Management Of Irrigation Systems Research Articles

Analysis of Irrigation Water Requirements In The Secondary Channel of Rentang Irrigation Area

Effective management and planning of irrigation systems critically depend on accurately understanding the overall irrigation water requirements. This study aims to evaluate and project the irrigation water needs in the secondary channel of BSD 1, located in the Rentang Irrigation Area, Majalengka Regency, which serves an irrigation area of 620 hectares and is supplied by the Jatigede Dam and Cimanuk River. Factors such as effective rainfall, water layer changes, seepage, consumptive use, and land preparation are considered in determining water needs. The research employs the kp-01 concept, allowing manual calculations to determine irrigation requirements. Results indicate that irrigation water is necessary starting in early November due to the rice planting pattern. The manual calculations reveal a maximum irrigation water requirement of 1.55 m³ per second during the second period of May and a minimum requirement of 0.06 m³ per second during the second period of December. The findings highlight the importance of understanding seasonal variations and optimizing water resource allocation to ensure efficient irrigation practices. This study concludes that adopting precise calculation methods like kp-01 is essential for sustainable irrigation management and can serve as a reference for similar agricultural planning efforts in other regions.

Optimizing Irrigation Infrastructure Management with Web-Based Technologies and OpenStreetMap Integration

Irrigation is essential for increasing agricultural productivity in Indonesia, directly impacting national food security and farmer welfare by ensuring the sustainability of irrigation systems. Despite its importance, significant challenges persist, including damage to 46% of the irrigation infrastructure across 7.1 million hectares, with responsibilities divided among central, provincial, and city governments. These challenges are compounded by issues such as inadequate information access, limitations in real-time monitoring, and poor stakeholder coordination. To address these issues, integrating technological tools such as Geographic Information Systems (GIS) and platforms such as OpenStreetMap (OSM) is essential. These technologies offer real-time information, facilitate land change tracking, and enable spatial mapping, which supports proactive management and planning while improving decision making through data-driven insights. The adoption of Rapid Application Development (RAD) and Decision Support Systems (DSS) further enhances the efficiency of irrigation system management. The RAD methodology ensures swift development cycles with user interaction, while DSS provides structured decision-making capabilities. This approach supports robust system design, user interface development, and geographic data visualization. In practice, efficient data collection and stakeholder participation enable the system to effectively report and track irrigation conditions, promoting public participation, and fostering long-term infrastructure sustainability. The developed online monitoring system demonstrates its effectiveness by providing interactive map features and transparent reporting platforms, verified through successful implementation of testing scenarios. This system not only improves infrastructure management in areas like Batu City, but also sets a precedent for scalable participatory irrigation solutions, creating a foundation for future enhancements.

Effect of Irrigation System, Pesticide Use, and Human Resource Management on Productivity Growth of Oil Palm in Indonesia

Effect of Irrigation System, Pesticide Use, and Human Resource Management on Productivity Growth of Oil Palm in Indonesia

Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review.

This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this review, the automation of each component is examined in the irrigation management pipeline from data collection to application while analyzing its effectiveness, efficiency, and integration with various precision agriculture technologies. It also investigates the role of the interoperability, standardization, and cybersecurity of IoT-based automated solutions for irrigation applications. Furthermore, in this review, the existing gaps are identified and solutions are proposed for seamless integration across multiple sensor suites for automated systems, aiming to achieve fully autonomous and scalable irrigation management. The findings highlight the transformative potential of automated irrigation systems to address global food challenges by optimizing water use and maximizing crop yields.

Agronomic Performance and Technological Attributes of Sugarcane Cultivars Under Split-Irrigation Management

In addition to being an important instrument in the search for increasingly greater productivity, agricultural production with adequate use of irrigation systems significantly minimizes the impact on water resources. To meet high productivity and yield, as well as industrial quality, a series of studies on sugarcane cultivation are necessary. Despite being able to adapt to drought, sugarcane is still a crop highly dependent on irrigation to guarantee the best quality standards. Our study aimed to analyze the agronomic performance and technological attributes of two sugarcane cultivars, evaluating the vegetative and productive pattern, as well as the industrial quality of the cultivars RB92579 and SP80–1816, which were cultivated under split-irrigation management in the Sugarcane Research Unit of IF Goiano—Campus Ceres, located in the state of Goiás in the Central-West region of Brazil. A self-propelled sprinkler irrigation system (IrrigaBrasil) was used, duly equipped with Twin 120 Komet sprinklers (Fremon, USA). The cultivars were propagated vegetatively and planted in 0.25 m deep furrows with 1.5 m between rows. The experiment was conducted in a completely randomized design (CRD), with a bifactorial split-plot scheme (5 × 2), with four replications, where the experimental plots were subjected to one of the following five split-irrigation management systems: 00 mm + 00 mm; 20 mm + 40 mm; 30 mm + 30 mm; 40 mm + 20 mm; or 60 mm + 00 mm. At 60 and 150 days after planting (DAP), the following respective irrigation management systems were applied: 00 mm + 00 mm and 20 mm + 40 mm. Biometric and technological attributes, such as plant height (PH) and stem diameter (SD), were evaluated in this case at 30-day intervals, starting at 180 DAP and ending at 420 DAP. Measurements of soluble solids content (°Brix), apparent sucrose content (POL), fiber content (Fiber), juice purity (PZA), broth POL (BP), reducing sugars (RS), and total recoverable sugars (TRS) were made by sampling stems at harvest at 420 DAP. RB92579 showed total recoverable sugar contents 11.89% and 8.86% higher than those recorded for SP80–1816 under split-irrigation with 40 mm + 20 mm and 60 mm + 00 mm, respectively. Shoot productivity of RB92579 reached 187.15 t ha−1 under split-irrigation with 60 mm + 00 mm, which was 42.16% higher than the shoot productivity observed for SP80–1816. Both cultivars showed higher qualitative and quantitative indices in treatments that applied higher volumes of water in the initial phase of the culture, coinciding with the dry season. Sugarcane cultivar RB92579 showed a better adaptation to the prevailing conditions in the study than the SP80–1816 cultivar.

A case study of canal seepage quantification using gain/loss method and electrical resistivity tomography in an intensively managed water resource system in the Treasure Valley, Idaho, United States

Monitoring groundwater-surface water (GW-SW) interactions is essential for effectively managing the available water resources in semi-arid and arid environments. The focus of this study was to quantify how much SW is being exchanged with the shallow GW aquifer in the Treasure Valley (TV), Idaho, USA. Previous water budgets estimated regional canal seepage without incorporating canal variability and flow measurement uncertainty. To address this, we applied both direct (gain/loss) and indirect (electrical resistivity tomography (ERT)) techniques. Direct seepage measurements were taken on canals capturing a range of different characteristics in the TV. The discrete measurements were then used to estimate the total seepage anticipated to be lost from these canals during the irrigation season. Our findings showed high seepage variability across the canals. The ERT inversion approach was utilized before and after the irrigation season by applying an advanced inversion scheme to better constrain the canal seepage spatial variability and uncertainty by quantifying changes in the saturated zone with 2D-ERT results. Temporal changes in the subsurface resistivity were observed due to the lateral flow from the nearby surface canal during the irrigation season. The combination of these approaches improves our understanding of SW-GW interactions in intensively managed irrigation systems.

IoT-based smart irrigation management system to enhance agricultural water security using embedded systems, telemetry data, and cloud computing

Agriculture, the key sector for food production, faces challenges exacerbated by the growing global demand for food and the scarcity of water resources. Traditional irrigation methods often lead to inefficient use of water, resulting in wastage. Moreover, unpredictable weather conditions and the need to adopt sustainable farming practices are driving us to develop advanced irrigation systems. This paper proposed a smart irrigation management system using new technology like 1) embedded systems, 2) Internet of Things (IoT), 3) telemetry data, 4) cloud computing, communication protocol, and 5) sensors to collect and process real-time data of the smart agriculture. This new technology and intelligent algorithm are used in this paper to improve agricultural practices. The architecture of the proposed scheme comprises three layers: 1) IoT devices, 2) ThingsBoard cloud, and 3) the dashboard, each playing a pivotal role in ensuring seamless data flow, secure communication, and enhanced user interaction. The algorithm orchestrates system operations, incorporating sensor data reading, JavaScript Object Syntax (JSON) payload creation, and secure telemetry data transmission via Hypertext Transfer Protocol (HTTP) protocol to the ThingsBoard cloud. Pump activation decisions are governed by predefined thresholds, preventing an over-irrigation system. The evaluation of system performance involves deploying temperature, humidity, moisture, and water level sensors in a testing field. Further, before data is sent to the cloud, sensor values are calibrated using a functional map. Tests carried out with temperature, humidity, and water level sensors demonstrate the system's dynamic efficiency. By visualizing and analyzing environmental information via ThingsBoard, the results provide real-time data on environmental parameters of the system proposed, improving the efficiency of water use and the sustainability of farming practices. In addition, the system features e-mail notifications for alerts. The integration of e-mail notifications reinforces monitoring and management practices by alerting farm owners and users. This study showcases the efficacy of the proposed paper in enhancing sustainability, water usage, and supporting smart agriculture practices. Further, this paper integrates embedded systems, IoT, cloud computing, and advanced algorithms to optimize and enhance crop productivity and contribute to global food and water security.

Automated irrigation management system for tree species seedlings using weighing mini-lysimeters

Automated irrigation management system for tree species seedlings using weighing mini-lysimeters

Drainage lysimeter based measurement of water requirement and crop coefficient of bread wheat under semi-arid climate of Melkassa, Ethiopia

In view of the changing climate and growing global food demand, efficient water management is crucial for sustainable agriculture. Accurate measurement of evapotranspiration is essential for determining crop water demand and consequently for designing and managing irrigation systems. This study, conducted at Melkassa Agricultural Research Center in Ethiopia, utilized a drainage lysimeter to investigate the water requirements and crop coefficients of the Kingbird wheat variety during the December to March cropping season in 2021/22 and 2022/23. The experiment involved planting Kingbird wheat both inside and outside the lysimeter and irrigating using a watering can. Neutron probe measurement monitored the water balance in the soil. The study determined an average crop evapotranspiration of 427.28 mm and a reference evapotranspiration of 471.30 mm indicating a water requirement of 4273 m³ to fully grow wheat on a hectare of land. The derived average crop coefficient values were 0.43, 0.93, 1.15, and 0.30 for the initial, mid-season, and end growth stages, respectively. Furthermore, a fifth-order polynomial function was developed to predict crop coefficient values based on days after sowing. The findings provide valuable insights for enhancing the design and management of irrigated wheat production in the region. The specific crop coefficient values determined for different growth stages are crucial for optimizing irrigation scheduling and improving water-use efficiency, contributing to sustainable wheat production in semi-arid environment.

Irrigation technologies and management and their environmental consequences: Empirical evidence from Ethiopia

The main objective of this study is to understand the interlinkages between different irrigation technologies and management systems and environmental outcomes. We use a unique and comprehensive household and plot-level dataset covering ten districts of Ethiopia complemented with remotely sensed data and qualitative information collected from the study sites. The econometric results show that compared to open-access plots equipped with pump irrigation, other irrigated configurations, and especially private groundwater-based systems, have higher vegetation cover and show less susceptibility to the most common environmental concerns mentioned in the survey regions: water logging, soil salinity, and erosion externalities.

An adaptive neuro-fuzzy inference system-based irrigation sprinkler system for dry season farming

In recent years, the management of irrigation systems has emerged as one of the most pressing concerns in the agricultural industry, especially in areas that experience dry seasons. In this research, an adaptive neuro-fuzzy inference system (ANFIS)-based irrigation system that uses a hot and cold sprinkler mechanism is presented. The goal of the system is to reduce the amount of water needed for farming and increase crop output during dry seasons. Adaptive control of water release is achieved via the use of MATLAB and the ANFIS model. This is done in response to changes in soil moisture, ambient temperature, and crop water demand. According to the findings, the suggested system performs noticeably better than conventional irrigation methods in terms of both the amount of water used and the number of crops produced.

Hybridization of deep learning, nonlinear system identification and ensemble tree intelligence algorithms for pan evaporation estimation

A reliable pan evaporation (Epan) estimation over a daily scale is vital for sustainable water and agriculture management, especially for designing water use allocations, irrigation system management, and demand and utilization assessments. However, designing a reliable computational methodology is a challenging task for Epan estimation due to its stochastic and random characteristics. In this study, the potential of five machine learning (ML) models namely Hammerstein-Weiner (HW), Random Forest (RF), Boosted Regression Tree (BRT), Long Short-Term Memory (LSTM), and Stepwise Linear Regression (SWLR) models were evaluated for Epan modeling in Lake Hawassa catchment, Ethiopia. Afterward, hybrid SWLR-LSTM, SWLR-RF, SWLR-BRT, and SWLR-HW models were developed to benefit from the strength of the non-linear and linear characteristics of the models. The estimation results were evaluated using determination coefficient (R2), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE) and Root Mean Square Error (RMSE), Index of Agreement (IA) and Kling-Gupta efficiency (KGE). The performance analysis result of individual models showed that the RF model led to more accurate estimation with NSE = 0.938, IA = 0.956, R2 = 0.94, KGE = 0.852, RMSE = 0.483 mm/day and Pbias = -1.193 mm/day during the validation phase. Among the hybrid models, SWLR-RF provides the best predictive performance improving SWLR, HW, LSTM, BRT and RF by 26.563 %, 21.348 %, 15.577 %, 9.091 % and 3.514 %, respectively based on the validation phase NSE value. Generally, the results of the current study demonstrated the capability of deep learning, ensemble tree and hybrid linear-nonlinear models for Epan estimation in the data-scarce catchment.

Efficient Soil Health Monitoring and Irrigation Management System Using Fuzzy Logic

Abstract: The need for sustainable agriculture is acutely felt due to ever increasing global water crisis. Efficient irrigation management is crucial in this regard. The proposed system employs a network of soil moisture sensor distributed over a agricultural field, where continuous acquisition of real time data on soil moisture levels is done. Soil moisture measurement is a vital parameter in terms of precision agriculture, which affects the irrigation scheduling for ensuring optimal crop growth. Traditional methods related face challenges adapting to changing soil patterns at different conditions. The system proposes application of fuzzy logic algorithm for processing of data. Key advantages of using fuzzy logic in soil moisture measurement include improved precision in moisture detection, enhanced ability to make more informed irrigation decisions. The proposed system delivers a model for promoting water conservation, enhancing crop productivity and maintaining soil health.

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.

Farmland mulching and optimized irrigation increase water productivity and seed yield by regulating functional parameters of soybean (Glycine max L.) leaves

In both arid and semi-arid regions, adopting field mulching can effectively optimize soil moisture distribution, enhance crop yields, and improve water productivity. While acknowledging its advantages, field mulching seems insufficient for maintaining high crop productivity due to the increasing frequency of extreme weather. Furthermore, drought often coincides with critical crop growth stages, necessitating the implementation of agricultural irrigation to ensure normal crop growth. Accordingly, we conducted a three-year field experiment from 2021 to 2023 including three typical field mulching methods (no mulching, NM; straw mulching, SM; plastic film mulching, FM) and three supplementary irrigation strategies (irrigated at the branching stage (V4), W1; irrigated at the pod-filling stage (R2), W2; irrigated at both the V4 and R2 stage, W3). Throughout the entire growth period, we monitored soil moisture conditions for each treatment, measured leaf physiological parameters at crucial growth stages, and assessed soybean yields and water productivity (WP). Our findings indicated that, relative to SM and NM, FM maintains optimal soil moisture balance, augments chlorophyll content, and enhances photosynthesis, resulting in an average yield increase of 17.0% and 38.3% over three growing seasons. Additionally, supplementary irrigation also significantly affects the growth and seed yield of soybean. FMW2 achieved the higher seed yield (4307.5 kg ha−1, 3-year averaged), had insignificant difference with the highest seed yield of 4568.6 kg ha−1, both significantly higher than other treatments. Similarly, the leaf area index, chlorophyll content, net photosynthetic rate (Pn) and transpiration rate (Tr) also presented insignificant difference between FMW2 and FMW3, while WUEleaf (Pn/Tr) of FMW2 obviously higher than that of FMW3. As a result, FMW2 achieved the highest WP of 12.2 kg ha−1 mm−1 over the three growing seasons, compared to the three-year average of the other treatments, the increase ranges from 5.6% to 46.7%. In summary, the FMW2 treatment optimized water distribution to meet the water demands of soybeans during the reproductive growth stages, achieving a beneficial balance between soybean seed production and WP by regulating leaf functional parameters. Future research will explore more specific irrigation scheduling techniques (e.g., precision irrigation, deficit irrigation, and sensor-based irrigation management systems) while integrating innovative agricultural film materials (e.g., biodegradable films) to further enhance crop resilience and productivity under evolving climatic conditions.

Defining the optimization strategy for solar energy use in large water distribution networks: A case study from the Valle Inferior irrigation system, Spain

Irrigation modernization in Spain has brought significantly increased energy requirements. To provide the needed power, solar energy is rising in popularity. However, matching the availability of solar energy to irrigation demand remains a challenge. In the Valle Inferior irrigation system, while enough solar energy is produced on an annual basis, only 52 % of the energy consumed for irrigation is from own solar production, mainly due to the practice of night-time irrigation. For this system, this study explores two solar energy optimization scenarios. The first aims to optimize use of own solar energy by adjusting the current 24-h irrigation schedule to 8 or 12 h. The analysis finds that in situations where water availability is limited, this change can enable the annual percentage of own solar energy to be increased to 98 % of the total energy consumed. Nonetheless, such optimization of solar energy use gives rise to difficulties in the system itself, as farmers prefer to irrigate at night. In the second scenario, economic profits are optimized with the sale of excess solar energy. In this scenario, besides water availability and irrigation schedule, the optimum also depends on energy prices. Optimization is thus found to differ, depending on the optimization target, as well as water availability and the irrigation schedule used. This poses new operational challenges for large irrigation system management.

Determinants of Farmers’ Acceptance of the Volumetric Pricing Policy for Irrigation Water: An Empirical Study from China

Volumetric-based pricing for irrigation water was introduced as part of a comprehensive reform of agricultural water prices in China. However, operational deficiencies and farmers’ lack of willingness to adopt the volumetric pricing policy (VPP) hinder the coordinated implementation of the reform. To address these practical challenges, we employed a binary logistic regression model to analyse farmers’ acceptance of the VPP for agricultural irrigation water usage in Suqian City, Jiangsu Province. A variable set was formed by selecting potential variables from four types of influencing factors: the subject (water users), the object (water supply departments), natural condition factors, and social condition factors. Our results revealed seven factors that determine whether farmers accept the VPP: irrigation water measurement at the water inlet of a lateral canal, the irrigation water-saving rewards scale, enforcement efforts of charging by volume, the irrigation water source type, the use of agricultural water-saving for trade, financial investment in water-saving technology, and the level of irrigation water pricing. We determined the degree of influence of the seven determining factors, among which the irrigation water-saving rewards scale and enforcement efforts of charging by volume most influence farmers’ decisions on the VPP for irrigation water. The results of this study can be used as a reference for innovation of the agricultural water-saving system in Suqian City, optimisation of an accurate fiscal subsidy scale, quantification of irrigation water rights, optimisation of the measurement facility layout, and effective implementation of agricultural water rights trading. More broadly, this study provides a valuable reference for solving the difficulties faced in the comprehensive reform of agricultural water pricing in China, which includes irrigation water pricing mechanisms, management systems, subsidy mechanisms, and water-saving incentive measures.

DEVELOPMENT OF A GIS-BASED SYSTEM FOR IRRIGATION MANAGEMENT AND FLOW DISTRIBUTION

Abstract. The systematic management of irrigation operation of medium and large gravity irrigation systems (IS) significantly affects the delivery efficiency of irrigation from the source to the farm level. An interactive spatial database that integrates system data handling and irrigation distribution assessment functionality can be implemented, which has not yet been developed in existing NIS in the Philippines. This study proposes a framework model in developing a geographic information system (GIS) database for irrigation systems that integrates a GIS-based irrigation network distribution analysis model. The creation of a model framework for the development of a functional GIS-based database for IS management and flow distribution is comprised of five stages namely: (1) selection and determination of significant irrigation system data, (2) determination of required estimation parameters, (3) GIS development, (4) irrigation network distribution model development, and (5) selection of applicable water distribution assessment techniques. The GIS-based database and integrated model for irrigation provide essential data records of the irrigation system that could easily be viewed systematically and integrated into any GIS. In addition, data packages can easily be shared and run even on low-end computers. Features of the proposed irrigation model include flow volume simulation, time of delivery simulation, irrigation scheduling, remote sensing model integration for automated evapotranspiration computation, volumetric assessment, assessment of the timing of irrigation, and distribution optimization.

Water scarcity conflicts in a community managed irrigation system in Northern Tunisia: Supporting dialogue and negotiation

Water scarcity conflicts in a community managed irrigation system in Northern Tunisia: Supporting dialogue and negotiation

Empowerment of farmers through participatory irrigation management in Bihar, India

AbstractPoorly performing irrigation schemes in India require technical, managerial and institutional support on an urgent basis. In recent years, participatory irrigation management (PIM) has gained momentum as a strategy to improve the performance of irrigation systems, increase water use efficiency and promote equitable water distribution. This approach involves the active involvement of farmers and other stakeholders in the planning, implementation and management of irrigation systems. The state of Bihar has primarily an agrarian economy and irrigation is essential for agricultural production. However, Bihar's irrigation potential is being underutilized due to improper management of irrigation systems. In Bihar, the experiment of PIM in the Paliganj distributary of the Sone irrigation scheme was attempted as a local initiative by WALMI Patna, which was funded by USAID, and, thus, the need for an overall state policy and strategic plan was realized. Therefore, PIM has been implemented through the formation of water users' associations (WUAs) and farmers’ organizations (FOs) at different levels under the Bihar Irrigation Act of 1997, and Bihar Irrigation, Flood Management and Drainage Rules, 2003. The Bihar model of PIM is based on the macro‐to‐micro approach because of its geographical conditions and the size of the schemes in the state. This paper examines the implementation of PIM in Bihar and its impacts on farmers.