Sustainable Irrigation Practices Research Articles

Unravelling the potential of Big Data-driven decision-making in sustainable water irrigation: An AI perspective

Big Data-driven initiatives have transformed several industries by using large databases to extract vital information and enhance operations. In the field of sustainable water irrigation, Big Data enables intelligent water management, allowing for exact resource allocation, monitoring and analysis. This combination enables smart decision-making, increasing productivity in agriculture while reducing water consumption and environmental impacts. The revolutionary influence of data-driven approaches on managing water resources for sustainable and efficient irrigation practices has significant potential. The research highlights the importance of Big Data-driven approaches in water management by examining their applicability. It explores the artificial intelligence (AI) techniques for water irrigation that is sustainable and determines significant affecting factors efficient water management. The study encourages AI in intelligent water management systems and explains the application of big data in irrigation. It illustrates how smart water management which includes hydrated irrigation and predictive water infrastructure can contribute to AI-driven water management. To handle complicated water supply problems, the research presents an estimated water level model, highlighting the integration of AI and indicating the revolutionary potential of these techniques in attaining sustainable water irrigation. We highlight the novel possibilities of big data-driven methods for sustainable water irrigation, illustrating the essential part of AI techniques in effective water resource allocation.

Assesment of Borewell Irrigation Water Quality in Hiriyur Taluk Chitradurga District, Karnataka, India

In the quest for understanding the suitability of borewell groundwater for irrigation purposes in Hiriyur Taluk, Chitradurga District, we embarked on a comprehensive assessment of key physico-chemical parameters. Our study involved the collection and analysis of thirty water samples, drawn from both banana leaf margin-affected and their healthier counterparts within the taluk. These samples were meticulously scrutinized for pH levels, electrical conductivity (EC), calcium, magnesium, sulphate, nitrate, Exchangable sodium percentage (ESP), chloride, residual sodium carbonate (RSC) and sodium adsorption ratio (SAR), all vital components in the evaluation of irrigation water quality. The findings from this study not only underscore the importance of assessing and understanding these parameters but also advocate for proactive measures in promoting the development of borewell groundwater for sustainable and productive irrigation practices within the region.

I-RAT: A discussion support system to rapidly assess economic and environmental impacts of different sugarcane irrigation practices

Water pollution and climate change are among the greatest threats to the iconic Great Barrier Reef (GBR). To improve the GBR’s long-term outlook, making improvements on these fronts is required. Owing to the complex interactions between soils, climate, and farm management, a tool is needed to guide agricultural land managers about which management practice changes are more likely to deliver improved water quality and climate resilience outcomes whilst maintaining profitability. Using concepts drawn from social studies of science and technology, a ‘discussion’ support system (DSS) named Irrigation Rapid Assessment Tool (i-RAT) was developed through participatory processes designed to enhance co-learning from the development of this DSS. i-RAT is a rapid assessment-visualisation tool in an interactive web application (https://i-rat.net) to visualise the impacts of changed irrigation practices for sugarcane farmers and extension staff. Specifically, i-RAT compares these impacts regarding farm economics, water quality, productivity, carbon and nitrogen cycles, and greenhouse gas emission. The core of i-RAT is the sugarcane module from the Agricultural Production Systems sIMulator (APSIM) modelling platform. i-RAT was first developed for the Burdekin sugarcane growing region in Queensland (Australia’s largest sugarcane producing region). Various soil types and management scenarios representing farming practices in the Burdekin region were used for simulations (1971–2021) to generate a datacube. In this paper, we describe how fundamental learnings about more (drought) resilient farming systems and more sustainable irrigation practices can be extracted from i-RAT. Details of the participatory approach with research and industry partners that informed the design and function of i-RAT and how APSIM was set up and parameterised are described. A ‘what-if’ analysis demonstrated the i-RAT features and application.

Environmental and Agro-Economic Sustainability of Olive Orchards Irrigated with Reclaimed Water under Deficit Irrigation

Increasing the economic and environmental sustainability of irrigated agriculture is a vital challenge for the Mediterranean crop production sector. This study explores the effects of the adoption of reclaimed water (RW) as source of irrigation in conjunction with the application of deficit irrigation strategies in an olive orchard (different genotypes) located within the “Valle dei Margi” farmhouse (Eastern Sicily). Specifically, the RW was obtained in situ by treating the wastewater coming from the farmhouse throughout a nature-based treatment wetland system (TW). The effects of RW on crop water status (CWS) was assessed by conducting plant-based measurements (i.e., leaf water potential, Ψ; and leaves’ relative water content, RWC) and determining satellite-based biophysical indicators. An economic and environmental evaluation of the proposed sustainable irrigation practices was carried out using the life cycle assessment (LCA) approach. The RW quality showed high variability due to fluctuations in the number of customers at the farmhouse during the COVID-19 pandemic period. A strong impact on the variation in Ψ was observed among the olive orchard under the different water regimes, evidencing how CWS performances are conditioned by the genotype. However, no differences in leaves’ RWC and in satellite-based biophysical indicators were detected. Finally, the results of the LCA analysis underlined how the use of RW may permit us to obtain important economic and environmental gains, representing an added value for olive growing for operating in accordance to more sustainable development models.

Acoustic signal-based indigenous real-time rainfall monitoring system for sustainable environment

The rainfall weather station employs a tipping bucket rain gauge, which serves as a specialized instrument for the meticulous assessment and documentation of various rainwater parameters. The implementation of a tipping bucket rain gauge for rainfall monitoring bears significant implications for both societal productivity as well as improvement of human life. A noteworthy example can be the constructive influence of rainwater over the sustainable agricultural irrigation practices, wherein the precise monitoring of rainfall through a tipping bucket rain gauge enables the formulation of tedious irrigation strategies. The rainfall monitoring if often handle using rain gauge which majorly faces two challenges named as mechanical devices failure and high installation and maintenance cost. Considering the challenges, we propose the fully automated rain gauge (RG) based on the principle of sound and its properties for rainfall monitoring. The working prototype is part of our work whose primary task is to collect the rainfall acoustic value and store it in the cloud. Our mechanism is to use the acoustic property of rain data to categorize rainfall intensity. We perform blind signal separation on the received signal (acoustic signal recorded with the help of microphone sensor) and feed the separated signal to a recurrent convolution neural network (RCNN). The source separation of the collected acoustic signals is primarily being done using independent component analysis and principal components analysis. The proposed solution can be able to make the classification of rain intensity with more than 80% accuracy. In addition to this, the developed method provides the sustainable solution to the challenges with the low-cost and application-specific acceptable threshold criteria and supplement rain measurement techniques.

Smart Irrigation Systems using (IoT) – A Survey

In India a very vast population is dependent on farming activities and most of them still uses traditional methods of irrigation. Water availability plays main role in this sector. As the world is advancing and population of India is on the rise scarcity of water is becoming a common problem in most part of the country. This survey paper provides a comprehensive overview of smart irrigation systems, focusing on their integration with IoT, deep learning, sensors, and AI to optimize agricultural practices. The paper conducts an extensive literature review, exploring challenges and opportunities in the domain, including IoT applications in agriculture, deep learning techniques, sensor technologies, and AI-driven decision-making. The methodology involves developing and integrating key components like sensor deployment, data collection, deep learning model training, and AI-based decision-making algorithms. Performance evaluation compares the system's effectiveness with traditional methods, analyzing irrigation decisions, water consumption, crop yield, and water conservation. The results demonstrate how the smart irrigation system optimizes water usage, maintains optimal soil moisture levels, and improves crop health, leading to increased agricultural productivity and reduced water waste. Overall, this survey paper contributes valuable insights for researchers, practitioners, and policymakers, emphasizing the potential of IoT, deep learning, sensors, and AI in revolutionizing agriculture and promoting sustainable and efficient irrigation practices to address water scarcity challenges. Keyword:- IoT(internet of things), ML (Machine Learning), LDR(light dependent register), DHT (Dihydrotestosterone)

Development of a solar powered smart irrigation control system Kit

The adverse effects of climate change and climate variability are hindering agricultural productivity in developing countries. When combined with the laborious nature of irrigated agriculture, many smallholder farmers in these countries abandon irrigation systems or occasionally fail to implement an irrigation event due to other commitments. Therefore, this necessitates smart technology advances in agriculture to deal with irrigated agriculture problems of energy use efficiency, cost, water conservation, and drudgery. This study aimed at developing a mobile solar-powered control system for real-time scheduling using feedback from soil moisture sensors. A smart solar-powered irrigation control system (Smart Irri-Kit) was developed to schedule and automate water delivery to crops based on soil moisture levels. Itincorporates an automated tank water level control system that triggers pump activation during irrigation. The Kit was designed, fabricated, programmed, and field tested at Makerere University Agricultural Research Station Kabanyolo, using sandy clay loam soil. In this study, efficient irrigation scheduling and relay of soil moisture updates to the farmers were achieved. The Smart Irri-Kit soil moisture sensors were able to detect soil moisture with no significant difference from the gravimetric method. The development of the solar-powered Smart Irri-Kit presents a sustainable and automated solution for optimizing irrigation practices, contributing to water conservation and improved crop yield. The integration of solar power enhances its autonomy and reduces operational costs. Future research directions include the incorporation of weather parameters and advanced communication capabilities for remote monitoring and control. Overall, the Smart Irri-Kit holds great potential in promoting efficient and sustainable irrigation practices in agriculture

Water and Carbon Footprints of Biomass Production Assets: Drip and Center Pivot Irrigation Systems

Studies on the environmental footprints of agricultural production have strong links with 4 out of the 17 Sustainable Development Goals (SDG) established by the United Nations. Irrigation systems are essential tools for increasing agricultural yields, particularly in arid regions. However, the production and assembly of these systems can have significant environmental impacts, including excessive water consumption and greenhouse gas emissions. Although studies have approached biomass production, few of them have provided data about asset depreciation, such as irrigation systems, machinery, etc. Trying to fill this gap, this study aimed at determining the water and carbon footprints of two commonly used irrigation systems: center pivot and drip. Several variables, including the irrigated area, pump power, filter type, system flow, and pipe length, were analyzed to determine the carbon and water footprints of each component of the irrigation systems. The results reveal that the materials used for pipes and filters had the most significant impact on the water and carbon footprints, with galvanized steel pipes and sand filters having the highest footprints. Additionally, the irrigated area affected the center pivot and drip systems differently, with the depreciation of the irrigation systems being a significant variable for both water and carbon footprints. These results can support the development of sustainable irrigation practices that reduce environmental impacts while enhancing agricultural yields. Decision-makers can use this information to establish a life-cycle database and evaluate the impact of irrigation systems on water and carbon footprints.

Analyzing Water Conservation Techniques and Sustainable Irrigation Practices to Ensure the Availability of Clean Water for Future Generations

To keep ecosystems functioning, keep people alive, and propel social and economic progress, there must be access to clean water. The quantity and quality of freshwater resources are under jeopardy due to growing water scarcity, pollution, and climate change. In light of these issues, one of the most important things we can do to protect the supply of potable water for generations to come is to implement sustainable irrigation practices and water conservation measures. In light of the impending water crisis, this introduction lays the groundwork for an in-depth examination of sustainable irrigation practices and water conservation methods, discussing their significance, obstacles, and consequences. In light of increasing environmental challenges and water demands, it is critical to take immediate and decisive action to safeguard and responsibly manage water supplies. In light of changing environmental, social, and economic dynamics, this analysis aims to shed light on how to achieve water security and resilience by examining the concepts, innovations, and practical implementations of sustainable irrigation and water conservation. The main aim of this study is to analyze water conservation techniques & strategies of sustainable irrigation practices to provide insight for ensuring availability of clean water for future generations.

Economic efficient use of soilless techniques to maximize benefits for farmers

<abstract> <p>The main challenge of the agricultural sector is the increasing pressure on natural resources, mainly water and arable land. Consequently, an urgent imperative arises to explore technological advancements that can boost food production in alignment with the growing demands. The Soilless Production System (SPS) emerges as a proficient approach for managing irrigation water, thereby making a significant contribution to food security. This research focuses on the efficient use of SPS and identifies the best economic use of the soilless techniques for different crops within an area characterized by limited land and water availability. The database for the study was generated through a farm survey to investigate the benefits of adopting SPSs as a sustainable agricultural practice. A linear programming approach was applied to develop an optimization model for resource allocation and crop mix selection, considering the development opportunities through the SPSs. Different scenarios were applied in the model. The results proved that adopting SPSs is a sustainable irrigation practice, since the technique promotes water use efficiency, generates profitability, and conserves the associated natural resources. The SPSs ensure sustainable use of water resources by increasing water use efficiency. The hydroponics cultivation system had 11 ± 1.7 times higher yields but required 82 ± 11 times more energy in comparison to the lettuce crop produced by the conventional production system (CPS). The result of the optimal solution shows that the total revenue of scenarios of the study is 109% of the revenue of the original value of the largest farmer. The water resources and the investment cost as constraints in the model are totally used, but the operational costs in the optimal solutions are 74% of the total operational cost in the original data. The optimal solution showed the importance of using computerized systems in which the control of the fertigation is better.</p> </abstract>

Spatiotemporal modeling to predict soil moisture for sustainable smart irrigation

In this paper, we have proposed spatiotemporal soil moisture modeling to improve understanding of soil moisture and variability for sustainable irrigation practices. Increasing population growth and climatic changes have intensified the need to implement effective measures to conserve water in irrigation practices. Effective irrigation in parkland influences the overall plant growth processes including the final appearance of the plants. Furthermore, over-irrigation and poor water management in parks and gardens lead to wastage of water which may result in seepage, runoff, and leaching of nutrients into nearby streams. Conversely, under-irrigation results in reduced plant growth and unappealing appearance. Thus, appropriate irrigation management is required to maintain a lush green landscape. Due to large variability in soil properties, environmental conditions and landscape features, the soil moisture level might not be uniformly distributed within a given landscape. Hence, it is essential to understand the soil moisture distribution in the field. There are few existing soil moisture modeling exist but these modelings have not considered multiple vertical depth and time domain. Using Internet of Things(IoT) enabled Cyber–physical system and machine learning techniques, this paper has presented a soil moisture modeling for multiple vertical soil depth for a robust understanding of soil’s and plants need for sustainable sprinkling of the water. The proposed model has used both statistical and deep learning techniques to understand moisture variability in both vertical depth and time. The proposed model has used Correlation Index (CI) to understand multi depth moisture variability on the influence of soil types and local weather parameters, such as precipitation and temperature. To predict moisture data in multi depth, the model has evaluated the use of both statistical machine learning models, such as Support vector regression(SVR) and linear regression (LR), and deep learning models, such as Long Short-Term Memory (LSTM) using seasonal and non-seasonal dataset. The experiment has revealed closely related variability pattern between wind speed and soil moisture in multi depth whereas soil type shows a loosely related variability pattern with moisture in higher depth. With these diverse datasets, the proposed machine learning model has achieved almost 90% success rate to predict moisture content in higher depth using data of lower depth to reduce cumbersome sensors array deployment in higher depth for live moisture visualization. This proposed multiple depth and time domain-based model can enable a smart water dispersing system to conserve water and make a positive contribution to a sustainable future.

Assessing the Role of Irrigation as an Adaptive Measure to Climate Change Induced Water Insecurity: Case Study of the Market Gardening Sector in Parts of the Northwest and West Regions of Cameroon

Climate change induced extreme weather events are having major repercussions on availability and accessibility to water and water resources especially for farmers across the globe. This is more so for small-scale farmers in the developing world who largely depend on rain-fed agriculture. The market gardening sector in particular is known for its high dependence on suitable weather conditions for adequate productivity. However, in recent years, market garden crop cultivation has been severely threatened by climate change induced extreme weather events such as prolong dry spells, scanty and erratic rainfall, rising temperatures, extreme sunshine and storms. This study was undertaken to understand the role of irrigation as an adaptive measure to climate change induced water insecurity, with focus on the market gardening sector in parts of the west and northwest regions of Cameroon. Data were collected through a survey of 260 market gardeners involved in different irrigation practices geared toward countering water insecurity problems induced by climate change. Findings indicated that water insecurity is induced by extreme weather events such as prolong dryness, scanty and erratic rainfall, extreme sunshine and rising temperatures. A majority of the market gardeners surveyed reported the existence of water insecurity which has led to crop failure in many instances placing them in financial difficulties and seriously hampering their livelihood. Market gardeners were involved in different types of irrigation practices including sprinkler, drip, furrow, and manual with some taking to no irrigation practice. The main factors influencing market gardeners' practice of irrigation in the face of extreme weather induced water scarcity/insecurity were household income, age of market gardeners, educational level, farm size, number of farm plots, proximity to source of water, gender, water requirement of crop, support from government and NGOs, extension services, access to credit, membership in farming group and membership in common initiative group (CIG). Climate change has therefore induced water insecurity forcing market gardeners to indulge in different irrigation practices all year round in a bid to improve crop productivity and reduce recurrent crop failures. On the basis of these findings, the use of more sustainable irrigation methods in order to conserve water and water resources is recommended as this will go a long way to phase out the problem of water insecurity induced by climate change. Policy makers need to craft and implement favorable policies that encourage more market gardeners to adopt sustainable irrigation practices in the face of climate change induced water scarcity/insecurity.

Agricultural Irrigation Effects on Hydrological Processes in the United States Northern High Plains Aquifer Simulated by the Coupled SWAT-MODFLOW System

Groundwater use for irrigation has a major influence on agricultural productivity and local water resources. This study evaluated the groundwater irrigation schemes, SWAT auto-irrigation scheduling based on plant water stress (Auto-Irr), and prescribed irrigation based on well pumping rates in MODFLOW (Well-Irr), in the U.S. Northern High Plains (NHP) aquifer using coupled SWAT-MODFLOW model simulations for the period 1982–2008. Auto-Irr generally performed better than Well-Irr in simulating groundwater irrigation volume (reducing the mean bias from 86 to −30%) and groundwater level (reducing the normalized root-mean-square-error from 13.55 to 12.47%) across the NHP, as well as streamflow interannual variations at two stations (increasing NSE from 0.51, 0.51 to 0.55, 0.53). We also examined the effects of groundwater irrigation on the water cycle. Based on simulation results from Auto-Irr, historical irrigation led to significant recharge along the Elkhorn and Platte rivers. On average over the entire NHP, irrigation increased surface runoff, evapotranspiration, soil moisture and groundwater recharge by 21.3%, 4.0%, 2.5% and 1.5%, respectively. Irrigation improved crop water productivity by nearly 27.2% for corn and 23.8% for soybean. Therefore, designing sustainable irrigation practices to enhance crop productivity must consider both regional landscape characteristics and downstream hydrological consequences.

Economic and environmental impact assessment of sustainable future irrigation practices in the Indus Basin of Pakistan

Pakistan’s agriculture is characterized by insecure water supply and poor irrigation practices. We investigate the economic and environmental feasibility of alternative improved irrigation technologies (IIT) by estimating the site-specific irrigation costs, groundwater anomalies, and CO2 emissions. IIT consider different energy sources including solar power in combination with changes in the irrigation method. The status quo irrigation costs are estimated to 1301 million US$ year−1, its groundwater depletion to 6.3 mm year−1 and CO2 emissions to 4.12 million t year−1, of which 96% originate from energy consumption and 4% via bicarbonate extraction from groundwater. Irrigation costs of IIT increase with all energy sources compared to the status quo, which is mainly based on diesel engine. This is because of additional variable and fixed costs for system’s operation. Of these, subsidized electricity induces lowest costs for farmers with 63% extra costs followed by solar energy with 77%. However, groundwater depletion can even be reversed with 35% rise in groundwater levels via IIT. Solar powered irrigation can break down CO2 emissions by 81% whilst other energy sources boost emissions by up to 410%. Results suggest that there is an extremely opposing development between economic and ecological preferences, requiring stakeholders to negotiate viable trade-offs.

Levels of Salmonella enterica and Listeria monocytogenes in Alternative Irrigation Water Vary Based on Water Source on the Eastern Shore of Maryland.

ABSTRACTIrrigation water sources have been shown to harbor foodborne pathogens and could contribute to the outbreak of foodborne illness related to consumption of contaminated produce. Determining the probability of and the degree to which these irrigation water sources contain these pathogens is paramount. The purpose of this study was to determine the prevalence of Salmonella enterica and Listeria monocytogenes in alternative irrigation water sources. Water samples (n = 188) were collected over 2 years (2016 to 2018) from 2 reclaimed water plants, 3 nontidal freshwater rivers, and 1 tidal brackish river on Maryland’s Eastern Shore (ESM). Samples were collected by filtration using modified Moore swabs (MMS) and analyzed by culture methods. Pathogen levels were quantified using a modified most probable number (MPN) procedure with three different volumes (10 liters, 1 liter, and 0.1 liter). Overall, 65% (122/188) and 40% (76/188) of water samples were positive for S. enterica and L. monocytogenes, respectively. For both pathogens, MPN values ranged from 0.015 to 11 MPN/liter. Pathogen levels (MPN/liter) were significantly (P < 0.05) greater for the nontidal freshwater river sites and the tidal brackish river site than the reclaimed water sites. L. monocytogenes levels in water varied based on season. Detection of S. enterica was more likely with 10-liter filtration compared to 0.1-liter filtration. The physicochemical factors measured attributed only 6.4% of the constrained variance to the levels of both pathogens. This study shows clear variations in S. enterica and L. monocytogenes levels in irrigation water sources on ESM.IMPORTANCE In the last several decades, Maryland’s Eastern Shore has seen significant declines in groundwater levels. While this area is not currently experiencing drought conditions or water scarcity, this research represents a proactive approach. Efforts, to investigate the levels of pathogenic bacteria and the microbial quality of alternative irrigation water are important for sustainable irrigation practices into the future. This research will be used to determine the suitability of alternative irrigation water sources for use in fresh produce irrigation to conserve groundwater.

Sustainable Irrigation through Application of Hydrogel: A Review

Agricultural sustainability is essential to enhance food and water security, particularly in the context of climate change. In the recent past, applications of hydrogels in agriculture have received substantial attention among researchers as well as among farmers. This review elaborates on various aspects of hydrogels such as classifications, ideal properties for agricultural application, and interaction mechanisms with soil and plants. The experimental methods for determining hydrogel properties were given specific attention as properties such as swelling, retention, slow release, and degradation are of vital importance to agricultural sustainability. The studies conducted over the years on the effect of hydrogel application on different crops are reviewed. Several hydrogel studies demonstrate significant improvement in water consumption, water use efficiency, crop growth and yield parameters. The review looks into hydrogel degradation mechanisms in soil and also the ‘test methods’ for assessment of biodegradation. The gap areas in research on hydrogels and agricultural sustainability have also been identified. The need for developing a framework for the evaluation of the suitability of hydrogel for agricultural applications has been brought out. The review would encourage further research on enhancing the ideal properties of hydrogel and synthesizing with natural polymers. The use of hydrogel in agriculture for controlled nutrient release could be an alternative to conventional release methods for fertilizers and pesticides, thereby reducing the environmental footprint. The reduction in water footprint in major crops such as paddy and wheat through hydrogel might establish a shift towards sustainable irrigation practices if adopted on a large scale. Integrating innovative solutions with environmental-friendly hydrogels in the coming decades will contribute to the pursuit of achieving sustainable development goals.

Growth and yield traits of pickling cucumber plants to measure the impact of different irrigation management practices

Cucumber (Cucumis sativus L) is mostly cultivated by family-based farmers worldwide and processed to pickles by small to mid-sized industries generating employment and income. But irrigation management needs better investigation for adapting adequate sustainable practices. The rational use of irrigation water still has been neglected nowadays, but can improve cucumber production. The objective was to evaluate different irrigation levels on growth and yield parameters of the Amour F1 cucumber hybrid through time under greenhouse. Treatments consisted of five irrigation levels (amounts of water applied of 62, 93, 124, 155 and 186 mm), ranging from water stress to excess water. The experimental design was randomized blocks, with four replications. Vegetative growth and yield showed different responses to the irrigation levels. Plant height and internode length were less influenced by the irrigation levels, throughout the time, than stem diameter and root length. Cucumber yield was low with 62 mm and 93 mm, higher with 124 mm irrigation, and delayed in time at 155 mm and 186 mm. Water excess (186 mm) was not beneficial for pickling cucumber plants, and the amount of 124 mm was satisfactory for their development and yield. The results of this study may allow adoption of sustainable irrigation practices with no waste of agricultural water, a scarce resource worldwide

Performance of Satellite-Based Evapotranspiration Models in Temperate Pastures of Southern Chile

Farmers in the temperate zone of southern Chile have started to irrigate historically rainfed pastures during recent years to reduce dairy productivity losses against increasingly severe summer droughts. The lack of information on pasture water requirements (i.e., evapotranspiration), however, hampers the implementation of efficient irrigation programs. Here, we use in-situ observations to evaluate the skill of four remote sensing Surface Energy Balance (SEB) models and two satellite-based global evapotranspiration products (PML_V2 and GLEAM) to estimate actual evapotranspiration (ETa) of pastures in southern Chile during 2014–2017. Daily ETa measured at an evaluation site over the period ranges between 1.2 mm and 6.2 mm day−1 during the growing season (October–March), with an annual maximum of about 4.8 mm day−1 in January and a minimum 0.6 mm day−1 in June. Only the Simplified SEB (SEBS) model and its operational variant (SSEBop) and the PML_V2 global evapotranspiration product perform well, capturing 63–79% of the variance of in-situ evapotranspiration with an error between 0.75 mm day−1 and 1.1 mm day−1. The readily available PML_V2 product can be used as a convenient way to determine average water footprint of pastures and the two SEBs models can be implemented to monitor irrigation requirements in near-real time from field to regional scales. These results demonstrated a high potential of satellite observations for monitoring evapotranspiration and quantify the water footprint of pastures in southern Chile for a sustainable irrigation practice.

Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California

Environmental policies to address water quality impairments in the San Joaquin River of California have focused on the reduction of salinity and selenium-contaminated subsurface agricultural drainage loads from westside sources. On 31 December 2019, all of the agricultural drainage from a 44,000 ha subarea on the western side of the San Joaquin River basin was curtailed. This policy requires the on-site disposal of all of the agricultural drainage water in perpetuity, except during flooding events, when emergency drainage to the River is sanctioned. The reuse of this saline agricultural drainage water to irrigate forage crops, such as ‘Jose’ tall wheatgrass and alfalfa, in a 2428 ha reuse facility provides an economic return on this pollutant disposal option. Irrigation with brackish water requires careful management to prevent salt accumulation in the crop root zone, which can impact forage yields. The objective of this study was to optimize the sustainability of this reuse facility by maximizing the evaporation potential while achieving cost recovery. This was achieved by assessing the spatial and temporal distribution of the root zone salinity in selected fields of ‘Jose’ tall wheatgrass and alfalfa in the drainage reuse facility, some of which have been irrigated with brackish subsurface drainage water for over fifteen years. Electromagnetic soil surveys using an EM-38 instrument were used to measure the spatial variability of the salinity in the soil profile. The tall wheatgrass fields were irrigated with higher salinity water (1.2–9.3 dS m−1) compared to the fields of alfalfa (0.5–6.5 dS m−1). Correspondingly, the soil salinity in the tall wheatgrass fields was higher (12.5 dS m−1–19.3 dS m−1) compared to the alfalfa fields (8.97 dS m−1–14.4 dS m−1) for the years 2016 and 2017. Better leaching of salts was observed in the fields with a subsurface drainage system installed (13–1 and 13–2). The depth-averaged root zone salinity data sets are being used for the calibration of the transient hydro-salinity computer model CSUID-ID (a one-dimensional version of the Colorado State University Irrigation Drainage Model). This user-friendly decision support tool currently provides a useful framework for the data collection needed to make credible, field-scale salinity budgets. In time, it will provide guidance for appropriate leaching requirements and potential blending decisions for sustainable forage production. This paper shows the tie between environmental drainage policy and the role of local governance in the development of sustainable irrigation practices, and how well-directed collaborative field research can guide future resource management.

Salinity and salt fluxes in a polluted tropical river: The case study of the Athi river in Kenya

Study regionThis study was undertaken in the Athi-Sabaki river basin in Kenya in East Africa. Study focusThe study focused on the determination of the influence of streamflow variability on salt fluxes. This involved monitoring of river discharge and river salinity in the period between 2012 and 2018. New hydrological insights: This study demonstrates that Athi-Sabaki river discharges significant quantity of salt to the sea. There are significant seasonal and inter-annual variations in salt fluxes that are due to variations in river discharge and rainfall in the basin. The relationship between streamflow variations and variations of salinity in the river was inverse with highest salinity concentrations and fluxes occurring during low flow conditions. The river salinity and TDS concentrations decreased with an increase in river discharge due to dilution effect and flushing of salt from the river. The highly polluted sub-basins draining through the City of Nairobi exhibited relatively higher salinity and salt fluxes as compared to non-polluted ones draining rural areas. The total salt flux in the basin ranged between 29 × 103 and 261 × 103 tons year−1. The relatively high salinity and salt fluxes were attributed to the discharge of wastewaters, seepage of groundwater and irrigation return flows. The study calls for water pollution control, sustainable irrigation and landuse practices in the basin.