Agricultural Water Management Research Articles

Cross-Regional Crop Classification Based on Sentinel-2

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM.

Artificial Intelligence in Water Management for Sustainable Farming: A Review

Artificial Intelligence (AI) is capable of enhancing water management for sustainable farming. The growing demand for agricultural productivity and sustainability in the context of finite water resources and climate change drives the necessity for more efficient water management practices. AI technologies, through automated and precision irrigation systems, AI-based predictive models, and AI-driven water quality monitoring, offer significant improvements in water efficiency and agricultural output. These systems optimize irrigation scheduling based on real-time data, enhance the precision of water application, and ensure water quality, thus supporting sustainable agricultural practices. However, the implementation of AI in water management is not without challenges. Technical difficulties in adapting AI to diverse agricultural environments, data privacy and security concerns, ethical considerations, and barriers to adoption among small-scale farmers are critical issues that need addressing. This study addresses both the transformative impacts and the inherent challenges of integrating AI technologies. Furthermore, the review identifies a gap in research regarding AI’s adaptability to variable climates and its integration with socio-economic data, suggesting that future studies focus on these areas. Policy recommendations are also discussed, emphasizing the need for developing standards and best practices, increasing funding and incentives for AI research, promoting training and capacity building, and establishing robust regulatory frameworks for data management. By tackling these challenges and leveraging AI’s full potential, water management in agriculture can be significantly improved, leading to enhanced global water security and sustainability in farming practices. The review concludes that while AI presents a promising future for agricultural water management, strategic and thoughtful approaches are required to overcome obstacles and fully realize the benefits of this technology.

Impact of rice straw mulching on water consumption and productivity of orange trees [Citrus sinensis (L.) Osbeck]

Due to the adverse effect of global climate change on agricultural water management, maintaining soil moisture in the root zone is essential for optimal crop productivity. For effective irrigation, we used organic mulching to increase the yield and water productivity (WP) of Valencia orange in arid/desert climates. Here, the effect of rice straw mulching and irrigation rates [100, 85, and 70% of evapotranspiration reference (ETc)] on nutrient contents, quality, and yield of orange trees was evaluated. Under field conditions, the application of straw mulch significantly increased the concentration of some essential elements and photosynthetic pigments but reduced proline contents in orange leaves. Soil mulching significantly increased fruit and inflorescence retention, and resulted in a higher number and weight of fruit on branches, without compromising their taste or texture, compared to the treatment without mulching. Both irrigation and mulching also affected crop productivity and water use efficiency (WUE). This was evident when yield/fed increased by 22.7–23.8, 20.7–31.5, and 6.2–16.9% under mulching at 100, 85, and 70% ETc, respectively, compared to traditional conditions at 100% ETc without mulch. Although the maximum WUE under rice straw mulch was 5.72–5.84 kg/m3 at 70% ETc, it was 5.31–5.40, and 4.57–4.64 kg/m3 at 85, and 100% ETc, respectively. Organic mulching showed superior results with respect to increasing yields and WP, while saving 15.4% water and comparable benefit/cost with the traditional irrigation when orange trees were irrigated with 85% ETc. Together, this study could be a promising strategy for climate change adaptation and sustainable water management.

AI ENABLED WATER CONSERVATION FOR IRRIGATION USING IOT

This project introduces a smart irrigation system that utilizes Internet of Things (IoT) technology and machine learning algorithms to enhance water management in agriculture. The system employs a series of IoT sensors distributed across the field to consistently monitor key environmental factors such as soil moisture levels, temperature, humidity, and others. The collected data is analysed using machine learning Cat Boosting algorithm. This algorithm analyze the data to determine the optimal irrigation schedule based on crop water requirements, soil conditions, weather forecasts, and historical data. The system controls irrigation equipment such as pumps, valves, and sprinklers to deliver precise amounts of water to crops at the right time. Continuous feedback from the system allows for refinement of irrigation schedules, leading to improved water conservation, increased crop yield, and cost savings for farmers. This project discusses the benefits of such a system, including water conservation, increased crop yield, cost savings, environmental sustainability, and remote monitoring and control capabilities. Overall, the integration of IoT and machine learning technologies offers a powerful solution for sustainable agriculture, enabling data-driven decision-making processes to optimize water usage and maximize crop productivity. Keywords— Smart irrigation system, Internet of Things(IOT)technology, Water conservation, Environmental parameters, Soil moisture monitoring, Increased crop yield, Machine learning algorithms

A comprehensive analytical and computational assessment of soil water characteristics curves in Atlantic Canada: Application of a novel SelectKbestbased GEP model

For sustainable agriculture, irrigation is one of the most important management practices in a semi-arid climate. Determining the site-specific soil type-based irrigation water requirements requires the development of a soil water characteristics curve (SWCC). This study focuses on exploring the suitability of analytical SWCC models based on the observed data of SWCC developed using the pressure membrane apparatus. A novel SelectKbest gene expression programming (GEP) model is used to create the generalized machine learning SWCC model for Atlantic Canadian soils. The composite soil samples with representative soil textures of loamy sand, loam, and sandy loam are collected from the selected locations in Prince Edward Island and New Brunswick provinces of Canada. The suitability of models at three regions shows that at all sites, including PEI-1 (loam soil) 1, PEI-2 (loamy sand), and NB-3 (sandy loam) if SWCC data has four measured suction potential levels, then McBee & Bomb and Tani exponential models may be selected. Van Genuchten, double exponential, Ruso, Kosuki, and Gardner's four parameters models should be a good choice for five-section potential. Similarly, for six measured suction potential values, the Fredlund & Xing, Omuto, and Van Genuchten models having parameters should be selected for determining the optimum soil hydraulic parameters. The HydroMe program with Brooks and Corey, four, and five parameters Van Genuchten models performed well. The HydroMe program is calibrated and validated with standard RETC for selected regions of Atlantic Canada, which suggests that the HydroMe program is flexible in developing efficient agricultural water management algorithms. Various performance metrics like R2 and RMSE are employed to assess the robustness of the model. A generalized SWCC model (R2=0.976 and RMSE=0.04) is developed using a novel SelectKbest-based GEP modeling approach for Atlantic Canadian soils. These novel approaches can significantly improve site-specific agricultural water management and decision support systems.

Determinants of farmers’ willingness to pay for irrigation improvements in Northcentral Ethiopia

Investing in agricultural water management by improving irrigation schemes helps to establish climate-resilient and sustainable agri-food systems, thus contributing to sustainable poverty reduction. The purpose of this study was to identify the determinants of farmers’ willingness to pay (WTP) for irrigation water improvements in Northcentral Ethiopia. Primary data collected from a random sample of 132 households were analyzed using the double-bounded contingent valuation method and binary logit regression model to identify what determines farmers’ WTP for irrigation improvement. The findings show that farmers’ mean WTP for irrigation water improvements is 141.60 Birr/ha/year (2.50 USD/ha/year). The results of bivariate Probit model revealed that crop type, education, relative location to the irrigation scheme, irrigated plot size, and perceived drought risk statistically and positively determine farmers’ WTP for irrigation water improvement. These findings offer valuable policy implications on how best to guide practical agricaltural water management. Policy interventions aimed at enhancing farmers' behavior, awareness, and perception of drought-related issues, while also promoting cash crop production, are likely to drive more farmers towards a positive WTP for irrigation water improvement.

Risks associated with wastewater reuse in agriculture: investigating the effects of contaminants in soil, plants, and insects

Rapid urbanization has heightened the urgency of the necessity for sustainable water management in agriculture. This review focuses on the impacts of using reused wastewater in agricultural practices, specifically highlighting the nutrient benefits and consequences of pollutants on important environmental elements. It investigates the impact of contaminants on agricultural ecosystems by assessing the soil composition and nutrient equilibrium. This research also examines the impact of pollution exposure on plants and insects, elucidating the behavioural adaptations and their broader ecological consequences in agricultural environments. Eventually, a comprehensive analysis was conducted to consolidate these findings, emphasizing the challenges and significance of implementing sustainable practices. This study highlights the necessity of addressing the health and environmental concerns associated with the agricultural reuse of wastewater, while also giving valuable information to guide future regulations.

Comparative Analysis of Earth Observation Methodologies for Irrigation Water Accounting in the Bekaa Valley of Lebanon

This study extensively examines the estimation of irrigation water requirements using different methodologies based on Earth Observation data. Specifically, two distinct methods inspired by recent remote sensing and satellite technology developments are examined and compared. The first methodology, as outlined by Maselli et al. (2020), focuses on using Sentinel-2 MSI data and a water stress scalar to estimate the levels of actual evapotranspiration and net irrigation water (NIW). The second methodology derives from the work of D’Urso et al. (2021), which includes the application of the Penman–Monteith equation in conjunction with Sentinel-2 data for estimating key parameters, such as crop evapotranspiration and NIW. In the context of the Bekaa Valley in Lebanon, this study explores the suitability of both methodologies for irrigated potato crops (nine potato fields for the early season and eight for the late season). The obtained NIW value was compared with measured field data, and the root mean square errors were calculated. The results of the comparison showed that the effectiveness of these methods varies depending on the growing season. Notably, the Maselli method exhibited better performance during the late season, while the D’Urso method proved more accurate during the early season. This comparative assessment provided valuable insights for effective agricultural water management in the Bekaa Valley when estimating NIW in potato cultivation.

Evaluating the impacts of climate change and land-use change on future droughts in northeast Thailand

The impacts of climate change (CC) on droughts are well documented, but the effects of land-use change (LUC) are poorly understood. This study compares the projected individual and combined impacts of these stressors on future droughts (2021–2050), with respect to baseline (1981–2010) in one of the major tributaries of the Mekong River. LUC impacts on hydrological droughts are minimal compared to CC, with the latter expected to shorten the recurrence interval of a 20-year return period event to every 14 years. Both CC and LUC have significant impacts on agricultural droughts with heightened sensitivity. ‘Once in a Decade’ agricultural droughts will be 40% (35%) longer and 88% (87%) more severe under the CC (LUC) scenario. Under both stressors, the events occurring every 20 years will be twice as frequent. Results highlight the intensification of future droughts and the urgency for actions to mitigate/adapt to climate change and manage land use. Future policy shall holistically address agricultural water management, sustainable land use management, and crop management to cope with future droughts. We recommend developing resilient agricultural practices, enhanced water resource management strategies, and incorporating drought risk into land-use planning to mitigate the compounded impacts of CC and LUC.

Addressing water scarcity in agricultural irrigation: By exploring alternative water resources for sustainable irrigated agriculture

AbstractThis review paper addresses challenges in the water sector, particularly in irrigated agriculture, aiming to propose solutions for meeting irrigation demands while promoting global food security and sustainable development, notably SDG 6. Structured around three facets: empowering farmers, strengthening conventional sources of irrigation water and harnessing non‐conventional water resources, it emphasizes the significance of exploring blue water resources due to precipitation variability. Many irrigation systems operate below efficiency, offering productivity enhancement opportunities. Water management in agriculture spans various levels, involving farmers as key stakeholders. In addition to surface water, alternative sources like rainwater, grey water, recycled wastewater and groundwater can meet irrigation needs. Rainfed agriculture, facing challenges from erratic rainfall, can benefit from rainwater harvesting and under‐irrigation practices. Wastewater emerges as a pivotal resource, particularly in periurban areas, necessitating appropriate safety measures. This paper presents the General Report of Congress Question 64 of the ICID Congress in Visakhapatnam. It provides a unique opportunity to focus on how alternative water resources might enhance the resilience of irrigation systems and bridge the gap between water supply and demand. The subdivision of the paper into three distinct subtopics guides research contributions, encouraging responses that delve into the specific themes of reinforcing, harnessing and empowering, all within the context of sustainable irrigated agriculture.

The effects of improved subsurface drainage on runoff and nitrogen leaching from a clayey field section

AbstractThe aim of this study was to investigate the effects of improved subsurface drainage installation on nitrogen (N) loss in drain discharge (DD) and topsoil layer runoff (TLR). Data on DD and TLR, as well as on concentrations of total, nitrate and ammonium N in the runoff components, were collected from four sections of an experimental field in southern Finland (June 2007–December 2018). Supplementary drains were installed in one of the field sections in June 2014, and the data from that section were compared with those from three reference sections. Differences between the sections were statistically analysed based on annual and monthly values of runoff components and concentrations, as well as the loads of N fractions. The results revealed that improved drainage increased the N load in the DD, reducing the load in the TLR. Changes in N loads were more clearly driven by changes in the runoff volumes rather than by changes in the N concentrations in the runoff waters. Before the drainage improvement, most of the total N load was nitrate (53%), while the share of rest N (fraction of the total N after the mineral N fractions were subtracted) was 45%. After improved drainage, the percentages of nitrate and rest N were 73 and 26%, respectively. The results demonstrate the importance of agricultural water management as the key driver for controlling nutrient loads.

Combining Crop and Water Decisions to Manage Groundwater Overdraft over Decadal and Longer Timescales

Coordinating management of groundwater, surface water, and irrigated crops is fundamental economically for many arid and semi-arid regions. This paper examines conjunctive water management for agriculture using hydro-economic optimization modeling. The analysis is integrated across two timescales: a two-stage stochastic decadal model for managing annual and perennial crops spanning dry and wet years and a far-horizon dynamic program embedding the decadal model into a longer groundwater policy setting. The modeling loosely represents California’s San Joaquin Valley and has insights for many irrigated arid and semi-arid regions relying on groundwater with variable annual hydrology. Results show how conjunctive water management can stabilize crop decisions and improve agricultural profitability across different water years by pumping more in dry years and increasing recharging groundwater in wetter years. Using groundwater as a buffer for droughts allows growing more higher-value perennial crops, which maximizes profit even with water-scarce conditions. Nevertheless, ending overdraft in basins with declining groundwater for profit-maximizing farming reduces annual crops to maintain more profitable perennial crops through droughts. Results are affected by economic discount rates and future climates. Operating and opportunity costs from forgone annual crops can reduce aquifer recharge early in regulatory periods.

Evaluating the impacts of flooding on crop yields by different meteorological indices: A regional case study in the middle-lower reach of the Yangtze River, China

Flooding is a worldwide destructive disaster that severely restricts agricultural production. Meteorological indices are common tools for regionally assessing the impact of flooding on crop yields, but their performances are rarely compared. In this work, six convenient meteorological indices, namely, precipitation (P), standardized precipitation anomaly (PA), China Z index (CZI), standardized precipitation index (SPI), standardized precipitation and evapotranspiration index (SPEI), and standardized antecedent precipitation and evapotranspiration index (SAPEI), were employed to establish correlations between flooding intensity and crop meteorological yield (FL-Ym correlation). Four major crops in the middle-lower reach of the Yangtze River (cotton, oilseed rape, wheat, and maize) were selected as the study crops. The results indicated that the flooding intensities quantified by the SPI, SPEI, and CZI had strong interconnections, whereas those quantified by the P and SAPEI were less related to the others. In the cases with stronger negative FL-Ym correlations, different indices were more likely to yield consistent identifications. In terms of the districts witnessing significant FL-Ym correlations, in only 26%, 41%, 44%, and 75% of them (respectively corresponding to cotton, wheat, maize, and oilseed rape), the correlations were consistently identified as significant by the majority of the indices, demonstrating the nonnegligible influence of index selection. The relative performances of the examined indices varied with the employed calculation periods (whole growth period and single critical stage), whereas the SPEI was generally the best performer. The SAPEI performed best in assessing the flooding impact during crop critical growth stages, in sharp contrast with its mediocre performance over the whole crop growth period. According to the results of multiple indices, flooding during the middle stages of the study crops exerted the greatest negative impact. Additionally, oilseed rape and Anhui Province were identified as the crop and the region most affected by flooding. This work can provide support for flooding disaster assessment and agricultural water management.

Exploring ways to improve agricultural water management in two Mediterranean irrigated systems: promises of wireless low-tech sensor networks

Exploring ways to improve agricultural water management in two Mediterranean irrigated systems: promises of wireless low-tech sensor networks

Decoupling Agricultural Grey Water Footprint from Economic Growth in the Yellow River Basin

Decoupling agricultural economic growth from agricultural water pollution is of great importance to regional sustainable development. It is necessary to further explore the decoupling state and key driving factors connecting agricultural water pollution and agricultural economic growth on the basis of accurate measurement of agricultural water pollution. Accordingly, taking the Yellow River Basin (YRB) as the research object, this study combined the water footprint theory, the Logarithmic Mean Divisia Index (LMDI) model and the Tapio decoupling model (TDM) to conduct an in-depth decoupling analysis of the connection between the agricultural grey water footprint (AGWF) and agricultural economic growth in the YRB. Specifically, this study first calculated the AGWF of the YRB during 2016–2021 and objectively evaluated the water resource utilization in this region based on the AGWF. Then, the LMDI model was used to explore the driving factors of the AGWF in the YRB. Finally, the decoupling states between the AGWF and its driving factors with agricultural GDP (AGDP) were studied using the TDM. The main results are as follows: (1) The overall AGWF in the YRB showed a decreasing trend and a slow increase, decreasing by 5.39% in 2021 compared to 2016. (2) The primary promoting factor and inhibiting factor of AGWF reduction are the efficiency effect and agricultural economic effect, respectively. (3) The decoupling states of the AGWF and AGDP presented strong decoupling (SD) and then weak decoupling (WD) in the YRB during the research period. The decoupling states between the agricultural grey water footprint intensity (AGWFI) and AGDP changed from expansive negative decoupling (END) to SD. The decoupling state of population and AGDP remained SD. This study will contribute to alleviating agricultural water pollution in the YRB and help policymakers in water-stressed countries to formulate agricultural water management policies.

Irrigation intelligence-enabling a cloud-based Internet of Things approach for enhanced water management in agriculture.

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.

Evaluating the machine learning based Efficacy of Decision Tree and Support Vector Machines in Smart Irrigation Systems for Precise Irrigation Status Classification for Optimizing Water Management in Agriculture

The research paper deals with the improvement of water management practices in agriculture through the use of machine learning in smart irrigation systems. The efficiency of the Decision Tree and the Support Vector Machine models with real-time sensor data was analyzed. The Internet of Things system was developed to analyze the data collected at the field with the help of various sensors, including temperature, humidity, and soil moisture sensors. The layout and choice of sensors were based on the official parameters required for most average plants and corps. After data collection and development of classifiers, the Decision Tree and Support Vector Machine models were used and analyzed regarding their efficiency for making irrigation-related decisions. As the result, it was found that both models showed a good performance, but the SVM model was slightly better due to a smaller number of false positives and false negatives, which allow it to divide the data into the corresponding categories more accurately. At the same time, both models use history data, and the more recent data are better for decision as it was also noted during the development when the use of models based on history data for irrigation decision result in regular irrigation, which in long term would cause the loss of productivity of plans, due to the issue that ancient and no crops are watered too. As the final results, it can be stated that the models show good performance, and the algorithms that were tested can be stated viable for the use at practice. At the same time, the SVM model shows slightly better performance than DT.

Automatic gate controlling system for sustainable agriculture water management in Sri Lanka: A review

This research introduces an automatic gate-controlling system explicitly tailored for sustainable paddy cultivation in Sri Lanka. Paddy fields, critical to the nation's food security, often face water management and resource efficiency challenges. Given the pressing concerns of water scarcity and the need for efficient resource utilization in Sri Lanka's agricultural landscape, the automatic gate-controlling system emerges as a cutting-edge solution. This research explores the transformative potential of this technology in revolutionizing traditional irrigation practices. It provides a detailed examination of the system's components and functionalities, emphasizing the integration of smart sensors to gather real-time data on soil moisture levels, weather conditions, and crop water requirements. This data is then processed through advanced analytics, facilitating informed decision-making for the automated control of irrigation gates. These data inputs are then processed by an intelligent control system that automates the operation of water gates. By precisely regulating water flow, the system minimizes water wastage and mitigates over-irrigation risks, contributing to sustainable paddy cultivation practices. The automated nature of the system reduces the dependency on manual labor, providing farmers with an efficient and reliable tool for water management. By adopting this technology, the study envisions increased yields in paddy cultivation while promoting responsible use of water resources. This research marks a significant stride towards sustainable agriculture in Sri Lanka, particularly in the crucial paddy cultivation sector.

Optimized water management for precision agriculture using IoT-based smart irrigation system

This project introduces a Smart Irrigation System aimed at enhancing water efficiency in agriculture through the integration of an Arduino Uno micro-controller and a variety of sensors. The system incorporates a soil moisture sensor, a DHT11 sensor for monitoring temperature and humidity, an LCD, a buzzer for alerts, and a relay module to control a pump motor. The main goal is to create an automated irrigation system that adjusts to environmental conditions, thereby optimizing water usage for plant growth. The soil moisture sensor continuously evaluates soil moisture levels, while the DHT11 sensor keeps tabs on temperature and humidity. The Arduino Uno processes this data and displays it on an LCD screen. When the soil moisture falls below a predefined threshold, the system activates a buzzer and triggers the relay to turn on the pump motor. The LCD provides real-time feedback on environmental conditions and the irrigation status. This Smart Irrigation System offers several advantages, including water conservation, energy efficiency, and improved plant health. By automating irrigation based on real sensor data, the system minimizes water wastage and ensures that plants receive optimal moisture levels. Additionally, temperature and humidity monitoring contribute to effective microenvironment management. The project serves as a demonstration of IoT-based solutions for agricultural challenges, featuring a modular design that allows for scalability. Future developments may include incorporating additional sensors, enabling wireless connectivity for remote monitoring, and integration with weather data. In summary, the system provides a practical and cost-effective solution for sustainable agriculture, promoting water conservation and efficient farming practices.

Education Of Water Needs And Management For Agriculture In Garagahan Irrigation Area

Irrigation Areas (DI) were built to meet various needs of life, especially to achieve the level of community welfare. D I Sangkir Garagahan was built by the Department of Water Resources and Construction Development in 1986 and is located in the Lubuk Basung sub-district, Agam regency. The current irrigation management is 671 hectares covering 3 working areas, namely Nagari Garagahan which has an area of ​​415 hectares of irrigated rice fields, Nagari Kampuang Tangah 136 hectares and Nagari Manggopoh 120 hectares. On April 15, 2022 an educational training was conducted to determine the needs and management of water for agricultural land. The methods used are lectures, discussions and training on the use of formulas to regulate plant water and soil management. The training was attended by the Nagari Garagahan community who are members of the Water User Farmers Association (P3A) and Water User Farmers Association (GP3A). Availability of water by adjusting plant water in the field and at the entrance gate for an area of ​​415 hectares of irrigated rice fields, the water requirement is 0.7204 m3/second. Soil management for agriculture is carried out by biological, non-biological and mechanical methods. By adjusting the capacity of the nagari for its implementation