Management Of Irrigation Systems Research Articles

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.

An Energy-Efficient Cluster-based data aggregation for agriculture irrigation management system using wireless sensor networks

Wireless Sensor Network (WSN) are becoming one of the demanding platforms, where sensor nodes are sensing and monitoring the physical or environmental conditions and transmit the data to the base station via multi-hop routing. Agriculture sector also adopted these networks to promote innovations for environmentally friendly farming methods, lower the management cost, and achieve scientific cultivation. This combination can improve crop yield, quality, and efficiency while saving energy and water. It is important to note that irrigation is one of the essential factors for agricultural success. In an agriculture irrigation application, a WSN is deployed in a specific area to control the irrigation in the field. But using WSN will generate massive redundancy in the data processing. So, there is lacking an efficient system for extensive sensor data processing. This research work presents an Energy-Efficient Cluster-Based Data Aggregation (E2CDA) Technique for agriculture irrigation systems in WSN. An improved probabilistic clustering method is used for preserving the optimal number of Cluster Head (CH) in each round of iteration. The proposed work is implemented in MATLAB, according to simulation results; the developed E2CDA method provides better result when compared with the existing approaches such as Low Energy Adaptive Clustering Hierarchical (LEACH) routing and centralized LEACH (LEACH-C) in terms of energy savings, network lifetime, packet delivery ratio, end-to-end delay, and packet drop ratio.

IoT-based solar-powered smart irrigation system with solar tracker for rice fields

Water management in irrigated agricultural fields is a critical issue in Bangladesh due to a lack of improved irrigation facilities for efficient use of irrigation water, and the cost of crop production is high with low agricultural productivity in agriculture. Therefore, this research aimed to design and implement an internet of things (IoT)-based solar-powered smart irrigation system by sensing the soil moisture levels and periodically controlling the pump operations when the soil moisture is below the required level. A sun-tracking solar system was developed to generate more electricity by tracking the orientation of the sun. A comparison was made between the energy of a fixed-panel solar system with a 24.95° tilt angle and a solar tracking system with no tilt angle. The solar tracker device utilizes a Light Dependent Resistor (LDR) sensor to track the sun, aligning the 150 W solar panel perpendicular to maximize energy capture. This energy is converted into electrical energy and stored in a 12V battery for powering the operation of the submersible pump (DC 12V, 150 W). A wireless sensor network was employed to monitor pumping status and energy comparison in a rice field. A sub-station transferred water level data wirelessly to a main station, where a microcontroller processed the data. The relay module automatically triggered pumping, and the data was stored in the cloud. The solar tracking system outperformed the fixed-panel system with a fixed tilt angle of 24.95° in terms of output power by a remarkable 26.72%. There is a huge potential for solar irrigation systems in Bangladesh, and they can provide sustainable solutions with low irrigation costs. Therefore, this IoT-based solar-powered smart irrigation system could play a vital role in improving irrigation management systems with increased water use efficiency.

Analysis of Modernization Readiness of Irrigation in The Downstream Brantas River Basin Under The Authority of The Central Government

In carrying out the management of the irrigation system has obstacles are inadequate infrastructure conditions caused, the irrigation infrastructure has exhausted its technical age which causes the decline in the performance function of the irrigation network. Government to farmers has not been carried out intensively. In an effort to overcome these obstacles, in addition to the operation, maintenance and rehabilitation required a renewal thoroughly, both institutional, technical, managerial, and human resources. Modernization irrigation is an effort to realize a participant irrigation management system that is oriented toward fulfilling irrigation service levels in an effective, efficient, and sustainable. Limited time, cost and human resources are obstacles in the implementation of irrigation modernization, so an assessment is needed to measure the level of readiness of an irrigation area in carrying out irrigation modernization activities. This result of analyze the readiness of irrigation modernization show that Mrican Kanan area is ready for irrigation modernization with IKMI value 81,69%. Meanwhile DI. Mrican Kiri, DI. Siman, DI. Menturus, DI, Delta Brantas needs maintenance around 1- 2 years. DI. Mrican Kanan can start from the Peterongan Secondary Channel. SI. Pare Peterongan, SI. Tunggorono, SS. Melik dan SS. Sentul.

ESTIMATION OF SOIL WETTING PATTERN USING DIFFERENT IRRIGATION REGIMES IN CONJUCTION WITH SUPER ABSORBENT POLYMER

The design and management of irrigation systems require soil water movement and distribution knowledge. Finding the actual ability of drip irrigation to supply sufficient water uniformly and timely requires optimizing the operational management parameters. The objective of the study was to estimate the water distribution pattern with drip irrigation under different irrigation frequencies, durations and Belsap super absorbent polymer. A factorial experimental design layout of drip irrigation system was set up in Noiwet, Mogotio Sub County, Baringo County. The same quantity of water with three irrigation frequencies were applied once daily, after two days and three days and three durations which were full, half and a third the irrigation time. The total number of treatments were 18 with three replications making a total of 54 experiments. The wetted depths and widths of the soil were measured directly in the field using a ruler immediately after irrigation. ANOVA statistical analysis was carried out to determine the significance of the treatments on the soil wetting pattern of the clay soil. The results from the study revealed that the super absorbent polymer had no effect on the soil wetting pattern while the decrease in the irrigation frequency and increase in irrigation duration led to an enlargement in both the diameter and depth of the soil wetted zone. Information on depths and widths of wetted zone of soil under drip irrigation have a great significance in the design and management of drip irrigation system for delivering required quantity of water and chemicals to plants.

Influence of the sampling time interval of canopy temperature on the dynamic zoning of variable rate irrigation

Generating spatial distribution maps of canopy temperature (Tc) is the basis for the dynamic management of a variable rate irrigation (VRI) system based on infrared thermometers (IRTs). To improve the accuracy of management zone delineation based on Tc maps for summer maize and winter wheat with IRTs mounted on a moving sprinkler irrigation system, the influence of sampling time interval of Tc was studied and the VRI prescription maps were compared with those obtained from unmanned aerial vehicle (UAV) thermal imaging on the same day. The experimental sites were located in Shunyi, Beijing, for summer maize and in Dacaozhuang, Hebei Province, for winter wheat. The sampling time intervals were selected as 0.5–26 min. The results demonstrated that the nRMSE of the predicted and measured values for Tc increased and that their correlation coefficient decreased with the extension of the sampling time interval. When the sampling time interval ranged from 0.5 to 5 min, the correlation coefficient and nRMSE between the predicted and measured values for Tc changed little, and the overlap percentage of the same management zones delineated with NRCT between 5 min and the minimum sampling time interval (0.5 or 1 min) was greater than 79%. For any two measuring dates of Tc, their overlap percentage changed greatly, with a maximum value of 67% for NRCT. The average irrigation amount based on the prescription maps of the IRTs system and UAV thermal imaging system on the three observation days was equal. Our results demonstrated techniques to collect IRTs data and to generate VRI prescription map based on this data, and a sampling time interval of no more than 5 minutes was recommended for Tc.

OPTIMAL DESIGN OF A PUMPED IRRIGATION SYSTEM OPERATING IN SHIFTS

The optimization of pumping systems is important because it improves energy efficiency, increases production, reduces operating and maintenance costs, and contributes to sustainability. To address this challenge, it is essential to effectively manage irrigation systems. In this research, an optimization algorithm was used to minimize the economic cost of irrigation system design. The implemented methodology includes the following steps: determining the fictitious continuous flow rate (qfc) and the number of shifts, functionally designing the irrigation network with pumping operations in shifts, and finally designing the network using Linear Programming (LP). Inputs to the model include network topology, set point pressures, design speed ranges, and pump characteristic curve. The model output provides the diameters of the irrigation system lines. In consequence, this methodology was applied in the irrigation system of the community of San Pedro de la Bendita in the province of Loja. The study demonstrates a significant cost reduction in the hydraulic design of the irrigation network when operating in shifts using LP compared to the cost of a conventional hydraulic design.

Sustainable Water Management of Drip-Irrigated Asparagus under Conditions of Central Poland: Evapotranspiration, Water Needs and Rainfall Deficits

Water is a valuable yet scarce resource in agriculture. Optimizing crop production relies on irrigation, but within the framework of sustainable agriculture, efforts should prioritize reducing irrigation water usage. Unfortunately, climate change increases plant water requirements, consequently heightening the need for irrigation. The aim of the research was to estimate the water needs of asparagus during the fern growth period (21 June–31 August) using the Blaney–Criddle method. The study also aimed to determine trends in changing water needs and estimate precipitation deficits. Calculations were carried out for the years 1981–2020 across four provinces in central Poland. The research indicated that water needs varied between 233 mm and 242 mm, depending on the province. Over the forty-year period, all provinces displayed an increasing trend in water needs, with a rise of 3.1 mm to 6.2 mm per decade. Between 21 June and 31 August, rainfall deficits occurred with varying intensity in all provinces during normal, medium dry and very dry years. The values of water needs and rainfall deficit, calculated as a result of the research, are of key importance in the design, construction, and management of drip irrigation systems for asparagus plantations in central Poland.