which will support sustainable agricultural water management and the realization of national “dual carbon” targets.

Hydroponic spinach (Spinacia oleracea) production requires diligent nutrient solution management. Nutrient imbalances may compound over time in recirculating systems, which affects quality and yield. There is a lack of studies comparing nutrient solution refill (NSR) strategies on crop yield, quality, and resource use across diverse production settings. This study aimed to assess six NSR strategies for spinach production: weekly drain and refill with fresh nutrient solution, and the remaining NSR included full and half strength topping off, controlling electrical conductivity (EC), refill strength from water use efficiency, and individual nutrient replacement. Each strategy was studied under photosynthetic photon flux density of 250, 500, and 700 µmol·m−2·s−1 and in a greenhouse (ambient CO2) or a vertical farm (elevated CO2). Strategies that used diluted refill solutions, replenished based on plant water use efficiency, or nutrient-specific fertilizer additions resulted in a 91% reduction in fertilizer application compared with full-strength refilling, EC maintenance, and drain/refill methods. We found no change in yield, leaf tipburn incidence, or chlorosis among all light intensities and production systems. These results demonstrate that spinach yield and quality can be maintained with NSR strategies that reduce water and fertilizer use in greenhouses and vertical farms.

How reliable are long time-series reanalysis and model-based soil moisture products for agricultural soil water stress monitoring? Insights from a five-dataset evaluation across China

Groundwater usage characterization in a tribal stretch infected with chronic kidney disease of unknown etiology (CKDu).

Assessing environmental impacts of agricultural water table management: A global meta-analysis

Enhanced non-structural carbohydrate metabolism and transport contribute to yield improvement in salt-tolerant rice under brine irrigation.

Evaluation of carrying capacity of agricultural water and soil resources and targeted regional management in Northwest China.

Lake Urmia, one of the world’s largest hypersaline lakes, has experienced severe drought in recent decades. This study investigates the combined impacts of agricultural expansion and climate variability on river inflows from 1985 to 2020. A hybrid framework incorporating statistical models and Convolutional Neural Networks was employed to estimate river discharge and disentangle the effects of hydroclimatic and anthropogenic drivers. Results indicate a persistent snow drought beginning in the late 1990s, concurrent with exceeding fourfold increase in irrigated lands. Scenario-based analysis, restoring key parameters to pre-1999 levels revealed that reverting agricultural water use was the dominant factor driving changes in river inflows, accounting for approximately 66% (95% CI: 56%–76%) of the total impact. In contrast, restoring precipitation and evaporation contributed 25% (95% CI: 18%–33%) and 9% (95% CI: 7%–12%), respectively, while restoring both simultaneously explained 34% (95% CI: 26%–43%) of the change. These results underscore the primary role of agricultural water demand amplified by declining snowpack and climatic shifts in altering basin hydrology. The findings highlight the urgent need for integrated water resource management, with a focus on climate adaptation, snowpack monitoring, and sustainable agricultural practices to address ongoing environmental degradation and ensure long-term water security.

Soil moisture (SM) is crucial for ecosystems and agriculture. Since the root systems of plants absorb water at different depths with different intensities, monitoring multi-layer SM can better respond to the water demand of plants and offer a crucial technical backing for drought monitoring and precision irrigation. Synthetic aperture radar (SAR) and multispectral (MS) have been widely used in SM estimation; however, their combined application for multi-layer SM profiling remains underexplored. Existing research based on these two data types has primarily focused on surface soil moisture (SSM), with limited investigation into estimating SM at deeper or varying depths. Therefore, the aims of this research are to integrate Sentinel-1 SAR and Sentinel-2 MS data and employ machine learning algorithms to estimate multi-layer SM in the Shandian River Basin. The results showed that (1) MS + SAR-based SM estimation significantly outperformed single-source data (MS or SAR alone). Specifically, MS data performed better in the root-zone estimation, while SAR data showed superior performance in SSM estimation. (2) The BKA-CNN estimation accuracy significantly outperformed RF and XGBoost. The results of its five-fold cross-validation are as follows: R2 = 0.768 ± 0.011 at 3 cm, R2 = 0.777 ± 0.013 at 5 cm, R2 = 0.799 ± 0.011 at 10 cm, R2 = 0.792 ± 0.01 at 20 cm, and R2 = 0.782 ± 0.011 at 50 cm. (3) The BKA-CNN model performed better in grassland than in farmland. These findings indicate that the BKA-CNN model proposed in this study effectively improves the estimation precision of multi-layer SM by fusing SAR and MS data, demonstrating considerable generalization ability and robustness. It holds potential application value in ecological protection and agricultural water resource management.

This study conducted a simulation on a test-bed area to evaluate the feasibility of water supply based on annual rainwater usage, depending on the capacity of rainwater storage tanks and daily consumption levels. The analysis showed that larger storage tank capacities led to an increase in the number of days rainwater could be used annually and improved utilization rates, which allowed for the estimation of an appropriate water supply quantity (for agricultural use). Although the rainwater utilization rates remained below 60% for year-round use across various storage tank sizes, the systems were nearly fully utilized during periods of concentrated agricultural water demand when designed at an appropriate scale. Additionally, the larger the storage tank, the higher the seasonal usage efficiency. Also, lower daily usage led to better overall efficiency. Considering monthly and seasonal variability, the study found that an efficient and sustainable continuous supply was relatively better in areas with lower total annual rainfall but minimal seasonal fluctuations and fewer drought periods than in areas with higher but uneven rainfall. This study is expected to provide foundational data for water resource planning by assessing both the soundness of urban water circulation and the feasibility of water supply through rainwater use.

According to the results of the water quality assessment conducted in Amarah City, the physical, chemical, and microbiological characteristics of the Tigris River have drastically decreased particularly true in contrast to the water that enters the city (from the upstream) and exits it (outfall).The amounts of nutrients (phosphate and nitrate), organic matter (BOD and COD), and dissolved salts (as determined by a conductivity test) are markers of significant waste water, agricultural, and industrial water pollution. These results indicate that environmental restrictions must be enforced immediately, and effluent must be cleaned before being released into waterways.

Surface-enhanced Raman spectroscopy (SERS) has rapidly emerged as a powerful analytical technique for the sensitive and selective detection of organophosphorus pesticides (OPPs) in complex food matrices. This review summarizes recent advances in substrate engineering, emphasizing structure–performance relationships between nanomaterial design and molecular enhancement mechanisms. Functional groups such as P=O, P=S, and aromatic rings are highlighted as key determinants of Raman activity through combined chemical and electromagnetic effects. State-of-the-art substrates, including noble metals, carbon-based materials, bimetallic hybrids, MOF-derived systems, and emerging liquid metals, are critically evaluated with respect to sensitivity, stability, and applicability in typical matrices such as fruit and vegetable surfaces, juices, grains, and agricultural waters. Reported performance commonly achieves sub-μg L−1 to low μg L−1 detection limits in liquids and 10−3 to 10 μg cm−2 on surfaces, with reproducibility often in the 5–15% RSD range under optimized conditions. Persistent challenges are also emphasized, including substrate variability, quantitative accuracy under matrix interference, and limited portability for real-world applications. Structure–response correlation models and data-driven strategies are discussed as tools to improve substrate predictability. Although AI and machine learning show promise for automated spectral interpretation and high-throughput screening, current applications remain primarily proof-of-concept rather than routine workflows. Future priorities include standardized fabrication protocols, portable detection systems, and computation-guided multidimensional designs to accelerate translation from laboratory research to practical deployment in food safety and environmental surveillance.

ABSTRACT Water pricing, often pursued under the goal of getting the prices right, is widely promoted as a pathway to efficiency and sustainability in agricultural water use. However, this long-standing and often polarized debate frequently overlooks the complexities of real-world implementation, where pricing decisions intersect with deep-rooted social, economic, and environmental concerns. Reform advocates argue that subsidies encourage inefficiency, strain public finances, and limit investment in critical infrastructure. Conversely, opponents view such subsidies as necessary welfare mechanisms that support vulnerable communities and safeguard national food security. This analytical review paper critically examines the validity of both perspectives, offering a new conceptual framing that situates the subsidy pricing debate within the emerging context of desalinated water use and broader water governance reform. It investigates whether pricing reforms alone can meaningfully alter water use behavior among agricultural users and what contextual factors shape their success or failure. The analysis suggests that pro- and anti-reform arguments are not mutually exclusive. Artificially low prices are increasingly unsustainable; yet abrupt or poorly designed reforms may be equally harmful. A transitional, context-sensitive approach, combining targeted support mechanisms with gradual reform, offers a more viable path, particularly in settings where full cost recovery may be unrealistic.

Under extreme drought conditions, water shortage loss assessment is essential for regional water use system management decisions and the development of drought mitigation measures. This study addresses current research on economic losses in water use systems, which often overlooks the social and ecological losses caused by drought mitigation measures. The impact of measures such as water use compression, industrial structure adjustment, and hierarchical, zoned and time-divided water allocation on various water use subsystems is analysed. A comprehensive framework for quantifying regional water shortage losses was established using emergy analysis, enabling unified evaluation of multidimensional impacts. The framework was applied to quantify water use system losses in Chuxiong Prefecture, Yunnan Province, during the severe drought event in 2009 ~ 2012. The results show that the overall trend of water shortage losses in the water use system initially increased before gradually declining, with subsystem losses ranked as follows: domestic water > agricultural water > industrial water > ecological water. This study offers a new perspective for evaluating water shortage losses under extreme drought conditions.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-21673-4.

This research presents the design and implementation of a wireless, computer-based water level monitoring and control system. The system is developed to provide an efficient and reliable solution for real-time monitoring and management of water levels. At its core, the system utilizes an ESP32 microcontroller, a calibrated resistive water level sensor, and a 20x4 LCD display for visual feedback. A 12V DC relay is integrated to enable automatic or manual control of an external water pump, with manual override facilitated by a push button. The system is powered through a stepped-down, rectified mains supply. A mobile application, developed, offers a user-friendly interface for configuration and remote monitoring. Wireless communication between the ESP32 and the application enables real-time data transmission and control, using a suitable protocol to ensure reliability. The system is tested extensively to validate its performance, accuracy, and responsiveness, demonstrating its potential for use in residential, agricultural, and industrial water management applications.

Climate variability, population growth, and agricultural expansion increasingly strain groundwater resources in arid and hyper-arid regions. In West Mallawi, El-Minya Governorate, Egypt-where annual rainfall is less than 10mm-the Middle Eocene and Oligocene-Pleistocene aquifers serve as the main sources of domestic and agricultural water. This study aims to support sustainable groundwater management by comparatively assessing their hydrochemical characteristics, water quality, and potential health risks. In 2024, 86 groundwater samples were collected and analyzed for physicochemical parameters, hydrochemical facies, and water quality indices. The deeper Middle Eocene aquifer exhibited higher total dissolved solids (TDS) and greater mineralization than the shallower Oligocene-Pleistocene aquifer. Hydrochemical facies analysis revealed predominant sulfate-chloride-sodium water types, influenced by evaporite dissolution, cation exchange, and prolonged water-rock interaction. Water Quality Index (WQI) evaluations indicated that most Oligocene-Pleistocene samples ranged from "good" to "poor," whereas many Middle Eocene samples were classified as "poor" to "unsuitable" for drinking, with similar trends observed for irrigation suitability. Health risk assessment highlighted elevated hazard levels in the Middle Eocene aquifer, emphasizing the need for targeted treatment, routine monitoring, and strengthened groundwater management strategies. This comparative approach provides new insights into the vulnerabilities of aquifers under extreme arid conditions and offers evidence-based guidance for sustainable resource management.

Among the Central Asian republics, Uzbekistan is unique in that approximately 80% of its territory lies within a plain, characterized by an arid geographic zone and dry climate. Agricultural production in these regions is possible only through artificial irrigation. In recent years, global climate change and challenges related to transboundary water use have led to a reduction in water availability. The average annual water allocation to Uzbekistan is estimated at 51–53 billion m3, of which 90–91% is consumed by the agricultural sector. Due to the uneven distribution of water resources and the complex topography of irrigated lands, water supply is supported by numerous pumping stations operated by the state, water users associations, farms, and clusters. Additionally, well-based pumping systems are employed to maintain groundwater levels and ensure irrigation. On average, these facilities consume around 8.0 billion kWh of electricity annually. The agricultural sector faces several critical challenges, including crop water deficits caused by water shortages, slow adoption of water-saving technologies, and limited implementation of drip irrigation on household plots, dachas, and greenhouses that play a key role in food supply. Moreover, the delivery of water to fertile lands situated far from main power lines and water sources remains problematic. This article aims to explore the integration of solar energy solutions to support drip irrigation in both large-scale agricultural lands (ω = 1.0–100.0 ha and above) and small-scale areas such as homestead plots, dachas, and greenhouses (ω = 0.01–1.0 ha), as well as their application in small- to medium-sized pumping stations. Based on the research and experimental design work carried out, three mobile photovoltaic units—MPPU-8-500-4000, MPPU-2-550-1100, and MPPU-4-500-2000—were developed and implemented to address water and energy shortages in agriculture.

Abstract The Barind tract in northwest Bangladesh faces severe groundwater challenges due to excessive extraction and limited recharge capacity, primarily caused by its thick top clay layer and intensive agricultural water demand. This study develops and evaluates an innovative horizontal flow treatment unit integrated with Managed Aquifer Recharge (MAR) to address severe groundwater challenges in the Barind tract of northwest Bangladesh. The developed system demonstrated exceptional treatment efficiency, achieving 97.06% turbidity reduction (from 68 to 2 NTU), 87.31% removal of total suspended solids, and significant bacterial contamination reduction of 81.82% (TC) and 93.75% (FC), while maintaining all chemical parameters within ECR 2023 drinking water standards. The system's recharge performance was evaluated under both controlled and natural conditions. Under controlled conditions, the initial recharge rate achieved 168.06 lpm, while during monsoon flooding, natural condition recharge ranged from 154.11 to 167.38 lpm. The system demonstrated remarkable sustainability, with 96% recovery of the original recharge rate possible through backwashing and maintenance, showing only a minimal annual decline rate of 3.43 lpm/year due to minor clogging. Long-term monitoring revealed significant groundwater improvement, with a 2.01 m water table rise over seven years (2015–2021), while maintaining groundwater quality within drinking water standards. The innovative aspects of the system include its novel horizontal flow design that reduces clogging potential, integrated pre-treatment for suspended solids removal, and an efficient self-cleaning mechanism through backwashing. The design's cost-effectiveness and local maintainability make it particularly suitable for widespread implementation. This study demonstrates that the integrated MAR-treatment system provides a sustainable solution for groundwater management in water-stressed regions, effectively addressing both water quantity and quality challenges while ensuring long-term operational viability.

Water scarcity and inefficient irrigation practices pose significant challenges to sustainable agriculture, necessitating advanced solutions for efficient water management. This research presents a Smart Water Management System (SWMS) that integrates Internet of Things (IoT) sensors and Machine Learning (ML) algorithms to optimize irrigation scheduling and water conservation. The system employs Deep Q-Networks (DQN) and Artificial Neural Networks (ANNs) for real-time soil moisture prediction and autonomous irrigation control, achieving higher accuracy (RMSE = 0.08, MAE = 0.07) than traditional models. Experimental results demonstrate a 40% reduction in water consumption, a 35% decrease in operational costs, and a latency of 2.5 seconds, ensuring efficient and timely irrigation adjustments. Compared to existing AI-based irrigation models, the proposed system exhibits superior adaptability, scalability, and real-time decision-making capabilities, supported by edge computing for reduced reliance on cloud-based processing. Despite its effectiveness, challenges such as AI-driven weather forecasting, blockchain-based data security, and large-scale field deployment remain areas for future research. This study contributes to the advancement of intelligent, cost-effective, and sustainable irrigation solutions, supporting global agricultural water conservation efforts.