Search Results (114)

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

Urbanization is causing a decrease in agricultural land. This leads to changes in cropping patterns, irrigation water availability, and water allowance. Therefore, change in cropping pattern, irrigation water availability, and water allowance were investigated in the Multan region of Pakistan using remote sensing and GIS techniques. The multi-temporal Landsat images with 30 m resolution were acquired for both Rabi (winter) and Kharif (summer) seasons for the years of 1988, 1999 and 2020. The image processing tasks including layer stacking, sub-setting, land use/land cover (LULC) classification, and accuracy assessment were performed using ERDAS Imagine (2015) software. The LULC maps showed a considerable shift of orchard area to urban settlements and other crops. About 82% of orchard areas have shifted to urban settlements and other crops from 1988 to 2020. The LULC maps for Kharif season indicated that cropped areas for cotton have decreased by 42.5% and the cropped areas for rice have increased by 718% in the last 32 years (1988–2020). During the rabi season, the cropped areas for wheat (Triticum aestivum L.) have increased by 27% from 1988 to 2020. The irrigation water availability and water allowance have increased up to 125 and 110% due to decrease in agricultural land, respectively. The overall average accuracies were found as 87 and 89% for Rabi and Kharif crops, respectively. The LULC mapping technique may be used to develop a decision support system for evaluating the changes in cropping pattern and their impacts on net water availability and water allowances. Full article

The determination of the actual crop water requirement (CWR) today represents an important prerogative for combating climate change. A three-year trial was conducted to ad-dress the need to provide adequate support to processing tomato growers in defining the correct amounts of water to be supplied. In fact, the objective of this work was to calculate the water requirement of processing tomatoes, specifically analyzing their irrigation needs using the CROPWAT 8.0 software and through capacitive and tensiometric probes. Furthermore, for both methods, the tomato yield was evaluated both by supplying 100% of its water requirement and by supplying, through regulated deficit irrigation (RDI), 70% of its water requirement. Subsequently, for each irrigation strategy employed and for each CWR calculation method, the water footprint was calculated by analyzing the blue, green, and grey components. In the years 2022 and 2023, there was an overestimation of CWR of 13.5% for IR100 and 13.94% for IR70, and 14.53% for IR100 and 11.65% for IR70, respectively, while in 2024 there was an underestimation, with values of 9.17% and 5.22% for the IR100 and IR70 treatments compared to the values obtained with the probes. The total WF of tomatoes varied between 33.42 and 51.91 m³ t⁻¹ with the CROPWAT model and between 35.82 and 47.19 m³ t⁻¹ with the probes for IR100, while for RDI70, the values ranged between 38.72 and 59.44 m³ t⁻¹ with the CROPWAT method and between 35.81 and 53.95 m³ t⁻¹ with the probe method. In water-scarce regions, integrating the CROPWAT 8.0 model (enhanced with real-world data) and implementing smart systems can significantly improve water management, refine decision-making processes, and mitigate environmental impacts. This approach directly addresses the urgent need for water security within sustainable agriculture. Full article

The agriculture sector is a major contributor to the economy of Alberta, Canada, accounting for almost 2.8% of the total GDP. Considering its importance, implementing efficient and cost-effective irrigation systems is vital for promoting sustainable agriculture in semi-arid regions like Lethbridge County, Alberta, Canada. Although irrigation is primarily carried out using the Oldman River and its allied reservoirs, groundwater pumping becomes a supplementary necessity during periods of limited surface water availability or droughts. This research investigates the potential of renewable energy resources, such as wind and solar energy, to meet the energy requirements for crop irrigation. The study begins by identifying and calculating the water requirements for major crops in Lethbridge County, such as wheat and barley, using the United Nations Food and Agriculture Organization’s CROPWAT 8.0 software. Subsequently, energy calculations were conducted to meet the specific crop water demand through the design of a hybrid energy system using Homer Pro 3.16.2. A technoeconomic analysis of the renewable hybrid system has been carried out to demonstrate the efficiency and novelty of the proposed work. Outcomes revealed that the proposed system is both efficient and economical in fulfilling the crop water requirement through groundwater pumping, promoting sustainable agriculture, and helping to ensure food security in the region. Full article

Sprinkler system performance enhancement has been a key area of research due to concerns about water shortages and rising energy costs. This study evaluated the hydraulic performance of the newly designed Spiral Fluidic Sprinkler (SFS) with various nozzles under different operating pressures. MATLAB R2020b software was used to simulate sprinkler uniformities under various operating pressures and the droplet diameter, velocity, and kinetic energy were measured using a 2DVD video raindrop spectrometer. The results showed that larger nozzle sizes generally improved application uniformity and efficiency. The 4 mm nozzle at 200 kPa achieved the lowest coefficient of variation (CV) at 6.2%, while the 3 mm nozzle showed a higher CV of 10.4%. Under 200 and 250 kPa of pressure, a statistically significant difference (p < 0.05) was observed between the CVs for the 4 mm nozzle. Droplet size distributions revealed that over 90% of droplets produced by the 4 mm nozzle were under 3 mm in diameter across all pressures. Kinetic energy analysis indicated that droplet momentum increased with pressure, enhancing coverage but potentially increasing drift at higher levels. Overall, the SFS demonstrated strong potential for water conservation and improved irrigation efficiency in controlled agricultural environments. Full article

Climate change has a complex impact on the agricultural crop system, with knowledge of the processes being necessary to assist decisions that guide the adaptation of society to profound structural changes. This study aims to highlight the main changes generated by the modification of climatic parameters (increasing air temperature, humidity and precipitation and decreasing wind speed) on agricultural crops in a region with important changes in its economic profile due to urban extension and land use modification. The analysis methodology is based on the Cropwat software to highlight the temporal variability of crop evapotranspiration, effective rain and water requirements for different crops—strawberry, sunflower and pea—and the possibility of using other types of crops with higher yield and lower water needs. The methodology used highlights this fact, showing that major changes are needed in the choice of crop schemes and future technological processes in the current context of climate change. The current results of the study, conducted over a period of 30 years (1991–2020), showed that the climatic, land use and economic changes in the study area have led to a decrease in evapotranspiration and crop water requirements due to the amounts of precipitation that can provide for the water needs of strawberry, sunflower and pea crops. The irrigation requirements during the analysis period 1991–2020 varied from <10 mm/year to 120 mm/year for strawberry crops, and can exceed 300 mm/year for sunflower and pea crops, having higher values in years with a precipitation deficit (effective rain less than 100 mm). Analyzing the irrigation requirements during the vegetation growing seasons shows that for pea and strawberry the trend is decreasing, but without a significance level. Only for the sunflower crop is an increasing trend recorded in the initial and late stages. The results obtained provide a methodological framework as well as concrete information for decision-makers in the field of agriculture who must build adaptation mechanisms for climate challenges. Full article

This study examined groundwater dynamics under saline–alkali improvement measures in a 3.66 × 10⁷ m² study area in Wuyuan County, Hetao Irrigation District, where agricultural sustainability is constrained by soil salinization. This work investigated the spatiotemporal evolution patterns and influencing factors of the groundwater environment in the context of soil salinity–alkalinity improvement, as well as the impact of irrigation on the ionic characteristics of groundwater. Furthermore, based on this analysis, a groundwater numerical model and a prediction model for the study area were developed using Visual MODFLOW Flex 6.1 software to forecast the future groundwater levels in the study area and evaluate the effects of varying irrigation scenarios on these levels. The key findings are as follows: (1) The groundwater depth stabilized at 1.63 ± 0.15 m (0.4 m increase) post-improvement measures, maintaining equilibrium under current irrigation but increasing with reductions in water supply. The groundwater salinity increased by 0.59–1.2 g/L across the crop growth period. (2) Spring irrigation raised the groundwater total dissolved solids by 15.6%, as influenced by rock weathering (38.2%), evaporation (31.5%), and cation exchange (30.3%). (3) Maintaining current irrigation systems and planting structures could stabilize groundwater levels at 1.60–1.65 m over the next decade, confirming the sustainable hydrological effects of soil improvement measures. Reducing irrigation to 80% of the current water supply of the Yellow River enables groundwater level stabilization (2.05 ± 0.12 m burial depth) within 5–7 years. This approach decreases river water dependency by 20% while boosting crop water efficiency by 18.7% and reducing root zone salt stress by 32.4%. Full article

To study the impact of soil salinity type and degree in irrigated lands on the process of crop yield formation, multiparametric and single-parameter mathematical models were used. The methodological basis of the study was the materialist theory of scientific knowledge (analysis and synthesis) and the laws of ecology, using graph-analytical methods based on artificial intelligence and the applied software product Microsoft Office. To create the database, an empirical method of generalizing research results was used to study the effect of soil salinity type and degree in irrigated lands on the yield of agricultural crops in various natural and climatic zones of Central Asia for the period from 1932 to 2020. Based on plotting graphs of the dependence of the relative yield of agricultural crops on the dimensionless (relative) value of soil salinity type and degree, based on research data, the following results were obtained: first, differential equations describing the studied process were derived; second, within the framework of a very high determination index confirming a strong correlation between the function arguments and yield, a system of exponential, logarithmic, and polynomial equations was obtained using the applied software product Microsoft Office, which enables the management of agricultural crop yields on saline soils; and third, it creates prerequisites for the design of ecologically sustainable agro-landscapes. Full article

This research focuses on developing an intelligent irrigation solution for agricultural systems utilising solar photovoltaic-thermal (PVT) energy applications. This solution integrates PVT applications, prediction, modelling and forecasting as well as plants’ physiological characteristics. The primary objective is to enhance water management and irrigation efficiency through innovative digital techniques tailored to different climate zones. In the initial phase, the performance of PVT solutions was evaluated using ANSYS Fluent software R19.2, revealing that scaled PVT systems offer optimal efficiency for PV systems, thereby optimising electrical production. Subsequently, a comprehensive approach combining integral feature selection (IFS) with machine learning (ML) and deep learning (DL) models was applied for reference evapotranspiration (ETo) prediction and water needs forecasting. Through this process, 301 optimal combinations of predictors and best-performing linear models for ETo prediction were identified. Achieving R² values exceeding 0.97, alongside minimal indicators of dispersion, the results indicate the effectiveness and accuracy of the elaborated models in predicting the ETo. In addition, by employing a hybrid deep learning approach, 28 best models were developed for forecasting the next periods of ETo. Finally, an interface application was developed to house the identified models for predicting and forecasting the optimal water quantity required for specific plant or crop irrigation. This application serves as a user-friendly platform where users can input relevant predictors and obtain accurate predictions and forecasts based on the established models. Full article

Water management in arid regions faces significant challenges due to limited water resources and increasing competition among sectors. Climate change (CC) exacerbates these issues, highlighting the need for advanced modeling tools to predict trends and guide sustainable resource management. This study employs Water Evaluation And Planning (WEAP) software to develop a Decision Support System (DSS) to evaluate the impact of climate change and water management strategies on the Triassic aquifer of “Sahel El Ababsa” in southeast Tunisia up to 2050. The reference scenario (SC0) assumes constant climatic and socio-economic conditions as of 2020. CC is modeled under RCP4.5 (SC1.0) and RCP8.5 (SC2.0). Additional scenarios include Seawater Desalination Plants (SDPs) (SC3.0 and SC4.0), water harvesting techniques (SC5.0) to highlight their impact on the recharge, and irrigation management strategies (SC6.0). All these scenarios were further developed under the “SC1.0” scenario to assess the impact of moderate CC. The initial aquifer storage is estimated at 100 Million cubic meters (Mm³). Under (SC0), storage would decrease by 76%, leaving only 23.7 Mm³ by 2050. CC scenarios (SC1.0, SC2.0) predict about a 98% reduction. The implementation of the Zarat SDP (SC3.0) would lead to a 45% improvement compared to reference conditions by the end of the simulation period, while its extension (SC4.0) would result in a 69.5% improvement. Under moderate CC, these improvements would be reduced, with SC3.1 showing a 59% decline and SC4.1 a 35% decline compared to the reference scenario. The WHT scenario (SC5.0) demonstrated a 104% improvement in Triassic aquifer storage by 2050 compared to the reference scenario. However, under CC (SC5.1), this improvement would be partially offset, leading to a 29% decline in aquifer storage. The scenario maintaining stable agricultural demand from the Triassic aquifer under CC (SC6.1) projected an 83% decrease in storage. Conversely, the total “Irrigation Cancellation” scenario (SC7.1) under CC showed a significant increase in aquifer storage, reaching 59.3 Mm³ by 2050—an improvement of 250% compared to the reference scenario. The study underscores the critical need for alternative water sources for irrigation and integrated management strategies to mitigate future water scarcity. Full article

Water planning and governance strategies must adapt to challenges associated with population growth, climate change, and projected water shortages. In the Western United States, agriculture is the dominant water use, and agricultural water users are being asked to conserve or share their water with other uses. Managing scarce water supplies at the local level often involves creative solutions, many of which are not well documented, especially in the agricultural sector. It is therefore critical to understand ideas to manage scarce water resources from the perspective of agricultural water users and those who work with them. In our research, we used interviews to explore how agricultural water users are managing increasing water scarcity in the Middle Rio Grande basin of central New Mexico and what enables or prevents them from taking innovative action to manage water scarcity. We hypothesized that we would find undocumented water use innovations born out of water users’ responses to lower and more variable water availability in recent years. We primarily recruited interviewees through snowball sampling, with a total of 42 (47%) agricultural water users, decision makers, and non-profit leaders influencing agricultural water governance in the basin accepting our invitation to participate. Our approximately one-hour, semi-structured and open-ended interviews explored agricultural water users’ lived experiences with water governance and opportunities to manage water scarcity. The interviews were recorded, transcribed, coded, and analyzed using HyperRESEARCH software (version 4.5.4). Our results did not support our hypothesis. Instead, we found that agricultural water users struggled to implement well-known innovations amid the pressures of water scarcity, supply uncertainty, administrative complexity, and constraints on their time, labor, and money. Water users and decision makers were mutually interested in implementing innovations in crop choice, flexibility in water storage, use, and management, stricter enforcement of water use efficiency, and access to more efficient irrigation equipment. However, high costs, a lack of knowledge, education, and training, and challenges related to water distribution and scheduling prevented agricultural water users from accessing these and other innovations. Recommendations include incentive-based policies to promote agricultural water use innovations that require high initial costs, improved water accounting at the basin and regional levels to promote flexible and reliable access to agricultural water, targeted education and outreach programming on alternative irrigation methods and cropping patterns, and improved access to irrigation scheduling information to support agricultural water users in planning for water scarcity. Full article

HYDRUS-3D is a widely used software for modeling variably saturated water flow, but its performance under field conditions requires validation, particularly given the challenges of soil moisture detection. This study aimed to validate the accuracy of HYDRUS-3D in simulating soil water infiltration under drip irrigation using advanced horizontal mobile sensor systems (HMSSs). We designed a three-dimensional soil water infiltration experiment for drip-irrigated fields, employing two HMSSs which were orthogonally placed at 0.2 m and 0.4 m depths from the horizontal plane, and formed a trapezoidal configuration (height: 0.2 m, top edge width: 1 m) on the soil surface. These measurements provided three-dimensional data in the central area and two-dimensional data in the sloped sections. HYDRUS-3D simulations were compared to HMSS measurements, showing strong agreement in both the central area and sloped sections, with high R² values and low RMSE, indicating excellent model accuracy. These results confirm HYDRUS-3D’s reliability in simulating soil water movement under real-world conditions. In particular, the model effectively captured the horizontal fusion process of adjacent drip emitters, which advances its validation for field-scale applications and supports its use in precision agricultural water management. Full article

Precision agriculture (PA) is a farm management data-driven technology that enhances production with efficient resource usage. Existing PA methods rely on data processing, highlighting the need for a portable computing device for real-time, infield decisions. Raspberry Pi, a cost-effective multi-OS single-board computer, addresses this gap. However, information on Raspberry Pi’s use in PA remains limited. This review consolidates details on Raspberry Pi versions, sensors, devices, algorithm deployment, and PA applications. A systematic literature review of three academic databases (Scopus, Web of Science, IEEE Xplore) yielded 84 (as of 22 November 2024) articles based on four research questions and screening criteria (exclusion and inclusion). Narrative synthesis and subgroup analysis were used to synthesize the results. Findings suggest Raspberry Pi can be a central unit to control sensors, enabling cost-effective automated decision support for PA, particularly in plant disease detection, site-specific weed management, plant phenotyping, biomass estimation, and irrigation systems. Despite focusing on these areas, further research is essential on other PA applications such as livestock monitoring, UAV-based applications, and farm management software. Additionally, Raspberry Pi can be used as a valuable learning tool for students, researchers, and farmers and can promote PA adoption globally, helping stakeholders realize its potential. Full article

The study highlights the increasing significance of understanding sediment sources and their contributions within a watershed, particularly in relation to different land use types. As the demand for effective source apportionment grows, this research aims to quantify how various land uses—specifically rangeland, rainfed agriculture, irrigated agriculture, and orchards—contribute to sediment yield over time. To achieve this, the researchers employed geochemical tracers and ¹³⁷Cs to assess sediment contributions in a small sub-basin located in western Iran. The methodology involved creating a working unit map by overlaying land use maps from 1967 and 2021 with a slope map of the region. A total of 75 and 31 soil samples were systematically collected across different land uses to ensure a representative analysis of ¹³⁷Cs and geochemical methods, respectively. The study utilized specific models to calculate the average contributions of each land use type. For non-agricultural lands, a diffusion and migration model was applied, while agricultural lands were analyzed using a mass balance type II model. The FingerPro program in R software 4.2.2 facilitated the selection of suitable tracers and allowed for the determination of sediment source contributions through a multivariate mixed model algorithm. The findings revealed significant changes in sediment yield contributions over the past 60 years. In 2021, rainfed agriculture accounted for 72.26% of sediment yield, down from 85.49% six decades earlier. Conversely, irrigated agriculture showed an increase from 1.80% to 15.06%. Rangeland and orchard contributions remained relatively stable but low, at approximately 8% and 4%, respectively. The total erosion rate for the sub-basin was estimated at 526.87 t y⁻¹, with rainfed agriculture being responsible for the majority at 450.43 t y⁻¹. Full article

The development of water demand analysis methodologies to maintain agricultural crops at an optimal production level, in relation to current climate changes, is a necessity for many geographical areas. The methodology used uses CROPWAT 8.0 software, in the desire to highlight for an important agricultural region in Romania the need to optimize the water requirement for winter wheat crops. The methodology used was able to highlight this fact, as major changes are needed in future technological processes in the current context of climate change. Based on the modeling of evapotranspiration, effective precipitation, and irrigation requirements, it was obtained that the winter wheat needs four additional irrigations per year (in April, May, and July). The irrigations at critical depletion led to a 100% efficiency of reducing the harvest deficit, during the middle and late vegetation stages. The irrigation required by winter wheat depends on precipitation efficiency, and it is very important for improving crop yield up to 100%. The obtained results provide a methodological framework, but also concrete information for decision-makers in the field of agriculture. Full article