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A zombie idea is one that has been repeatedly refuted by analysis and evidence, and should have died, but clings to life for reasons that are difficult to understand without further investigation. The perception that investments in modern irrigation systems automatically save water constitutes a zombie idea. On face value, most would accept that modernizing irrigation systems makes sense: agriculture represents 70% of global water withdrawals while physical irrigation efficiencies range between 25% and 50% worldwide—that is, most of the water entering the irrigation system never makes it to the targeted crop. However, the impacts of modern irrigation systems are complex, and as we show, usually have the opposite effect to that intended through altered cropping and water application decisions by farmers, that aggravate water scarcity. This paper investigates how this zombie idea forms; why it persists, even when proven wrong by scientific evidence; and how to overcome it.

Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1°) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1° to 0.25°. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales (1-, 3-, and 6-months) and self-calibrated Palmer Drought Severity Index (sc-PDSI). Moreover, the IBIS had experienced increasing drought episodes, e.g., eight drought episodes were detected within the years 2010 and 2016 with WSDI of −1.20 and −1.28 and total WSD of −496.99 mm and −734.01 mm, respectively. The Partial Least Square Regression (PLSR) model between WSDI and climatic variables indicated that potential evaporation had the largest influence on drought after precipitation. The findings of this study will be helpful for drought-related decision-making in IBIS.

Precision agricultural greenhouse systems indicate considerable scope for improvement of irrigation management practices, since growers typically irrigate crops based on their personal experience. Soil-based greenhouse crop irrigation management requires estimation on a daily basis, whereas soilless systems must be estimated on an hourly or even shorter interval schedule. Historically, irrigation scheduling methods have been based on soil or substrate monitoring, dependent on climate or time with each having both strengths and weaknesses. Recently, plant-based monitoring or plant reflectance-derived indices have been developed, yet their potential is limited for estimating the irrigation rate in order to apply proper irrigation scheduling. Optimization of irrigation practices imposes different irrigation approaches, based on prevailing greenhouse environments, considering plant-water-soil relationships. This article presents a comprehensive review of the literature, which deals with irrigation scheduling approaches applied for soil and soilless greenhouse production systems. Irrigation decisions are categorized according to whether or not an automatic irrigation control has the ability to support a feedback irrigation decision system. The need for further development of neural networks systems is required.

Irrigation system performance regards as a function of climatic conditions. The present study was carried out to study this phenomenon. Sugar beet and sesame corps were cultivated during two agricultural seasons of 2017/2018 and 2018/2019 irrigated with drip and sprinkler systems. The drip and sprinkler systems performance was evaluated in terms of hydraulic characteristics added to irrigation water requirements. The recorded monthly values were compared to the traditional estimation method. The results revealed that irrigation system efficiency was increased by increasing ambient temperature for the drip irrigation system, and vice versa was noticed with the sprinkler irrigation system. Emission uniformity and application efficiency of emitters were increased by increasing ambient temperature. While the sprinkler flow rate and distribution uniformity were decreased by increasing ambient temperature. For drip irrigation system, the average total amount of irrigation water requirements using traditional estimation for sugar beet (2372 m 3 /fed) was less than the actual calculated (2439 m 3 /fed), while for sesame crop, the traditional estimation method (2556 m 3 /fed) was higher than actual calculated (2477 m 3 /fed). Using a sprinkler system, the average total amount of irrigation water requirements by the traditional estimation (2689 and 2897 m 3 /fed) was less than the actual calculated (2709 and 3044 m 3 /fed) for sugar beet and sesame crops, respectively. So, it is important to consider the effects of climatic conditions through the agricultural season.

The exogenous application of osmoprotectants [e.g., proline (Pro)] is an important approach for alleviating the adverse effects of abiotic stresses on plants. Field trials were conducted during the summers of 2017 and 2018 to determine the effects of deficit irrigation and exogenous application of Pro on the productivity, morph-physiological responses, and yield of maize grown under two irrigation systems [surface irrigation (SI) and drip irrigation (DI)]. Three deficit irrigation levels (I 100 , I 85 , and I 70 , representing 100, 85, and 70% of crop evapotranspiration, respectively) and two concentrations of Pro (Pro 1 = 2 mM and Pro 2 = 4 mM) were used in this study. The plants exposed to drought stress showed a significant reduction in plant height, dry matter, leaf area, chlorophyll content [soil plant analysis development (SPAD)], quantum efficiency of photosystem II [Fv/Fm, Fv/F0, and performance index (PI)], water status [membrane stability index (MSI) and relative water content (RWC)], and grain yield. The DI system increased crop growth and yield and reduced the irrigation water input by 30% compared with the SI system. The growth, water status, and yield of plants significantly decreased with an increase in the water stress levels under the SI system. Under the irrigation systems tested in this study, Pro 1 and Pro 2 increased plant height by 16 and 18%, RWC by 7 and 10%, MSI by 6 and 12%, PI by 6 and 19%, chlorophyll fluorescence by 7 and 11%, relative chlorophyll content by 9 and 14%, and grain yield by 10 and 14%, respectively, compared with Pro 0 control treatment (no Pro). The interaction of Pro 2 at I 100 irrigation level in DI resulted in the highest grain yield (8.42 t ha –1 ). However, under the DI or SI system, exogenously applied Pro 2 at I 85 irrigation level may be effective in achieving higher water productivity and yield without exerting any harmful effects on the growth or yield of maize under limited water conditions. Our results demonstrated the importance of the application of Pro as a tolerance inducer of drought stress in maize.

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy.

Drought and salinity stress severely inhibits the growth and productivity of crop plants by limiting their physiological processes. Silicon (Si) supplementation is considerd as one of the promising approaches to alleviate abiotic stresses such as drought and salinity. In the present study, a field experiment was conducted over two successive growth seasons (2019-20) to investigate the effect of foliar application of Si at two concentrations (1 and 2 kg Si ha -1 ) on the growth, yield and physiological parameters of three maize cultivars (ES81, ES83, and ES90) under three levels of irrigation salinity) [1000 (WS 1 ), 2000 (WS 2 ) and 3000 (WS 3 ) mg L -1 NaCl]. In this study, A trickle irrigation system was used. Si application significantly mitigated the harsh effects of salinity on growth and yield components of maize, which increased at all concentrations of Si. In irrigation with S3 salinity treatment, grain yield was decreased by 32.53%, however, this reduction was alleviated (36.19%) with the exogenous foliar application of Si at 2 kg Si ha -1 . At salinity levels, Si application significantly increased maize grain yield (t ha -1 ) to its maximum level under WS of 1000 mg L -1 , and its minimum level (Add value) under WS of 3000 mg L -1 . Accordingly, the highest grain yield increased under Si application of 2 kg Si ha -1 , regardless of salinity level and the cultivar ES81 achieved the highest level of tolerance against water salinity treatments. In conclusion, Application of Si at 2 kg Si ha -1 as foliar treatment worked best as a supplement for alleviating the adverse impacts of irrigation water salinity on the growth, physiological and yield parameters of maize.

Urban areas face significant challenges, including a lack of green spaces, scarce water resources, environmental pollution, and elevated heat emissions, particularly in developing countries experiencing rapid population growth. Therefore, the study aims to advance sustainable urban agriculture by designing and evaluating a solar-powered smart rooftop irrigation system for peppermint cultivation. The system incorporates two drip irrigation setups—conventional and smart irrigation—powered by photovoltaic (PV) panels. The smart system integrates real-time monitoring of critical variables, including (1) soil moisture, (2) relative humidity, (3) PV panel temperature, and (4) PV panel current and voltage. Key performance metrics such as water and energy consumption, water use efficiency, energy productivity, and carbon dioxide emissions were evaluated for both systems. In addition, the economic analysis of the smart system was determined. Results revealed that the smart system reduced water and energy consumption by 28.1% compared to conventional irrigation. Additionally, the smart system achieved a notable reduction in carbon footprint, with CO 2 emissions of 0.181 kg CO₂/m 2 /year compared to 0.252 kg CO₂/m 2 /year for the conventional system. The system’s economic analysis demonstrated a payback period of 5.6 years, highlighting its financial viability. This study underscores the transformative potential of solar-powered smart irrigation systems in enhancing food security, conserving water, reducing energy consumption, and mitigating carbon emissions in urban agriculture.

Improving irrigation water management is a key concern for the agricultural sector, and it requires extensive and comprehensive tools that provide a complete knowledge of crop water use and requirements. This study presents a novel methodology to explicitly estimate daily gross and net crop water requirements, actual crop water use, and irrigation efficiency of center pivot irrigation systems, by mainly utilizing the Sentinel-2 MultiSpectral Instrument (MSI) imagery at the farm scale. ETMonitor model is adapted to estimate actual water use (as the sum of canopy transpiration and evaporation of water intercepted by canopy and evaporation from soil) at daily/10-m resolution, benefiting from the high-resolution Sentinel-2 data and thus to assess the irrigation efficiency at the farm scale. The gross irrigation water requirement is estimated from the net crop water requirement and the water loss, including the water droplet evaporation directly into the air during application before droplets fall on the canopy and canopy interception loss. The method was applied to a pilot farmland with two major crops (wheat and potato) in the Inner Mongolia Autonomous Region of China, where modern equipment and appropriate irrigation methods are deployed for efficient water use. The estimated actual crop water use showed good agreement with the ground observations, e.g. the determination coefficients range from 0.67 to 0.81 and root mean square errors range from 0.56 mm/day to 1.24 mm/day for wheat and potato when comparing the estimated evapotranspiration with the measurement by the eddy covariance system. It also showed that the losses of total irrigated volume were 25.4% for wheat and 23.7% for potato, respectively, and found that the water allocation was insufficient to meet the water requirement in this irrigated area. This suggests that the amount of water applied was insufficient to meet the crop water requirement and the inherent water losses in the center pivot irrigation system, which imply the necessity to improve the irrigation practice to use the water more efficiently.

This research aims to carry out a systematic review of the available literature about smart irrigation systems. It will be focused on systems using artificial intelligence techniques in urban and rural agriculture for soil crops to identify those that are currently being used or can be adapted to urban agriculture. To this end, a modified PRISMA 2020 method is applied, and three search equations are formulated. From those filters, and after a screening process, 170 articles are obtained. These articles are analyzed through VantagePoint, a text processing software. After this, they are taken through a detailed analysis phase in which 50 sources are selected as the most relevant to be read and analyzed by topic. Finally, the different phases of the analysis are used to draw conclusions that might be interesting for researchers working in this specific field or for the general public interested in rural and urban agriculture and its automation.