A simple Capillary Device for Real-time Monitoring of barley water requirements under arid environment

A simple Capillary Device for Real-time Monitoring of barley water requirements under arid environment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A simple Capillary Device for Real-time Monitoring of barley water requirements under arid environment Salah Zereg, Khaled Belouz, Salim Matallah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6571549/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In Algeria, as in many other developing countries, irrigation faces major challenges, including water wastage, overexploitation of groundwater, and soil salinization—primarily due to inadequate water management practices. Achieving efficient irrigation relies on the precise estimation of crop water requirements. However, this is often lacking, as it depends on accurate assessments of the reference evapotranspiration (ET₀) and the experimental determination of crop-specific coefficients using high resolution lysimeters. This study aims to develop and evaluate a simple, low-tech irrigation scheduling system based on the wick irrigation technique. The proposed system—a Low-Tech Self-Watering Capillary Device (LTSWCD)—enables real-time measurement of the crop evapotranspiration (ETc) of a selected crop (barley) grown in a one-square-meter micro-plot under arid conditions. The amount of water consumed over several days is then directly applied to larger experimental blocks. Daily measurements were compared to the standard barley estimates recommended in the FAO-56 guidelines, and irrigation scheduling was studied under two treatments: 100% (full irrigation) and 130% (over-irrigation) of the water depth recorded by the LTSWCD. The main results reveal that the measured crop evapotranspiration (ETc) captured the same trend as the based FAO56-PM ETc at different time intervals, with a correlation coefficient R = 0.95 at weekly and RMSE = 4 mm week -1 . The seasonal Bareley’s measured ETc was 327 mm, and the obtained Kc shows a good agreement with the standard Kc. Moreover, under two irrigation treatments (100 and 130%), barley’s grain and straw yields show no significant differences at a 0.05 significance level, which demonstrates that increasing irrigation beyond the measured crop water needs does not significantly enhance barley yields under the conditions of this experiment. These results highlight, in the absence of high-resolution lysimetric data, the effectiveness of the LTSWCD as a simple device that farmers could use to directly measure real-time irrigation water requirements under conditions similar to those in the study area. Irrigation scheduling device Crop water requirements Wick irrigation Barley CROPWAT model Arid environment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Irrigation is essential for food production, especially in arid and semi-arid areas (Pescod 1992). It is noted that more than 70% of all global freshwater is used for agriculture and food production (D’Odorico et al. 2020; FAO 2021), and that poor management of irrigation leads to low water use efficiency. Therefore, accurately determining crop water requirements is a significant challenge (Ferreira et al. 2019) for many farmers worldwide, both in developed and developing countries. This issue is exacerbated by ongoing climate change and the diminishing availability of water resources (Palmgren and Shabala 2024; Pravalie 2016). In developing countries, this knowledge gap often stems from research lack and limited access to accurate, localized data (Roy et al. 2021), leading farmers and irrigation managers to rely on traditional practices. Unfortunately, this reliance can result in inefficient irrigation, manifesting as either overirrigation or underirrigation. Algeria, like other countries in the MENA region, is facing severe water shortages, largely due to limited rainfall (Plan Bleu 2025; Agoumi 2003, Tanarhte et al. 2024; García-Ruiz et al. 2011) and increasing demand from key sectors such as agriculture, and domestic consumption (Mohammed and Al-Amin 2018). Reports from the Ministry of Water Resources (MWR) indicate that agriculture is the largest consumer of available freshwater, accounting for 64% of total withdrawals (FAO 2023), with irrigation being the dominant use. The situation is worsened by inefficient water management, poor valuation of water resources, and declining water quality. However, irrigation efficiency is significantly hindered by soil salinization, groundwater depletion, and inconsistent rainfall patterns, making water management increasingly challenging (Abouelmagd and Ahmed 2024; Hartani and Semar 2024; Boutera et al. 2012). Furthermore, climate change causes frequent droughts that increase the demand for irrigation water and thus contribute to significant water losses (Haile et al. 2024), Mouhouche and Guemraoui (2004) stated that obsolescence of the networks in large irrigated perimeters alone was responsible for 40% of water losses, not including those related to evaporation and deep percolation due to poor irrigation management. Therefore, efficient irrigation water management is essential to address the critical questions of when, how, and how much to irrigate (Allen et al. 1998; Levidow et al. 2014), ensuring that crops receive the required water precisely when they need it. Efficient irrigation can reduce water losses and improve crop productivity (Perry et al. 2017). For estimating crop water requirements, Doorenbos and Pruitt (1977) introduced the concept of crop coefficient (Kc), defined as the ratio between crop evapotranspiration (ETc) and reference evapotranspiration (ETo): Kc = ETc/ETo (1) In the FAO-24 paper, Doorenbos and Pruitt (1977) proposed Kc values for a list of crops grown under typical soil wetting and irrigation management conditions. Later, Allen et al. (1998) published a revised list in the FAO-56 paper, where Kc values were adjusted (FAO56–Kc) using the Food and Agriculture Organization Penman-Monteith reference evapotranspiration (FAO56PM–ETo) equation instead of the modified Penman method from FAO–24: Where: FAO56PM–ETo in (mm day − 1 ), reference evapotranspiration; R n in (MJ.m − 2 .day − 1 ), net radiation at crop’s surface; G in (MJ.m − 2 .day − 1 ), the heat-flux density of the soil; T in (°C), mean-daily air temperature; u 2 in (m s − 1 ) wind speed; e s and e a in (kPa), saturation and actual vapor pressure, respectively; γ in (kPa °C − 1 ), the psychrometric constant; and Δ in (kPa °C − 1 ), he slope of the saturation vapor pressure curve Until now, in the absence of lysimetric studies, the practical approach for estimating crop evapotranspiration (ETc in mm day − 1 ) has been to multiply the crop coefficient (FAO56–Kc) by FAO56PM–ETo (mm day − 1 ). Therefore, from Eqs. (1) and ( 2 ), ETc is expressed as: FAO56–ETc = FAO56–Kc×FAO56PM–ETo (3) Allen et al. (1998) recommended that the Kc values should be obtained empirically for irrigated crops using lysimetric data while taking into account local climatic conditions. However, there is very little research on ETc for field crops, and the Kc values obtained through lysimeters have not been improved for important crops under arid and semi-arid conditions (Benli et al., 2006). Moreover, the Kc values obtained in standard environments may not accurately reflect farm conditions and real farming operations, making it difficult or requiring adjustments (Allen et al., 1998) to apply such data effectively in the field, which could lead to imprecise irrigation management strategies. Developing a simple, cost-effective, and reliable irrigation scheduling tool that can be easily adopted by farmers will be crucial (Rouzaneh et al., 2021). In this context, low-tech irrigation scheduling systems based on real-time measurement of crop water requirements will offer a promising solution for improving water use efficiency, particularly in regions that face water scarcity. These systems should be characterized by their simplicity, sustainability, accessibility, and affordability, making them ideal for farmers in such regions (Vandôme et al., 2023). In recent years, several studies have explored the effectiveness of wick irrigation systems and highlighted their potential to improve water use efficiency, reduce labor and resource inputs, and enhance crop yield and quality. Zarei et al. 2024 ; Vyas et al., 2022 ; Ferrarezi and Testezlaf 2016 ; Chi-Won et al. 2010). Based on this irrigation technique, this study consists of developing, and testing a self-regulating device namely, low-tech self-watering capillary device (LTSWCD), that measures barley’s ( Hordeum Vulgare ) water needs from a micro-plot under arid environment zone in Algeria and using the recorded measurements to schedule irrigation of the crop grown in larger experimental plots. 2. Materials and methods 2.1 Site description The field experiment was conducted at the Technical Institute of Saharan Agriculture (ITDAS) experimental station in Biskra region, southeast of Algeria (Fig. 1 ). It is situated at 34.808° N latitude and 5.655° E longitude at an elevation of 153 m asl (above sea level). The climate is arid with annual precipitation below 100 mm and evaporation exceeding 2500 mm per year. Agriculture is the main economic sector in Biskra, heavily dependent on groundwater for irrigation. The region cultivates a diverse range of crops, including greenhouse vegetables, date palms, olives, cereals, and alfalfa. Due to its arid climate, adopting efficient irrigation methods is essential for maintaining sustainable agricultural production. These conditions make Biskra a suitable site for assessing irrigation technologies, such as the low-tech irrigation control device examined in this study, which seeks to optimize water usage in water-limited environments. 2.2 Crop and experimental Design 2.2.1 The crop Barley ( Hordeum Vulgare L ) is the second most-produced cereal in Algeria after wheat, with mean area harvested more than 1 M ha year − 1 and 1.4 mean yield of 1.4 t ha − 1 (FAO 2024) was selected for this study due to its critical role as a staple crop for both food and animal feed. Efficient irrigation is an essential parameter for optimizing barley’s productivity and water use efficiency, particularly in arid and semi-arid environment like Biskra, Algeria. For the experiment, the variety Fouara was chosen and sown on December 28, 2023. The thousand kernel weight (TKW) of the seeds was 33.53 g, and the sowing density was set at 400 plants m − ², corresponding to a sowing rate of 172 kg ha − 1 . The field experiment has a sandy soil type, At the beginning of the experiment, the soil had an organic matter content of less than 1%, an electrical conductivity of 2.26 dS m-1 and a volumetric moisture content of 60 mm m − 1 . For the need for validation of the LTSWCD measurements (observed ETc), the barley’s Kc values given by the FAO56 database were used to estimate crop evapotranspiration (ETc) based on FAO56PM–ETo estimates. Figure 2 shows the commonly Kc’s four growth stages, as illustrated in the CROPWAT model (Smith 1992), the barley’s crop cycle accounts for 120 days, starting from planting date (28/12/2023) to proposed harvest day (26/04/2024): Initial stage (Kc = 0.30, 15 days): It represents the germination and early growth period where ETc is low; Development stage (Kc increases gradually, 25 days): during this period the plant starts growing actively, and ETc rises; Mid-season stage (Kc = 1.15, 50 days): This stage represents the crop at its peak growth, requiring maximum ETc; Late season stage (Kc decreases to 0.25, 30 days): The crop matures, and ETc declines. The critical depletion fraction indicates the soil moisture level below which the crop begins to experience water stress: 0.55 during the initial stage, 0.55 and 0.50 during the development and mid-season stages, and 0.90 in the late season, showing the crop can tolerate more dryness at this stage. The Ky coefficient represents the yield response to water deficit: 0.20 for the initial stage; 0.60, 0.50, 0.40 for the development, mid-season, and late-season stages, respectively. These values indicate the sensitivity of barley yield to water stress, with the development stage being the most sensitive. 2.2.2 Experimental design The study followed a randomized complete block design (RCBD) with irrigation as the main factor. Two irrigation treatments were tested: I1: 100% of the crop water requirements (full irrigation). I2: 130% of the crop water requirements (over-irrigation). Each treatment was replicated three times. The experimental plots were irrigated using a drip irrigation system. The spacing between plots was 0.5 m, and between blocks was 1m. Pressure-compensating emitters, with a discharge rate of approximately 8 liters per hour, were installed along 16 mm diameter polyethylene lines. Each plot contains 11 lines (laterals). The lateral spacing was set at 20 cm, while emitter spacing varied between 20 cm for the fully irrigated treatment (I1, 100%) and 15 cm for the over-irrigated treatment (I2, 130%). Water used for irrigation had a total dissolved solids (TDS) concentration of 2.7 g L − 1 , suitable for barely crop. These treatments were designed to assess the accuracy of the designed device, water use efficiency, and yield, under the specific environmental conditions of Biskra, Algeria. 2.2.3 Description of the designed LTSWCD The LCSWCD included a micro-plot of 1 m² sowed and fertilized at the same calendar of the experimental plot, where crop evapotranspiration is continuously measured. Figure 3 shows a descriptive diagram of the low-tech self-watering capillary-device prototype. It consists of four interconnected plastic tanks (A, B, C, and D): Above-Ground Tank (Tank A): This elevated tank measures 0.92 m × 1.12 m × 1 m (depth) and serves as the primary irrigation water reservoir. It is connected at its base to Tank B via a 16 mm diameter multilayer pipe, which terminates in a smart float valve mounted on Tank B’s wall. This valve ensures a consistent water level in Tank B. The transparent Tank A is equipped with a graduated measuring tape, allowing for easy monitoring of daily water consumption. Topsoil Tank (Tank B): This tank consists of a 110 mm diameter closed PVC pipe, buried in the topsoil and encircling Tank C on all four vertical sides. Wick emitters extend from Tank B into the topsoil of Tank C, providing water as shown in the vertical cross-section (Fig. 3 -c). The distribution of wick emitters is illustrated in Fig. 3 -a. Underground Tank (Tank C): Measuring 0.92 m × 1.12 m × 1 m (depth), this tank contains a 15 cm gravel layer at the bottom to facilitate drainage water infiltration. Above the gravel, a 75 cm layer of soil (substrate) supports plant growth for water consumption measurement. Tank C is connected at its bottom to Tank D via a 20 mm diameter PVC pipe. Drainage Tank (Tank D): Positioned in a ditch, this 0.4 m × 0.4 m × 0.5 m (depth) tank collects drainage water, if any, particularly during the first month of crop growth. The water circulates in a closed circuit: it flows very slowly from tank (A) to tank (B) by gravity, then from tank (B) to tank (C) via capillary wicks through capillarity and soil suction, and from tank (C) to tank (D) by gravity. Finally, the drainage water is returned from tank (D) to tank (A) automatically by a pump equipped with a booster (see Fig. 3 -b). Each wick is made of a bundle of eight braided capillary strings covered with a flexible sheath. Each string has a diameter of 4 mm and a length varying from 38 to 58 cm (Fig. 4 ). The two ends of the wick are bare, one submerged in the water of tank (B) and the other buried in the soil of tank (C) at a distance of 7 cm below the soil surface. The difference in level between the water in tank (B) and the buried ends of the wicks is 6 cm. There are 58 wicks, ensuring the soil profile is fully moistened and meets the crop's peak water demand. This number was determined by trial and error, adding wicks and monitoring the soil's moisture profile until an optimal configuration was achieved. The distribution of the wicks in the soil is shown in Fig. 3 -c. Each wick takes the form of an inverted L (Ί). The assembly of tanks (B) and (C) is surrounded by a 20 cm thick layer of soil to ensure thermal insulation of the substrate (soil). Tanks (A) and (C) have the same geometric shape and surface area so that the lowering of the water level in tank (A) corresponds to the water consumption of the crop sown in the substrate (soil) during the chosen time step. For example, a lowering of the water level in tank (A) by 5 mm corresponds to a crop water consumption of 5 mm (= 50 m³ ha − 1 ). 2.2.5 Irrigation and Fertilization Management Irrigation and fertilization are known to significantly impact water use efficiency and crop growth: the fertilization was applied uniformly across all plots with the following nutrient inputs: Nitrogen (N): 200 kg N ha − 1 as urea, split into three equal applications at growth stages DC12, DC22, and DC31 according to the Zadoks (1974) scale. Phosphorus (P2O5): 100 kg P 2 O 5 ha − 1 as superphosphate, applied at sowing. Potassium (K2O): 100 kg K 2 O ha − 1 as potassium sulfate. Phonological development was monitored following the Zadoks scale (Zadoks et al. 1974) to ensure appropriate timing of growth stage-specific interventions and grass weeds were controlled manually by hand throughout the growing season. The irrigation treatments consisted of two levels: 100% and 130% of the crop's water requirement measured by the LTSWCD. These levels were selected to test the reliability of the measurements of the device in term of optimal irrigation (100%) and slightly excessive irrigation (130%) on barley yield. The 100% irrigation treatment was provided by the LTSWCD, which uses real-time measurements of crop evapotranspiration in a 1 m² micro-plot. The 130% treatment was adjusted relative to the LTSWCD’s measurements. 2.2.6 Data collection and measurements Meteorological data from the experimental site are essential for the daily reference evapotranspiration (ETo) calculation using the benchmark FAO56PM equation (Allen et al. 1998). The required data were sourced from multiple datasets and estimation procedures. Aggregated daily minimum and maximum temperatures and relative humidity were recorded locally during the 2023–2024 experimental period using a temperature and humidity logger. The mean daily wind speed (10 m height above ground level) data were obtained from the Tutiempo network (Tutiempo.net 2024) and adjusted to standard height (2 m) using 0.75 conversion factor as given by Allen et al. (1998). Also taking into account the presence of a 6 m-high windbreak at the experimental site by applying a reduction coefficient (taken 25%), as suggested in the literature (Cleugh 1998; Brandle and Finch 1991; Salem, 1989). Missing values of solar radiation and sunshine hours were estimated using the built-in estimation function of CROPWAT version 8.0, following the procedure outlined by Allen et al. (1998). Since the LTSWCD operates as a simplified lysimeter, barley water consumption (observed ETc) in the micro-plot was monitored daily at 8:00 a.m. The ETc was determined from the soil water balance (Allen et al., 1998) following Eq. ( 4 ), $$\:{Dr}_{i}={Dr}_{i-1}-{\left(P-RO\right)}_{i}-{I}_{i}-{CR}_{i}i+{ETc}_{i}+{DP}_{i}$$ 4 So, $$\:{ETc}_{i}=\:{Dr}_{i}-{Dr}_{i-1}+{\left(P-RO\right)}_{i}+{I}_{i}+{CR}_{i}i-{DP}_{i}$$ 5 Where, Dr i : root zone depletion at the end of day i (in mm); Dr i−1 : soil moisture content in the root zone at the end of the day i−1 (in mm); P i : precipitation on day i (in mm); RO i : runoff from the soil surface on day i (in mm); I i : net irrigation depth on day i (in mm); CR i : capillary rise from the water table on day i (in mm); ETc i : crop ET on day i (in mm); DP i : percolated water out of root zone on day i (in mm); The ETc was measured directly based on the decrease in the water level in tank A of the LTSWCD (Fig. 3 ). During the crop season P was neglected (P ≈ 0 mm), there were no considerable precipitations (only 6 mm during the mid-season (in one day) and 7.8 mm (in 3 days) during the late season), wick network ensured irrigation without RO losses (i.e., RO = 0), and that the crop was grown in conditions without water stress. Which involves, that soil moisture depletion was maintained between field capacity (FC) and below the readily available moisture threshold (i. e. FC ≤ Dr ≤ RAM). DP assumed to be zero, because the soil water moisture in the root zone was maintained, along the crop cycle, below FC (i.e., Dr, i > 0). So, Eq. ( 5 ) becomes Eq. ( 6 ), $$\:{ETc}_{i}=\:{Dr}_{i}-{Dr}_{i-1}+{I}_{i}$$ 6 At the beginning of the experiment, the soil was manually filled to its field capacity (60 mm). The cumulative water amount over a few days (within a week) was then applied to the larger experimental plots. To capture variability across different time scales and identify broader ETc trends, measured ETc values were compared at daily, 3-day, and weekly intervals against corresponding ETc estimates using Eq. (3) (FAO56PM–ETc). This analysis is crucial for assessing the device's accuracy and its applicability in irrigation scheduling. 2.2.7 Yield components and statistical analysis The harvest took place on May 3, 2024, and yield data were collected from a 0.5 m² area at the center of each plot to reduce edge effects. Yield parameters were measured at harvest and included: Weight of 1000 kernels: Measured by weighing a representative sample of 1000 kernels. Grain yield: Expressed in g m − ² and converted to t ha − 1 , calculated from the harvested area of 0.5 m² in the middle of each plot. Straw yield: After harvesting each plot, straw was collected, bagged, and weighed to determine the straw yield, expressed in g m − ² and converted to t ha − 1 . For the statistical analysis, two one-way analyses of variance (ANOVA) were conducted to assess differences in grain and straw yields among irrigation treatments using the data analysis toolpack in Microsoft Excel v. 2019 (Microsoft 2018). Mean differences between treatment were considered significant at ( P-value < 0.05), based on Fisher’s least significant difference (LSD) test. 3. Results and discussions 3.1 Reference evapotranspiration (ETo) Reference evapotranspiration (ET₀) values were automatically calculated using the standard FAO56 Penman-Monteith approach (Eq. ( 2 )), as recommended by Allen et al. (1998). Table 1 presents the monthly aggregated ET₀ values and meteorological data, both derived from the daily time series of the study area that are represented in Fig. 5 . The collected and recorded data reveals Biskra's arid climate (Table 2 ) marked by high thermal amplitudes, with summer peaks of more than 40°C and winter lows near 9°C. The relative humidity ranged from 24% in July to 55% in October, and intense solar radiation, particularly from June to August. Wind speeds ranged from 1.5 to 4.2 m s − 1 , predominantly blowing from the northwest and north-northwest combined with high radiation levels, contributing to significant evapotranspiration. Reference evapotranspiration (ETo) peaks at 10.6 mm day − 1 in July 2024. During the barley growing season, ETo varies from 2.0 mm day − 1 in December 2023 to a peak of 5.1 mm day − 1 in April 2024. Table 1 Aggregated monthly data from December 2023 to November 2025 and ETo estimates values using CROPWAT 8.0 software Month Min Temp Max Temp Relative Humid. Eff. Rain mm Wind speed Sunsh. Durat. Solar radiation ETo °C °C % m s − 1 Hours MJ m − ² day − 1 mm day − 1 January 9.7 19.2 49 4.9 1.5 5.4 9.6 2.0 February 11.3 20.4 51 8.5 2.0 5.5 11.8 2.7 March 13.2 24.4 40 2.5 1.9 7.5 16.9 4.0 April 16.4 27.4 38 8.7 2.0 8 20.1 5.1 May 21.0 33 34 0 3.6 9.3 23.4 7.9 June 26.5 38.6 30 0 4.2 9.7 24.5 9.9 July 30.0 43.1 24 0 3.5 10.2 24.9 10.6 August 29.3 40.8 29 0 2.9 8.5 21.3 8.6 September 25.3 36.2 29 0 2.8 7.8 18 7.0 October 20.5 31.1 32 0 2.7 6.6 13.8 5.3 November 16.2 25 46 0 3.8 5.1 9.7 3.9 December 10.9 19.6 52 0 2.0 4.8 8.4 2.1 Average 19.2 29.9 38 24.6 2.7 7.4 16.9 5.77 3.2 Barley ETc and crop coefficient The evolution of daily crop evapotranspiration of barley, measured using the LTSWCD (observed ETc) and soil water status throughout the crop growth cycle are presented in Fig. 6 a–c. To help identify trends in observed ETc, a 7-day moving average and cumulative ETc over 3-day intervals spanning 120 days after sowing are shown alongside FAO56PM–ETc values. Figure 6 highlights the condition of no water stress, where soil moisture depletion remains below the readily available water (RAW) threshold (Fig. 6 c), and illustrates the four standard growth stages of barley, providing insights into changes in water demand throughout the crop growth cycle Initial Stage (1–15): During the first 10 days after sowing (December 28), mean observed ETc was 0.2 mm day -1 and 0.5 mm day -1 for FAO56PM–ETc estimates. This was due to the micro-plot of the LTSWCD being covered with a transparent plastic film to enhance germination, considering the late sowing date, and to protect seeds from bird predation. The plastic barrier prevented evapotranspiration, leading to no water loss during this period. The cumulative observed ETc during this stage was 3 mm compared to 7.1 mm for FAO56PM–ETc (recommended). Development Stage (16–40): Following the removal of the plastic film, observed ETc gradually increased as the crop developed. Observed ETc ranged from 1.0–3.0 mm day -1 , while FAO56PM–ETc ranged from 0.6–2.7 mm day -1 . Both methods showed a steady rise in ETc, with observed ETc estimates being slightly higher in most periods. This phase marks the initial establishment of the crop, where water demand remains relatively low but steadily increases as canopy cover expands, the cumulative observed ETc during this stage was 44 mm (13% higher) against 39 mm for FAO56PM–ETc. Mid-Season (41–90): During this phase, ETc values reached their peak as the crop experienced its highest water demand. Observed ETc ranged from 2.0 to 5.0 mm day-1, while FAO56PM–ETc ranged from 2.7 to 7.2 mm day -1 . Both methods demonstrated a strong agreement, with FAO56PM estimates showing more fluctuations and are slightly higher. This period represents the most critical stage for irrigation management, as water uptake is at its maximum due to active crop growth and increased transpiration. The cumulative observed ETc during this stage was 12% lower than the recommended value, totaling 177 mm compared to 201.5 mm for FAO56PM–ETc. Late Season (91 - End): As the crop approached maturity, ETc declined significantly. Observed ETc ranged from 1.4 to 4.2 mm day -1 , while FAO56PM–ETc varied between 1.4 and 6.4 mm day -1 . Both methods captured the downward trend, with observed ETc slightly exceeding FAO56PM estimates in some instances. The reduction in water demand during this stage is attributed to senescence and reduced canopy transpiration, marking the end of the crop cycle. The cumulative observed ETc during this stage was 2% higher than recommended value, totaling 103 mm against 101.1 mm for FAO56PM–ETc. In the same way Fig. 6 represents the 7 days period moving average (Fig. 6 A) and cumulated of three days of the ETc (Fig. 6 B) for both methods, observed ETc follows a similar pattern, with a gradual increase after sowing, peaking around days 80–90, and then declining with barley’s growth cycle. Initially, both methods are closely aligned, but observed ETc records slightly higher values during early to mid-growth stages before converging with FAO56PM–ETc later. The barley’s seasonal observed ETc, using LTSWCD measurements, was 327 mm against 348.9 mm computed FAO56-PM ETc. The slight differences suggest that LTSWCD may better capture localized conditions or microclimate effects that the FAO Penman-Monteith method might not fully account for in the study area. Also, it highlights the potential for using the LTSWCD in real-time irrigation scheduling, as it may better reflect actual field conditions compared to the standard FAO method. In general, whether using daily (Fig. 6 a) or 3-day intervals (Fig. 6 b), both observed ETc and FAOPM-ETc follow the same trend over time. The ETc measured using LTSWCD remains consistent, while FAO56PM–ETc tends to give slightly higher estimates, especially during early and mid-growth stages. These results follow the same trend and are in comparable order of magnitude with previous lysimetric studies on barley found by (López-Urrea et al. 2021) and Hashemi and Sepaskhah (2020). The differences are possibly related to the differential varietal response, the duration of the crop cycle (120 days in the present study, 150 days in López-Urrea et al. (2021), and 200 days in Hashemi and Sepaskhah (2020)), as well as the prevailing climate and local conditions. Figure 7 illustrates the scatter plot representing the relationship between the weekly observed ETc and FAO56PM–ETc values. The coefficient of determination R 2 = 0.92 indicates a strong positive correlation between the two methods. The data points generally align along the best-feet line, which supports the reliability of the LTSWCD as a practical tool for estimating crop water requirements and validating its accuracy. The scatter plots of the residuals (Fig. 8 ) don’t show any patterns (the points of the residuals are randomly distributed between the two sides of the origin axis) which indicates good performance. For the Kc (Fig. 9 ), observed Kc values (blue dots), obtained using Eq. (1), depicted noisy variability around the FAO56–Kc smoothed representation curve, based on barley grown under standard conditions, This account for daily time resolution effects and site-specific conditions such as soil type, irrigation method, or microclimate differences. The 10-period moving average helps reduce this noise, making trends more comparable to FAO56–Kc. The comparison between FAO56–Kc and Observed Kc across different growth phases as suggested in Fig. 2 , highlights key deviations due to real-world conditions. During the initial phase (1–15 days), observed Kc values range from 0 to 0.6, while FAO56–Kc remains between 0.3 and 0.5. Notably, in the first 10 days, the LTSWCD microplot was covered by plastic microfilm, effectively reducing water demand to zero and resulting in an observed Kc of zero during this period. In the development phase (16–40 days), observed Kc shows high variability (0.4–1.8), often exceeding the FAO56 range (0.5–1.15), likely due to fluctuating weather and irrigation differences. The mid-season (41–90 days) exhibits more stability, with observed Kc values (0.8–1.6) closely following FAO56 (1.15), though occasional peaks occur. In the late-season (91–120 days), observed Kc declines closely (1.0-0.3) with FAO56 (1.15–0.3). Overall, while FAO56–Kc serves as a generalized reference, real-time observations capture daily fluctuations and site-specific influences, which are crucial for precise water management. Which suggests that FAO-56 model provides a good reference for estimating barley’s Kc, but local conditions introduce variability. This comparison highlights the importance of site-specific calibration of Kc values for improved irrigation management and water use efficiency. 3.3 Crop yield and statistical analysis The Table 2 summarizes the experiment agronomic performance under two irrigation treatments: 100% and 130% of water depletion measured using the LCSWCD, focusing on grain yield, straw yield, and mean kernel weight. The LTSWCD treatment produced the highest grain and straw yields, at approximately 6.1 and 13.3 t ha⁻¹, respectively, significantly outperforming the other treatments. This can be attributed to the use of fresh water for irrigation, with TDS below 0.1 g L − 1 . For the 100% treatment plot, grain yield averaged 4.2 t ha − 1 , with a straw weight of 4.6 t ha − 1 and a TKW of approximately 34.4 mg, which aligns with findings in term of grain yield from numerous barley studies conducted under semiarid and arid environments, for example, in Iran (Jahromi et al. 2023) and in USA (Lazicki et al. 2016). The mean TKW of 34.4 mg is higher than TKW of seeds (33.5 g). Under 130% irrigation, grain yield increased only marginally to an average of 4.40 t ha − 1 ; however, straw yield declined to 4.10 t ha − 1 , and 1000 kernel weight dropped to 32.53 mg. Table 2 agronomic performance results under the LTSWCD, 100%, and 130% irrigation treatments Treatments Grain yield (t ha − 1 ) Straw yield (t ha − 1 ) TKW (g) LTSWCD 6.1 13.3 24.17 4.26 4.03 35.36 100% 3.66 3.79 36.31 4.66 5.95 31.45 Average 4.19 4.59 34.37 4.08 2.79 31.36 130% 4.98 6.29 39.90 4.14 3.23 26.33 Average 4.40 4.10 32.53 3.3.1 Analyze of variance Two one-way ANOVAs were conducted to compare grain and straw yields between the two irrigation treatments: 100% (full irrigation), representing water consumption measured using the low-tech self-watering capillary device (LTSWCD), and 130% (over-irrigation), which exceeds the measured water requirement by 30%. The ANOVA results indicated that differences in both grain and straw yields were not statistically significant at the 0.05 level, suggesting that increasing irrigation beyond the crop’s measured water needs does not significantly enhance yields. These findings highlight the effectiveness of LTSWCD in measuring barley irrigation water requirements and emphasize that overirrigation does not effectively increase grain yield and may even lead to trade-offs, such as reduced straw yield and grain quality, due to nutrient leaching, which is well documented in sandy soils. This suggests that optimal water use is crucial to balance productivity and irrigation water use efficiency. 4. Conclusion This study demonstrates the effectiveness of the designed low-tech self-watering capillary device as a simple, easy to use, and reliable tool for real-time and direct measurement of barley water needs. The results indicate that the measured ETc follows the FAO56-PM ETc trends and in a comparable order of magnitude in term of seasonal water requirement, confirming the device as a promising accurate tool in irrigation scheduling. Furthermore, the comparison between the 100% (full irrigation) and 130% (over-irrigation) treatments reveals that increasing irrigation beyond the measured crop water requirements does not lead to significant improvements in barley grain and straw yields. These results highlight, in the absence of high-resolution lysimetric data, the effectiveness of the LTSWCD as a simple and easy-to-use device for direct measuring in real time irrigation water requirements under conditions similar to the study area. These findings highlight the importance of precise irrigation management in optimizing water use efficiency. The LTSWCD provides, in the absence of high-resolution lysimetric data, a simple and scalable solution for sustainable irrigation, particularly in arid environments. By ensuring that irrigation is based on actual crop water demands, this low technology can contribute to reducing water waste while maintaining optimal crop productivity. Future research could focus on testing an enhanced version of the device by integrating soil moisture sensors and water depth data loggers in place of visual control to improve real-time monitoring and automation. Additionally, evaluating its performance across different soil types, crops, and climatic conditions would further validate its applicability and scalability. Statements and Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by S. Z., K. B., and S. M.. The first draft of the manuscript was written by S. Z., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript Acknowledgement The authors would like to acknowledge the help of the Technical Institute of Saharan Agriculture (ITDAS) in Biskra, Algeria for providing the experimental site that made this research possible. References Abouelmagd A, Ahmed M (2024) Groundwater in North Africa: Effects of Climatic and Anthropogenic Pressures on Groundwater Availability. Springer, Cham. https://doi.org/10.1007/978-3-031-48299-1_11. Agoumi A (2003). Vulnerability of the Maghreb countries to climate change: real and urgent need for an adaptation strategy and means for its implementation. International Institute for Sustainable Development and Climate Change. Knowledge Network. Allen R G, Pereira L S, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109. 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In: Climate Change and Environmental Degradation in the MENA Region (pp. 175-187). Cham: Springer Nature Switzerland. Hashemi M, Sepaskhah A R (2020) Evaluation of artificial neural network and Penman–Monteith equation for the prediction of barley standard evapotranspiration in a semi-arid region. Theoretical and Applied Climatology, 139(1), 275-285. https://doi.org/10.1007/s00704-019-02966-x Jahromi M N, Razzaghi F, Zand-Parsa S (2023) Strategies to increase barley production and water use efficiency by combining deficit irrigation and nitrogen fertilizer. Irrigation Science, 41(2), 261-275. https://doi.org/10.1007/s00271-022-00811-0 Lazicki P, Geisseler D, Horwath W (2016) Barley production in California. University of California Davis, California Levidow L, Zaccaria D, Maia R, Vivas E, Todorovic M, Scardigno A (2014) Improving water-efficient irrigation: Prospects and difficulties of innovative practices. Agricultural Water Management, 146, 84-94. https://doi.org/10.1016/j.agwat.2014.07.012 López-Urrea R, Pardo J J, Simón L, Martínez-Romero Á, Montoya F, Tarjuelo J M, Domínguez A (2021) Assessing a removable mini-lysimeter for monitoring crop evapotranspiration using a well-established large weighing lysimeter: a case study for barley and potato. Agronomy, 11(10), 2067. https://doi.org/10.3390/agronomy11102067 Microsoft Corporation. (2018) Microsoft Excel (Version 2019) [Computer software]. Microsoft. https://www.microsoft.com/ Mohammed T and Al-Amin A Q (2018) Climate change and water resources in Algeria: vulnerability, impact and adaptation strategy. Economic and Environmental Studies (E&ES), 18(1), 411-429. https://doi.org/10.25167/ees.2018.45.23 Mouhouche B and Guemraoui M (2004) Réhabilitation des grands périmètres d'irrigation en Algérie. In Séminaire sur la modernisation de l'agriculture irriguée (pp. 13-p). IAV Hassan II. https://hal.science/cirad-00189190/ Palmgren M and Shabala S (2024) Adapting crops for climate change: regaining lost abiotic stress tolerance in crops. Frontiers in Science, 2, 1416023. https://doi.org/10.3389/fsci.2024.1416023. Perry C, Steduto P KF (2017) Does improved irrigation technology save water? FAO, Cairo Pescod M B (1992) Wastewater treatment and use in agriculture-FAO irrigation and drainage paper 47. Food and Agriculture Organization of the United Nations, Rome. Plan Bleu (2025) MED 2050 The Mediterranean by 2050, A foresight by Plan Bleu. https://planbleu.org/wp-content/uploads/2025/01/RAPPORT-MED-2050-EN.pdf. Pravalie R (2016) Drylands extent and environmental issues. Earth-Science Reviews, 161, 259–278. https://doi.org/10.1016/j.earscirev.2016.08.003. Rouzaneh D, Yazdanpanah M, Jahromi A B (2021) Evaluating micro-irrigation system performance through assessment of farmers' satisfaction: implications for adoption, longevity, and water use efficiency. Agricultural Water Management, 246, 106655. https://doi.org/10.1016/j.agwat.2020.106655. Roy DK, Lal A, Sarker KK, Saha KK, Datta B (2021) Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system. Agric Water Manag 255:107003. https://doi.org/10.1016/j.agwat.2021.107003. Salem B B (1989) Arid zone forestry: A guide for field technicians. Food and Agriculture Organization (FAO), Roma Smith M (1992) CROPWAT: A computer program for irrigation planning and management. FAO Irrigation and Drainage Paper 46. Food and Agriculture Organization of the United Nations, Roma Tanarhte M, Vries A J De, Zittis G, Chfadi T (2024) Severe droughts in North Africa: A review of drivers, impacts, and management. Earth-Science Reviews, 250, 104701. https://doi.org/10.1016/j.earscirev.2024.104701. Tutiempo Network (n.d.) (2024) Historical weather data. Tutiempo.net. https://en.tutiempo.net/. Accessed 26 December 2024 Vandôme P, Leauthaud C, Moinard S, Sainlez O, Mekki I, Zairi A, Belaud G (2023) Making technological innovations accessible to agricultural water management: Design of a low-cost wireless sensor network for drip irrigation monitoring in Tunisia. Smart Agricultural Technology, 4, 100227. https://doi.org/10.1016/j.atech.2023.100227 Vyas U, Darji K, Bhatt N, Vakharia V, Patel D (2022, August) Evaluation of Movement of Wetting Front Under Wick Irrigation in Black Cotton Soil. In International Conference Innovation in Smart and Sustainable Infrastructure (pp. 63-72). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-3557-4_6 Zadoks J C, Chang T T, Konzak C F (1974) A decimal code for the growth stages of cereals. Weed Research, 14(6), 415–421. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x Zarei Z, Heidari H, Honarmand S J, Bafkar A (2024) Improving grain yield and water use efficiency in maize by wick irrigation. Irrigation Science, 42(4), 785-800. https://doi.org/10.1007/s00271-023-00906-2 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6571549","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481603069,"identity":"8ddc1ee3-db8b-42d2-a4ac-61d96f8d516d","order_by":0,"name":"Salah 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localization\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6571549/v1/6f0a92c81416e472c6f682ff.png"},{"id":86310625,"identity":"46a5a3a6-5804-4f17-b0aa-5e47925cdddd","added_by":"auto","created_at":"2025-07-09 08:08:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50196,"visible":true,"origin":"","legend":"\u003cp\u003ethe barley’s crop diagram in CROPWAT (Smith, 1992)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6571549/v1/a62655576a16c0affe453638.png"},{"id":86310612,"identity":"e572d507-50b5-4e8e-8734-a5156d23ca45","added_by":"auto","created_at":"2025-07-09 08:08:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":232527,"visible":true,"origin":"","legend":"\u003cp\u003eTechnical diagram of the prototype of the LTSWCD irrigation scheduling system, (a) The distribution of the ends of the wicks in the soil, (b) Horizontal cross section through tanks B and C, and (c) vertical cross section through tanks B and C\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6571549/v1/54dba62336e0c7d53a737510.png"},{"id":86310623,"identity":"00174edb-d206-45bb-8d0f-84fec37b89e3","added_by":"auto","created_at":"2025-07-09 08:08:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80682,"visible":true,"origin":"","legend":"\u003cp\u003ePhoto of the wick emitter\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6571549/v1/ab98f67996d0c7cec8d8af34.png"},{"id":86310620,"identity":"28b84790-018b-4d55-9bb6-580d73f97987","added_by":"auto","created_at":"2025-07-09 08:08:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":121676,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variations of meteorological data during December 2023 to November 2025\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6571549/v1/c7701c60a6b971f4cd70f1a9.png"},{"id":86310617,"identity":"a5f56e12-f23e-4efb-b6ed-83f3717bc6b0","added_by":"auto","created_at":"2025-07-09 08:08:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":150260,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of crop evapotranspiration (ETc) and soil water status throughout the crop growth cycle. (a) Daily ETc estimated using FAO56 PM method vs. observed ETc, with different growth stages highlighted, (b) Three-day cumulative ETc trends, showing variations over time, and (c) Soil water depletion dynamics, depicting root-available moisture (RAM), total available moisture (TAM), and field capacity over the growing period\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6571549/v1/5b51ee7aedc5d204b5f0b3f1.png"},{"id":86310622,"identity":"39d4ce4e-e22c-422c-bbb9-070c0bcb4b37","added_by":"auto","created_at":"2025-07-09 08:08:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":16918,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of observed ETc values and those based on FAO56PM–ETo\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6571549/v1/6c47196c42ea55e9417d861e.png"},{"id":86310630,"identity":"6ac7cfac-7407-4ed0-b1e6-177daa298740","added_by":"auto","created_at":"2025-07-09 08:08:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":11003,"visible":true,"origin":"","legend":"\u003cp\u003escatter plot of residuals versus weekly mean observed ETc\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6571549/v1/b55af0d21286322e82496b34.png"},{"id":86310611,"identity":"ec786957-e77b-4a2a-9bff-0cfba262a3ce","added_by":"auto","created_at":"2025-07-09 08:08:01","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":56819,"visible":true,"origin":"","legend":"\u003cp\u003escatter plot of obtained Kc vs FAO56–Kc\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6571549/v1/c88e2d76e3505d8847899514.png"},{"id":94472551,"identity":"a991cf44-16da-4074-8f66-288165f289b6","added_by":"auto","created_at":"2025-10-27 15:41:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2042779,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6571549/v1/f8018cc3-e39c-4710-8ef9-e93510e7ffb2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A simple Capillary Device for Real-time Monitoring of barley water requirements under arid environment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIrrigation is essential for food production, especially in arid and semi-arid areas (Pescod 1992). It is noted that more than 70% of all global freshwater is used for agriculture and food production (D\u0026rsquo;Odorico et al. 2020; FAO 2021), and that poor management of irrigation leads to low water use efficiency. Therefore, accurately determining crop water requirements is a significant challenge (Ferreira et al. 2019) for many farmers worldwide, both in developed and developing countries. This issue is exacerbated by ongoing climate change and the diminishing availability of water resources (Palmgren and Shabala 2024; Pravalie 2016). In developing countries, this knowledge gap often stems from research lack and limited access to accurate, localized data (Roy et al. 2021), leading farmers and irrigation managers to rely on traditional practices. Unfortunately, this reliance can result in inefficient irrigation, manifesting as either overirrigation or underirrigation.\u003c/p\u003e\n\u003cp\u003eAlgeria, like other countries in the MENA region, is facing severe water shortages, largely due to limited rainfall (Plan Bleu 2025; Agoumi 2003, Tanarhte et al. 2024; Garc\u0026iacute;a-Ruiz et al. 2011) and increasing demand from key sectors such as agriculture, and domestic consumption (Mohammed and Al-Amin 2018). Reports from the Ministry of Water Resources (MWR) indicate that agriculture is the largest consumer of available freshwater, accounting for 64% of total withdrawals (FAO 2023), with irrigation being the dominant use. The situation is worsened by inefficient water management, poor valuation of water resources, and declining water quality. However, irrigation efficiency is significantly hindered by soil salinization, groundwater depletion, and inconsistent rainfall patterns, making water management increasingly challenging (Abouelmagd and Ahmed 2024; Hartani and Semar 2024; Boutera et al. 2012). Furthermore, climate change causes frequent droughts that increase the demand for irrigation water and thus contribute to significant water losses (Haile et al. 2024), Mouhouche and Guemraoui (2004) stated that obsolescence of the networks in large irrigated perimeters alone was responsible for 40% of water losses, not including those related to evaporation and deep percolation due to poor irrigation management.\u003c/p\u003e\n\u003cp\u003eTherefore, efficient irrigation water management is essential to address the critical questions of when, how, and how much to irrigate (Allen et al. 1998; Levidow et al. 2014), ensuring that crops receive the required water precisely when they need it. Efficient irrigation can reduce water losses and improve crop productivity (Perry et al. 2017).\u003c/p\u003e\n\u003cp\u003eFor estimating crop water requirements, Doorenbos and Pruitt (1977) introduced the concept of crop coefficient (Kc), defined as the ratio between crop evapotranspiration (ETc) and reference evapotranspiration (ETo):\u003c/p\u003e\n\u003cp\u003eKc\u0026thinsp;=\u0026thinsp;ETc/ETo (1)\u003c/p\u003e\n\u003cp\u003eIn the FAO-24 paper, Doorenbos and Pruitt (1977) proposed Kc values for a list of crops grown under typical soil wetting and irrigation management conditions. Later, Allen et al. (1998) published a revised list in the FAO-56 paper, where Kc values were adjusted (FAO56\u0026ndash;Kc) using the Food and Agriculture Organization Penman-Monteith reference evapotranspiration (FAO56PM\u0026ndash;ETo) equation instead of the modified Penman method from FAO\u0026ndash;24:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAdQAAABBCAYAAABo+1UoAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAABEVSURBVHhe7d3PaxP5/wfwZ753rUl7WkSWpoctsnRZZ7PixkMFM8U9LS6k7oIsCK0JIniwtal72q46WXFPxioURJREEHYvrVZhC6aKlrYksNKDnbCIeJpY3f4B7+/hk/ebmXcmbZJm/dE+HzDQvGfmncm8p/N6v9/znpmAEEKAiIiINuT/9AQiIiJqHAMqERFRCzCgEhERtQADKhERUQswoBIREbUAAyoREVELMKASERG1AAMqERFRCzCgEm0i5XIZfX19CAQC6OrqQrFYBAAUi0V0dXUhEAggnU6r5UdHR9WypVLJlRMRNYoBlWgTuXPnDjo7OyGEwNjYGK5evQoAOH/+PMbGxuA4Di5cuIByuYxisYj79+/DcRwMDAxgYmJCz46IGsCASrSJHDx4EPfv30coFMKff/6J169fAwDevHmDnTt3or29HZFIBEtLS1hdXUV7ezva29uxb98+LCws6NkRUQMYUIk2kc7OTiwvL+P169d48+YNjh49CgDYsWMHnj17hnK5jOXlZXR3d2Pbtm1YXl5GuVzGs2fPsGfPHj07ImoAAyptaqVSCclkUl1LfB9yuZzqen0XisUi+vv78d133+HQoUMAgJGREYyMjKCjowMDAwNob29HT08PYrEYOjo68Ntvv+HYsWN6Vi31IZQF3kN50BYiiFrEcRxhmqYAIEzTFI7j6IsolmUJ0zQ9adlsVgSDQREMBkU2m1XphUJBhMNhAUCEw2HPPMm2bQFA2Lat0uT2uLcjlUoJAGqKx+NrbmcjbNsW8Xjck7eUz+dFKpXyLC9ZliUsy9KTNxW/snif1ioPomaxhUot8/PPP+PAgQMQQmDHjh24dOmSvggAYGpqCouLi5402Xp5/vw5ZmZmcOTIEZTLZQDA4cOHcfr0aQghcPr0aSSTSc+6AJBKpWAYBl69eqXSJiYmcPLkSbS3t6u0Y8eOIRgMQggBx3Fg2zaWlpbU/GaVy2UYhoEvv/wSjuOgUCh45kejUdi2vWVH0vqVxfu01cuD/hsMqNQyuVwO+/btAwAcPXrUd5BLqVTCjRs3cOLECU/6gwcPEIlEVFekYRg1A10kEvF8vnr1Knp7exGLxfDy5UuVfu3aNXz99deeZefm5tDf3w8AWFpagm3b+OSTTzzLNGNiYgKRSARDQ0PqN+RyOc8yvb29uHPnjietFnmby1dffQVUAnZXVxempqb0Rd8ZeUtOKBRSaf39/VW/049fWaTTaYRCIQQCgQ13wTaTVyPlQVQPBlRqmZWVFfX39u3bPfOkZDKJy5cvq8/ynsi3b9+6loKnJXP9+nWMjIwgEAhgZGQEZ8+eVfNKpRJmZmYwODiItrY2vHjxQs2zbbuqRfTw4UNcuXIFgUAAY2NjmJmZQWdnp2cZP4FAwHeSrl27hpMnT3rW0e3evdvzWeYxPDyM4eFhT56rq6t4+vQp5ufngUrAjsViOHToUNU2vKtpaWkJmUzG8xvu37+PgwcPetL86GWRTqfxzz//YH5+HqlUCjMzM57l3fTtkJPUSF5uenkQbRQDKrVMMBhUf//777+eeQAwOzuL6elpdHR0YP/+/Ziensbw8DAAoK2tzbOs7O4FgFOnTiGTyUAIgfPnz+Onn35S81KpFM6dO6c+64FZl8vlUCgUUCgUMDc3h507dwKVFmEoFEI6nVatMPfgGSGE7yTZtq1aYOl0GoFAAH19fWq+H5mHZVmwLMuTZzQaVQGoVCphcXFRBTN9G97VFI1Gsbq6qlr4s7OzCIfDajtHR0dVK9H98Ag/Fy5cwJUrV2AYBlZWVlQlSz5oIhAIqP2vb4ec1ssrl8shFAohFAqplj7Rf4kBlVomFovh7t27AIAbN27gwIEDAIC+vj6k02lEo1F1Mszn8zBNU50Y9+7di7m5OZRKJRSLRdi2je7ubqASrKS2tjZ1b6Xs6pUtTP1eynA47AnMs7OzWFlZQU9PD3p6ehAKhVSX3+rqKm7evIkLFy4gk8kgEolgdXVVrbuecDiMp0+fApXtAIBbt255lnn8+LHnc70uXrzoadW/T9PT0/j0009RLBZx6tQpxGIx1Q29sLCAmZkZFAoF/PXXX5719LJYWVmB4zh4/vw54KoI3b59G0IIpFIp9PT0qOXX4peXvCY/Pz+Pmzdv+gbUZsuDqCZ9lBJRs9yjcd0jOk3TrBrFKkfbutNlmj7KN5vNekb55vN54TiOCAaDnhG78nsly7I8+RiG4fnORCIhAIh8Pi9E5fvl8uFwWK1XDzlCWW5DIpHQFxHxeNwzCllaa5Sve/ukQqFQ9bsBiEKh4FnuvzA+Pq5+4+TkpADg2ceO44hUKlW1LXpZyLKW5Slls1lhGEbV+mvxy8uyLDE+Pi5EZZvl3261yoOoWQyotGk1equGaZrCtm1h27YwTbMqkG1EM7dpyO3QZbNZMT4+LmzbVoHfb7l3zXEcYVmWcBynqkJRT1mkUinhOI6YnJzc8O+RAbxQKIhgMCgmJyc95dlMeRCtZ1MFVP2fmMi2bd8Wk59gMChEZZ1gMFiz1dgoGQAblUql1jzpZ7PZmse8ZVlVFYJUKqVa0fK35fN5TytX3js7OTnp6W2oh2zxy0m3XlnI9WUA3AjZek4kEiKVSolwOKxao82Wx8fGtm11fLjLRj9m9M/UvOqjvkGWZXn+idxTrROS7K5ba75lWeof2m/ZWt/rp1AoiEQiIYLBoO8/kp4HfG74d5949G0Rrn/gRrur3Gr9JgDqxKqn61Oz300fn0Qi4fuQC1EjoJqmKQqFgigUCipI5vN5lYdlWSroyC7TWq1k+rDJyyRCO07kecx93GSz2apjhZrjH4EaZBiGp5YjA2atQpI1Zb+akeM4wjAMYRiGWl8eBO5gaPk8acePvLaVzWZrXi+RwVDOz+fzvtsnr9Xo3+s4jgr+G61Z6/tSpo2Pj6trZ3K/uJeV+4i2Dr/Km1l5UpV7clcAa13jtG27qqIoK3hrtZLpw6P3XOjnPdNnTIN+zqHmtOQM7BdIal0vyefzqkD1wCQqAwWCwWDVuqZpepavtb6bDKb6yUMnW8Nu+vfJtHg8XpWeSCTUI+f07W6U376Mx+OqJSH/ERzHqapp6ttFm5ddedRiLX4tVNnz4/hc49R7ZGQLVVQGaOl50Ycpn89Xnct0pmlW9Wystw7Vp/Z/ZJ1ka65ehmGo2rAeAGQLTC9s4RPg/NbX+XUV+zFN01MLly1svWYut819InNXEAzD8CzfKPn7pbW69PRWtR9ZUcAaz8B1M31aN/q03j6nd0OWVa3j2y+guq+juY+zycnJqksh8jiH9kxi+rChxvnTzS94uq8xU/M2HFBlN6h70ltYkuW6PcAvIMquYD/hcNhTq5atT3fAcH+vDDgy0KFyLVY/cciWnlzXrjzgPBgMeg4wOfLQ3bUqu3oLhYIwDKMqADdK3pLgnmod5KlUas0ALvelPKnKvDfagiaiD5NfT5sukUhUnVNkb4eeTo3bcEDVA0mtAtUHN1iWVRU89VaoJAu8Vs1LdmG5Dwqrcv1nfHxcBREZVNxdwDLwuqd4PF7VTZxKpVReqFzHlBUEPShLtVp8fr9RVLrd3JWGtQKmvt/dZEvXvT2yIqC3WlpF/42cOH0s02ahNzp0lnYvsCTPDQyoG7eho6lWIPFTK7i4oUYXVqIyQnet1pXcFtkClQFVp3+HHFK/HtkSFZU8xsfHVcCTQXmt7auHHDy1HlnBqLXf/Sorele1n1pl5J5qVQaI6P2R54RaFWb3+AtdIpGo6xxI69vQowcfPHgAAFVvkdBNTU2hXC6jEsAhhMDk5KS+GILBYNWzWIvFIq5cuYIzZ85UPejcTV9v165dQOU5qDr3c2Nv376NWCzmma+TechHoZmmiePHj+P3338HADx69AiGYay5fespFotYWVnxvElFvmFENzc3B6yx3xcXF6veyHL9+nXE43FPmu7evXueMvKb7t27p69GRB8Ivzcn5XI5PHz4EENDQ/osoPKCg4GBAT2ZmqFH2EbIFs1a5AAfvTUlu1/d3QyypiTTJicnfW9fEZVWosxTdvm6W7HyexOJhEqTLTeZv+zqqFVzk+R1WCkej6vuVnkdda3u2XqMj497aonyN/m1CNfb7zIv27Y9+0bvxiaizcF2PTXLLavdQpPNZj29YPX0XFH9mt6Tskt1rW5A27bVgCD39T4ZyKCNIHSf/FF5SEKtLtB4PK4GJMl89IAhb2CvtYxMxxpdJe5t9Qu87vz95tej4HoGrj7pedaz3x3HUbfx+P1uItp89ICqj+r2O2eEeUtUSwXE/wILEf1HSqUSLl68iMHBwbrfoPKhy+VyePv2LQYHB/VZ9J709fU1dEkmnU5j165d6nV8tHEbuoZKtNmUSiUEAgHfa+/pdHrd93zqyuUykskkfvnlF08wzeVyvtfHJfcLtN3c7/jM5XL6bJRKJfT19al3ivptr3zn60b09/dj9+7dGB0d1WfRe3L27FnfY8LP7OwsUClHah0GVCKXVCoFwzDw6tUrfVZTJiYmcPLkSc+ANRnk3O95rYd8x+fz588xMzODI0eOeN4xCgCvXr3Cjh071MA/+QJ3aWpqCouLi560ZkWjUdi27Vv5oHcvGo3i4cOHenKVZDKJsbGxmoOUqHkMqEQV8oXlsVgML1++1Gf7KhaL6OrqUi+wliOz5Uu3r127VjUae2hoqKmWwYMHDxCJRNDe3o6enh4YhoGlpSXPMtFoFLlcDuVyGY8ePUI2m1XzSqUSbty4gRMnTnjWka3eq1evAq7fVI/e3l71knZ6/zKZjJ5UJZPJNNQ1TPVjQCWqBJuZmRkMDg6ira0NL168UPNk9+nw8DCGh4fVZwBYXV3F06dPMT8/D1RapLFYDIcOHQIqrdB6b6dKp9OevPVuW/3WsFr5zs7OoqOjA7dv38b27dtVejKZxOXLl9Vnd3fwmTNn1N9PnjxZ9xYraffu3XoS0ZbFgEpU6eo9d+6c+uwOXvIeXMuyYFmW+oxKi1AGtlKphMXFxbpaCX6GhoY8ecu/Zdec+/5pVFrDfqLRKIQQuH79Or799lugEmSnp6fR0dGB/fv3Y3p6WnUH9/f3Y3FxEQcPHgQA/PHHH/jmm29UfvKabCgUUi1vIqrGgEpbnuzq7ezsBADs27cPCwsL+mLrunjxoqcFCADhcNg38BWLRWCNoOhn7969mJubQ6lUQrFYhG3b6O7uxuzsrGrVptNpNVBo27Ztal0ZZIUQyOfzME1TBW5UHnDS2dmJ0dFRzM3N4bPPPlMDV5aXl+E4Ds6fP4+///5brQMAjx8/9nwm2soYUGlLK5fLGBkZwfHjx1UX6/79+/XF6vLDDz9UdcMODAyoJ4pJfX19+OKLLwAAHR0dviNx/fT09CCRSCAcDqO3txeZTKbq+0zTxMLCAgKBAA4fPuy5hirdvXsX09PTnu8NBoMIhUL4/PPPEYlEEI/H0d3dDVQC6tu3b1EsFnHs2DFXTv97Ktf333/vSSPaqngfKtEGydG3fgM9yuUyfvzxR9y6dasq+H0sisUinjx5gsHBQSSTSdWlPTs7i7t37+LXX3/VVyHakthCJdqgiYkJ7NmzR08GKgOHMpkMLl26pLp5PzaHDx9WLXh5i0wul8OzZ88YTIlc2EIlIiJqAbZQiYiIWoABlYiIqAUYUImIiFqAAZWIiKgFGFCJiIhagAGViIioBRhQiYiIWoABlYiIqAUYUImIiFqAAZWIiKgFGFCJiIhagAGViIioBRhQiYiIWoABlYiIqAUYUImIiFqAAZWIiKgFGFCJiIhagAGViIioBRhQiYiIWoABlYiIqAUYUImIiFrg/wEgiwnDLujIdQAAAABJRU5ErkJggg==\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere: FAO56PM\u0026ndash;ETo in (mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), reference evapotranspiration; R\u003csub\u003en\u003c/sub\u003e in (MJ.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), net radiation at crop\u0026rsquo;s surface; G in (MJ.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), the heat-flux density of the soil; T in (\u0026deg;C), mean-daily air temperature; u\u003csub\u003e2\u003c/sub\u003e in (m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) wind speed; e\u003csub\u003es\u003c/sub\u003e and e\u003csub\u003ea\u003c/sub\u003e in (kPa), saturation and actual vapor pressure, respectively; \u0026gamma; in (kPa \u0026deg;C\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), the psychrometric constant; and \u0026Delta; in (kPa \u0026deg;C\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), he slope of the saturation vapor pressure curve\u003c/p\u003e\n\u003cp\u003eUntil now, in the absence of lysimetric studies, the practical approach for estimating crop evapotranspiration (ETc in mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) has been to multiply the crop coefficient (FAO56\u0026ndash;Kc) by FAO56PM\u0026ndash;ETo (mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Therefore, from Eqs.\u0026nbsp;(1) and (\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), ETc is expressed as:\u003c/p\u003e\n\u003cp\u003eFAO56\u0026ndash;ETc\u0026thinsp;=\u0026thinsp;FAO56\u0026ndash;Kc\u0026times;FAO56PM\u0026ndash;ETo (3)\u003c/p\u003e\n\u003cp\u003eAllen et al. (1998) recommended that the Kc values should be obtained empirically for irrigated crops using lysimetric data while taking into account local climatic conditions. However, there is very little research on ETc for field crops, and the Kc values obtained through lysimeters have not been improved for important crops under arid and semi-arid conditions (Benli et al., 2006). Moreover, the Kc values obtained in standard environments may not accurately reflect farm conditions and real farming operations, making it difficult or requiring adjustments (Allen et al., 1998) to apply such data effectively in the field, which could lead to imprecise irrigation management strategies.\u003c/p\u003e\n\u003cp\u003eDeveloping a simple, cost-effective, and reliable irrigation scheduling tool that can be easily adopted by farmers will be crucial (Rouzaneh et al., 2021). In this context, low-tech irrigation scheduling systems based on real-time measurement of crop water requirements will offer a promising solution for improving water use efficiency, particularly in regions that face water scarcity. These systems should be characterized by their simplicity, sustainability, accessibility, and affordability, making them ideal for farmers in such regions (Vand\u0026ocirc;me et al., 2023). In recent years, several studies have explored the effectiveness of wick irrigation systems and highlighted their potential to improve water use efficiency, reduce labor and resource inputs, and enhance crop yield and quality. Zarei et al. 2024 ; Vyas et al., 2022 ; Ferrarezi and Testezlaf 2016 ; Chi-Won et al. 2010). Based on this irrigation technique, this study consists of developing, and testing a self-regulating device namely, low-tech self-watering capillary device (LTSWCD), that measures barley\u0026rsquo;s (\u003cem\u003eHordeum Vulgare\u003c/em\u003e) water needs from a micro-plot under arid environment zone in Algeria and using the recorded measurements to schedule irrigation of the crop grown in larger experimental plots.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Site description\u003c/h2\u003e\u003cp\u003eThe field experiment was conducted at the Technical Institute of Saharan Agriculture (ITDAS) experimental station in Biskra region, southeast of Algeria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is situated at 34.808\u0026deg; N latitude and 5.655\u0026deg; E longitude at an elevation of 153 m asl (above sea level). The climate is arid with annual precipitation below 100 mm and evaporation exceeding 2500 mm per year.\u003c/p\u003e\u003cp\u003eAgriculture is the main economic sector in Biskra, heavily dependent on groundwater for irrigation. The region cultivates a diverse range of crops, including greenhouse vegetables, date palms, olives, cereals, and alfalfa. Due to its arid climate, adopting efficient irrigation methods is essential for maintaining sustainable agricultural production. These conditions make Biskra a suitable site for assessing irrigation technologies, such as the low-tech irrigation control device examined in this study, which seeks to optimize water usage in water-limited environments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Crop and experimental Design\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 The crop\u003c/h2\u003e\u003cp\u003eBarley (\u003cem\u003eHordeum Vulgare L\u003c/em\u003e) is the second most-produced cereal in Algeria after wheat, with mean area harvested more than 1 M ha year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1.4 mean yield of 1.4 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (FAO 2024) was selected for this study due to its critical role as a staple crop for both food and animal feed. Efficient irrigation is an essential parameter for optimizing barley\u0026rsquo;s productivity and water use efficiency, particularly in arid and semi-arid environment like Biskra, Algeria. For the experiment, the variety \u003cem\u003eFouara\u003c/em\u003e was chosen and sown on December 28, 2023. The thousand kernel weight (TKW) of the seeds was 33.53 g, and the sowing density was set at 400 plants m\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup2;, corresponding to a sowing rate of 172 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe field experiment has a sandy soil type, At the beginning of the experiment, the soil had an organic matter content of less than 1%, an electrical conductivity of 2.26 dS m-1 and a volumetric moisture content of 60 mm m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor the need for validation of the LTSWCD measurements (observed ETc), the barley\u0026rsquo;s Kc values given by the FAO56 database were used to estimate crop evapotranspiration (ETc) based on FAO56PM\u0026ndash;ETo estimates. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the commonly Kc\u0026rsquo;s four growth stages, as illustrated in the CROPWAT model (Smith 1992), the barley\u0026rsquo;s crop cycle accounts for 120 days, starting from planting date (28/12/2023) to proposed harvest day (26/04/2024):\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eInitial stage (Kc\u0026thinsp;=\u0026thinsp;0.30, 15 days): It represents the germination and early growth period where ETc is low;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDevelopment stage (Kc increases gradually, 25 days): during this period the plant starts growing actively, and ETc rises;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMid-season stage (Kc\u0026thinsp;=\u0026thinsp;1.15, 50 days): This stage represents the crop at its peak growth, requiring maximum ETc;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLate season stage (Kc decreases to 0.25, 30 days): The crop matures, and ETc declines.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe critical depletion fraction indicates the soil moisture level below which the crop begins to experience water stress: 0.55 during the initial stage, 0.55 and 0.50 during the development and mid-season stages, and 0.90 in the late season, showing the crop can tolerate more dryness at this stage. The Ky coefficient represents the yield response to water deficit: 0.20 for the initial stage; 0.60, 0.50, 0.40 for the development, mid-season, and late-season stages, respectively. These values indicate the sensitivity of barley yield to water stress, with the development stage being the most sensitive.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Experimental design\u003c/h2\u003e\u003cp\u003eThe study followed a randomized complete block design (RCBD) with irrigation as the main factor. Two irrigation treatments were tested:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eI1: 100% of the crop water requirements (full irrigation).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eI2: 130% of the crop water requirements (over-irrigation).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eEach treatment was replicated three times. The experimental plots were irrigated using a drip irrigation system. The spacing between plots was 0.5 m, and between blocks was 1m.\u003c/p\u003e\u003cp\u003ePressure-compensating emitters, with a discharge rate of approximately 8 liters per hour, were installed along 16 mm diameter polyethylene lines. Each plot contains 11 lines (laterals). The lateral spacing was set at 20 cm, while emitter spacing varied between 20 cm for the fully irrigated treatment (I1, 100%) and 15 cm for the over-irrigated treatment (I2, 130%). Water used for irrigation had a total dissolved solids (TDS) concentration of 2.7 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, suitable for barely crop.\u003c/p\u003e\u003cp\u003eThese treatments were designed to assess the accuracy of the designed device, water use efficiency, and yield, under the specific environmental conditions of Biskra, Algeria.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Description of the designed LTSWCD\u003c/h2\u003e\u003cp\u003eThe LCSWCD included a micro-plot of 1 m\u0026sup2; sowed and fertilized at the same calendar of the experimental plot, where crop evapotranspiration is continuously measured.\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows a descriptive diagram of the low-tech self-watering capillary-device prototype. It consists of four interconnected plastic tanks (A, B, C, and D):\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAbove-Ground Tank (Tank A): This elevated tank measures 0.92 m \u0026times; 1.12 m \u0026times; 1 m (depth) and serves as the primary irrigation water reservoir. It is connected at its base to Tank B via a 16 mm diameter multilayer pipe, which terminates in a smart float valve mounted on Tank B\u0026rsquo;s wall. This valve ensures a consistent water level in Tank B. The transparent Tank A is equipped with a graduated measuring tape, allowing for easy monitoring of daily water consumption.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTopsoil Tank (Tank B): This tank consists of a 110 mm diameter closed PVC pipe, buried in the topsoil and encircling Tank C on all four vertical sides. Wick emitters extend from Tank B into the topsoil of Tank C, providing water as shown in the vertical cross-section (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-c). The distribution of wick emitters is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-a.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUnderground Tank (Tank C): Measuring 0.92 m \u0026times; 1.12 m \u0026times; 1 m (depth), this tank contains a 15 cm gravel layer at the bottom to facilitate drainage water infiltration. Above the gravel, a 75 cm layer of soil (substrate) supports plant growth for water consumption measurement. Tank C is connected at its bottom to Tank D via a 20 mm diameter PVC pipe.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDrainage Tank (Tank D): Positioned in a ditch, this 0.4 m \u0026times; 0.4 m \u0026times; 0.5 m (depth) tank collects drainage water, if any, particularly during the first month of crop growth.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe water circulates in a closed circuit: it flows very slowly from tank (A) to tank (B) by gravity, then from tank (B) to tank (C) via capillary wicks through capillarity and soil suction, and from tank (C) to tank (D) by gravity. Finally, the drainage water is returned from tank (D) to tank (A) automatically by a pump equipped with a booster (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-b). Each wick is made of a bundle of eight braided capillary strings covered with a flexible sheath. Each string has a diameter of 4 mm and a length varying from 38 to 58 cm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The two ends of the wick are bare, one submerged in the water of tank (B) and the other buried in the soil of tank (C) at a distance of 7 cm below the soil surface. The difference in level between the water in tank (B) and the buried ends of the wicks is 6 cm. There are 58 wicks, ensuring the soil profile is fully moistened and meets the crop's peak water demand. This number was determined by trial and error, adding wicks and monitoring the soil's moisture profile until an optimal configuration was achieved. The distribution of the wicks in the soil is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-c. Each wick takes the form of an inverted L (Ί).\u003c/p\u003e\u003cp\u003eThe assembly of tanks (B) and (C) is surrounded by a 20 cm thick layer of soil to ensure thermal insulation of the substrate (soil).\u003c/p\u003e\u003cp\u003eTanks (A) and (C) have the same geometric shape and surface area so that the lowering of the water level in tank (A) corresponds to the water consumption of the crop sown in the substrate (soil) during the chosen time step. For example, a lowering of the water level in tank (A) by 5 mm corresponds to a crop water consumption of 5 mm (=\u0026thinsp;50 m\u0026sup3; ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.5 Irrigation and Fertilization Management\u003c/h2\u003e\u003cp\u003eIrrigation and fertilization are known to significantly impact water use efficiency and crop growth: the fertilization was applied uniformly across all plots with the following nutrient inputs:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eNitrogen (N): 200 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as urea, split into three equal applications at growth stages DC12, DC22, and DC31 according to the Zadoks (1974) scale.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePhosphorus (P2O5): 100 kg P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as superphosphate, applied at sowing.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePotassium (K2O): 100 kg K\u003csub\u003e2\u003c/sub\u003eO ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as potassium sulfate.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003ePhonological development was monitored following the Zadoks scale (Zadoks et al. 1974) to ensure appropriate timing of growth stage-specific interventions and grass weeds were controlled manually by hand throughout the growing season.\u003c/p\u003e\u003cp\u003eThe irrigation treatments consisted of two levels: 100% and 130% of the crop's water requirement measured by the LTSWCD. These levels were selected to test the reliability of the measurements of the device in term of optimal irrigation (100%) and slightly excessive irrigation (130%) on barley yield. The 100% irrigation treatment was provided by the LTSWCD, which uses real-time measurements of crop evapotranspiration in a 1 m\u0026sup2; micro-plot. The 130% treatment was adjusted relative to the LTSWCD\u0026rsquo;s measurements.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.6 Data collection and measurements\u003c/h2\u003e\u003cp\u003eMeteorological data from the experimental site are essential for the daily reference evapotranspiration (ETo) calculation using the benchmark FAO56PM equation (Allen et al. 1998). The required data were sourced from multiple datasets and estimation procedures. Aggregated daily minimum and maximum temperatures and relative humidity were recorded locally during the 2023\u0026ndash;2024 experimental period using a temperature and humidity logger. The mean daily wind speed (10 m height above ground level) data were obtained from the Tutiempo network (Tutiempo.net 2024) and adjusted to standard height (2 m) using 0.75 conversion factor as given by Allen et al. (1998). Also taking into account the presence of a 6 m-high windbreak at the experimental site by applying a reduction coefficient (taken 25%), as suggested in the literature (Cleugh 1998; Brandle and Finch 1991; Salem, 1989). Missing values of solar radiation and sunshine hours were estimated using the built-in estimation function of CROPWAT version 8.0, following the procedure outlined by Allen et al. (1998).\u003c/p\u003e\u003cp\u003eSince the LTSWCD operates as a simplified lysimeter, barley water consumption (observed ETc) in the micro-plot was monitored daily at 8:00 a.m. The ETc was determined from the soil water balance (Allen et al., 1998) following Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e4\u003c/span\u003e),\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{Dr}_{i}={Dr}_{i-1}-{\\left(P-RO\\right)}_{i}-{I}_{i}-{CR}_{i}i+{ETc}_{i}+{DP}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSo,\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{ETc}_{i}=\\:{Dr}_{i}-{Dr}_{i-1}+{\\left(P-RO\\right)}_{i}+{I}_{i}+{CR}_{i}i-{DP}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere,\u003c/p\u003e\u003cp\u003eDr\u003csub\u003ei\u003c/sub\u003e: root zone depletion at the end of day\u003csub\u003ei\u003c/sub\u003e (in mm);\u003c/p\u003e\u003cp\u003eDr\u003csub\u003ei\u0026minus;1\u003c/sub\u003e: soil moisture content in the root zone at the end of the day\u003csub\u003ei\u0026minus;1\u003c/sub\u003e (in mm);\u003c/p\u003e\u003cp\u003eP\u003csub\u003ei\u003c/sub\u003e: precipitation on day\u003csub\u003ei\u003c/sub\u003e (in mm);\u003c/p\u003e\u003cp\u003eRO\u003csub\u003ei\u003c/sub\u003e: runoff from the soil surface on day\u003csub\u003ei\u003c/sub\u003e (in mm);\u003c/p\u003e\u003cp\u003eI\u003csub\u003ei\u003c/sub\u003e: net irrigation depth on day\u003csub\u003ei\u003c/sub\u003e (in mm);\u003c/p\u003e\u003cp\u003eCR\u003csub\u003ei\u003c/sub\u003e: capillary rise from the water table on day\u003csub\u003ei\u003c/sub\u003e (in mm);\u003c/p\u003e\u003cp\u003eETc\u003csub\u003ei\u003c/sub\u003e: crop ET on day\u003csub\u003ei\u003c/sub\u003e (in mm);\u003c/p\u003e\u003cp\u003eDP\u003csub\u003ei\u003c/sub\u003e: percolated water out of root zone on day\u003csub\u003ei\u003c/sub\u003e (in mm);\u003c/p\u003e\u003cp\u003eThe ETc was measured directly based on the decrease in the water level in tank A of the LTSWCD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). During the crop season P was neglected (P\u0026thinsp;\u0026asymp;\u0026thinsp;0 mm), there were no considerable precipitations (only 6 mm during the mid-season (in one day) and 7.8 mm (in 3 days) during the late season), wick network ensured irrigation without RO losses (i.e., RO\u0026thinsp;=\u0026thinsp;0), and that the crop was grown in conditions without water stress. Which involves, that soil moisture depletion was maintained between field capacity (FC) and below the readily available moisture threshold (i. e. FC\u0026thinsp;\u0026le;\u0026thinsp;Dr\u0026thinsp;\u0026le;\u0026thinsp;RAM). DP assumed to be zero, because the soil water moisture in the root zone was maintained, along the crop cycle, below FC (i.e., Dr, i\u0026thinsp;\u0026gt;\u0026thinsp;0). So, Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e5\u003c/span\u003e) becomes Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e6\u003c/span\u003e),\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{ETc}_{i}=\\:{Dr}_{i}-{Dr}_{i-1}+{I}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAt the beginning of the experiment, the soil was manually filled to its field capacity (60 mm). The cumulative water amount over a few days (within a week) was then applied to the larger experimental plots. To capture variability across different time scales and identify broader ETc trends, measured ETc values were compared at daily, 3-day, and weekly intervals against corresponding ETc estimates using Eq.\u0026nbsp;(3) (FAO56PM\u0026ndash;ETc). This analysis is crucial for assessing the device's accuracy and its applicability in irrigation scheduling.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.2.7 Yield components and statistical analysis\u003c/h2\u003e\u003cp\u003eThe harvest took place on May 3, 2024, and yield data were collected from a 0.5 m\u0026sup2; area at the center of each plot to reduce edge effects. Yield parameters were measured at harvest and included:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eWeight of 1000 kernels: Measured by weighing a representative sample of 1000 kernels.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGrain yield: Expressed in g m\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup2; and converted to t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, calculated from the harvested area of 0.5 m\u0026sup2; in the middle of each plot.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStraw yield: After harvesting each plot, straw was collected, bagged, and weighed to determine the straw yield, expressed in g m\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup2; and converted to t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFor the statistical analysis, two one-way analyses of variance (ANOVA) were conducted to assess differences in grain and straw yields among irrigation treatments using the data analysis toolpack in Microsoft Excel v. 2019 (Microsoft 2018). Mean differences between treatment were considered significant at (\u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), based on Fisher\u0026rsquo;s least significant difference (LSD) test.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results and discussions","content":"\u003cp\u003e\u003cb\u003e3.1 Reference evapotranspiration (ETo)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eReference evapotranspiration (ET₀) values were automatically calculated using the standard FAO56 Penman-Monteith approach (Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e2\u003c/span\u003e)), as recommended by Allen et al. (1998). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the monthly aggregated ET₀ values and meteorological data, both derived from the daily time series of the study area that are represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The collected and recorded data reveals Biskra's arid climate (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) marked by high thermal amplitudes, with summer peaks of more than 40\u0026deg;C and winter lows near 9\u0026deg;C. The relative humidity ranged from 24% in July to 55% in October, and intense solar radiation, particularly from June to August. Wind speeds ranged from 1.5 to 4.2 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, predominantly blowing from the northwest and north-northwest combined with high radiation levels, contributing to significant evapotranspiration. Reference evapotranspiration (ETo) peaks at 10.6 mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in July 2024. During the barley growing season, ETo varies from 2.0 mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in December 2023 to a peak of 5.1 mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in April 2024.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAggregated monthly data from December 2023 to November 2025 and ETo estimates values using CROPWAT 8.0 software\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMin Temp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMax Temp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRelative Humid.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEff. Rain mm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWind speed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSunsh. Durat.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSolar radiation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eETo\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003em s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMJ m\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup2; day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003emm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJanuary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFebruary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c1\"\u003e\u003cp\u003eApril\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJuly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAugust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOctober\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNovember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Barley ETc and crop coefficient\u003c/h2\u003e\u003cp\u003eThe evolution of daily crop evapotranspiration of barley, measured using the LTSWCD (observed ETc) and soil water status throughout the crop growth cycle are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u0026ndash;c. To help identify trends in observed ETc, a 7-day moving average and cumulative ETc over 3-day intervals spanning 120 days after sowing are shown alongside FAO56PM\u0026ndash;ETc values. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e highlights the condition of no water stress, where soil moisture depletion remains below the readily available water (RAW) threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), and illustrates the four standard growth stages of barley, providing insights into changes in water demand throughout the crop growth cycle\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eInitial Stage (1\u0026ndash;15): During the first 10 days after sowing (December 28), mean observed ETc was 0.2 mm day\u003csup\u003e-1\u003c/sup\u003e and 0.5 mm day\u003csup\u003e-1\u003c/sup\u003e for FAO56PM\u0026ndash;ETc estimates. This was due to the micro-plot of the LTSWCD being covered with a transparent plastic film to enhance germination, considering the late sowing date, and to protect seeds from bird predation. The plastic barrier prevented evapotranspiration, leading to no water loss during this period. The cumulative observed ETc during this stage was 3 mm compared to 7.1 mm for FAO56PM\u0026ndash;ETc (recommended).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDevelopment Stage (16\u0026ndash;40): Following the removal of the plastic film, observed ETc gradually increased as the crop developed. Observed ETc ranged from 1.0\u0026ndash;3.0 mm day\u003csup\u003e-1\u003c/sup\u003e, while FAO56PM\u0026ndash;ETc ranged from 0.6\u0026ndash;2.7 mm day\u003csup\u003e-1\u003c/sup\u003e. Both methods showed a steady rise in ETc, with observed ETc estimates being slightly higher in most periods. This phase marks the initial establishment of the crop, where water demand remains relatively low but steadily increases as canopy cover expands, the cumulative observed ETc during this stage was 44 mm (13% higher) against 39 mm for FAO56PM\u0026ndash;ETc.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMid-Season (41\u0026ndash;90): During this phase, ETc values reached their peak as the crop experienced its highest water demand. Observed ETc ranged from 2.0 to 5.0 mm day-1, while FAO56PM\u0026ndash;ETc ranged from 2.7 to 7.2 mm day\u003csup\u003e-1\u003c/sup\u003e. Both methods demonstrated a strong agreement, with FAO56PM estimates showing more fluctuations and are slightly higher. This period represents the most critical stage for irrigation management, as water uptake is at its maximum due to active crop growth and increased transpiration. The cumulative observed ETc during this stage was 12% lower than the recommended value, totaling 177 mm compared to 201.5 mm for FAO56PM\u0026ndash;ETc.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLate Season (91 - End): As the crop approached maturity, ETc declined significantly. Observed ETc ranged from 1.4 to 4.2 mm day\u003csup\u003e-1\u003c/sup\u003e, while FAO56PM\u0026ndash;ETc varied between 1.4 and 6.4 mm day\u003csup\u003e-1\u003c/sup\u003e. Both methods captured the downward trend, with observed ETc slightly exceeding FAO56PM estimates in some instances. The reduction in water demand during this stage is attributed to senescence and reduced canopy transpiration, marking the end of the crop cycle. The cumulative observed ETc during this stage was 2% higher than recommended value, totaling 103 mm against 101.1 mm for FAO56PM\u0026ndash;ETc.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIn the same way Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e represents the 7 days period moving average (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and cumulated of three days of the ETc (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) for both methods, observed ETc follows a similar pattern, with a gradual increase after sowing, peaking around days 80\u0026ndash;90, and then declining with barley\u0026rsquo;s growth cycle. Initially, both methods are closely aligned, but observed ETc records slightly higher values during early to mid-growth stages before converging with FAO56PM\u0026ndash;ETc later. The barley\u0026rsquo;s seasonal observed ETc, using LTSWCD measurements, was 327 mm against 348.9 mm computed FAO56-PM ETc.\u003c/p\u003e\u003cp\u003eThe slight differences suggest that LTSWCD may better capture localized conditions or microclimate effects that the FAO Penman-Monteith method might not fully account for in the study area. Also, it highlights the potential for using the LTSWCD in real-time irrigation scheduling, as it may better reflect actual field conditions compared to the standard FAO method. In general, whether using daily (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) or 3-day intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), both observed ETc and FAOPM-ETc follow the same trend over time. The ETc measured using LTSWCD remains consistent, while FAO56PM\u0026ndash;ETc tends to give slightly higher estimates, especially during early and mid-growth stages.\u003c/p\u003e\u003cp\u003eThese results follow the same trend and are in comparable order of magnitude with previous lysimetric studies on barley found by (L\u0026oacute;pez-Urrea et al. 2021) and Hashemi and Sepaskhah (2020). The differences are possibly related to the differential varietal response, the duration of the crop cycle (120 days in the present study, 150 days in L\u0026oacute;pez-Urrea et al. (2021), and 200 days in Hashemi and Sepaskhah (2020)), as well as the prevailing climate and local conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the scatter plot representing the relationship between the weekly observed ETc and FAO56PM\u0026ndash;ETc values. The coefficient of determination R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.92 indicates a strong positive correlation between the two methods. The data points generally align along the best-feet line, which supports the reliability of the LTSWCD as a practical tool for estimating crop water requirements and validating its accuracy. The scatter plots of the residuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) don\u0026rsquo;t show any patterns (the points of the residuals are randomly distributed between the two sides of the origin axis) which indicates good performance.\u003c/p\u003e\u003cp\u003eFor the Kc (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), observed Kc values (blue dots), obtained using Eq.\u0026nbsp;(1), depicted noisy variability around the FAO56\u0026ndash;Kc smoothed representation curve, based on barley grown under standard conditions, This account for daily time resolution effects and site-specific conditions such as soil type, irrigation method, or microclimate differences. The 10-period moving average helps reduce this noise, making trends more comparable to FAO56\u0026ndash;Kc.\u003c/p\u003e\u003cp\u003eThe comparison between FAO56\u0026ndash;Kc and Observed Kc across different growth phases as suggested in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, highlights key deviations due to real-world conditions. During the initial phase (1\u0026ndash;15 days), observed Kc values range from 0 to 0.6, while FAO56\u0026ndash;Kc remains between 0.3 and 0.5. Notably, in the first 10 days, the LTSWCD microplot was covered by plastic microfilm, effectively reducing water demand to zero and resulting in an observed Kc of zero during this period. In the development phase (16\u0026ndash;40 days), observed Kc shows high variability (0.4\u0026ndash;1.8), often exceeding the FAO56 range (0.5\u0026ndash;1.15), likely due to fluctuating weather and irrigation differences. The mid-season (41\u0026ndash;90 days) exhibits more stability, with observed Kc values (0.8\u0026ndash;1.6) closely following FAO56 (1.15), though occasional peaks occur. In the late-season (91\u0026ndash;120 days), observed Kc declines closely (1.0-0.3) with FAO56 (1.15\u0026ndash;0.3). Overall, while FAO56\u0026ndash;Kc serves as a generalized reference, real-time observations capture daily fluctuations and site-specific influences, which are crucial for precise water management. Which suggests that FAO-56 model provides a good reference for estimating barley\u0026rsquo;s Kc, but local conditions introduce variability. This comparison highlights the importance of site-specific calibration of Kc values for improved irrigation management and water use efficiency.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Crop yield and statistical analysis\u003c/h2\u003e\u003cp\u003eThe Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the experiment agronomic performance under two irrigation treatments: 100% and 130% of water depletion measured using the LCSWCD, focusing on grain yield, straw yield, and mean kernel weight.\u003c/p\u003e\u003cp\u003eThe LTSWCD treatment produced the highest grain and straw yields, at approximately 6.1 and 13.3 t ha⁻\u0026sup1;, respectively, significantly outperforming the other treatments. This can be attributed to the use of fresh water for irrigation, with TDS below 0.1 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor the 100% treatment plot, grain yield averaged 4.2 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a straw weight of 4.6 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and a TKW of approximately 34.4 mg, which aligns with findings in term of grain yield from numerous barley studies conducted under semiarid and arid environments, for example, in Iran (Jahromi et al. 2023) and in USA (Lazicki et al. 2016). The mean TKW of 34.4 mg is higher than TKW of seeds (33.5 g).\u003c/p\u003e\u003cp\u003eUnder 130% irrigation, grain yield increased only marginally to an average of 4.40 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; however, straw yield declined to 4.10 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 1000 kernel weight dropped to 32.53 mg.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eagronomic performance results under the LTSWCD, 100%, and 130% irrigation treatments\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatments\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrain yield\u003c/p\u003e\u003cp\u003e (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStraw yield\u003c/p\u003e\u003cp\u003e (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTKW\u003c/p\u003e\u003cp\u003e (g)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLTSWCD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e130%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Analyze of variance\u003c/h2\u003e\u003cp\u003eTwo one-way ANOVAs were conducted to compare grain and straw yields between the two irrigation treatments: 100% (full irrigation), representing water consumption measured using the low-tech self-watering capillary device (LTSWCD), and 130% (over-irrigation), which exceeds the measured water requirement by 30%. The ANOVA results indicated that differences in both grain and straw yields were not statistically significant at the 0.05 level, suggesting that increasing irrigation beyond the crop\u0026rsquo;s measured water needs does not significantly enhance yields. These findings highlight the effectiveness of LTSWCD in measuring barley irrigation water requirements and emphasize that overirrigation does not effectively increase grain yield and may even lead to trade-offs, such as reduced straw yield and grain quality, due to nutrient leaching, which is well documented in sandy soils. This suggests that optimal water use is crucial to balance productivity and irrigation water use efficiency.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study demonstrates the effectiveness of the designed low-tech self-watering capillary device as a simple, easy to use, and reliable tool for real-time and direct measurement of barley water needs. The results indicate that the measured ETc follows the FAO56-PM ETc trends and in a comparable order of magnitude in term of seasonal water requirement, confirming the device as a promising accurate tool in irrigation scheduling. Furthermore, the comparison between the 100% (full irrigation) and 130% (over-irrigation) treatments reveals that increasing irrigation beyond the measured crop water requirements does not lead to significant improvements in barley grain and straw yields.\u003c/p\u003e\u003cp\u003eThese results highlight, in the absence of high-resolution lysimetric data, the effectiveness of the LTSWCD as a simple and easy-to-use device for direct measuring in real time irrigation water requirements under conditions similar to the study area.\u003c/p\u003e\u003cp\u003eThese findings highlight the importance of precise irrigation management in optimizing water use efficiency. The LTSWCD provides, in the absence of high-resolution lysimetric data, a simple and scalable solution for sustainable irrigation, particularly in arid environments. By ensuring that irrigation is based on actual crop water demands, this low technology can contribute to reducing water waste while maintaining optimal crop productivity.\u003c/p\u003e\u003cp\u003eFuture research could focus on testing an enhanced version of the device by integrating soil moisture sensors and water depth data loggers in place of visual control to improve real-time monitoring and automation. Additionally, evaluating its performance across different soil types, crops, and climatic conditions would further validate its applicability and scalability.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by S. Z., K. B., and S. M.. The first draft of the manuscript was written by S. Z., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge the help of the Technical Institute of Saharan Agriculture (ITDAS) in Biskra, Algeria for providing the experimental site that made this research possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbouelmagd A, Ahmed M (2024) Groundwater in North Africa: Effects of Climatic and Anthropogenic Pressures on Groundwater Availability. Springer, Cham. https://doi.org/10.1007/978-3-031-48299-1_11.\u003c/li\u003e\n \u003cli\u003eAgoumi A (2003). Vulnerability of the Maghreb countries to climate change: real and urgent need for an adaptation strategy and means for its implementation. International Institute for Sustainable Development and Climate Change. 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Accessed 26 December 2024\u003c/li\u003e\n \u003cli\u003eVand\u0026ocirc;me P, Leauthaud C, Moinard S, Sainlez O, Mekki I, Zairi A, Belaud G (2023) Making technological innovations accessible to agricultural water management: Design of a low-cost wireless sensor network for drip irrigation monitoring in Tunisia. Smart Agricultural Technology, 4, 100227. https://doi.org/10.1016/j.atech.2023.100227\u003c/li\u003e\n \u003cli\u003eVyas U, Darji K, Bhatt N, Vakharia V, Patel D (2022, August) Evaluation of Movement of Wetting Front Under Wick Irrigation in Black Cotton Soil. In International Conference Innovation in Smart and Sustainable Infrastructure (pp. 63-72). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-3557-4_6\u003c/li\u003e\n \u003cli\u003eZadoks J C, Chang T T, Konzak C F (1974) A decimal code for the growth stages of cereals. Weed Research, 14(6), 415\u0026ndash;421. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x\u003c/li\u003e\n \u003cli\u003eZarei Z, Heidari H, Honarmand S J, Bafkar A (2024) Improving grain yield and water use efficiency in maize by wick irrigation. Irrigation Science, 42(4), 785-800. https://doi.org/10.1007/s00271-023-00906-2\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Irrigation scheduling device, Crop water requirements, Wick irrigation, Barley, CROPWAT model, Arid environment","lastPublishedDoi":"10.21203/rs.3.rs-6571549/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6571549/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn Algeria, as in many other developing countries, irrigation faces major challenges, including water wastage, overexploitation of groundwater, and soil salinization—primarily due to inadequate water management practices. Achieving efficient irrigation relies on the precise estimation of crop water requirements. However, this is often lacking, as it depends on accurate assessments of the reference evapotranspiration (ET₀) and the experimental determination of crop-specific coefficients using high resolution lysimeters.\u003c/p\u003e\n\u003cp\u003eThis study aims to develop and evaluate a simple, low-tech irrigation scheduling system based on the wick irrigation technique. The proposed system—a Low-Tech Self-Watering Capillary Device (LTSWCD)—enables real-time measurement of the crop evapotranspiration (ETc) of a selected crop (barley) grown in a one-square-meter micro-plot under arid conditions. The amount of water consumed over several days is then directly applied to larger experimental blocks. Daily measurements were compared to the standard barley estimates recommended in the FAO-56 guidelines, and irrigation scheduling was studied under two treatments: 100% (full irrigation) and 130% (over-irrigation) of the water depth recorded by the LTSWCD.\u003c/p\u003e\n\u003cp\u003eThe main results reveal that the measured crop evapotranspiration (ETc) captured the same trend as the based FAO56-PM ETc at different time intervals, with a correlation coefficient R = 0.95 at weekly and RMSE = 4 mm week\u003csup\u003e-1\u003c/sup\u003e. The seasonal Bareley’s measured ETc was 327 mm, and the obtained Kc shows a good agreement with the standard Kc. Moreover, under two irrigation treatments (100 and 130%), barley’s grain and straw yields show no significant differences at a 0.05 significance level, which demonstrates that increasing irrigation beyond the measured crop water needs does not significantly enhance barley yields under the conditions of this experiment. These results highlight, in the absence of high-resolution lysimetric data, the effectiveness of the LTSWCD as a simple device that farmers could use to directly measure real-time irrigation water requirements under conditions similar to those in the study area.\u003c/p\u003e","manuscriptTitle":"A simple Capillary Device for Real-time Monitoring of barley water requirements under arid environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 08:07:55","doi":"10.21203/rs.3.rs-6571549/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b3f1fc6-c050-4997-a692-5e749b4ac905","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T14:21:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-09 08:07:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6571549","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6571549","identity":"rs-6571549","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}