Irrigation Water Use Research Articles

Effect of Irrigation Regime and Soil Nutrients on the Growth of the Paddy Weed Heteranthera reniformis and Rice Grain Yield

The growth of Heteranthera reniformis, an invasive alien paddy weed, can be affected by cultivation practices. The experiments were conducted using herbicide-free soil to understand the effects of irrigation regimes and nutrient treatments on the growth of H. reniformis, as well as yield parameters while competing with a pre-existing seedbank. The pot experiments were conducted in a randomized complete block design (RBCD) with three replicates and twelve treatments. The four irrigation regimes (IRs): continuous irrigation (CI), soil condition at near saturation (non-puddled) (S), alternate wetting and drying (AWD) irrigation under two conditions [rewatered when the soil water potential reached −25 kPa (25P) and −35 kPa (35P)], and three nutrient treatments (NTs) of 0–0–0 NPK (NT0), 40–25–30 NPK (NT1), 80–50–60 NPK (NT2), kg ha−1 were established. The IRs had a significant effect on the growth of H. reniformis and other paddy field weeds, and the growth of H. reniformis was suppressed in the AWD regimes. NT2 resulted in more rice panicles, higher grain yield, and increased irrigation water use efficiency (IWUE). The highest grain yield and protein content were observed in S–NT2 and 25P–NT2 treatments. The IR and NT can be maintained to prevent yield penalties and reduce the invasiveness of weeds.

Learning-based multi-agent MPC for irrigation scheduling

Amid concerns about freshwater scarcity, the agricultural sector faces challenges in water conservation and optimizing crop yields, highlighting the limitations of traditional irrigation scheduling methods. To overcome these challenges, this paper introduces a unified, learning-based predictive irrigation scheduler that integrates machine learning and Model Predictive Control (MPC), while also incorporating multi-agent principles. The proposed framework incorporates a three-stage management zone delineation process, utilizing k-means clustering and hydraulic parameters estimates for optimized agro-hydrological modeling. Long Short-Term Memory (LSTM) networks are employed for accurate and computationally efficient root zone soil moisture modeling. The scheduler, formulated as a mixed-integer MPC with zone control, utilizes the identified LSTM networks to maximize root water uptake while minimizing overall water consumption and fixed irrigation costs. Additionally, the learning-based scheduler adopts a multi-agent MPC paradigm, where decentralized hybrid actor–critic agents and the concept of a limiting irrigation management zone are employed to enhance computational efficiency. Evaluating the performance on a 26.4-hectare field in Lethbridge for the 2015 and 2022 growing seasons demonstrates the superiority of the proposed scheduler over the widely-used triggered scheduling approach in terms of Irrigation Water Use Efficiency (IWUE) and total prescribed irrigation. Notably, the proposed approach achieves water savings between 7 to 23%, coupled with IWUE increases ranging from 10 to 35%.

Optimizing crop water productivity and delineating root architecture and water balance in cotton–wheat cropping system through sub-surface drip irrigation and foliar fertilization strategy in an alluvial soil

Water-intensive agricultural practices in tandem with conventional irrigation methods such as flood irrigation, has caused severe water scarcity in south-Asian Indo-Gangetic Plains (IGPs). The adoption of micro-irrigation systems (sprinker, surface drip and sub-surface drip) instead of flooding offers a great promise in water saving and enhanced fertilizer-use efficiency, and thus, improved productivity. The change in irrigation and fertilization methodology may affect the behavior of plant root growth, root architecture and, crop and water productivity. Hence, current study aimed at evaluating the influence of sub-surface drip-irrigation and fertigation strategy on crop and water productivity besides delineating root architecture and soil water balance in cotton–wheat cropping system (CWCS). Results showed that sub-surface drip irrigation (SSDI) enhanced the root system by stimulating the development of new secondary roots which actively participated in physiological responses. Root length at boll opening stage of cotton increased significantly under SSDI at 60% ETc (60% of evapo-transpiration) over other treatments. Root mass density in wheat was also significantly higher under SSDI particularly at 15–30 cm depth over surface drip and flood irrigation during 2020–21. SSDI at 100% ETc (100% of evapo-transpiration) produced statistically similar seed cotton yield, wheat grain yield and the system productivity (wheat equivalent yield) to 80% ETc. SSDI at 80% ETc (80% of evapo-transpiration), resulted in water-saving of ∼43.2% over flood irrigation in CWCS. Further, fertigation through SSDI resulted in ∼20% reduction in fertilizer needs, while achieving the yield equivalent to that of conventional fertilization. Irrigation water productivity and bio-physical water productivity of this system were ∼46.1 and 5.7% higher under 80% ETc along with F80 (80% recommended dose of nitrogen, RDN) and Mfoliar (foliar application of KNO3 & MgSO4 at flower initiation and boll development stages of cotton) over flood irrigation. Overall, SSDI at 80% of evapo-transpiration along with 80% RDN and foliar application of KNO3 & MgSO4 may prove highly beneficial to enhance the crop and water productivity in CWCS, besides saving irrigation water-use by ∼43.2% over flood irrigation in water-scarce agro-ecologies of south-Asia.

Cover crops and irrigation impacts on corn production and economic returns

Increases in irrigated crop acreage and frequent droughts during the growing season have caused continual groundwater decline of the Mississippi River Valley Alluvial Aquifer in the Mississippi Delta region. A field experiment was conducted from 2019 to 2021 to determine if combinations of irrigation scheduling thresholds and cover crops (CCs) could improve corn (Zea mays L.) production, water productivity (WP), irrigation water use efficiency (IWUE), and net returns. The irrigation thresholds used for irrigation scheduling were a wet threshold (−40 kPa), a dry threshold (−90 kPa), and no irrigation. The CC treatments were cereal rye (Secale cereale L.), hairy vetch (Vicia villosa R.), wheat (Triticum aestivum L.)-radish (Raphanus sativus L.)-turnip (Brassica rapa L.) mix, and no cover crop. In 2020, CCs reduced corn yield by 9–26% compared to no cover crop. In 2021, wet irrigation threshold treatments had 8% and 9% higher corn yield than no irrigation and dry irrigation threshold treatments, respectively. No irrigation treatments showed higher WP than dry and wet irrigation thresholds, while hairy vetch had higher in WP among CC treatments in year two of this study. Hairy vetch under the dry irrigation threshold had higher IWUE than all other treatments. The no-CC treatments generated $167, $340, and $58 ha−1 more under the wet, dry, and no irrigation treatments, respectively, when averaged over both years. Water conservation was affected by CC selection and irrigation management.

Impact of the use of water from fish farming in the irrigation of pepper crops (Capsicum annuumL.) in the greenhouse in the Biskra region

ABSTRACT The conservation of water and its rational management in crop irrigation is important for sustainable agriculture in arid and semi-arid regions, particularly with the appearance of the effects of climate change in these regions. The paper reports a study of the impact of using water from fish farming in the irrigation of pepper crops grown in greenhouses under salinity conditions. The results obtained show that the use of fish water in irrigation improves the vegetative growth of plants and their fruit yields. It also has a beneficial effect on the soil by increasing its level of organic matter, total Nitrogen and other nutrients. The value that this work adds is the conservation of the soil by improving its quality and the reduction of water pollution by reusing this wastewater in irrigation. The reduced use of synthetic fertilisers facilitates the application of organic farming. These advantages benefit the environment and consumers of the crops.

Uncovering Current and Future Variations of Irrigation Water Use Across China Using Machine Learning

AbstractAccurately characterizing changes in irrigation water use (IWU) is crucial for formulating optimal water resource allocation policies, particularly in the context of climate change. However, existing IWU estimation methods suffer from uncertainties due to limited data availability and model constraints, restricting their applicability on a national scale and under future climate change scenarios. We present a robust framework leveraging machine learning and multiple data sets to estimate IWU across China. Forced with an ensemble of climate and socio‐economic projections, we appraise future trends and additional costs of IWU. Our model shows high accuracy in reproducing IWU, with coefficient of determination (R2) ranging from 0.86 to 0.91 and root mean square error from 0.261 to 0.361 km3/yr when compared to reported values in Chinese prefectures. Independent validation at 11 cropland sites further confirms the model's predictive power (R2 = 0.67). Under different emissions scenarios, China's IWU is projected to increase by 8.5%–17.1% (6.8%–34.8%) by 2050s (2100s) compared to the historical period (1981–2010), with higher emissions leading to more significant increases. This rise in IWU by 2050s (2100s) comes with an estimated additional cost of US $1.65–3.91 ($2.28–6.5) billion/year, highlighting the urgency for sustainable water management. Our study provides an effective approach for estimating current and future IWU using machine learning techniques, transferable to other countries facing increasing irrigation demands.

Hybrid Theory‐Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China

AbstractCurrent irrigation water use (IWU) estimation methods confront uncertainties warranting further attention, primarily stemming from constraints within model structure and data quality. This study proposes a hybrid framework that integrates multiple machine learning (ML) methods with theory‐guided strategies to calculate IWU for three principal cereal crops within the Chinese agricultural landscape. We generated high resolution time series data sets of evapotranspiration and surface soil moisture (SM) using remote sensing resources. ML techniques, along with the Bayesian three‐cornered hat ensemble, were employed to drive multiple remote sensing‐derived data sets in IWU calculation. We applied two theory‐guided mechanisms to quantify irrigation signals: first, converting original SM values into logarithmic terms, and second, extracting process‐based SM residuals. Proposed framework has been validated at 12 field stations across China, yielding coefficient of determination (R2) ranging from 0.54 to 0.70, and root mean square error (RMSE) spanning 278–335 mm/yr. Our framework demonstrates considerable strength in IWU estimation when compared to reported IWU values form 341 cities across China. Specifically, for rice, wheat, and maize, the R2 values range from 0.78 to 0.83, 0.68 to 0.76, and 0.53 to 0.64, respectively, with corresponding RMSE measuring 0.22–0.25, 0.10–0.12, and 0.11–0.13 km3/yr, respectively. These findings highlight the effectiveness of theory‐guided strategies in discerning irrigation‐related information, thereby improving overall model performance. Attention should be directed toward the uncertainties in evapotranspiration and precipitation products on model performance, which remained modest, with a relative change of less than 5%.

Effects of pan evaporation-based drip irrigation levels on performance of guava grown in Udaipur and Rewa regions of India

A field experiment was conducted for three years (2019-20, 2020-21 and 2021-22) on 4 years old guava orchard established at 3×2 m spacing with drip irrigation treatments at two locations viz. Udaipur Rajasthan and, Rewa, Madhya Pradesh. Plant growth, yield contributing parameters, fruit yield and water use efficiency was significantly affected by different pan evaporation-based drip irrigation levels (70, 80, 90 & 100% of Epan) over local control. In existing climatic conditions of Udaipur and Rewa regions, the daily irrigation water requirement of high-density planting guava tree was varied from 7.8 to 26.3 and 4.5 to 26.5 liter/plant/day, respectively. Among all the pan evaporation-based drip irrigation levels, the irrigation supplied at 80% and 90% of daily pan evaporation were found as best approach for irrigating high density plantation (HDP) guava orchard through drip irrigation in Udaipur & Rewa regions with maximum fruit yield (37.3 & 30.7tha-1), irrigation water use efficiency (0.359 & 0.263tha-1-cm) along with significant water saving (29.2 & 22.2%), respectively over local control. Results will help farmers, policy makers and irrigation managers to conserve available fresh water resources in water scares regions of Rajasthan and Madhya Pradesh.

The effects of high temperature and low humidity on crop water stress index of seed pumpkin plants (Cucurbita pepo L.) in semi-arid climate conditions

This study aimed to evaluate the effects of high temperature and low humidity on the crop water stress index (CWSI) of seed pumpkin plants grown under semi-arid climate conditions to determine the optimum irrigation time. This research unveils the critical impact of high temperature and low humidity on seed pumpkin growth, emphasizing the vital role of the CWSI in optimizing irrigation strategies and seed yield. Moreover, the relationship between CWSI, physiological parameters, and seed yield of the pumpkin was investigated. The mean CWSI values in the I70 (0.40) and I35 (0.56) treatments were 42% and 100% higher, respectively than those in the full irrigation (I100) treatment (0.28). While the I70 treatment showed manageable water stress with minimal impact, the I35 treatment experienced severe stress, significantly reducing crop growth and yield. The mean seed yield (SY) in the I70 treatment increased to 1245.2 kg ha–1 compared to I35 (903.3 kg ha–1) but remained lower than I100 (1339.3 kg ha–1). The CWSI had negative correlations (p ≤ 0.01) with seed yield, chlorophyll content, and leaf area index, while it had positive correlations with water use efficiency and irrigation water use efficiency (p ≤ 0.01). This study showed that pumpkins could be grown successfully at 30% water deficit conditions, and a water deficit higher than 30% may cause a significant seed yield loss in semi-arid climate conditions. In addition, the results highlight the importance of optimal irrigation and CWSI monitoring for informed irrigation decisions and sustainable agricultural practices. Therefore, moderate water deficit (I70) can be adopted in pumpkin cultivation as an alternative to full irrigation.

County‐Level Evaluation of Large‐Scale Gridded Data Sets of Irrigated Area Over China

AbstractThe reliability of irrigated area (IA) information dominates the performance of irrigation water use and crop modeling accuracy. IA is typically mapped using Food and Agriculture Organization (FAO) agricultural census and remote sensing indices. Recent advances in machine learning and sampling techniques further improve IA mapping. However, the relative performances of different IA mapping approaches and their capability in capturing long‐term IA temporal variability remain unknown. Here, 1861 county‐level IA information from Government Censored Data (GCD) during 2000–2021 are collected, cross‐validated, and employed to evaluate commonly used gridded IA data sets. Results show that IA data sets based on the direct interpolation of FAO agricultural census can accurately capture the spatial distribution of IA. However, FAO statistics are only available in a particular year, which cannot capture inter‐annual irrigation variations. In contrast, IA products solely based on vegetation indices are prone to positive biases over humid regions due to the lack of contrast in vegetation dynamics. Overall, the latest GCD‐based machine learning IA data sets are relatively more accurate, but they are also problematic in estimating IA trends due to the use of temporally static training samples. Such biases are tightly related to agricultural suitability (AS calculated using precipitation and potential evapotranspiration). This suggests that AS should be employed as an endogenous variable in future machine learning based IA mapping algorithms.

Impact of Deficit Irrigation Strategies Using Saline Water on Soil and Peach Tree Yield in an Arid Region of Tunisia

Sustainable fruit orchard development in arid areas is severely affected by the scarcity of fresh water. To mitigate the lack of fresh water, the use of low-quality water for irrigation is becoming a common practice in several margin areas. However, salinity is considered one of the most important environmental constraints limiting the successful crop production. Therefore, the effects of deficit irrigation strategies using saline water (3.1 dS m−1) on soil water content, soil salinity, and yield of commercial peach orchard were investigated. Three irrigation treatments were considered: a Control, full irrigated (FI); and partial root-zone drying (PRD50); and deficit irrigation (DI) strategies irrigated at 50% ETc. These levels of water supply allowed for contrasting watering conditions with clear distinction between irrigation treatments. The differential pattern in soil moisture was accompanied by that of soil salinity with an increase in all FI treatments (16–25%). The results indicated that soil salinity increased with increasing water supply and evaporative demand during the growing season from January (3.2 dS m−1) to August (6.6 dS m−1). Deficit irrigation strategies (DI, PRD50) induced more soil salinity along the row emitter compared to the Control due to insufficient leaching fractions. By the end of the growing season, the soil salinity under long-term saline drip irrigation remained stable (5.3–5.7 dS m−1). An efficient leaching action seemed to be guaranteed by rainfall and facilitated by sandy soil texture, as well as the high evaporative demand and the important salt quantity supplied, which maintain the deficit irrigation strategies as valuable tools for water saving and improving water productivity. The significant water saving of 50% of water requirements induced a fruit yield loss of 20%. For this reason, DI and PRD50 could be reasonable irrigation management tools for saving water and controlling soil salinity in arid areas and on deep sandy soil.

Evaluating the Effect of Deficit Irrigation on Yield and Water Use Efficiency of Drip Irrigation Cotton under Film in Xinjiang Based on Meta-Analysis.

Water scarcity constrains the sustainable development of Chinese agriculture, and deficit irrigation as a new irrigation technology can effectively alleviate the problems of water scarcity and water use inefficiency in agriculture. In this study, the drip irrigation cotton field under film in Xinjiang was taken as the research object. Meta-analysis and machine learning were used to quantitatively analyze the effects of different farm management practices, climate, and soil conditions on cotton yield and water use efficiency under deficit irrigation, to investigate the importance of the effects of different factors on cotton yield and water use efficiency, and to formulate appropriate optimization strategies. The results showed that deficit irrigation significantly increased cotton water use efficiency (7.39%) but decreased cotton yield (-15.00%) compared with full irrigation. All three deficit irrigation levels (80~100% FI, 60~80% FI, and 40~60% FI; FI: full irrigation) showed a significant decrease in cotton yield and a significant increase in water use efficiency. Under deficit irrigation, cotton yield reduction was the smallest and cotton water use efficiency increased the most when planted with one film, two tubes, a six-row cropping pattern, an irrigation frequency ≥10 times, a nitrogen application of 300~400 kg·ha-1, and a crop density ≥240,000 per hectare, and planted with the Xinluzhong series of cotton varieties; deficit irrigation in areas with average annual temperature >10 °C, annual evapotranspiration >2000 mm, annual precipitation <60 mm, and with loam, sandy soil had the least inhibition of cotton yield and the greatest increase in cotton water use efficiency. The results of the random forest showed that the irrigation amount and nitrogen application had the greatest influence on cotton yield and water use efficiency. Rational irrigation based on optimal management practices under conditions of irrigation not less than 90% FI is expected to achieve a win-win situation for both cotton yield and water use efficiency. The above results can provide the best strategy for deficit irrigation and efficient water use in drip irrigation cotton under film in arid areas.

Farmers’ Knowledge on Agricultural Irrigation Programs: The Case of Altınekin District of Konya Province

Climate change-induced droughts have various negative impacts on different sectors, especially on agricultural sector. Altınekin District, where the research was conducted, has the lowest precipitation in Konya Province, therefore is expected to be most affected by drought. For sustainable use of water resources, each stage of agricultural production should be carried out within the framework of a specific program. With this study, knowledge level of farmers engaged in irrigated agriculture in Altınekin district regarding irrigation and irrigation programs they applied were determined through a face-to-face survey. Present findings revealed that 38.7% of the participant farmers are aged 50 years and over and a large proportion of them (54.7%) are primary school graduates. Participant farmers were all (100%) using groundwater resources in agricultural production and 88% of them stated that they did not receive any training on irrigation. While 32% of the farmers participating in the survey stated that they had knowledge about the concept of irrigation program, 68% reported that they did not have any knowledge. Incorrect and improper practices regarding efficient use of irrigation water in the region, especially regarding the irrigation program, were identified and solutions were proposed for these problems.

River water pollution in Lebanon: the country's most underestimated public health challenge.

Due to the several interconnected crises that Lebanon has been facing for the past 4 years, many important social and environmental issues have been overlooked until more "pressing" ones are dealt with. Consequently, water pollution in Lebanon continues to worsen. This study aimed to describe the microbiological and chemical properties of the 10 main rivers in Lebanon and to assess their suitability for irrigation, while exploring some of the solutions to the problem. This cross-sectional study evaluated the pollution level of water from 10 rivers in Lebanon in June 2023 and their suitability for irrigation. Samples were collected at 3°C and their quality parameters were measured. Statistical analysis was conducted using R statistical software version 4.0.2. Compared to the Food and Agriculture Organization (FAO) guidelines for safe irrigation water use, 4 out of the 10 samples had pH levels exceeding the permissible threshold, resulting in severe limitations on their usability. Three rivers had nitrate concentrations that exceeded the approved range, thus constraining their severe usage. Among the rivers, 60% had Escherichia coli levels higher than the permissible spectrum and 40% had faecal coliform counts exceeding FAO's upper limit recommendation. All water sources, however, had total dissolved solid levels that were within the recommended range. Polluted water can have a negative impact on human, wildlife and ecosystem health. Most of the assessed rivers in our study contained bacterial colonies, above the maximum recommended internationally. There is therefore an urgent need to address pollution issues in Lebanese waters to make them suitable for irrigation and other uses.

Response of tomato (Solanum lycopersicum) to varying levels of irrigation and nitrogen under trickle fertigation

An experiment was conducted during 2022 and 2023 at Lovely Professional University, Jalandhar, Punjab to study the response of tomato (Solanum lycopersicum L.) to 3 irrigation levels [I1, 70%; I2, 85% and I3, 100% of IWR] and 3 nitrogen fertilization levels [N1, 70%; N2, 85% and N3, 100% of RDN] in split plot design. On individual effect basis, the fruit yield first increased with the increase in amount of applied irrigation water in I2 (95.6 t/ha) and then decreased in I3 (93.5 t/ha). Irrigation water use efficiency (IWUE) was the highest in I2 (3.73 t/ha-cm). Plant growth, tomato yield and IWUE were significantly higher in N2 than that were found in N1 and N3. Nitrogen use efficiency (NUE) decreased with increasing nitrogen levels (N2 and N3) but it increased with increasing amount of water applied (I3). The treatments having deficit irrigation level as I1 and I2, significantly enhanced the lycopene content (27 to 33 µg/g) and reduced the nitrate content (74 to 95 mg/kg) in the fruit over full irrigation level I3. Nitrate content was highest in N3 (100 mg/kg). Among all the combination of irrigation and nitrogen fertilization levels the interaction of I2N2 (85% of IWR and RDN) was found the best to grow the drip-irrigated tomato crop. This combination gave optimum plant growth (87.5 cm), fruit yield (100 t/ha), IWUE (3.93 t/ha-cm), NUE (0.79 t/kg) and good quality fruit. The findings can be utilized for irrigation planning and nitrogen management in tomato cultivation and to conserve available fresh water resources in water scare regions of Punjab. In order to promote trickle fertigation at village level, the awareness programs for farmers could be conducted.

Adjustment of the AquaCrop model in maize with different levels of irrigation in southern Uruguay temperate climate conditions

The AquaCrop model allows evaluating and designing irrigation strategies that improve the use of irrigation water. The objective of this research was to calibrate and validate the AquaCrop model for maize to the climatic conditions of southern Uruguay, with different irrigation water management. This model was calibrated and validated for corn using experimental data from irrigation trials with different deficit levels in three seasons, 2015-16 and 2016-17 (calibration) and 2014-15 (validation). Three maximum irrigation depths were evaluated: 3, 6 and 9 mm day-1, and rainfed (rainfall only). The crop was parameterized for local conditions and water stress coefficients were adjusted. The calibration simulated the yield, biomass and soil moisture in the irrigated treatments with good performance. All the statistic indexes used to evaluate the adjustment between the observed and simulated data model indicated a good model performance, with the exception of the efficiency coefficient of the Nash-Sutcliffe (EF) model. The model underestimated the yield in the rainfed treatment (EF of -0.52) when root depth was limited to 0.7 m. However, the test soil allowed for greater radical exploration than the initially used. At 0.90 m root depth, the model was good at simulating the yields in the rainfed treatment, mainly in dry years (EF of 0.79). The model predicts the yield with good adjustment in different irrigation and rainfall situations if the stress coefficients are adjusted and the crop is properly parameterized, mainly the root depth.

Using the AquaCrop model to assess the cotton yield response to three irrigation schedules in the Río Dulce Irrigation System, Santiago del Estero, Argentina

This work evaluates the cotton response to irrigation scheduling using AquaCrop, in the Río Dulce Irrigation System (SRRD), Santiago del Estero, Argentina. The model was calibrated and validated to simulate the cotton´s growth and yield for the SRRD, where most of the cotton is grown in a cropping system called narrow rows (0.52 to 0.76 meter between rows, 200,000 to 220,000 plants per hectare). The model adaptation to different cultivars and agronomical practices was noteworthy. Then, the impact of three different irrigation schedules on cotton production was assessed using a series of 35 years of daily climatic data. The irrigation scenarios were defined based on the farmers’ practices and on the rotational water delivery of the SRRD. The highest yields were attained when irrigation was applied at 25 and 55 days after sowing (DAS), followed by 55 DAS, and, finally, 55 and 85 DAS. Considering both the yields and the water use, irrigating at 25 and 55 DAS would be the best option for a normal season in the SRRD. This work shows the usefulness of combining the use of crop simulation models, field measurements and long-term weather data to analyze yield trends and irrigation water use under different scenarios.

An inter-comparison of approaches and frameworks to quantify irrigation from satellite data

Abstract. This study provides the first inter-comparison of different state-of-the-art approaches and frameworks that share a commonality in their utilization of satellite remote-sensing data to quantify irrigation at a regional scale. The compared approaches vary in their reliance on either soil moisture or evapotranspiration data or their joint utilization of both. The two compared frameworks either extract irrigation information from residuals between satellite observations and rainfed hydrological models in a baseline framework or use soil water balance modeling in a soil-moisture-based inversion framework. The inter-comparison is conducted over the lower Ebro catchment in Spain where observed irrigation amounts are available for benchmarking. Our results showed that within the baseline framework, the joint approach using both soil moisture and evapotranspiration (ET) remote-sensing data only differed by +37 mm from the irrigation benchmark (922 mm) during the main irrigation season over 2 years and by +47 and −208 mm for approaches relying solely on soil moisture and ET, respectively. A comparison of the different frameworks showed that the main advantage of the more complex baseline framework was the consistency between soil moisture and ET components within the hydrological model, which made it unlikely that either one ended up representing all irrigation water use. However, the simplicity of the soil-moisture-based inversion framework, coupled with its direct conversion of soil moisture changes into actual water volumes, effectively addresses the key challenges inherent in the baseline framework, which are associated with uncertainties related to an unknown remote-sensing observation depth and the static depth of the soil layers in a conceptual model. The performance of the baseline framework came closest to the irrigation benchmark and was able to account for the precipitation input, which resulted in more plausible temporal distributions of irrigation than what was expected from the benchmark observations.

Increasing Water Use Efficiency of Irrigated Rice through Water Saving Techniques in Yobe Basin, North East, Nigeria

Increasing world population and dwindling water resources is exerting pressure to develop strategies for producing more food using less water.&#x0D; Aims: To investigate the effect of different irrigation schedules on grain yield and water use efficiency (WUE) of FARO 44 rice variety.&#x0D; Study Design: The research was laid out in a split plot arrangement. Twelve treatments replicated three time comprising of 3 irrigation intervals as main plot and 4 irrigation depth as sub plot.&#x0D; Place and Duration of Study: Gashua, Yobe State on the floodplains of river Yobe, Nigeria between March 2023 and July 2023.&#x0D; Methodology: Comprised of 3, 5 and 7 days irrigation intervals as main plot, with irrigation depth (amount) at 100% of crop water requirement (ETc), 85% of ETc, 70% of ETc and the farmers flooding practice as the sub plot treatments. Some growth, yield and components of the rice were determined. Reference evapotranspiration (ETo) and Crop evapotranspiration (ETc) were computed using Cropwat model. Crop and irrigation water use efficiencies were also calculated.&#x0D; Results: The 3 days irrigation interval produced the maximum growth parameters of rice along with both application of 100% of crop water requirement and the traditional flooding type of irrigation. The interaction of 3 days irrigation interval and 100%ETc and 85% of ETc significantly produced the highest grain yield (6484.85 kg ha-1.) 3 and 5 days irrigation interval with 100% and 85% of ETc irrigation depths significantly produced the highest water use efficiencies. A well fitted linear relationship (R2 = 0.834) existed between the irrigation scenarios and the rice grain yield.&#x0D; Conclusion: It can be concluded that irrigation frequency and amount can be reduced to arrive at highly acceptable yield in the study area. High rice yield can be maintained with 3 days irrigation interval and 85% of the crop water requirement and also improve its water use efficiency.

Automated Detection of On-Farm Irrigation Reservoirs in Two Critical Groundwater Regions of Arkansas

In eastern Arkansas, the use of surface water for crop irrigation is steadily increasing in response to declining aquifers. Effective conjunctive water management requires accurate and timely information on the locations, sizes, and numbers of on-farm irrigation reservoirs. A method for remotely locating and characterizing on-farm reservoirs was developed using relative elevation and near-infrared imagery. With 62% accuracy, the method automatically identified 429 irrigation reservoirs within a 1.9-Mha area in less than an hour using an off-the-shelf laptop. Reservoirs not accurately identified (i.e., false negatives) were caused by the presence of vegetation or turbidity within the reservoirs. There were no false positive detections. This approach for identifying elevated reservoirs is applicable across the Mississippi Alluvial Plain (MAP) that encompasses over 4-Mha of irrigated cropland and other agricultural areas having low-relief.