Search Results (38)

Climate change is intensifying hydroclimatic extremes and agricultural water scarcity, sharpening trade-offs among yield stability, water saving, and farm incomes in major grain regions. Existing studies often optimise cropping patterns or irrigation schedules separately, seldom embedding yield robustness and policy instruments in one decision framework. We propose an integrated Machine-learning–System-dynamics–Non-dominated-sorting-genetic-algorithm-II (ML–SD–NSGA-II) framework linking long-horizon meteorological scenario generation, crop–water–economy feedback and multi-objective optimisation of crop areas and irrigation depths. ML models generate daily climate sequences to drive an SD model of soil moisture, yield formation, basin-scale allocable water, and farm returns; NSGA-II searches Pareto-optimal strategies that maximise profit and irrigation water productivity while minimising yield deviation. Applied to a rice–wheat irrigation system in the middle Yangtze River Basin, knee-point solutions lift irrigation water productivity by about 14%, maintain near-baseline profits, and reduce yield deviation. Scenario tests with block tariffs, quota-based subsidies, and extreme drought show pricing mainly curbs low-value water use in normal years, while under drought, physical scarcity dominates and economic tools offer limited buffering. This reveals the existence of a scarcity-regime threshold beyond which economic instruments become second-order relative to binding biophysical constraints. The framework supports transparent ex ante testing of tariff–subsidy packages for irrigation governance and adaptation. Full article

The Jordan Valley, a critical agro-ecosystem in Jordan, faces escalating challenges from chronic water scarcity compounded by environmental and socio-economic pressures, necessitating a systems perspective to understand cross-sector interactions beyond isolated sectoral issues. This study interprets socio-ecological interactions influencing sustainability outcomes in the region and identifies key feedback loops and adaptive responses under water scarcity through an integrated Socio-Ecological Systems (SES) and Water–Energy–Food–Ecosystems (WEFE) framework. Employing a qualitative document analysis (QDA) design, a purposive collection of peer-reviewed studies and institutional publications (n = 50) published between 2002 and 2025 was assembled and systematically coded using a structured deductive–inductive strategy grounded in SES components and WEFE domain interactions. Results reveal seven interconnected themes: water scarcity as a structural constraint, agricultural intensification and resource pressures, climate change as a stress multiplier, ecosystem degradation and service loss, pollution and environmental quality challenges, socio-economic vulnerability and livelihood constraints, and fragmented governance with coordination gaps. These themes highlight reinforcing loops where scarcity promotes groundwater reliance and non-conventional water use, intensification heightens salinity and contamination risks, climate variability escalates irrigation demands, and ecological degradation diminishes buffering capacity, while socio-economic limitations hinder adaptation and governance fragmentation impairs integrated planning and enforcement. While prior studies have examined water scarcity, agricultural intensification, or climate impacts in isolation, this study advances the literature by synthesizing these dynamics through an integrated SES–WEFE analytical lens, revealing reinforcing system feedbacks and governance constraints that are not visible within single-sector or descriptive syntheses. Full article

Sustainable soil–water management under climate and socio-economic pressures requires predictive capability that is both mechanistic and continuously corrected by observations. Data assimilation (DA) provides the formal machinery to merge models with heterogeneous measurements—from satellite evapotranspiration and soil moisture to cosmic-ray neutron sensing, proximal geophysics, lysimeters, and groundwater hydrographs—while propagating uncertainty. This review (based on 90 references) synthesizes frontiers in DA and modeling for soil–water systems across scales, emphasizing (i) multi-source observation operators and scaling; (ii) coupled crop–vadose–groundwater modeling frameworks and their structural hypotheses; (iii) modern DA methods (ensemble, variational, particle-based, and hybrid physics–ML) for joint estimation of states, parameters, and biases; and (iv) emerging digital twins that enable predict-then-verify management loops for irrigation, recharge enhancement, and drought risk reduction. We highlight how tracer-aided and isotope-informed components can improve evapotranspiration partitioning and recharge threshold detection, and how agent-based or socio-hydrological coupling can represent human decision feedback. Finally, we outline research gaps in uncertainty quantification, benchmarking, reproducibility, and governance needed to operationalize trustworthy soil–water digital twins for resilient food and water systems. Full article

This work aims to demonstrate the successful, long-term human use of an automatic scheduling-manual operation (AS-MO) precision irrigation tool by farmers on a medium-scale Jordanian farm. Innovation in low-cost, accessible, and water-efficient irrigation technologies is critical as water resources become scarce, especially on resource-constrained farms in the drought-prone Middle East and North Africa (MENA) region. Prior work has shown that a proposed AS-MO decision support tool could bridge the gap between fully manual irrigation—a common practice on many MENA farms—and existing precision agriculture solutions, which are often too expensive or complex for medium-scale farmers to adopt. Recent developments have also demonstrated that the scheduling theory behind the proposed AS-MO tool uses up to 44% less water compared to fully manual irrigation. However, a functional design of the AS-MO tool has not been realized nor has it been demonstrated on a farm with farmer users. This work documents the detailed design of an AS-MO tool’s human–machine interaction (HMI) and validates the human execution of the tool in context. Through an 11-week case study conducted on a Jordanian farm, we show that farmers used a functional prototype of the AS-MO tool as intended. The functional tool prototype was designed to deliver a long-term AS-MO user experience to study participants. The prototype monitored local weather conditions, generated water-efficient schedules using an existing scheduling theory, and notified users’ phones when they should manually open or close valves. The irrigation practices of participants using the AS-MO prototype were measured, and participants demonstrated successful use of the tool. Users correctly confirmed 93% of the scheduled events using the tool’s HMI. Despite manual operation, a majority of confirmed irrigation event durations fell within 15% of the automatically scheduled durations; relative to the length of scheduled irrigation event durations, the medians of confirmed and scheduled durations were 102% and 88%, respectively. These results demonstrate the success of the tool’s decision support ability. Feedback from study participants can support the AS-MO tool’s next design iteration and can inform the development of other decision support systems designed for resource-constrained, medium-scale farms. This work presents an important step towards developing a precision irrigation tool that, if adopted at scale, could increase the adoption of water-efficient irrigation practices on resource-constrained farms that are not served by existing technology, improving sustainable agriculture in MENA. Full article

To address the issue of low fertilizer proportioning accuracy in irrigation and fertilization systems due to neglecting the influence of target ions in raw water, this study designed a high-precision online automatic water–fertilizer mixing device that can directly mix raw water (without water purification treatment) with fertilizer stock solution. This device is capable of preparing mixed fertilizer solutions containing N, K, and Ca elements. It employs ion-selective electrodes and flow meters for online detection and feedback of target ion concentrations in the fertilizer solution and flow rate information, and adopts an online fertilizer mixing control strategy that uses a constant raw water flow rate and a fuzzy PID control method to dynamically adjust the pulse frequency of metering pumps, thereby changing the injection volume of nutrient solution. Simulation and experimental analyses show that the piping system of the device is reasonably designed, ensuring stable and smooth fertilizer injection. The temperature-compensated concentration detection models for the three target ions in the fertilizer solution, constructed using a stepwise fitting method, achieve average relative detection errors of 1.94%, 1.18%, and 2.87% for K⁺, NO₃⁻, and Ca²⁺, respectively. When preparing single-element or mixed fertilizer solutions, the device achieves an average steady-state error of no more than 4% and an average steady-state time of approximately 40 s. Compared with deionized water, the average relative errors for potassium ions, nitrate ions, and calcium ions when preparing fertilizer solutions with raw water are 1.33%, 1.12%, and 1.19%, respectively. Compared with the theoretical errors of fertilizer preparation with raw water, the fertilizer proportioning errors of this device for potassium ions, nitrate ions, and calcium ions can be reduced by a maximum of 10.55%, 66.84%, and 62.71%, respectively, which is superior to the performance requirements for water–fertilizer integration equipment specified in the national industry standard DG/T 274-2024. Additionally, the device achieves accurate and stable fertilizer proportioning with safe and reliable operation during 6 h of continuous operation. This device significantly reduces the impact of raw water on fertilizer proportioning accuracy, improves the adaptability of the device to irrigation water sources, and provides theoretical basis and technical support for water-fertilizer integration systems in cost-sensitive agriculture. Full article

The narrow margin for irrigation error in aeroponics necessitates advanced control strategies beyond fixed timer-based approaches. This study evaluates a plant-driven irrigation method based on real-time leaf turgor feedback in aeroponic romaine lettuce (Lactuca sativa L. var. longifolia) cultivation. A leaf thickness–turgor sensor was interfaced with an Arduino Mega 2560 to activate misting events dynamically. Two identical aeroponic systems were operated in a fully controlled environment: a conventional timer-based control (TC) system applying mist every 10 min and an Arduino-controlled (AC) system triggered by turgor changes. Over two independent 37-day cultivation cycles, the AC strategy reduced total water use by an average of 15.9% and pump activations by 17.2% while improving water use efficiency by 17.8% and nutrient use efficiency for N, P, and K by an average of 17.8%, with no statistically significant differences in shoot biomass, height, or yield. Although root dry weight was significantly higher under TC, the AC treatment led to a 45.0% reduction in leaf nitrate accumulation and non-significant increases in phenolic content. These findings demonstrate the potential of turgor-responsive irrigation for enhancing sustainability, resource use efficiency, and the quality of produce in aeroponic systems, thereby supporting its broader integration into controlled-environment agriculture (CEA). Full article

Groundwater plays an important role in areas facing water scarcity, which can cause many problems if poorly managed. In Northern Cyprus, in the Güzelyurt region, where agriculture is thriving, excessive and inappropriate groundwater use has caused a sharp decrease in water levels and electrical conductivity in many coastal areas. This study explores this problem using system dynamics tools designed to analyze feedback loops and causal links. The qualitative system dynamics approach is employed to investigate complex systems by focusing on structural and behavioral patterns through qualitative elements such as feedback loops, causal relationships, and system archetypes, rather than relying solely on numerical data. For this purpose, group model building is used, for which a basic model is built using library studies, and then the model is developed and improved through numerous interviews and meetings held with policymakers, farmers, soil and water managers, university professors, and representatives from the local community. The study examines water management practices, including transferring water from Turkey to Northern Cyprus and allocating a portion for agricultural use in Güzelyurt. It also explores agricultural strategies and the employment of advanced irrigation methods. In the tourism and urban consumption sectors, raising public awareness and educating citizens about water scarcity linked to climate change are highlighted as essential measures in promoting sustainable water usage. Full article

Soil environmental monitoring is crucial for ensuring the sustainability and productivity of agriculture. This study aims to develop a wireless soil monitoring system that utilizes Narrowband Internet of Things (NB-IoT), solar energy, and Global Positioning System (GPS) technologies to address the issues of high labor demand, high costs, and delayed feedback in traditional soil monitoring methods. This system can collect soil temperature, humidity, and meteorological data in real time, transmit them to a cloud platform for analysis and visualization, and predict future soil data. It employs multiple learning algorithms to build models and uses the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization. Field stability experiments were conducted on the system, and the performance of the soil moisture prediction model was evaluated. During the 84-day stability experiment, the system operated stably for 80 days, with a data collection success rate of 95.87%. In the performance evaluation of the soil moisture model, the GBDT model achieved a coefficient of determination (R²) of 0.9838 on the validation set and a root-mean-square error (RMSE) of 0.0013, with an RMSE of 0.0013 on the test set as well. The experimental results demonstrate that the system is stable and reliable, featuring low power consumption, wide coverage, and high accuracy. It can effectively predict soil moisture, providing timely and accurate support for irrigation and farming decisions. Full article

In the context of growing concerns over food security and climate change, research on sustainable agricultural development increasingly emphasizes the interconnections within agricultural systems. This study developed a regionally integrated optimization and prediction agricultural model to systematically analyze the impacts of climate change on agricultural systems and their feedback mechanisms from a water-energy-food-carbon (WEFC) nexus perspective. Applied to the Pearl River Basin, the model evaluates future trends in grain yield, water use, energy consumption, and carbon emissions under various climate scenarios throughout this century. The results indicate that rising temperatures significantly reduce crop yields, particularly in the western basin, increasing the environmental footprint per unit of grain produced. However, the CO₂ fertilization effect substantially offsets these negative impacts. Under the SSP585 scenario, CO₂ concentrations rising from 599.77 ppm to 1135.21 ppm by the century’s end led to a shift in crop yield trends from negative (Z = −7.03) to positive (Z = 11.01). This also reduces water, energy, and carbon footprints by 12.82%, 10.62%, and 10.59%, respectively. These findings highlight the critical importance of adaptive management strategies, including precision irrigation, optimized fertilizer use, and climate-resilient practices, to ensure sustainable agricultural production. Despite these insights, the model has limitations. Future research should incorporate uncertainty analysis, diverse adaptation pathways, and advanced technologies such as machine learning and remote sensing to improve predictive accuracy and applicability. This study offers valuable guidance for mitigating the adverse impacts of climate change on the WEFC nexus, supporting sustainable agricultural practices and science-based policy development. Full article

Groundwater, a crucial natural resource on a global scale, plays a significant role in Texas, impacting various essential ecosystem services either directly or indirectly. Despite efforts of state- and community-level regulations and conservation efforts, there is an ongoing trend of declining groundwater levels in the state of Texas. In this study, we utilized the systems thinking and system dynamics modeling approach to better understand this problem and investigate possible leverage points to achieve more sustainable groundwater resource levels. After conceptualizing a causal loop diagram (CLD) of the underlying feedback structure of the issue (informed by the existing literature), a small system dynamics (SD) model was developed to connect the feedback factors identified in the CLD to the stocks (groundwater level) and flows (recharge rate and groundwater pumping) that steer the behaviors of groundwater systems across time. After completing model assessment, experimental simulations were conducted to evaluate the current state relative to simulated treatments for improved irrigation efficiency, restricted pumping rates, cooperative conservation protocols among users, and combination strategy (of all treatments above) in the long-term. Results showed that groundwater stress (and the associated repercussions on related ecosystem service) could be alleviated with a combination strategy, albeit without complete groundwater level recovery. Full article

Due to its rich nutritional composition, biogas slurry can serve as a special liquid fertilizer. However, the application of slurry in agricultural fields currently faces challenges such as reliance on skilled famers’ experience, low precision, and difficulty in accurately controlling the irrigation dosage. To address these issues, an agricultural biogas slurry mixing agricultural machinery and its system has been designed and developed with the aim of enhancing the precision and safety of slurry application. The structure of the device has been designed, filter components have been selected, and improvements have been made to the structure of traditional connectors. Taking into account factors such as soil and humidity, an algorithm based on biogas slurry conductivity for slurry mixing decisions and the feedback control mechanism has been designed. After assembling the prototype, experiments were conducted, and the results showed that after processing by the system, compared to the calculated theoretical optimum, the concentration error of each component in the mixed fertilizer was controlled within 10%, and the conductivity fluctuation range was within 5%. This indicates that the overall ratio accuracy and stability of the biogas slurry mixing system are high. The biogas slurry mixing agricultural machinery and its system provide a novel intelligent equipment solution for the precise application of slurry, effectively enhancing the accuracy and safety of slurry application, reducing the use of chemical fertilizers during agricultural irrigation, and minimizing pollution to the environment and soil. Full article

Many climatic variables are projected to occur with more intense and frequent extreme events, possibly unpredictable patterns and negative feedback loops with other environmental processes. Agriculture has faced uncertainty regarding ground temperature and rainfall distribution during the last few years, making water availability one of the major concerns for farm management. In this scenario, rainwater harvesting could represent a powerful tool to mitigate this problem, and consequently, the research community has been fostering new technical solutions. On the other hand, a few studies on agronomic assessment of rainwater harvesting systems are present in scientific literature. The present study reports preliminary data of a long-term study on a Flexible Water Storage System (FWSS) evaluating the possibility of enhancing agriculture systems resilience, shifting from rainfed production to irrigated agriculture relying on excessive rainfall, collectible from extreme events. The idea of intercepting excess rainfall, which is generally lost, thanks to an innovative water harvesting system, and using it to mitigate drought stress for crops is in line with sustainable approaches aiming to improve the resilience of agricultural systems. The results highlighted that the system studied could potentially collect an annual average of 831.7 m³ of water, mitigating the excess of water in the ditch that can potentially cause flooding and storing fresh water to provide irrigation during dry periods. Full article

Soil microbial taxa have different functional ecological characteristics that influence the direction and intensity of plant–soil feedback responses to changes in the soil environment. However, the responses of soil microbial survival strategies to wet and dry events are poorly understood. In this study, soil physicochemical properties, enzyme activity, and high–throughput sequencing results were comprehensively anal0079zed in the irrigated cropland ecological zone of the northern plains of the Yellow River floodplain of China, where Oryza sativa was grown for a long period of time, converted to Zea mays after a year, and then Glycine max was planted. The results showed that different plant cultivations in a paddy–dryland rotation system affected soil physicochemical properties and enzyme activity, and G. max field cultivation resulted in higher total carbon, total nitrogen, soil total organic carbon, and available nitrogen content while significantly increasing α–glucosidase, β–glucosidase, and alkaline phosphatase activities in the soil. In addition, crop rotation altered the r/K–strategist bacteria, and the soil environment was the main factor affecting the community structure of r/K–strategist bacteria. The co–occurrence network revealed the inter–relationship between r/K–strategist bacteria and fungi, and with the succession of land rotation, the G. max sample plot exhibited more stable network relationships. Random forest analysis further indicated the importance of soil electrical conductivity, total carbon, total nitrogen, soil total organic carbon, available nitrogen, and α–glucosidase in the composition of soil microbial communities under wet–dry events and revealed significant correlations with r/K–strategist bacteria. Based on the functional predictions of microorganisms, wet–dry conversion altered the functions of bacteria and fungi and led to a more significant correlation between soil nutrient cycling taxa and environmental changes. This study contributes to a deeper understanding of microbial functional groups while helping to further our understanding of the potential functions of soil microbial functional groups in soil ecosystems. Full article

Determining evapotranspiration (ET) is essential for water accounting and for the management of irrigated agriculture from farm to region. We describe here a collaborative initiative aimed at establishing a prototype ET network in six countries of North Africa and the Near East (NENA region). The network utilizes a low-cost and open-source system, termed the CORDOVA-ET, consisting of a base station and sensing nodes to collect the weather data needed to determine the reference and actual ET (ET_o and ET_a). Here, we describe the network-deployment processes, system architecture, data-collection methodology, quality-control procedures, and some of the ET results obtained so far during a four-year period, starting in 2018. The network has been developed as an iterative and collaborative process, where training and capacity building have been the main drivers. The feedback and experiences gathered from the users have helped improve the different versions of the prototypes and enhance their assembly, deployment, reliability, and ease of operation. At the same time, the involvement in the construction, maintenance, and data analysis has also provided valuable insight into calculating ET from energy-balance methods. The network operated during six cropping seasons and the results were mixed, while data integrity (hourly and daily) varied from 95 to 23% depending on the country and season. Validation of the ET estimates was performed using the ECMWF ERA5 dataset as an independent reference. The energy-balance algorithm implemented in the system to determine the ET_a was validated using the OpenCropLib Python library. While the results of the data validation demonstrated the reliability and accuracy of the CORDOVA-ET system, network operations required significant support and special motivation on the part of the users. It is concluded that collaboration among users, together with the support services and participation of different stakeholders interested in agricultural water management, would be essential elements to ensure the sustainability of the ET network. Full article

Modeling and control theory applied to precision agriculture irrigation systems have been essential to reduce water consumption while growing healthy crops. Specifically, implementing closed-loop control irrigation based on soil moisture measurements is an effective approach for obtaining water savings in this resource-intensive activity. To enhance this strategy, the work presented in this paper proposed a new set of water management strategies for the case in which multiple irrigation areas share a single water supply source and compared them with heuristic approaches commonly used by farmers in practice. The proposed water allocation algorithms are based on techniques used in real-time computing, such as dynamic priority and feedback scheduling. Therefore, the multi-area irrigation system is presented as a resource allocation problem with availability constraints, where water consumption represents the main optimization parameter. The obtained results show that the data-driven water allocation strategies preserve the water savings for closed-loop control systems and avoid crop water stress due to the limited access to irrigation water. Full article