Irrigation Water Management Research Articles

Assessment of Canal Storage Dynamics for Sustainable Water Management in Thrissur North Kole Lands

Aims: The aim of this study is to estimate the canal storage capacity in Thrissur North kole lands to support effective water management for irrigation, drainage and flood control. Study Design: The study involves estimating canal storage in Thrissur North kole lands using water depth measurements, bed slope data and trapezoidal rule calculations, conducted on a decadal basis. Place and Duration of Study: The study was conducted in Thrissur North kole lands, located in Kerala, India, during 2022. Methodology: Accurate estimation of canal storage capacity in Thrissur North kole lands is essential for managing water resources to support irrigation and mitigate floods. The study measured water depths in canals while the Enamakkal and Idiyanchira regulators were fully closed and calculated depth variations along the canals using bed slope data and measurements taken at multiple points (start, middle, and end). All canals were digitized within an ArcGIS environment to determine their total length. The trapezoidal rule was applied to estimate the storage capacity of each canal, enabling an accurate evaluation of the canal system's overall storage potential. Results: The total canal length in the Thrissur North kole lands was determined to be 77.02 km. Decadal storage capacity estimations for 2022 revealed a range of 29.0 to 53.86 Mm³, with an average storage capacity of 42.40 Mm³. Additionally, a relationship between the water level at the Enamakkal regulator and canal storage capacity was developed. Conclusion: The study concludes that accurate estimation of canal storage capacity using water depth measurements, bed slope data, and trapezoidal rule calculations offers valuable insights for optimizing water management strategies, ensuring effective irrigation and mitigating flood risks in the Thrissur North kole lands.

Modelling of Cowpea Response to Varying Irrigation and SAP Levels Under Greenhouse Conditions Using AquaCrop

ABSTRACTIrrigation water management is key to promoting resilient crop production amidst the increasing effects of climate change. Super absorbent polymer (SAP) is an innovative technology for conserving irrigation water within arid and semi‐arid lands (ASALs). AquaCrop, a water productivity model, has been used for irrigation water management. This study aimed to model the response of cowpea to various irrigation and SAP levels under greenhouse conditions via AquaCrop. The experiments included five SAP application rates [0% (control), 0.05%, 0.1%, 0.15% and 0.2% to weight of the soil substrate] and five irrigation levels [full irrigation (FI), and 80%, 70%, 60% and 30% FI]. The AquaCrop model simulations matched the observed values with a normalized root mean square error of less than 16%. The simulated yield and biomass for full irrigation and 0% SAP were 5.1 and 11.0 t/ha, respectively, whereas the observed values were 5.3 and 10.8 t/ha, respectively. Treatments with SAP showed increased yield, biomass, water productivity (WPET) and harvest index (HI) with increased water stress up to 137%, 17%, 10% and 117%, respectively. Thus, SAP can improve crop production, and the AquaCrop model is suitable for improving irrigation water productivity within ASALs where water is scarce.

Evaluation of Micrometeorological Models for Estimating Crop Evapotranspiration Using a Smart Field Weighing Lysimeter

Accurate estimation of crop water use, which is expressed as evapotranspiration (ET) is an important task for effective irrigation and agricultural water management. Although direct field measurement of actual evapotranspiration (ETa) is the most reliable method, practical and economic limitations often make it difficult to acquire, especially in developing countries. Consequently, crop evapotranspiration (ETc) is calculated using reference evapotranspiration (ETo) and crop-specific coefficients (Kc) to support irrigation water management practices. Several ETo models have been developed to address varying environmental conditions; however, their transferability to new environments often leads to under or over estimation of ETo, which has an impact on ETc estimation. This study evaluated the accuracy of 30 ETo micrometeorological models to estimate ETc under different seasonal and micro-climatic conditions using ETa data directly measured using a smart field weighing lysimeter as a benchmark. Local Kc values were derived from field-based measurements, while statistical metrics were applied for the evaluation process. A cumulative ranking approach was used to assess the accuracy and consistency of the models across four cropping seasons. Results demonstrated the Penman–Monteith model to be the most consistent model in estimating ETc, which outperformed other models across all cropping seasons. The performance of alternative models differed significantly with seasonal conditions, indicating their susceptibility to seasonality. The findings demonstrated the Penman–Monteith model as the most reliable approach for estimating ETc, which justifies its application role as a benchmark for validating other ETo models in data-limited areas. The study emphasizes the importance of site-specific validation and calibration of ETo models to improve their accuracy, applicability, and reliability in diverse environmental conditions.

Evaluate the integrative effects of irrigation water level, furrow irrigation methods, and nitrogen fertilizer rate on tomato yield in semi-arid southern Ethiopia.

This study investigates the integrative effects of irrigation water management allowable depletion (MAD), furrow irrigation methods (FIM), and nitrogen fertilizer application rate (NFAR) on tomato yield components. These yield components include marketable, unmarketable, and total yield. Additionally, the study examines crop agronomy components such as plant height, number of branches, and root depth in semi-arid Southern Ethiopia. The research employs a factorial and split-plot design. Three types of FIM are assigned as the main plot, while three levels of irrigation water (50%, 75%, and 100% MAD) and four levels of NFAR (0%, 50%, 100%, and 150% NFAR) serve as the subplots, with three replications. The study results reveal significant impacts of these factors. All p-values for tomato yield and agronomy-measured components are less than 0.05. FIM, irrigation water level, and NFAR interaction affect marketable, unmarketable, and total tomato yield. The results ranged from 2.3 tons ha⁻1-43.9 tons ha⁻1 for marketable yield, 0.8 tons ha⁻1-8 tons ha⁻1 for unmarketable yield, and 6.3 tons ha⁻1-45.2 tons ha⁻1 for total yield. The integration of 75% MAD with 100% NFAR under conventional furrow irrigation, and 100% MAD with 100% NFAR under alternative furrow irrigation, was most preferable. This approach resulted in no loss of nitrogen fertilizer and saved 25% of irrigation water without reducing tomato yield. However, the interaction of deficit nitrogen fertilizer rate application, with or without full application of MAD and conventional or other furrow irrigation methods, caused the total yield to decrease. CFIM and AFIM with 100% MAD and 100NFAR treatments yielded the highest profits, while the FFIM approach under 50%MAD and 0%NFAR conditions negatively impacted profitability. Therefore, by adopting the preferable practices, farmers can achieve higher productivity and sustainability in tomato cultivation. This approach addresses the challenges posed by water scarcity and nutrient limitations.

Assessment of irrigation water management using FAO-CROPWAT 8 in the Paliganj distributary of the Sone irrigation scheme in Bihar: a case study

ABSTRACT Around 15% of India's GDP comes from agriculture, which is the foundation of the country's economy. India's economic development and efforts to combat poverty have already benefited greatly from its agricultural sector. Sustainable management of water resources can increase agricultural productivity. The study performed in the Paliganj distributary is a part of the Sone canal system in south Bihar, India. The Paliganj distributary command area lies wholly in the Gangetic plain. The estimation of crop evapotranspiration is carried out using the modified Penman–Monteith equation for the period 1980–2015. The crop coefficient of the crops has been estimated by using a single crop coefficient and a dual crop coefficient. The crop water requirement, irrigation water requirement, and irrigation scheduling of crops grown in the study area are determined using CROPWAT 8.0 software. The results conclude that the maximum, minimum, and average values of ET0 were found to be 7.65, 1.31, and 3.86 mm/day, respectively. The crop water requirement and irrigation requirement of crops grown in kharif, rabi, hot weather, and annual seasons in the study area were lower by using dual crop coefficient.

Sustainability of Irrigation Practices and Water-Pricing Tools in Water-Stressed Tropical Countries: A Way Forward for Sustainable Water Governance

Increasing water stress threatens agriculture, livelihood, and sustainability in Tropical countries. Asian and Sub-Saharan African countries in the Tropical region are projected as the future hotspots of water scarcity due to rising population pressure and climate variability. Around 87 out of 180 countries will turn into ‘water-stressed’ and 45 countries into ‘absolute water scarcity’ by 2050. Irrigation water demand accounts for 70% of all freshwater withdrawals globally. Due to varying climatic, topographical, and governance conditions, the agriculture techniques in the 54 tropical countries are indigenous to the local conditions. The main objective is to review the challenges in irrigation water management in seven water-stressed Tropical countries among the 54 countries. These countries are identified based on the baseline country data mined from FAO’s AQUASTAT database. A systematic literature review focusing on sustainable irrigation practices, the economic effects of water-pricing tools, and the role of policies and institutions were studied. The discussion centered on environmental sustainability, financial sustainability, institutional sustainability, and the resilience of agriculture to climate variability impacts. Community-based interventions, successful financial models, and policy and institutional reforms were perceived from successful irrigation systems. Implementation of climate-smart agricultural practices, shifting to water-saving technologies and irrigation methods, empowering the institutions, and enforcing policies and regulations to restrict resource overuse are recommended solutions for Sustainable Water Governance.

Soil moisture retrieval from dual-polarized Sentinel-1 SAR data over agricultural regions using a water cloud model.

The accurate retrieval of soil moisture plays a pivotal role in agriculture, particularly in effective irrigation water management, as it significantly affects crop growth and yield. The present study mainly focuses on the robustly investigated capability of dual-polarized Sentinel-1 SAR-derived vegetation descriptors in the water cloud model (WCM) in surface soil moisture (SSM) retrieval over wheat crops. The vegetation descriptors used in the study are radar vegetation index (RVI), backscattering ratio, polarimetric radar vegetation index (PRVI), dual polarization SAR vegetation index (DPSVI), and dual polarimetric radar vegetation index (DpRVI). The results of the WCM model illustrate that all the models show acceptable results, which confirms that this vegetative descriptor can be useful to estimate the accurate soil moisture over the wheat crop in the study area, except for DPSVI. Furthermore, the results revealed that model performances gradually decrease as the crop enters the complex stages. In summary, the overall finding demonstrates that PRVI outperformed other models in terms of statistical indicators value for calibration (R2 = 0.728, NSE = 0.727, PBIAS = - 2.67%, and RMSE = 2.985%) and validation (R2 = 0.728, NSE = 0.684, PBIAS = - 13.666%, and RMSE = 4.106%). Thus, overall results proved that the WCM model has considerable potential to retrieve SSM over wheat crops from Sentinel-1 satellite data. This study will be beneficial for regional water resources managers for proper allocation of irrigation water, effective irrigation management, and enhancedirrigation efficiency within the regions.

Pemberdayaan Petani Pemakai Air (P3A) Dalam Pengelolaan Irigasi di Daerah Irigasi Baumata Kabupaten Kupang

Water as a renewable natural resource, has the most fundamental and essential role in the sustainability of human life in supporting various activities, such as: agriculture, clean water for households, industry, aquaculture, tourism, mining and electric power. One use of water is for agriculture as irrigation water. Irrigation water managed by the Dua Serangkai P3A Group in the Baumata Irrigation Area is experiencing operational and maintenance (OM) obstacles due to limited water and limited OM soft skill capabilities of tertiary irrigation canals. The aim of this service is that irrigation water can be managed well through empowering P3A, improving the soft skills of tertiary irrigation canals OM, drafting a tertiary irrigation canals OM manual, agreeing on a water management system and assisting in drafting P3A AD/ART. Management of irrigation water in the Baumata Irrigation Area through P3A empowerment has experienced an increase in knowledge of soft skills for tertiary irrigation canals OM, drafting a tertiary irrigation canals OM manual, agreed on a water distribution system from upstream to downstream by: rainy season planting rice, setting regulated water distribution schedules P3A according to each block, while in the dry season horticulture/mixed planting, daily water arrangements are regulated by the clan according to the agreed schedule as well as assistance in preparing P3A AD/ART.

Effect of Deficit Irrigation at Different Growth Stages on the Yield and Water Productivity of Tomato at Adami Tulu Agricultural Research Center

Improving irrigation water management and increasing water productivity are critical to address future water scarcity in arid and semi-arid areas. A promising strategy is to maximize water productivity by exposing crops to a certain level of water stress. The experiment was conducted on-site at the Adami Tulu Agricultural Research Center to study the effect of deficit irrigation at different growth stages on agronomic parameters as well as yield and yield components as well as water productivity of tomato plants. Treatments consisted of a factorial combination of full and three-deficit irrigation with four plant growth stages. The results showed that the interaction effect between deficit irrigation and different plant growth stages significantly affected plant height, fruit height, fruit diameter, fruit yield and water productivity. The highest plant height (75.23 cm), fruit length (84.56 mm), fruit diameter (77.10 mm), marketable fruit yield (48.64 t/ha) and total fruit yield (50.09 t/ha) were obtained under continuous full irrigation achieves levels. While the lowest plant height (54.43 cm), fruit length (55.92 mm), fruit diameter (50.04 mm), marketable yield (22.51 tons/ha) and total yield (28.14 tons/ha) at 60% Etc achieved in the middle were stage treatment. The highest water productivity of 7.85 kg/ha was achieved with the application of 80% ETc in the late season, while the lowest (4.61 kg/ha) was achieved with 60% ETc in the middle treatment phase. Therefore, the results of this study suggest that applying 80% ETc deficit irrigation in the late season stage is the best solution for water conservation without affecting tomato yield while improving water productivity under water-stressed conditions.

A comprehensive approach to an urban water reuse system to meet irrigation demand and conserve water: City of Cape Coral, FL, USA

ABSTRACT Maximizing the reuse of domestic wastewater is a critical objective, spurred by the pressing need to avoid water loss, reduce environmental discharges, and manage irrigation water demand. In the City of Cape Coral (FL, USA), thousands of old-fashioned and inefficient irrigation systems have been installed over the past 50 years. The city has adopted year-round external water use restrictions, but demands continue to increase. In 2020, peak 24-h irrigation water demands exceeded 177,914 m3. In response to recurrent dry seasons and growing demand over the past 20 years, the city has diversified its water resources. The city uses brackish groundwater with reverse osmosis treatment to produce drinking water and reuses 100% of its wastewater effluent for irrigation by blending the treated wastewater with fresh water from 483 km of freshwater canals. To promote efficient irrigation water use among consumers, a new approach has been adopted to automate private irrigation systems. The Environmental Resource Assessment and Management System Integrated Urban Water Model was used to simulate water management scenarios. The simulation showed that increasing automatic irrigation efficiency can save millions of cubic meters of irrigation water and help the city meet the future build-out peak-day demand of 219,758 m3/d, as estimated by the Blaney–Criddle method.

Development and evaluation of site-specific evapotranspiration models in Malawi through a comparative analysis of existing models

Locally based information on reference evapotranspiration pertinent to efficient irrigation water management is scarce in developing countries. This compromises the accuracy in determining irrigation crop water requirements since most existing reference evapotranspiration models are empirically based and less accurate unless calibrated to local conditions. The FAO Penman-Monteith equation has been proven to estimate reference evapotranspiration for different environments worldwide. However, its intensive data input requirements impede utilization of the model in developing countries due to inadequate climate data collection and management capacities. Therefore, a need existed to develop site-specific models that are less data intensive. A study was conducted aimed at developing site-specific evapotranspiration models using comparative analysis of the Kharrufa, Linacre, Hargreaves Original and Hargreaves Modified empirical models during the wet and dry season. Performance of each model was compared with the FAO Penman-Monteith model, regarded as the standard model. The models whose Root Mean Square Error (RMSE), Mean Bias Error (MBE) and coefficient of determination (R2) were satisfactory were selected. The selected models were developed statistically through regression analysis. Performance of the Kharrufa model was satisfactory comparatively as observed from its RMSEs of 1.02 mm/day and MBE -0.34 to 0.8 for the dry season. However, the model less accurately estimated reference evapotranspiration during the wet season (RMSE 2.18 mm/day and MBE 1.03 to 2.13). The study recommends the use of the Kharrufa model in the study locations in dry seasons while utilization in wet season may require further studies to ascertain the model's useability and reliability.

Literature review on optimization of transboundary water for irrigation

ABSTRACT Transboundary water resources are essential for agricultural sustainability and regional development, and they are intrinsically linked to achieving the United Nations' SDGs and the water-food-energy nexus (WFE-NEXUS) concept. Despite challenges such as conflicting allocation and climate change impacts, effective transboundary water management for irrigation is crucial to meeting the SDGs of eradicating hunger, providing clean water and sanitation, offering affordable and sustainable energy, and taking climate action. This work synthesizes approaches to transboundary water for irrigation optimization, highlighting the significance of a holistic plan that considers both technical and social factors. Remote-sensing technologies, data forecasting, hydrology and hydraulic modelling, and water resource modelling all contribute to maximize water allocation and policy creation, particularly when paired with collaborative government features. This integrated approach to transboundary water for irrigation optimization fosters long-term development by improving livelihoods, resilience, and inclusive growth through efficient resource management.

Comprehensive Review of Crop Water Requirement Estimation Techniques: Approaches, Challenges, and Future Directions

In order to meet the growing demand for water, water scarcity is a significant issue that needs attention. Agriculture crops are not getting enough water because of this problem. Consequently, figuring out how much water a given crop needs requires using the right method. Accurate crop water requirements must be measured in order to schedule irrigation effectively, which in turn leads to efficient crop water management. To restore the lost moisture and promote the best possible growth for plants, irrigation is used. Water management and irrigation scheduling fundamentally depend on an accurate calculation of the crop's crop water requirements (ETc). Accurate evapotranspiration measurements are necessary for effective irrigation water management. Evapotranspiration (ET), a process that measures the amount of water lost from soil and crops through transpiration and evaporation processes, respectively, is dependent on a variety of meteorological factors. A significant factor in determining crop water requirements and irrigation schedules is reference evapotranspiration. There exist multiple theories and methodologies for estimating reference evapotranspiration, ranging from empirical to physical based. Reference Evapotranspiration is referred to the idea behind ET is to calculate ET based on a reference surface that is comparable to a deep surface of green grass that is consistently growing, completely covering the surface with enough water, and looking stable. The goal of irrigation futures is to choose a suitable model for estimating reference crop evapotranspiration. In order to calculate the amount of water needed for a crop, multiply ETo by the crop coefficient (Kc), which is dependent on the phases and length of a crop's growth. Regression, fuzzy logic, Penman-Monteith, Blaney-Criddle, Hargreaves, ANN and WNN, and other conventional and non-traditional methods are used to estimate ETo.

Innovative Solar Water Pump with Automatic Water Level Regulator for Efficiency Rice Field Irrigation

Nowadays, most of human activities are related to the use of technology. The invention of various innovative ideas that simplify human life is essential. One of the uses advancement technologies can applied in agricultural field, especially for irrigation processes since it becomes the critical step in agricultural activity since it ensures that plants receive an adequate water. Due to the importance of irrigation, this community service activity aimed to provide information on how to build a solar pump for irrigation purposes and implement an innovative solar water pump with automatic water level control to improve the efficiency of irrigation of rice fields in Bekiung Village. The system is designed using solar panels as the main energy source, minimizing dependence on conventional electricity and fossil fuels. With the integration of water level sensor and automatic control system, the pump is able to adjust the water supply in real-time based on the actual needs of rice plants. Field tests show that the system is able to provide water consistently and efficiently, reducing water and energy wastage. The implementation of this technology not only improves irrigation efficiency, but also supports environmentally friendly sustainable agriculture. The results of this study are expected to serve as a model for other villages facing similar challenges in irrigation water management.

Convolutional Neural Networks accurately predict soil matric potential from soil, weather, and satellite-derived data

Being able to predict soil moisture dynamics offers water managers the possibility to better plan irrigation events and prevent soil moisture deficits from reaching levels that reduce crop production. Machine learning (ML) model predictions can potentially assist farmers in managing irrigation water more efficiently. In this study, we aimed to assess the accuracy of a set of ML models in predicting soil matric potential seven days ahead in gravity-surface irrigated cotton paddocks and evaluate the models’ performance for longer term predictions (14 days). The ML models used past soil moisture, weather, and satellite-derived crop-related data as features for the input parameters. Input data were structured in tuples that were organised following a 20-day ‘window’ approach that ‘slid’ one position forward after each training round. A convolutional neural network (CNN) model outperformed a Long Short-Term Memory, Dense Multilayer Perceptron, and Linear Regression model, the latter of which produced the least accurate predictions. The accuracy of the soil matric potential predictions with the CNN model was stable over time (R2 ≥ 0.92 and root mean square deviation ≤ 7.5 kPa). However, less accurate predictions were obtained for a short period after emergence and at crop senescence. This study demonstrates the feasibility of producing accurate predictions of soil matric potential in cotton fields at 0.20 m soil depth with a CNN model, which can be integrated into irrigation decision support systems.

Examining the Barriers to Redesigning Smallholder Production Practices for Water-Use Efficiency in Numbi, Mbombela Local Municipality, South Africa

Smallholder farmers in South Africa face issues related to water shortages and poor irrigation water management. This study investigated barriers to improving water-use efficiency (WUE) in smallholder production practices in Numbi, South Africa. The objectives were to identify barriers in redesigning production practices for higher agricultural productivity and analyze the relationship between irrigation water supply and the adoption of WUE methods. From a population of 7696 people, 141 smallholder farmers were sampled using a simple random sampling technique through Taro Yamane’s sample size formula. The data were analyzed using Pearson’s correlation coefficient and descriptive statistics. Unreliable water supply (M = 3.78, SD = 0.85), poor soil water retention (M = 3.78, SD = 0.85), lack of water-efficient irrigation systems (M = 3.91, SD = 0.71), lack of water storage facilities (M = 3.85, SD = 0.93), limited access to credit (M = 4.09, SD = 0.85), income instability due to market fluctuations (M = 3.96, SD = 0.91), inadequate knowledge of irrigation management (M = 4.00, SD = 0.84), and harsh climatic factors were identified. A positive correlation (r = 0.339, n = 141, p < 0.001) between irrigation water source and WUE techniques was evident, indicating that irrigation water source had an insignificant impact on WUE methods. Resolving these barriers requires a holistic approach focusing on investments in irrigation infrastructure and targeted education initiatives by extension agents and other stakeholders, as this can enhance agricultural productivity.

An improved optical trapezoid model to identify wetlands and irrigation water ponds in the arid irrigation district using Google Earth Engine

ABSTRACT In arid regions, wetlands support ecosystem with high biodiversity and productivity. Meanwhile, irrigation during the fallow period often results in the temporary formation of water ponds in the cropland, which plays a critical role in agricultural productivity. The prompt identification of wetlands and irrigation water ponds in irrigation districts is important for the protection of local wetland ecosystem and the effective management of irrigation water. Utilizing the OPtical TRApezoid Model (OPTRAM) for soil moisture estimation, we have proposed an improved version of OPTRAM aimed to identify wetlands and irrigation water ponds in irrigation district using Sentinel-2 data through Google Earth Engine platform. Different criteria were used for the identification of wetland and irrigation water pond, given the greater diversity associated with wetlands. Parameters for the wet edge determined from OPTRAM were used to identify wetlands, while an additional threshold was added to the model and calibrated in accordance with the irrigated area to identify irrigation water ponds. The improved OPTRAM was applied in Hetao Irrigation District (HID) of Northwest China from 2016 to 2023, where autumn irrigation applied in late autumn after crop harvesting was considered. The identified distributions of wetland and autumn irrigation were validated with observations from field survey and statistical data, and were also compared with other remote sensing products. Results show that the proposed model is effective in identifying both wetlands and irrigation ponds. Considering the wetlands, the distances from identified boundaries to the actual boundaries obtained from field surveys were calculated, revealing the mean absolute error of 6.99 m, alongside a validation accuracy ratio of 0.94 for points located within wetlands. For autumn irrigation extent, the overall accuracy is 0.90 based on the observations from field survey, with mean absolute relative errors for irrigated areas across four sub-irrigation districts recorded as 20.55%, 8.10%, 12.83%, and 11.38%, respectively, when compared with statistical data. The majority of wetlands within HID are small in size and scattered, with different trends observed in the wetland areas of sub-irrigation districts and Wuliangsuhai Lake. Autumn irrigated croplands are mainly concentrated in Jiefangzha sub-irrigation district while being scattered across other sub-irrigation districts, depicting an overall decreasing trend in the autumn irrigated area. In summary, the proposed model performed well in identifying both wetlands and irrigation ponds, and can offer valuable support for the conservation of wetland ecosystem and irrigation management.

Simulating Agricultural Water Recycling Using the APEX Model

Irrigation plays a vital role in many agricultural crop production regions. Drainage water recycling (DWR) is a popular irrigation water management system that collects excess water drained from cropland fields and stores it in on-site reservoirs for reuse. The efficacy of these systems varies by location, climate, irrigation frequency, and crop demands. Simulating this system would be beneficial for assessing the impact of water and land management practices on agriculture and natural resources. This study presents the development of computational algorithms for DWR simulation with the Agricultural Policy Environmental eXtender (APEX) model, along with the results for 39 testing sites where both reservoir and drainage systems are adopted. Simulating a DWR system with the revised reservoir module, the APEX model simulates irrigation water reuse ranging between 29% and 93%; sediment reduction of around 66%; nitrogen loss reduction of 23% and 73% for the mineral and organic forms, respectively; and phosphorus loss reduction of 22% and 79% for the soluble and sediment-transported forms, respectively. In conclusion, the results provided by the APEX model for sediment loss reduction align with field data, but discrepancies for nitrogen and phosphorus losses emerged from this test.

Impact of Climate and Socio-Economic on Irrigation Water Management and Agricultural Water Productivity

Water security and food security are fundamental pillars of sustainable social and economic development [...]

Furrow Irrigation system in Ethiopia, a limitation in the rational use of water in agriculture and its performance

Ethiopia is among the many developing nations that prefer surface irrigation over underground and pressured irrigation systems due to its lower cost and energy consumption. This paper reviews the effects of furrow features, including length, flow rate, and influence on farm water productivity and yield gap. The furrow irrigation method can be highly effective when used correctly, but it can also be quite ineffective when used incorrectly. High efficiency can be attained if the design parameters of a furrow irrigation system, such as field length and flow rate, infiltration characteristics, and field slope, as well as its operational and management parameters, such as application depth, and frequency, are properly maintained. Over-irrigation in a furrow irrigation system can lead to a variety of detrimental consequences on crop productivity and the environment, including water runoff, reduced efficiency, deep percolation, waste of energy and resources, crop damage, and increased salinity. Therefore, checking the moisture content of the soil frequently to prevent over-irrigation by visual examination or soil moisture monitors, carefully planning furrows depending on soil type, crop requirements, and topography as well as gathering runoff water and reusing it improved farm irrigation water management in furrow irrigation system to reduce the yield gap and increase water productivity.