Improved Irrigation Management Research Articles

Analysis of the Status of Irrigation Management in North Carolina

Farmers in North Carolina are turning to irrigation to reduce the impacts of droughts and rainfall variability on agricultural production. Droughts, rainfall variability, and the increasing demand for food, feed, fiber, and fuel necessitate the urgent need to provide North Carolina farmers with tools to improve irrigation management and maximize water productivity. This is only possible by understanding the current status of irrigated agriculture in the state and investigating its potential weaknesses and opportunities. Thus, the objective of this study was to perform a comprehensive analysis of the current state of irrigation management in North Carolina based on 15-year data from the Irrigation and Water Management Survey by the United States Department of Agriculture–National Agricultural Statistics Service (USDA-NASS). The results indicated a reduction in irrigation acres in the state. Also, most farms in the state have shifted to efficient sprinkler irrigation systems from gravity-fed surface irrigation systems. However, many farms in North Carolina still rely on traditional irrigation scheduling methods, such as examining crop conditions and the feel of soil in deciding when to irrigate. Hence, there are opportunities for enhancing the adoption of advanced technologies like soil moisture sensors and weather data to optimize irrigation schedules for improving water efficiency and crop production. Precision techniques and data-based solutions empower farmers to make informed, real-time decisions, optimizing water use and resource allocation to match the changing environmental conditions. The insights from this study provide valuable information for policymakers, extension services, and farmers to make informed decisions to optimize agricultural productivity and conserve water resources.

Coupled weather and crop simulation modeling for smart irrigation planning: a review

ABSTRACT Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.

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

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

Investigating the compatibility of an irrigation decision support system with water rights and allocation in a selected irrigation network

AbstractIt is necessary to use different planning models, including decision support systems (DSSs), to allocate water resources. For this purpose, in this study, an irrigation decision support system (IDSS) was developed to improve irrigation management in the farming fields of Mahabad Plain located to the south‐east of Lake Urmia. Next, the compatibility of the IDSS with the conditions of the Mahabad irrigation and drainage network, water and soil resources, meteorological data and soil moisture (SM) were investigated. The statistical indices of coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (EF) and Wilmot agreement (d) were used to evaluate the adaptability of the IDSS. The results showed that the IDSS has reasonable compatibility with soil and water resources, crop yield and meteorological data. Irrigation scheduling provided by the IDSS led to a 13.9% reduction in water consumption and a 6.7% increase in crop yield. The IDSS estimated minimum and maximum temperature and sunshine hours to a satisfactory degree and relative humidity with an acceptable degree (NRMSE = 0.72–0.77) compared to regional synoptic station data. The performance of the IDSS in simulating SM is ranked from good to well (NRMSE = 0.75–0.83). The results indicate that the IDSS has a sufficient performance in estimating meteorological and soil moisture data with R2 = 0.90, RMSE = 4.65, NRMSE = 0.78, EF = 0.76 and d = 0.80. In addition, the IDSS provides the optimal irrigation schedule by considering the ability to deliver water from the irrigation and drainage network to the third‐grade canal and agricultural fields as the upstream condition.

Mulching effects on soil evaporation, crop evapotranspiration and crop coefficients: a review aimed at improved irrigation management

AbstractMulching is a widely adopted agronomic practice, often used as a water-saving strategy due to its effectiveness in reducing soil evaporation. However, effects vary depending on the materials used and the extent of mulch soil coverage. Consequently, the impacts of mulching may differ considerably across production systems, preventing the establishment of reliable guidelines for irrigation water management. The objective of this study is to comprehensively review existing literature that compares mulching versus no-mulching management, aiming to gain a deeper understanding of the effects of mulching on soil evaporation (Es), crop coefficients (Kc), and actual crop evapotranspiration (ETc act). 58 studies were selected. The impact of mulching was particularly notable in the early crop stages, when the soil is not fully covered. Data in literature shows that plastic films were more effective in reducing Kc than organic materials. However, this effect, while evident during the early crop stages, diminished throughout the rest of the season. Black plastic films were more effective during the early crop stages compared to other colored plastics, particularly relative to the decrease of Kc, but this effect also diminishes during the rest of the season. Building upon these findings, the study provides guidelines for expected reductions in Kc values based on the type of crop, crop stage, and the mulching material most used in each cropping system.

Deficit irrigation strategy to sustain available water resources using guar

The gradual depletion of irrigation water is a major threat to the agricultural economy in the arid and semi-arid regions of the world. Drought–tolerant crops and improved irrigation management practices may help the sustainability of agriculture in the region. A three–year field experiment (2018–2020) was conducted to assess the effects of pre–and in–season irrigations on the physiology, biomass production, yield, and yield components of two diverse guar (Cyamopsis tetragonoloba) cultivars. A split-split plot design was used, where the main plot was pre–irrigation, subplots consisted of in–season irrigation treatments, and sub-sub plots were cultivars. Each treatment was replicated four times each year. Pre–irrigation improved the averaged final seed yield by 32% compared to plots with no pre–irrigation. Seed yield was significantly affected by in–season growth stage based irrigation treatments. Averaged over the years, full irrigation achieved the highest seed yield, although not significantly different from no irrigation during the vegetative growth stage in any study year. Cultivar Kinman performed better than Monument in biomass production and seed yield. Overall, pre–irrigation improves guar growth and development, leading to greater seed yield and biomass production. The most sustainable strategy to utilize the available water resources will be to grow guar with restricted irrigation during the vegetative growth stage, save 22% of water, and maintain a similar seed yield to plots that received irrigation throughout the crop season.

Impact of calibrating a low-cost capacitance-based soil moisture sensor on AquaCrop model performance

Sensor data and agro-hydrological modeling have been combined to improve irrigation management. Crop water models simulating crop growth and production in response to the soil-water environment need to be parsimonious in terms of structure, inputs and parameters to be applied in data scarce regions. Irrigation management using soil moisture sensors requires them to be site-calibrated, low-cost, and maintainable. Therefore, there is a need for parsimonious crop modeling combined with low-cost soil moisture sensing without losing predictive capability.This study calibrated the low-cost capacitance-based Spectrum Inc. SM100 soil moisture sensor using multiple least squares and machine learning models, with both laboratory and field data. The best calibration technique, field-based piece-wise linear regression (calibration r2 = 0.76, RMSE = 3.13 %, validation r2 = 0.67, RMSE = 4.57 %), was used to study the effect of sensor calibration on the performance of the FAO AquaCrop Open Source (AquaCrop-OS) model by calibrating its soil hydraulic parameters.This approach was tested during the wheat cropping season in 2018, in Kanpur (India), in the Indo-Gangetic plains, resulting in some best practices regarding sensor calibration being recommended. The soil moisture sensor was calibrated best in field conditions against a secondary standard sensor (UGT GmbH. SMT100) taken as a reference (r2 = 0.67, RMSE = 4.57 %), followed by laboratory calibration against gravimetric soil moisture using the dry-down (r2 = 0.66, RMSE = 5.26 %) and wet-up curves respectively (r2 = 0.62, RMSE = 6.29 %). Moreover, model overfitting with machine learning algorithms led to poor field validation performance. The soil moisture simulation of AquaCrop-OS improved significantly by incorporating raw reference sensor and calibrated low-cost sensor data. There were non-significant impacts on biomass simulation, but water productivity improved significantly. Notably, using raw low-cost sensor data to calibrate AquaCrop led to poorer performances than using the literature. Hence using literature values could save sensor costs without compromising model performance if sensor calibration was not possible. The results suggest the essentiality of calibrating low-cost soil moisture sensors for crop modeling calibration to improve crop water productivity.

Smart control of soil water and salt content for improving irrigation management of tomato crop field: Kairouan area.

A good assessment of soil water and salt content is required for sustainable irrigation with brackish/saline water. The use of the Internet of Things (IoT) has been initiated for the tomato crop (Savera variety) as part of the PRIMA MEDITOMATO project. An experiment was carried out between February and June 2022 at a farmer's site. For continuous soil water and salt content assessment, TEROS (11/12) probes were implemented at depths of 0, 10, 20, 30, and 60 cm. The data logging process was performed by a ZL6 device and delivered by the ZENTRA Cloud web application (METER GROUPE Company). For the accuracy of the introduced sensors, calibration tests were first processed. Results of the calibration of the probes in the laboratory and in situ showed linear relationships between the humidity values measured by ZL6 (θZL6) and those determined by the gravimetric method, with high correlation coefficients (R2) of 0.86 and 0.96, respectively. There were also strong linear relationships between the ECbulk(ZL6) and the ECe measured on saturated paste extract with high correlation coefficients (R2) of 0.96 and 0.95. Corrected data, according to the determined linear regression equations, present the real-time assessment of soil water and salt content over the entire growth stage of tomatoes. The results of this monitoring showed that soil water content remained close to its status at field capacity (32%) at the beginning of the assessment and increased with the intensification of irrigation, reaching 46 and 54% at 20 and 30 cm, respectively, around mid-April. The salinity level was greater with depth. Indeed, it was low in topsoil with the increase in irrigation frequency and higher at 30 and 60 cm toward the end of the tomato cycle. According to this study, real-time data given by ZENTRA Cloud allows us to adjust irrigation management on time.

Using a crop water stress index to determine water use efficiency in a raspberry crop in the Mediterranean Central Chile

AbstractWater availability is projected to decrease in Mediterranean Central Chile, necessitating sustainable production strategies based on improved irrigation management. This study focuses on estimating water use efficiency (WUE) in raspberry crops at two validation sites using remote sensing and soil irrigation data. By employing the crop water stress index (CWSI), we demonstrate the potential of this tool in enhancing irrigation management and establishing sustainable production practices. The results demonstrate the successful estimation of actual evapotranspiration (ETa) using the Operational Simplified Surface Energy Balance (SSEBop) model (coefficient of determination [R2] = 0.92; root mean square error [RMSE] = 0.97 mm day−1), while the CWSI indicated high stress levels after 5 days of irrigation. Moreover, validation at two sites reveals significant differences in applied irrigation, with sites A and B receiving 17,097 and 3760 m3 ha−1, respectively, while the average water demand is close to 5300 m3 ha−1. These variations result in discrepancies in WUE, with values of 0.79 and 3.64 kg m−3. By integrating remote sensing indices and soil data, this study proposes that maintaining an 85% ETa rate during noncritical periods can enhance WUE. This work demonstrates the potential use of a water stress index to monitor crops in the Chilean central zone for efficient water resource use under future scarcity scenarios.

Use of interpretive machine learning and a crop model to investigate the impact of environment and management on soybean yield gap

Context Management and environmental conditions are the main factors influencing yield of soybean (Glycine max (L.) Merr.). Despite an increase in average soybean yield in recent years in Iran, a considerable gap remains between actual yield and potential yield. Aims The objective of this study was to identify critical climate and management factors affecting soybean yield in Iran’s major soybean production area. Methods A combination of machine learning approaches (using gradient boosted decision trees, XGBoost) and the SSM-iCrop2 simulation model was used. Critical management factors affecting soybean yield were determined through interpretive machine learning using information collected from 268 soybean fields over a 5-year period. Potential yield and water-limited potential yield at six weather stations were estimated for 30 years via the SSM-iCrop2 simulation model. Water limitation was determined by considering the ratio of water-limited yield potential to potential yield, and heat stress status was quantified as the number of days with maximum temperature >36°C during the soybean growing season. Key results The XGBoost models adequately described the observed changes in soybean yield. Root-mean-square error and Lin’s concordance correlation coefficient values of the calibrated model were 262 kg ha−1 and 0.96, respectively, which indicated that the predictor variables could describe most of the variation in soybean yield for the studied dataset. Conclusions We identified 15 climatic and management variables that affect soybean yield. A large part of the studied area is under high water stress and low heat stress. Implications Optimal planting date and improved irrigation management are the main options for reducing the yield gap in the study area.

Opinions on Irrigation Water Management Tools and Alternative Irrigation Sources by Farmers from the Delta Region of Mississippi

AbstractWater withdrawals for irrigation at an unsustainable rate resulted in a decline in the groundwater levels in the Mississippi River Valley Alluvial Aquifer (MRVAA) in the central southern USA. This drawdown of groundwater threatens agricultural production in the Mississippi Delta, an important agricultural region in the state of Mississippi, USA. Effective and efficient use of available resources is important to sustain and enhance agricultural productivity in this area. This study assessed the opinions of farmers on water conservation management practices and technologies that improve irrigation management and save water in the Mississippi Delta region based on data collected in an irrigation survey conducted in 2012. Most landowners believed that water conservation practices were effective in reducing irrigation water use without reducing maximum crop yields and have a positive return on investment. Land forming, tailwater recovery system, on‐farm storage, instream weirs to pond surface water, computerized hole selection for furrow irrigation, short irrigation runs, and irrigation scheduling were considered efficient water conservation technologies by landowners. Perceptions about use of different practices also depend upon the crops produced by the respondents. About 20 to 24% and 14.9 to 86% of survey respondents thought that on‐farm storage and center pivot, respectively, were inefficient water conservation practices for irrigating crops in the Mississippi Delta. The adoption of these practices may be increased if the landowners know the economic returns of implementing them.

Simulation of present and future land subsidence in the Rafsanjan plain, Iran, due to groundwater overexploitation using numerical modeling and InSAR data analysis

In the Rafsanjan plain, Iran, the excessive use of groundwater for pistachio irrigation since the 1960s has led to a severe water level decline as well as land subsidence. In this study, the advantages of InSAR analyses and groundwater flow modeling are combined to improve the understanding of the subsurface processes causing groundwater-related land subsidence in several areas of the region. For this purpose, a calibration scheme for the numerical groundwater model was developed, which simultaneously accounts for hydraulic aquifer parameters and sediment mechanical properties of land subsidence and thus considers the impact of water release from aquifer compaction. Simulation results of past subsidence are calibrated with satellite-based InSAR data and further compared with leveling measurements. Modeling results show that land subsidence in this area occurs predominantly in areas with fine-grained sediments and is therefore only partly dependent on groundwater level decline. During the modeling period from 1960 to 2020, subsidence rates of up to 21 cm year−1 are simulated. Due to the almost solely inelastic compaction of the aquifer, this has already led to an irreversible aquifer storage capacity loss of 8.8 km3. Simulation results of future development scenarios indicate that although further land subsidence cannot be avoided, subsidence rates and the associated aquifer storage capacity loss can be reduced by up to 50 and 36%, respectively, by 2050 through the implementation of improved irrigation management for the pistachio orchards.

Reclamation of Saline Agricultural Land

Abstract: The sustainability of (irrigated) agriculture in India is threatened by waterlogging, soil salinity, and alkalinity. To reverse declining agricultural productivity, a combination of surface and subsurface drainage, supplemented by improved irrigation management, has been identified as the most appropriate strategy. But subsurface drainage for salinity control is costly. Therefore, its sustained agricultural production benefits must be thoroughly investigated to establish its technoeconomic feasibility. The present study attempts to do this by analysing the cost of installing subsurface drainage, the direct onfarm benefits of subsurface drainage, and the financial feasibility of subsurface drainage. The study site selected for this study is Uran Islampur (Maharashtra State). The results say land use will intensify if drainage is installed because a sizeable area of formerly fallow land was brought under cultivation. It also says that the cropping pattern will be changed in favour of more remunerative crops, and crop yields will increase. These immediate gains from drainage are helping to increase land productivity, gainful employment of the farmers and, hence, farm income. The financial and economic feasibility of drainage in waterlogged and saline areas looks favourable, provided that sufficient water is available for leaching and irrigation and that a sustainable solution for the disposal of the low-quality drainage effluent is found. Concerning the latter, creating ponds to store drainage effluent temporarily is technically possible while not threatening the financial feasibility of the investment in drainage.

Measurement and simulation of irrigation performance in continuous and surge furrow irrigation using WinSRFR and SIRMOD models

The SIRMOD and WinSRFR models were used to model and assess the irrigation performance under continuous and surge irrigation strategies with two furrow lengths of 70 m and 90 m and stream sizes of 0.4 l/s and 0.6 l/s for each length. According to the normalized root mean squared error (NRMSE) and the relative error (RE), WinSRFR had, on average, excellent accuracy in the continuous and surge irrigation for simulating advance-recession times (NRMSE: 6.15 and 4.24% for advance time, and 2.20 and 5.20% for recession time), infiltrated water depth (NRMSE: 3.37 and 6.38%), and runoff volume (RE: 6.93 and 2.57%), respectively. SIRMOD had also, on average, excellent simulation in the continuous and surge irrigation for advance-recession times (NRMSE: 3.34 and 2.45% for advance time, and 2.28 and 6.41% for recession time), infiltrated water depth (NRMSE: 2.98 and 5.27%), and runoff volume (RE: 5.31 and 17.49%), respectively. The average of irrigation application efficiency (AE), distribution uniformity (DU), deep percolation (DP), and tail-water ratio (TWR) were 61.50, 90.25, 11.75, and 26.75% in continuous irrigation, and 72.03, 94.09, 8.39, and 19.57% in surge irrigation, respectively, which shows that surge irrigation increased AE (irrigation management performance) and DU (irrigation method performance) and reduced DP and TWR compared to continuous irrigation. Moreover, longer furrow lengths increased AE and DP under both irrigation methods, while it decreased TWR and DU. However, increasing the stream size decreased AE and DP and increased TWR under both continuous and surge irrigations. The higher stream size improved DU in continuous irrigation but reduced DU in surge irrigation. The results confirmed that both SIRMOD and WinSRFR are reliable analytical tools to evaluate furrow irrigation strategies for improving irrigation management. In conclusion, this study showed that surface irrigation models could be employed in practice by irrigation engineers and practitioners to design and define the optimized furrow length and stream size in arid and semi-arid areas where efficient and high performance irrigation strategies are required to save water and reduce water loss.

Application of a Generic Participatory Decision Support System for Irrigation Management for the Case of a Wine Grapevine at Epirus, Northwest Greece

In southern Europe, irrigation is the major water user and thus, development of operational tools that support decisions aiming to improve irrigation management, is of great importance. In this study, a web-based participatory decision support system for irrigation management (DSS), based on the principles of UN FAO’s paper 56, without requirement for any special monitoring hardware to be installed in each field, is evaluated for the case of a commercial wine grapevine (Vitis vinifera ‘Vertzami’) located at Epirus (northwest Greece), for two successive years (2021 and 2022). The soil moisture time series that were generated by the DSS’s model were compared to those measured by soil moisture sensors. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) ranged between 2.98–3.22% and 3.63–4.06%, respectively, under various irrigation practices and goals. Irrigation resulted very high yields and Crop Water Productivity (WPC) was 20–44% improved when following the DSS’s recommendations. The results also confirm potential pitfalls of sensor-based soil moisture monitoring and rainfall estimations using mathematical models. Finally, the value of water meters as practical sensors, which could support efficient irrigation management, is underlined. In every case, mindful application of decision support systems that require minimum or no hardware to be installed in each field, could extensively support growers and agronomic consultants to test, document and disseminate good practices and calculate environmental indices.

A Review on Regulation of Irrigation Management on Wheat Physiology, Grain Yield, and Quality.

Irrigation has been pivotal in sustaining wheat as a major food crop in the world and is increasingly important as an adaptation response to climate change. In the context of agricultural production responding to climate change, improved irrigation management plays a significant role in increasing water productivity (WP) and maintaining the sustainable development of water resources. Considering that wheat is a major crop cultivated in arid and semi-arid regions, which consumes high amounts of irrigation water, developing wheat irrigation management with high efficiency is urgently required. Both irrigation scheduling and irrigation methods intricately influence wheat physiology, affect plant growth and development, and regulate grain yield and quality. In this frame, this review aims to provide a critical analysis of the regulation mechanism of irrigation management on wheat physiology, plant growth and yield formation, and grain quality. Considering the key traits involved in wheat water uptake and utilization efficiency, we suggest a series of future perspectives that could enhance the irrigation efficiency of wheat.

Optimal Cost Structure in Operation and Maintenance Management of the Irrigation Systems in the Red River Delta, Vietnam

This study measured the cost efficiency and estimated the optimal cost structure in operating and maintaining the irrigation systems of eight provinces in the Red River Delta (RRD), Vietnam, from 2014 to 2016 using the input-oriented model of data envelopment analysis. The inputs included in the model were the costs of labor, materials, electric energy for irrigation, irrigation structure safety, maintenance, depreciation, administrative expenses, and overhead cost. The output was the irrigation area, which was converted to paddy areas. The findings of this study showed a large inefficiency in using the total input financial cost subsidized. The optimal technical efficiency scores under constant returns to scale (CRS) and variable returns to scale (VRS) were 0.86 and 0.98, respectively. The results implied that total input costs should be reduced by 14% under CRS and 2% under VRS to avoid cost inefficiency. The results also showed that scale efficiency occurred in the operation and maintenance (O&M) management of the irrigation systems. The O&M management in the irrigation systems in the RRD should allocate the major proportion of the costs to labor to be efficient. Application of the optimal input cost structure would improve irrigation management performance for the RRD region by 48.7% for the efficient input target (EIT) under CRS and by 5.03% for the EIT under VRS. These were the significant bases for recommending policy options for reviewing the irrigation tariff subsidy according to each input factor. And then it could create the incentives for private participation partnership in the irrigation sector.

Energy conservation prospects in water intensive Paddy-Wheat cropping system for groundwater pumping in the semi-arid region of Haryana.

The study was aimed at identifying the potential energy saving for groundwater pumping through enhanced efficiency of the pump sets. A total of 65 electrically powered tube wells were selected in the Sonipat district of the north Indian State of Haryana to study the energy use efficiency for groundwater pumping. The existing efficiency as well as the minimum expected overall efficiency of the tubewells were determined based on field survey, measurements, and applicable standard code of the Bureau of India Standards. The overall efficiencies of selected tube wells, computed based on actual measured power consumption, varied from 10.1% to 56.6% in the Sonipat block and 15.3% to 52.8% in the Rai block. The average energy requirement, for the selected tube wells, at the current efficiency level was 4,364.0 and 13,100.4 kWh for wheat and paddy crops, respectively, in the Sonipat block, while it was 3,424.8 and 10,280.9 kWh for wheat and paddy crops, respectively, in the Rai block. Analysis revealed that improving overall efficiency from the current level to the minimum expected level can lead to energy savings of 48.3% and 35.9% for tube wells in the Sonipat and Rai block, respectively. In the Rai block, where the groundwater level has declined significantly, the replacement of inefficient pumps should be done in tandem with crop diversification, improving water application efficiency and groundwater status by employing improved irrigation management practices and adopting groundwater recharge techniques.

Institution of Metal-Organic Frameworks as a Highly Sensitive and Selective Layer In-Field Integrated Soil-Moisture Capacitive Sensor.

The ongoing global industrialization along with the notable world population growth is projected to challenge the global environment as well as pose greater pressure on water and food needs. Foreseeably, an improved irrigation management system is essential and the quest for refined chemical sensors for soil-moisture monitoring is of tremendous importance. Nevertheless, the persisting challenge is to design and construct stable materials with the requisite sensitivity, selectivity, and high performance. Here, we report the introduction of porous metal-organic frameworks (MOFs), as the receptor layer, in capacitive sensors to efficiently sense moisture in two types of soil. Namely, our study unveiled that Cr-soc-MOF-1 offers the best sensitivity (≈24,000 pF) among the other tested MOFs for any given range of soil-moisture content, outperforming several well-known oxide materials. The corresponding increase in the sensitivities for tested MOFs at 500 Hz are ≈450, ≈200, and ≈30% for Cr-soc-MOF-1, Al-ABTC-soc-MOF, and Zr-fum-fcu-MOF, respectively. Markedly, Cr-soc-MOF-1, with its well-known water capacity, manifests an excellent sensitivity of ≈450% in clayey soil, and the analogous response time was 500 s. The noted unique sensing properties of Cr-soc-MOF-1 unveils the great potential of MOFs for soil-moisture sensing application.