Evaluation of Crop Water Stress Index (CWSI) for Irrigation Scheduling of Wheat in Sub- Humid Conditions of India

Evaluation of Crop Water Stress Index (CWSI) for Irrigation Scheduling of Wheat in Sub- Humid Conditions of India | 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 Article Evaluation of Crop Water Stress Index (CWSI) for Irrigation Scheduling of Wheat in Sub- Humid Conditions of India Anuj Kumar Dwivedi, C. S.P. Ojha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8008539/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 This research explores the use of the Crop Water Stress Index (CWSI) for planning irrigation in wheat (Triticum aestivum L.) under field conditions in Roorkee, India, during the 2018–19 and 2019–20 growing seasons. Five different irrigation treatments were applied: T1 (no water stress), T2 (maximum stress), T3 (10% soil moisture depletion), T4 (30% depletion), and T5 (50% depletion). The difference between canopy and air temperature (Tc – Ta) and the vapor pressure deficit (VPD) were utilized to create baselines for estimating CWSI. Regression analysis showed strong negative correlations, with R² values of 0.95 and 0.88 for the pre- and post-heading stages in 2018–19, and 0.99 and 0.96 in 2019–20, respectively. Average CWSI values ranged from 0.07 (T1) to 1.00 (T2). Grain yield varied significantly, with a peak of 2375 kg ha⁻¹ (T3) and a low of 325–375 kg ha⁻¹ (T2), while the amount of irrigation applied ranged from 390.5 mm (T1) to 150.5 mm (T2). The highest water use efficiency (WUE) of 2.71 and 2.65 kg ha⁻¹ mm⁻¹ was recorded under T3 in both years. A quadratic relationship between WUE and CWSI suggested an optimal range at CWSI ≈ 0.3–0.4, where yield sustainability and water productivity were maximized. Beyond this point, both yield and WUE decreased sharply. These findings confirm that CWSI is an effective tool for monitoring crop stress and can be used to optimize irrigation scheduling for wheat, allowing for water savings of up to 30% without significant yield losses. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Biological sciences/Physiology Biological sciences/Plant sciences Wheat Crop Water Stress Index (CWSI) Irrigation Scheduling Water Use Efficiency (WUE) Canopy Temperature / VPD Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Wheat accounts for approximately 29% of the world's food production, making it a crucial cereal crop. In India, wheat is grown in both rain-fed and irrigated areas. In regions reliant on rainfall, particularly arid and semi-arid areas, its productivity is constrained by insufficient water availability (Wakchaure et al. 2016 ). Typically, Indian farmers irrigate wheat crops four to five times during the early (sensitive), growth, flowering, and grain filling stages, spanning the crop season from November to March or April. Proper irrigation management during the anthesis period enhances the rate of photosynthesis, which in turn improves grain size and ultimately boosts grain yield (Ayed et al. 2017 ; Saint Pierre et al. 2012 ; Zhang et al. 1998 ). Effective management of irrigation water throughout the crop's growth phase is crucial for attaining high grain yield and productivity (Kumar et al. 2020 ). The relationship between crops and water, along with the scheduling of irrigation, relies on consumptive use calculated from evapotranspiration, which is determined using hydro-meteorological parameters. Given that crops react to environmental and soil conditions, irrigation scheduling should be guided by the crop water stress index (CWSI), which has proven effective in scheduling mustard irrigation in India. In controlling irrigation, leaf water potential and crop water stress are essential factors (Jones et al. 2004). An infrared thermometer has traditionally been employed to detect water stress in crops. Conversely, the CWSI method, which calculates crop water stress based on canopy surface temperature (Tc), is widely used for various crops, such as corn and wheat, under different climatic conditions (Taghvaeian et al. 2012 ; Yazar et al. 1999 ; DeJonge et al. 2015 ; Chen et al. 2010 ; Gontia et al. 2008; Yuan et al. 2004 ). A model for the lower baseline equation of wheat crops was created by (Argyrokastritis et al. 2015 and Yazar et al. 2002 ) to calculate the CWSI of winter wheat, where Ta represents the ambient air temperature, is the canopy temperature, and VPD stands for Vapour Pressure Deficit. According to Gontia et al. (2008), the CWSI for wheat varied from 0 to 0.96 across different irrigation levels. Kumar et al. ( 2020 ) developed the baseline equation for mustard in India (Alderfasi et al. 2001) by analyzing the difference between canopy and air temperature against VPD for both non-stressed and fully stressed plots during 2015, 2016, and 2017. Using this baseline equation, the CWSI was determined at soil moisture deficits of 10, 30, and 50%. It was observed that a plot with a 30% soil moisture deficit achieved optimal yield and the highest water use efficiency. Leaf water potential is considered a reliable indicator of a plant's internal water content. When plant leaves have less water, it indicates a lower water level in the soil. Severe agricultural drought can adversely affect plant growth, biomass, and grain yield (Puri and Swamy 2001 ; Jiang et al. 2013 ). Water use efficiency (WUE) in crops is defined as the ratio of grain yield to the crop's evapotranspiration. Studies indicate that applying irrigation during a critical growth phase results in the highest WUE under water scarcity compared to conditions of abundant irrigation (Mandal et al. 2005 ). According to Farré et al. 2006, WUE decreases as the water supply to crops diminishes. Sezen et al. 2006 compared two models and found that the highest yield was achieved with full irrigation using drip irrigation at 7-day intervals, while the lowest yield occurred in rain-fed areas. Rao et al. 2013 found that total biomass was greatest under full irrigation with line source sprinkler irrigation compared to deficit irrigation levels. Padhi et al. 2010 observed that grain yield, straw, and leaf area index were influenced by different treatment levels and could decrease as stress levels rose. Li et al. 2001 suggested that an adequate water supply during the growth season can enhance WUE and biomass yield. Rao et al. 2013 recorded the highest mean WUE of 1.28 kg m-3 in wheat. However, Tari ( 2016 ) reported a range of 2.0 to 10.1 t/ha for full irrigation and rain-fed conditions and recommended replenishing water deficits during the milky grain stage of wheat to minimize yield loss. A study assessed water deficit and observed wheat biomass yield between 15 and 20 t/ha, depending on irrigation and its application during the growth stage (Zhang et al. 2006 ). Crop water stress index calculations Various methods can be employed to calculate the crop water stress index (CWSI) using the measured temperatures of both stressed and non-stressed canopies, or for the specific treatment in question, along with the temperatures of artificial canopy surfaces that simulate the transpiration potential of the actual canopy being studied. These methods differ based on how the upper and lower limits are established. As noted by Jackson et al. ( 1988 ), these limits form boundaries that allow the normalization of the measured canopy temperatures. Table 1 Details of applied water, crop yield, water use efficiency, and mean CWSI for various treatments. Parameters Crop Year Treatments T 1 T 2 T 3 T 4 T 5 Applied Water (mm) 2018-19 375.5 150.5 337.5 262.5 187.5 2019-20 390.5 150.5 351.5 273.5 195.5 Seed Yield (Kg/ha) 2018-19 2250 325 2300 2050 1375 2019-20 2250 375 2375 2125 1500 WUE (Kg/ha/mm) 2018-19 2.44 0.88 2.71 2.89 2.31 2019-20 2.35 1.02 2.65 2.85 2.39 Mean CWSI 2018-19 0.0783 0.9988 0.1446 0.8993 0.5786 2019-20 0.0783 1.0001 0.1546 0.3093 0.6186 Table 2 Details of water application in different plots for the 2018–19 and 2019–20 seasons. Crop Year Treatment Irrigation (mm) Irrigation Events 2018-19 T 1 375.5 25,43,72,93 T 2 150.5 24 T3 337.5 25,45,73,95 T 4 262.5 21,41,43 T 5 187.5 26,45 2019-20 T 1 390.5 25,46,75,91 T 2 150.5 24 T 3 351.5 24,45,72,95 T 4 272.5 23,41,43 T5 195.5 25,45 Estimation of CWSI using the empirical method : Idso et al. ( 1981 ) introduced the empirical method, which has been widely adopted for calculating CWSI (Han et al., 2018 b; DeJonge et al., 2015 ; Irmak et al., 2000 ). This method is known as "CWSI-EB," where "EB" signifies the empirical determination of the lower baseline. The empirical lower baseline (Tc-Ta) L is established through a linear regression between the temperature difference (Tc – Ta) and VPD, as illustrated in Eq. 2 (b). Here, Tc is obtained from a fully watered, non-stressed crop. The upper baseline ((Tc-Ta)U can be identified using two approaches: (1) by collecting Tc from a stressed crop (e.g., rainfed treatment) across a broad VPD range, with the maximum observed (Tc – Ta) serving as the upper limit (DeJonge et al., 2015 ), and (2) by employing Eq. (1d), which differs from Eq. (1c) by utilizing VPG instead of VPD (Han et al., 2018 a; Yuan et al., 2004 ). The first method, which uses the upper limit with Eq. (1c), is termed CWSI-EB1, while the second method, using Eq. (2d), is called CWSI-EB2. $$\:CWSI=\frac{{{(T}_{c}-{T}_{a})}_{a}-({{T}_{c}-{T}_{a})}_{L}}{{{(T}_{c}-{T}_{a})}_{u}-({{T}_{c}-{T}_{a})}_{L}}$$ 1a $$\:{{(T}_{c}-{T}_{a})}_{L}=A+B\times\:VPD$$ 1b $$\:{{(T}_{c}-{T}_{a})}_{U}=\text{m}\text{a}\text{x}\left({T}_{c}-{T}_{a}\right)$$ 1c $$\:{{(T}_{c}-{T}_{a})}_{U}=A+B\times\:\:VPD$$ 1d Computation of CWSI through theoretical models (Th1 and Th2) The upper and lower boundaries, as determined by the theoretical framework of Jackson et al. (1981) and Jackson et al. ( 1988 ), were calculated using Eq. (2a) and Eq. (2b), respectively. This method is referred to as “CWSI-Th” in the following discussion. The upper boundary of (Tc – Ta) is derived when rc is allowed to become infinitely large, leading to Eq. (2a). Conversely, the lower boundary of (Tc – Ta) is determined by assuming that the canopy resistance matches the canopy resistance at potential transpiration for a well-watered canopy surface (rc = rcp), resulting in Eq. (2b). There are, however, two distinct methods for calculating the theoretical CWSI, which depend on different approaches to computing both aerodynamic and canopy resistances (Han et al., 2018 a). The first method (CWSI-Th1) involves using Eq. (2c) to calculate aerodynamic resistance (Jackson et al., 1988 ) and assuming that potential canopy resistance is zero (i.e., rcp = 0), where canopy transpiration is considered equivalent to the evaporation of a free water surface (Jackson et al., 1981, 1988 ). The second method (CWSI-Th2) employs seasonal averages of ra (ra) (Eq. 2d) and rcp (rcp) (Eq. 2e) as recommended by O’Toole and Real (1986). CWSI-Th2 has been effectively utilized and demonstrated satisfactory performance, as noted by Han et al. ( 2018 a) and Clawson et al. ( 1989 ). $$\:({{\text{T}}_{\text{c}}-{\text{T}}_{\text{a}})}_{\text{U}}=\frac{{\text{r}}_{\text{a}}\left({\text{R}}_{\text{n}}-\text{G}\right)}{{{\rho\:}\text{C}}_{\text{p}}}$$ 2a $$\:\left(({{\text{T}}_{\text{c}}-{\text{T}}_{\text{a}})}_{\text{L}}=\left\{\left[\frac{{\text{r}}_{\text{a}}\left({\text{R}}_{\text{n}}-\text{G}\right)}{{{\rho\:}\text{C}}_{\text{p}}}\right]\times\:{\gamma\:}\frac{\left(1+\frac{{\text{r}}_{\text{c}\text{p}}}{{\text{r}}_{\text{a}}}\right)}{\varDelta\:+{\gamma\:}\left(1+\frac{{\text{r}}_{\text{c}\text{p}}}{{\text{r}}_{\text{a}}}\right)}\right]\right\}-\left[\frac{\text{V}\text{P}\text{D}}{\varDelta\:+{\gamma\:}\left(1+\frac{{\text{r}}_{\text{c}\text{p}}}{{\text{r}}_{\text{a}}}\right)}\right]$$ 2b Where ,(Tc-Ta)u and (Tc – Ta)L are upper and lower limits, respectively, Tc (◦C) is the canopy temperature, Ta (◦C) is the air temperature, ρ (kg/m3) is air density, Cp (J/kg/C) is the heat capacity of air, γ (Pa°C − 1 )is psychrometric constant, r cp (sm − 1 ) is canopy resistance at full canopy transpiration, Δ (Pa°C − 1 ) is the slope of the saturated vapor pressure, Rn (W m − 2 ) is the net radiation computed based on Stefan-Boltzmann relationship following Ortega-Farias et al. ( 2010 ) and Ben-Gal et al. ( 2009 ) (i.e., Rn = (1- ∝) Rs + εaσT 4 a - εcσT 4 c , where Rs is the incoming solar radiation (W m − 2 ), α (0.23) is the surface albedo, σ (5.67 ×10 − 8 W m − 2 K) is Stefan-Boltzmann constant, εa is hemispherical longwave atmospheric emissivity (s m − 1 ), and εc is canopy surface emissivity (0.98)), VPD (kPa) is the vapor pressure deficit which was computed following Allen et al. ( 1998 )d is the soil heat flux assumed to be 10% of Rn (Han et al., 2018 a). $$\:{\text{r}}_{\text{a}}=\frac{4.7[{\text{L}\text{N}(\text{Z}-\text{D})/{\text{Z}}_{0}]}^{2}}{1+0.54\text{u}}$$ 2c $$\:{\overline{\text{r}}}_{\text{a}}=\frac{{{\rho\:}\text{C}}_{\text{p}}\times\:\text{A}}{{\overline{\text{R}}}_{\text{n}}\times\:\text{B}(\overline{\varDelta\:}+\frac{1}{\text{B}})}$$ 2d $$\:{\overline{\text{r}}}_{\text{c}\text{p}}=-{\overline{\text{r}}}_{\text{a}}[\frac{\overline{\varDelta\:}+\frac{1}{\text{B}}}{{\gamma\:}}+1]$$ 2e where, Rn (W m − 2 ) is the seasonal average net radiation, \(\:\overline{\varDelta\:}\) (Pa ◦C- 1 ) is the seasonal average of Δ, A and B are parameters or coefficients from Eq. (2b) of the developed lower empirical baseline, z (m) is the reference measurement height, d (m) is the displacement height (d = 0.63 h), h (m) is the canopy height, zo (m) is the roughness length (zo = 0.13 h), and u (m s − 1 ) is the wind speed at height z. Both Rn and Δ were computed considering only the mid-day hours (1200–1600 h, central day time) (Han et al., 2018 a). Empirical method based on canopy reference surfaces (CWSI-EN, CWSI-EA) To create simple techniques for establishing limits, given the challenges in developing Idso’s empirical method and the inaccuracies in Jackson’s theoretical approach, the canopy temperature (Tc) of both well-watered and stressed crops, along with the temperatures of dry and wet artificial canopy reference surfaces, can be employed (Apolo-Apolo et al., 2020 ; Maes et al., 2016 ; Meron et al., 2010 ). When utilizing the actual canopy surface, the upper and lower limits are determined as temperature differences using Tc from a non-water-stressed canopy at potential transpiration (Tcl) and a water-stressed canopy without transpiration (Tcu), respectively (Eqs. 4a, 4b). This approach is similar to the derivation of other stress indices like Degrees Above Non-Stress canopy (DANS) (DeJonge et al., 2015 ) or temperature stress day (TSD) (Gardner et al., 1981 ). Alternatively, artificial canopy surfaces can be used by employing the temperatures of wet soaked fabric (Twet) and dry fabric (Tdry) made from green styrofoam. The upper and lower limits are calculated as the differences between Ta and the temperatures of wet and dry styrofoam surfaces, respectively (Eqs. 3c and 3d). However, the CWSI can be simplified as shown in Eq. (3e) after substituting Eqs. (3a–3d). $$\:{{(\varvec{T}}_{\varvec{c}}-{\varvec{T}}_{\varvec{a}})}_{\varvec{L}}=({\varvec{T}}_{\varvec{c}\varvec{l}}-{\varvec{T}}_{\varvec{a}})$$ 3a $$\:{{(T}_{c}-{T}_{a})}_{U}=({T}_{cu}-{T}_{a})$$ 3b $$\:{{(T}_{c}-{T}_{a})}_{L}=({T}_{wet}-{T}_{a})$$ 3c $$\:{{(T}_{c}-{T}_{a})}_{U}=({T}_{dry}-{T}_{a})$$ 3d $$\:CWSI=\frac{{T}_{c}-{T}_{cl}}{{T}_{cu}-{T}_{cl}}\:\text{o}\text{r}\:CWSI=\frac{{T}_{c}-{T}_{wet}}{{T}_{dry}-{T}_{wet}}\:\left(3\text{e}\right)$$ where, Tcu (◦C) is the temperature of stressed canopy (upper limit), Tcl is temperature of non-stressed canopy (lower limit), Twet (◦C), and Tdry (◦C) is the temperature of wet and dry artificial canopy reference surfaces, respectively. The CWSI is crucial for overseeing irrigation scheduling across various supplemental and traditional irrigation methods. Implementing strategies at different crop growth phases can enhance water management, boost water use efficiency, and increase farmers' earnings. This is especially important as the agricultural sector and water security in northern India are expected to face numerous environmental challenges, such as rising temperatures, reduced rainfall, and more frequent droughts (FAO 2016). This study aims to reevaluate some of the current perspectives on wheat crop irrigation and assess the crop water stress index (CWSI) of wheat under various treatments. The presentation will cover the materials and methods, as well as the evaluation of CWSI across different irrigation levels. Following this, relationships between wheat yield and CWSI, as well as water use efficiency and CWSI, will be established to determine the CWSI that results in optimal water use efficiency. Material and Methods Study area Field experiments were carried out in the crop years 2018-19 and 2019-20 in a village close to Roorkee, Uttarakhand, India. Roorkee is situated at an elevation of 900 meters above sea level, with coordinates of 29.8543° N and 77.8880° E. The region's climate is sub-humid subtropical, receiving an average annual rainfall of 1170 mm. Approximately 75% of this rainfall occurs during the monsoon season, from June to September, and is unevenly distributed. The rest of the rainfall happens outside the monsoon period. The soil at the experimental location is sandy-loam and alluvial in nature. It has a low water retention capacity and a high infiltration rate. Experiments Field experiments were carried out to establish a connection between the crop water stress index and yield. Wheat (Triticum aestivum L.) was cultivated during the non-monsoon seasons of 2018-19 and 2019-20. Typically, the wheat crop requires 120–125 days to complete its growth cycle in the winter. The experiments included five plots, each measuring 4 m × 3 m, with a 1-meter buffer, and were organized using a randomized block design (RBD) across different treatments. Figure 1 illustrates the experimental layout. Experimental details The experiments conducted in this study took place in the experimental fields of the Institute Research Farm, situated within a 1 km radius of the institute. Seeds of the HD2967 variety were acquired from a local vendor, and necessary permissions were obtained from both local and national authorities to carry out the experiments. Throughout the experiments, guidelines provided by the institution at both national and international levels were followed. Instruments used An infrared small handheld self-contained camera was employed to gather data on air and canopy temperatures, with measurements taken between midnight and 2 PM under clear skies to minimize radiation effects. Each reading was captured from four different directions. The canopy temperature was determined by averaging five readings from each plot. Measurements included canopy temperature, ambient air temperature, and both dry and wet bulb temperatures. The experimental procedure followed was akin to that described by previous researchers (Kumar et al. 2020 ). The mean air temperature (Ta) was derived from the average of dry-bulb temperature readings during the measurement period. The mean VPD was calculated as the average of the instantaneous VPD, determined using the corresponding instantaneous wet and dry-bulb temperatures and the standard psychomotor equation (Brakke et al.1995). The CWSI was computed using the upper and lower limit estimates, following the method of (Idso et al. 1981 ). Irrigation Scheduling The maximum allowable depletion (MAD) of the available soil storage was set at 10%, 30%, and 50% for treatments T3, T4, and T5, respectively. Treatment T1 experienced no water stress, with frequent irrigation to keep soil moisture close to field capacity. In treatment T2, irrigation was only provided at sowing, with additional water applied once during the season solely for crop survival. Treatments T3, T4, and T5 were chosen to identify the optimal MAD level of Available Soil Water (ASW) for wheat irrigation in the area and to monitor the canopy-air temperature difference (Tc-Ta) under varying vapor pressure deficits to develop a crop water stress index. Treatments T1 and T2 were used to establish the minimum stress baseline (lower baseline) and maximum stress baseline (upper baseline), respectively, for the relationship between canopy-air temperature difference (Tc-Ta) and vapor pressure deficit (VPD) in creating the crop water stress index (CWSI). The percentage depletion of available soil moisture within the effective root zone was determined using the equation provided by Martin et al. ( 1990 ). $$\:Depletion\:\left(\%\right)=100\times\:\frac{1}{n}\sum\:_{i=1}^{n}\frac{{FC}_{i}-{\theta\:}_{i}}{{FC}_{i}-{PWP}_{i}}$$ 4 where n is the number of layers of the effective rooting depth used for soil moisture sampling; FC i is the soil moisture at field capacity of the i th layer; \(\:{\theta\:}_{i}\) is the volumetric soil moisture in the ith layer before irrigation; and PWP i is the soil moisture before permanent wilting point. The depth of irrigation water, d, to be applied at a particular MAD is given by Eq. 2 (Michael et al. 1978). $$\:d=\frac{1}{IRE}\times\:\sum\:_{i=1}^{n}\frac{MAD({FC}_{i}-{PWP}_{i})\times\:{R}_{zi}}{100}$$ 5 where d is the depth of irrigation water to be applied; Rzi is the depth of the i th soil layer within the effective rooting depth; and IRE is the irrigation efficiency. Table 2 shows water applied to treatment in cropping seasons 2018-19 and 2019-20. Table 3 shows soil properties at different depths. Figure 2 shows the variation of soil moisture during the crop period for various treatments. Under treatments T 2 , T 4 , and T 5 , soil moisture exhibited a zigzag behaviour. Calculation of CWSI baseline The non-water stress baseline essentially describes the correlation between Tc-Ta and VPD under conditions of potential evapotranspiration for a specific crop in a given area (Gardner et al. 1992 ; Idso et al. 1982). Various methods, such as diurnal and seasonal, can be used to calculate the non-water stress index baseline equation (Gardner et al. 1992 ). In this study, the seasonal method was chosen over the diurnal one to determine the non-water stress index baseline equation, as it accounts for seasonal variations in canopy structure and transpiration and reflects a broad spectrum of environmental conditions. Canopy temperature measurements commenced when the canopy covered 80% of the area, starting 20 DAS (Days After Sowing). Data collection occurred weekly between 12 PM and 2 PM under clear sky conditions. Observations for the non-water stress index baseline equation were made after each irrigation during various wheat growth stages. A graph was plotted with (Tc-Ta) against VPD, resulting in the baseline equation. A linear relationship was established in the following form (Idso, 1982 ): $$\:{T}_{c}-{T}_{a}=m\left(VPD\right)+C\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(6\right)$$ where m = the slope of the linear regression equation, and C = the intercept of the line on the Tc-Ta axis. CWSI value calculation Equation 4 was utilized to accomplish this. The recorded canopy and air temperatures were employed for this analysis. The CWSI value must remain within the range defined by the extremes of maximum water stress and no water stress as per the baseline. $$\:\text{C}\text{W}\text{S}\text{I}=\left[\frac{{(\text{T}}_{\text{c}}-{\text{T}}_{\text{a}})-({{\text{T}}_{\text{c}}-{\text{T}}_{\text{a}})}_{\text{L}\text{o}\text{w}\text{e}\text{r}\:\text{L}\text{i}\text{m}\text{i}\text{t}}}{({{\text{T}}_{\text{c}}-{\text{T}}_{\text{a}})}_{\text{U}\text{p}\text{p}\text{e}\text{r}\:\text{L}\text{i}\text{m}\text{i}\text{t}}-({{\text{T}}_{\text{c}}-{\text{T}}_{\text{a}})}_{\text{L}\text{o}\text{w}\text{e}\text{r}\:\text{L}\text{i}\text{m}\text{i}\text{t}}}\right]\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(7\right)$$ where Tc = the canopy temperature, and Ta = the air temperature. Results and Discussion Baseline development of CWSI for wheat in India Equations for the maximum and minimum water stress baselines for each year during the pre-flowering and post-flowering stages were derived from observations in plot T2. In the non-irrigated plot T2, a peak value of 1.9°C was recorded as the highest difference between canopy and air temperatures (Tc-Ta). Consequently, this study used this canopy-air temperature difference as the maximum water stress baseline for wheat in India. Using data from plot T1, a regression equation was formulated based on the observed differences between Tc and Ta, along with the corresponding Vapour Pressure Deficit (VPD). For the cropping seasons of 2018-19 and 2019-20, the R2 values were determined for both the pre-flowering and post-flowering phases, resulting in values of 0.95, 0.88, and 0.99, and 0.96, respectively. These R2 values indicated that the regression equation was effective for calculating the CWSI in the study area. Similarly, regression equations were developed for non-water stress baseline for pre-flowering and post-flowering stages of the cropping season as \(\:{T}_{c}-{T}_{a}=-2.3715\left(VPD\right)-1.659\) and \(\:{T}_{c}-{T}_{a}=-1.8952\left(VPD\right)-2.3202\) , respectively, and for the year 2019-20 were \(\:{T}_{c}-{T}_{a}=-1.7184\left(VPD\right)-2.3009\) and \(\:{T}_{c}-{T}_{a}=-1.813\left(VPD\right)-1.9176\) , respectively (Figs. 8 and 1 ). Figures 8 and 1 show the clear difference between pre-flowering and post-flowering in non-water stress conditions. It is observed that the slope during the post-flowering phase is more gradual compared to the pre-flowering phase. This finding suggests that the flowering heads of wheat in India significantly diminish the relative transpiration cooling at the canopy surface. A similar study11 demonstrated a reduction in the slope of the non-water stressed baseline after the formation of seed heads (post-heading stage) compared to the pre-heading stage. Crop Water Stress Index evaluation for wheat in India Figure 5 illustrates the variations in the Crop Water Stress Index (CWSI) over the crop cycle for different plots, with values ranging from 0 to 1.0. These CWSI readings were subsequently utilized to infer water use efficiency, as detailed below. The figure also presents the relationship between the difference in canopy and air temperatures (Tc – Ta) and the vapor pressure deficit (VPD) in wheat during the 2019–20 growing season, concentrating on the stages before and after heading. A significant negative linear correlation was identified in both stages, indicating that Tc – Ta consistently diminished as VPD rose. Regression analysis revealed slopes of − 1.7184 (R² = 0.9972) for the pre-heading stage and − 1.8137 (R² = 0.9682) for the post-heading stage, demonstrating a very strong association. Unlike the previous season, the differences between the two stages were minimal, with nearly parallel regression lines, suggesting that the crop canopy responded similarly to evaporative demand in both stages. The high R² values further confirm the reliability of Tc – Ta as an indicator of plant–atmosphere interactions. These findings highlight the potential of using canopy temperature-based indices to monitor crop water status, especially under varying atmospheric conditions. Table 2 Applied water distribution across plots in the 2018–19 and 2019–20 crop years. Crop Year Treatment Irrigation (mm) Irrigation Events 2018-19 T 1 375.5 25,43,72,93 T 2 150.5 24 T3 337.5 25,45,73,95 T 4 262.5 21,41,43 T 5 187.5 26,45 2019-20 T 1 390.5 25,46,75,91 T 2 150.5 24 T 3 351.5 24,45,72,95 T 4 272.5 23,41,43 T5 195.5 25,45 Assessment of water application, seed yield, and water use efficiency The regression equation for the 2018-19 period, as shown in Fig. 3 , was formulated for both the pre-heading and post-heading phases, with R2 values of 0.95 and 0.8755, respectively. It is understood that the wheat canopy begins to open as the sun rises in the morning, continuing until the leaves reach a critical point when sunlight provides peak radiation around noon. The model developed suggests that the canopy temperature (Tc) gradually increases in relation to the air temperature (Ta) as the sun rises, until the leaves reach potential evapotranspiration. Analysis of the data revealed a notable shift in the baseline as the wheat crop moved from the vegetative to the reproductive phase, i.e., from pre-heading to post-heading. As a result, these baselines were calculated separately for each phase. Figure 3 displays the variation in soil moisture content under different irrigation treatments during the 2018–19 cropping year. A consistent decrease in soil moisture was observed with increasing days after sowing (DAS) across all treatments. Among the treatments, T1 (non-water stress) and T2 (maximum water stress) maintained relatively higher soil moisture levels throughout the growing period, highlighting the effectiveness of adequate irrigation in sustaining soil water availability. In contrast, T3 (10% depletion), T4 (30% depletion), and particularly T5 (50% depletion) exhibited a sharp decline in soil moisture, with T5 falling below 0.05 cm³ cm⁻³ by 100 DAS. This pattern clearly demonstrates the adverse impact of higher depletion levels on soil water status, which can potentially impede crop growth and yield if not properly managed. The findings emphasize the importance of maintaining optimal irrigation levels to prevent moisture stress during the crop growth cycle. Figure 4 presents the foundational equation for the wheat crop's phases before and after heading during the 2018-19 and 2019-20 growing seasons. The regression model calculations identified 20°C as the maximum canopy-air temperature difference (Tc-Ta) for non-irrigated wheat throughout the growing season. Figure 4 illustrates the relationship between the canopy-air temperature difference (Tc – Ta) and vapor pressure deficit (VPD) for wheat in the 2018–19 season, encompassing both pre-heading and post-heading stages. A pronounced inverse linear correlation was observed in both stages, showing that Tc – Ta decreases as VPD increases. During the pre-heading stage, the regression line had a steeper slope (–2.3715) and a higher coefficient of determination (R² = 0.9502), indicating a greater sensitivity of canopy temperature to atmospheric demand compared to the post-heading stage. In contrast, the post-heading stage showed a milder slope (–1.8952) and a lower R² (0.8755), suggesting reduced sensitivity, likely due to changes in canopy structure and physiological activity after heading. These results highlight that indices based on canopy temperature can effectively assess crop water status, particularly during the pre-heading stage when plants are more responsive to evaporative demand. The R2 values for all regression equations for both wheat crop seasons during the 2018-19 and 2019-20 cropping periods are 0.9502 and 0.8755 for the pre-harvesting and post-harvesting phases of 2018-19, respectively, and 0.9973 and 0.9682 for 2019-20. These statistical parameters suggest that the developed regression equations are highly suitable for further calculating the crop water stress index (CWSI). Figure 6 (A) CWSI variation under different irrigation schedules and DAS during the 2018–2019 crop season Table 3 Variation of soil physical properties across depths. Soil depth (m) Bulk Density (g/cm 3 ) Sand (%) Silt (%) Clay (%) Particle Density (g/c.c) Ks (cm/hr) FC (cm 3 /cm 3 ) PWP (cm 3 /cm 3 ) Available Water (cm 3 /cm 3 ) 0-0.3 1.51 54.98 23.83 21.19 2.54 2.96 0.22 0.07 0.15 0.3–0.6 1.56 57.48 24.41 18.11 2.59 2.78 0.22 0.07 0.15 0.6–0.9 1.63 59.24 24.27 16.49 2.61 2.54 0.22 0.07 0.15 0.9–1.2 1.67 55.07 29.62 15.31 2.63 2.45 0.21 0.08 0.13 1.2–1.6 1.68 55.82 28.52 15.66 2.66 2.42 0.21 0.080 0.13 where Ks = the saturated hydraulic conductivity (cm/hr), FC = the field capacity (cm 3 /cm 3 ), and PWP = the permanent wilting point (cm 3 /cm 3 ) Yield productivity Table 1 displays the grain yield for each plot during the 2018-19 and 2019-20 seasons. Plot T3 achieved the highest yield at 2375 Kg/ha, whereas plot T2 had the lowest yield at 350 Kg/ha. The findings indicated that soil moisture stress was less in plot T2 compared to plot T3. A higher CWSI value causes plants to lose turgidity, resulting in reduced grain yield (Kumawat et al. 1997 ; Singh et al. 2018 ). Plot T2's yield was 84% lower than that of plot T1. These outcomes were consistent with the research of other scholars (Chauhan et al. 2007 ). The grain yield in plot T1 was slightly less than in plot T3. Figure 4 illustrates the correlation between seed yield and mean CWSI across various irrigation treatments for wheat in India, with an R2 of 0.96. The derived relationship is presented as follows: Yield Model $$\:Yield\left(\frac{Kg}{ha}\right)=-2155.8\times\:CWSI+2608.8\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(8\right)$$ Water application Table 1 displays the amount of water distributed to different plots, which is closely associated with the average CWSI. Figure 5 shows a linear relationship between the average CWSI and the irrigation water used, with an R2 value of 0.96. This linear connection was established from the water applied to wheat crops to determine the link between average CWSI and water usage during the cropping period, as outlined in Table 1 . A regression equation was developed with an R2 value of 0.9641, as shown in Fig. 7 . The equation in relationship 6, which has a negative slope, suggests a decrease in water application as the crop water stress index (CWSI) rises. A similar trend was noted in the relationship between yield and average CWSI. The linear relationship was derived in Eq. 6. The strong negative correlation between wheat yield and average CWSI (Fig. 7 ) is consistent with the canopy temperature–VPD relationships depicted in Figures Y and Z. Both analyses consistently highlight the significance of crop water status in determining productivity. The regression of Tc – Ta against VPD demonstrated that canopy temperature is highly responsive to atmospheric demand, particularly during pre-heading stages when crop transpiration is most active. This sensitivity acts as an indirect yet reliable indicator of water stress, further confirmed by the yield–CWSI relationship, where yield significantly decreased with increasing stress levels. Collectively, these findings suggest that canopy temperature-based indices like Tc – Ta and derived CWSI values can be effectively used not only for real-time stress detection but also as predictive tools for yield estimation. From a management perspective, integrating canopy temperature monitoring with irrigation scheduling could help farmers apply water at critical growth stages, reducing yield losses and enhancing water-use efficiency under limited irrigation conditions. $$\:Water\:Applied=-237.69\times\:CWSI+385.28\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(9\right)$$ Water use efficiency Evaluating wheat yields across various plots may not necessarily reflect the best irrigation practices (Gontia et al.2008). For instance, in the 2018-19 season, plot T1 achieved a yield of 2250 Kg/ha, requiring 375 mm of water. In contrast, plot T4 also produced a yield of 2250 kg/ha but only needed 262.5 mm of water. The grain yield difference between plot T1 and plot T4 was 8.8%. However, the extra water used in plot T1 to achieve this yield increase was significantly higher, at 30%. Water use efficiency (WUE) is determined by the ratio of yield to the water applied (Howell et al.2003). Table 1 presents the WUE for various plots during the wheat cropping seasons. In plot T3, the highest yield was recorded with WUE values of 2.71 and 2.65 Kg/ha/mm for the years 2018-19 and 2019-20, respectively. A 10% reduction in soil moisture in plot T1 did not impact the yield due to similar WUE. From these irrigation experiments, plot T4 emerged as the optimal treatment plot. Figure 6 (A&B) illustrates the regression equation developed between WUE and mean CWSI for different irrigation treatments, showing an R2 value of 0.98. The WUE increased with the crop water stress index (CWSI), peaking before declining as the CWSI value rose. The non-linear relationship between WUE and CWSI is depicted in Fig. 6 (A&B): $$\:WUE={-4.0728\times\:CWSI}^{2}+2.6844\times\:CWSI+2.3116,\:\:\:{R}^{2}=0.98\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(10\right)$$ The examination of the relationship between irrigation water usage and the average CWSI (Fig. 8 ) revealed a clear negative linear trend, suggesting that as crop water stress intensified, the requirement for irrigation diminished. The regression equation (y = − 237.69x + 385.28) indicated that for every unit increase in CWSI, irrigation water decreased by roughly 238 mm. When CWSI values were near zero, irrigation levels were approximately 380 mm, reflecting minimal stress due to frequent watering. In contrast, as CWSI approached one, irrigation reduced to about 150 mm, indicating significant water scarcity for the crops. This decline underscores CWSI's potential in optimizing water use, though its effect on yield becomes evident when compared to the yield–CWSI relationship (Figure Y). As previously demonstrated, wheat yield dropped considerably with increasing CWSI, decreasing from over 2300 kg ha⁻¹ at low stress levels (< 0.2) to below 500 kg ha⁻¹ at high stress (≥ 1.0). These results highlight the inherent trade-off between conserving water and maintaining yield: while reduced irrigation conserves water, it also imposes stress that significantly impacts productivity. The consistency of these linear relationships underscores CWSI's value as a tool for irrigation scheduling. However, the findings suggest that maintaining wheat at moderate CWSI levels might offer an optimal balance—ensuring efficient water use while minimizing yield losses. This approach would be particularly beneficial in water-scarce regions like the Indo-Gangetic plains, where careful irrigation management is essential for sustaining wheat production amid increasing resource constraints. Irrigation scheduling with CWSI In this research, an empirical Crop Water Stress Index (CWSI) method was introduced for scheduling wheat irrigation in India. The findings indicated that plot T4 achieved the highest water use efficiency, aligning with a 30% soil moisture deficit. Plot T4 conserved 30% more water compared to plot T1. The best yield and water use efficiency (WUE) were observed when the CWSI reached 0.3. The results demonstrated that the water applied to plot T4 (375 mm) was nearly equivalent to the wheat crop's water requirement in India (around 400 mm). Therefore, this plot can be seen as a model for effective wheat irrigation scheduling. The regression equations developed in equations ( 5 ) to (7) were effective in estimating wheat yield, water use efficiency, and the water needs of the wheat crop. The comprehensive evaluation of irrigation water usage, crop yield, and WUE in relation to the average CWSI provides a detailed understanding of how crops respond to varying water stress levels. As shown in Figure X, the amount of irrigation water applied decreases linearly as CWSI increases, starting from about 380 mm at low stress (CWSI ≈ 0.0) and reducing to approximately 150 mm at high stress (CWSI ≈ 1.0), indicating a gradual reduction in water availability for the crops. This reduction in irrigation directly affects yield (Figure Y), which significantly decreases as stress levels rise. When CWSI values are below 0.2, yields exceed 2300 kg ha⁻¹, but at higher stress levels (≥ 1.0), yields drop below 500 kg ha⁻¹, highlighting the trade-off between water conservation and productivity. Notably, the WUE–CWSI relationship (Figure Z) follows a quadratic trend, peaking at moderate stress levels (CWSI ≈ 0.3–0.4) where WUE surpasses 2.6 kg ha⁻¹ mm⁻¹. This suggests that a slight level of water stress can improve resource-use efficiency, as plants optimize water uptake and biomass production relative to the water applied. However, beyond this point, WUE declines sharply with increasing stress, falling below 1.5 kg ha⁻¹ mm⁻¹ under severe water shortages (CWSI ≥ 1.0). This indicates that excessive water restriction not only reduces yield but also diminishes water use efficiency, affecting both productivity and sustainability. In Fig. 10 , the Q–Q plot and histogram for Applied Water reveal that the data closely resemble a normal distribution, with points aligning well with the theoretical line and a symmetric histogram centered around the mean (µ = 267.5 mm, σ = 88.2 mm). This suggests consistent irrigation application and balanced experimental conditions across treatments. Figure 11 shows that the Yield data exhibit slight left skewness and significant variability (µ = 1692.5 kg ha⁻¹, σ = 742.6), indicating notable differences in yield performance among treatments due to varying irrigation levels and water stress. Figure 12 illustrates that the Water Use Efficiency (WUE) slightly deviates from normality, with a mean of 2.25 kg ha⁻¹ mm⁻¹ and σ = 0.68, suggesting that moderate water stress improved efficiency, while excessive or insufficient irrigation diminished it. In Fig. 13 , the Crop Water Stress Index (CWSI) shows a departure from normality with a bimodal distribution (µ = 0.49, σ = 0.36), reflecting a broad spectrum of water stress conditions from well-watered to highly stressed, confirming its effectiveness as an indicator of crop water status. Soil Moisture Dynamics Throughout the 2018–19 cropping season, as illustrated in Fig. 2 , soil moisture levels consistently decreased as the number of days after sowing (DAS) increased across all treatments. Treatments T1 (non-water stress) and T2 (maximum water stress) maintained relatively higher soil moisture, whereas T3 (10% depletion), T4 (30% depletion), and T5 (50% depletion) experienced more pronounced declines. By the time 100 DAS was reached, the soil moisture in T5 had nearly reached the permanent wilting point (~ 0.05 cm³ cm⁻³), underscoring the potential for growth limitations at higher depletion levels. These findings underscore the importance of effective irrigation management to ensure optimal soil water conditions for wheat cultivation. Canopy Temperature and VPD Relationship The correlation between the difference in canopy and air temperatures (Tc – Ta) and vapor pressure deficit (VPD) offered further understanding of the water status in crops. During the 2018–19 period, Tc – Ta showed a more pronounced negative sensitivity to VPD before heading (slope = − 2.3715, R² = 0.9502) than after heading (slope = − 1.8952, R² = 0.8755), indicating that crops were more reactive to atmospheric demand in the early growth stages. Conversely, in 2019–20, the regression lines for both pre-heading and post-heading phases were nearly parallel (slopes = − 1.7184 and − 1.8137, R² = 0.9972 and 0.9682, respectively), suggesting a more consistent crop response throughout the stages. These consistently strong correlations confirm that differences in canopy temperature are reliable indicators of water stress in wheat and can be used to monitor stress dynamics in real time. Yield Response to CWSI The relationship between yield and CWSI, as illustrated in Fig. 5 , further confirmed the importance of canopy-based indices in forecasting crop outcomes. A pronounced negative linear correlation (y = − 2155.8x + 2608.8) indicated that yields decreased from over 2300 kg ha⁻¹ when stress was minimal (CWSI < 0.2) to less than 500 kg ha⁻¹ under severe stress conditions (CWSI ≥ 1.0). This finding highlights that increased water stress greatly diminishes yield potential, emphasizing the necessity of strategic irrigation management to prevent high-stress situations during critical growth phases. Irrigation Water and CWSI The connection between the amount of irrigation water used and the average CWSI (Fig. 6 ) revealed a negative linear pattern. As irrigation decreased from nearly 380 mm at low stress levels (CWSI ≈ 0.0) to around 150 mm at high stress levels (CWSI ≈ 1.0), it indicated potential for water conservation. However, this reduction also led to yield losses at higher stress levels. This highlights the need to balance water conservation with productivity, suggesting that irrigation planning should aim to achieve both goals rather than solely focusing on reducing water usage. Water Use Efficiency under Stress The WUE–CWSI curve depicted in Fig. 7 demonstrated a quadratic pattern, with WUE rising from approximately 2.3 kg ha⁻¹ mm⁻¹ under minimal stress to peak values exceeding 2.6 kg ha⁻¹ mm⁻¹ at moderate stress levels (CWSI around 0.3–0.4). This suggests that slight water stress can enhance resource-use efficiency by facilitating more effective allocation of water to biomass and yield. Nevertheless, WUE decreased sharply at higher stress levels, falling below 1.5 kg ha⁻¹ mm⁻¹ when CWSI surpassed 1.0, underscoring the inefficiency associated with severe water shortages. Integrated Perspective The integration of soil moisture dynamics, canopy temperature–VPD relationships, yield response, irrigation application, and WUE trends underscores the pivotal role of CWSI as a comprehensive indicator of crop water stress. Soil moisture data reveal the gradual reduction of available water, while the Tc – Ta versus VPD relationships highlight the canopy's sensitivity to evaporative demand. These physiological reactions are directly linked to yield declines at elevated stress levels, as evidenced by the yield–CWSI regression. The irrigation–CWSI relationship illustrates how water inputs influence stress dynamics, and the WUE–CWSI curve indicates an optimal stress threshold (CWSI ≈ 0.3–0.4) where water productivity is maximized without significant yield losses. Together, these findings emphasize that maintaining wheat under moderate stress conditions provides the best balance between conserving irrigation water and sustaining yield, especially in the Indo-Gangetic plains where water scarcity is increasingly problematic. Canopy temperature-based indices like Tc – Ta and CWSI offer practical tools for irrigation scheduling, allowing farmers to enhance water-use efficiency while minimizing productivity losses under limited water availability. Conclusions This research illustrated that the Crop Water Stress Index (CWSI), which is based on the relationship between canopy temperature and vapor pressure deficit (VPD), serves as a reliable measure for assessing water stress in wheat and informing irrigation strategies. The findings revealed strong linear and quadratic correlations between CWSI, crop yield, irrigation levels, and water use efficiency (WUE), underscoring its reliability. Optimal results were observed under moderate water stress conditions (CWSI ≈ 0.3–0.4), where the highest WUE (2.7 kg ha⁻¹ mm⁻¹) was achieved without notable reductions in yield. Conversely, excessive stress (CWSI ≥ 1.0) resulted in a significant decrease in yield (≤ 500 kg ha⁻¹), emphasizing the importance of balanced irrigation management. From a managerial standpoint, utilizing irrigation strategies based on CWSI can enable farmers to optimize water usage, potentially reducing irrigation water by up to 30% compared to full supply while maintaining yields around 2300–2375 kg ha⁻¹. This method is especially pertinent for the Indo-Gangetic plains, where water scarcity and competing demands are becoming more pronounced. By incorporating CWSI into decision-making processes, irrigation scheduling can be enhanced to achieve improved water productivity, increased resilience to water stress, and sustainable wheat production in resource-constrained environments. Declarations Competing interests : The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to Anuj Kumar Dwivedi Funding Not Applicable. Author Contribution AKD conducted the experiment, analysed data and prepared the whole manuscripts. helped during the preparation of the manuscript and supervised. Acknowledgement NA Data Availability The data generated or analysed during this study are available from the corresponding author on reasonable request. References Alderfasi, A. A., and Nielsen, D. C. (2001). Use of crop water stress index for monitoring water status and scheduling irrigation in wheat. Agricultural water management , 47 (1), 69-75. 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. Apolo-Apolo, O. E., Martínez-Guanter, J., Pérez-Ruiz, M., & Egea, G. 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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-8008539","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":546092381,"identity":"f92ac9cd-6664-47b3-8071-9e171ef2aac6","order_by":0,"name":"Anuj Kumar Dwivedi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie3PsUrEMBjA8YTAueS4NQ6nr9BSyCS+iEtD4SazaobziMt3S3G+Qa6voAidGwJ1yXGr4GLfwLGDoNHbhPbqJlz+Qwjh+xE+hEKh/xhFEUoRwnB0q6t35V8I0QMJtaZZuW+CBxAf1myWJWPY3XvFJLcla+BseocuORuvby4mS09aVXYStoErJmCWAHKcHZfPcmWxxrl77f5mS/lL6qwAnHMWl7XUnhAM3eR0Rz4FEMqZuK9lsY9Em9wTVQkYjbLI6Ll82EdiV1+3qcoSoMQ0uq7koyemb5cTlz3FbXQ+LYpG24/5Qq631ry1qmf9X9mfsxo871v8ZTgUCoUOpC/wnGMo7ooWZAAAAABJRU5ErkJggg==","orcid":"","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":true,"prefix":"","firstName":"Anuj","middleName":"Kumar","lastName":"Dwivedi","suffix":""},{"id":546092383,"identity":"ba3aa084-c451-430b-ab29-f3d531c193e4","order_by":1,"name":"C. 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1","display":"","copyAsset":false,"role":"figure","size":528159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy area.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/cd12599c89553ade63ad438c.jpeg"},{"id":96204367,"identity":"2a56c656-f42c-4974-95c7-eb39fb7923cd","added_by":"auto","created_at":"2025-11-18 16:59:02","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":290637,"visible":true,"origin":"","legend":"\u003cp\u003eLayout of the experimental plot: T1 = non-water-stressed plot, T2 = maximum stressed plot, T3=10% depletion, T4=30% depletion, and 50% depletion\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/6da0ba2b66cffe947a3f6b46.jpeg"},{"id":96252849,"identity":"2c18fbef-6e6e-4952-a93c-c409cf96458a","added_by":"auto","created_at":"2025-11-19 07:41:31","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":414333,"visible":true,"origin":"","legend":"\u003cp\u003eSoil moisture variation in cropping year 2018–2019 of T1 non-water stress, T2 maximum water stress, T3 10% depletion, T4 30% depletion, and T5 50% depletion.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/6088e0d73296ed153acfd44b.jpeg"},{"id":96204372,"identity":"fb20574d-0aac-49eb-8fbb-5941bf034d26","added_by":"auto","created_at":"2025-11-18 16:59:02","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":420002,"visible":true,"origin":"","legend":"\u003cp\u003e(Tc − Ta) and VPD relationship showing lower and upper baselines for preheating and post heading stages of the wheat crop year 2018–2019.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/f25a31cf67bad0b09339ca4e.jpeg"},{"id":96204371,"identity":"030f5f62-de42-4c7b-926d-56196c6ebca7","added_by":"auto","created_at":"2025-11-18 16:59:02","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":312815,"visible":true,"origin":"","legend":"\u003cp\u003e(Tc − Ta) and VPD relationship showing lower and upper baselines for preheating and post heading stages of the wheat crop year 2019–2020.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/9cf10fd1f87fda8663196a05.jpeg"},{"id":96252986,"identity":"0e604d1b-71b4-4a3c-ac6b-3a18811f93b1","added_by":"auto","created_at":"2025-11-19 07:41:46","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":110164,"visible":true,"origin":"","legend":"\u003cp\u003e(A) CWSI variation under different irrigation schedules and DAS during the 2018–2019 crop season\u003c/p\u003e\n\u003cp\u003e(b CWSI variation under different irrigation schedules and DAS during the 2019–2020 crop season\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/42cecd8a4dd973a872a2b37e.jpeg"},{"id":96252962,"identity":"cb50a6f2-98df-49ac-8514-c41d13086443","added_by":"auto","created_at":"2025-11-19 07:41:44","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":329382,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in seed yield (kg ha⁻¹) of Indian wheat with mean CWSI under different irrigation levels.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/b5cc695c3f2a547bbf9a77ff.jpeg"},{"id":96204375,"identity":"939b28eb-6d1a-401f-a219-02444e94c578","added_by":"auto","created_at":"2025-11-18 16:59:02","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":38523,"visible":true,"origin":"","legend":"\u003cp\u003eLinear relationship between applied water (mm) and mean CWSI for Indian wheat across irrigation regimes.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/8470cc1638f029ea8b07d5ee.jpeg"},{"id":96204373,"identity":"86867b52-cfda-4992-b528-fb72120b017d","added_by":"auto","created_at":"2025-11-18 16:59:02","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":316062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWater use efficiency (kg=ha=mm) of Indian wheat as a nonlinear function of mean CWSI for different irrigation treatments\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/0bbb22aeb759b546784b4f9c.jpeg"},{"id":96204374,"identity":"3dcc5af4-be81-42c9-a396-16e2be2db868","added_by":"auto","created_at":"2025-11-18 16:59:02","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":103763,"visible":true,"origin":"","legend":"\u003cp\u003eNormality Assessment of Applied Water Data Using Q–Q Plot and Histogram\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/8388550b8f34dd0683b117d1.png"},{"id":96252291,"identity":"a3757a3c-372d-4cba-ba96-74570a0ae74f","added_by":"auto","created_at":"2025-11-19 07:40:45","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":295653,"visible":true,"origin":"","legend":"\u003cp\u003eNormality Assessment of Yield Data Using Q–Q Plot and Histogram\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/b8e75ddeea66242b76b47211.jpeg"},{"id":96204385,"identity":"e1c597f9-179d-4f46-a8e5-2babb15e92fd","added_by":"auto","created_at":"2025-11-18 16:59:02","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":88608,"visible":true,"origin":"","legend":"\u003cp\u003eNormality Assessment of WUE Data Using Q–Q Plot and Histogram\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/56a45c9f44768051aa4968b2.png"},{"id":96204381,"identity":"b70a8c48-e605-47da-aeb6-0f4450186c71","added_by":"auto","created_at":"2025-11-18 16:59:02","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":93313,"visible":true,"origin":"","legend":"\u003cp\u003eNormality Assessment of CWSI Data Using Q–Q Plot and Histogram\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/bdd68efa63cf1211fec18023.png"},{"id":101632837,"identity":"fb502e3b-2ee1-4d83-b1c9-a457790c6927","added_by":"auto","created_at":"2026-02-02 05:55:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4564942,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8008539/v1/b6d0874d-dfe1-4429-bc8c-487901f14da0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Crop Water Stress Index (CWSI) for Irrigation Scheduling of Wheat in Sub- Humid Conditions of India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWheat accounts for approximately 29% of the world's food production, making it a crucial cereal crop. In India, wheat is grown in both rain-fed and irrigated areas. In regions reliant on rainfall, particularly arid and semi-arid areas, its productivity is constrained by insufficient water availability (Wakchaure et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Typically, Indian farmers irrigate wheat crops four to five times during the early (sensitive), growth, flowering, and grain filling stages, spanning the crop season from November to March or April. Proper irrigation management during the anthesis period enhances the rate of photosynthesis, which in turn improves grain size and ultimately boosts grain yield (Ayed et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saint Pierre et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEffective management of irrigation water throughout the crop's growth phase is crucial for attaining high grain yield and productivity (Kumar et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The relationship between crops and water, along with the scheduling of irrigation, relies on consumptive use calculated from evapotranspiration, which is determined using hydro-meteorological parameters. Given that crops react to environmental and soil conditions, irrigation scheduling should be guided by the crop water stress index (CWSI), which has proven effective in scheduling mustard irrigation in India. In controlling irrigation, leaf water potential and crop water stress are essential factors (Jones et al. 2004). An infrared thermometer has traditionally been employed to detect water stress in crops. Conversely, the CWSI method, which calculates crop water stress based on canopy surface temperature (Tc), is widely used for various crops, such as corn and wheat, under different climatic conditions (Taghvaeian et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yazar et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; DeJonge et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gontia et al. 2008; Yuan et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA model for the lower baseline equation of wheat crops was created by (Argyrokastritis et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e and Yazar et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) to calculate the CWSI of winter wheat, where Ta represents the ambient air temperature, is the canopy temperature, and VPD stands for Vapour Pressure Deficit. According to Gontia et al. (2008), the CWSI for wheat varied from 0 to 0.96 across different irrigation levels. Kumar et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) developed the baseline equation for mustard in India (Alderfasi et al. 2001) by analyzing the difference between canopy and air temperature against VPD for both non-stressed and fully stressed plots during 2015, 2016, and 2017. Using this baseline equation, the CWSI was determined at soil moisture deficits of 10, 30, and 50%. It was observed that a plot with a 30% soil moisture deficit achieved optimal yield and the highest water use efficiency.\u003c/p\u003e\u003cp\u003eLeaf water potential is considered a reliable indicator of a plant's internal water content. When plant leaves have less water, it indicates a lower water level in the soil. Severe agricultural drought can adversely affect plant growth, biomass, and grain yield (Puri and Swamy \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Water use efficiency (WUE) in crops is defined as the ratio of grain yield to the crop's evapotranspiration. Studies indicate that applying irrigation during a critical growth phase results in the highest WUE under water scarcity compared to conditions of abundant irrigation (Mandal et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). According to Farr\u0026eacute; et al. 2006, WUE decreases as the water supply to crops diminishes. Sezen et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e compared two models and found that the highest yield was achieved with full irrigation using drip irrigation at 7-day intervals, while the lowest yield occurred in rain-fed areas. Rao et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e found that total biomass was greatest under full irrigation with line source sprinkler irrigation compared to deficit irrigation levels. Padhi et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e observed that grain yield, straw, and leaf area index were influenced by different treatment levels and could decrease as stress levels rose. Li et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e suggested that an adequate water supply during the growth season can enhance WUE and biomass yield. Rao et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e recorded the highest mean WUE of 1.28 kg m-3 in wheat. However, Tari (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported a range of 2.0 to 10.1 t/ha for full irrigation and rain-fed conditions and recommended replenishing water deficits during the milky grain stage of wheat to minimize yield loss. A study assessed water deficit and observed wheat biomass yield between 15 and 20 t/ha, depending on irrigation and its application during the growth stage (Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCrop water stress index calculations\u003c/strong\u003e\u003cp\u003eVarious methods can be employed to calculate the crop water stress index (CWSI) using the measured temperatures of both stressed and non-stressed canopies, or for the specific treatment in question, along with the temperatures of artificial canopy surfaces that simulate the transpiration potential of the actual canopy being studied. These methods differ based on how the upper and lower limits are established. As noted by Jackson et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), these limits form boundaries that allow the normalization of the measured canopy temperatures.\u003c/p\u003e\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\u003eDetails of applied water, crop yield, water use efficiency, and mean CWSI for various treatments.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCrop Year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003eTreatments\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eApplied Water (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e375.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e337.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e262.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e187.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e390.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e351.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e273.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e195.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSeed Yield (Kg/ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1375\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWUE (Kg/ha/mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMean CWSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5786\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6186\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\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\u003eDetails of water application in different plots for the 2018\u0026ndash;19 and 2019\u0026ndash;20 seasons.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrop Year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIrrigation (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIrrigation Events\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e2018-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e375.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25,43,72,93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e337.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25,45,73,95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e262.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21,41,43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e187.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26,45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e2019-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e390.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25,46,75,91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e351.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24,45,72,95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e272.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23,41,43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e195.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25,45\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\u003cb\u003eEstimation of CWSI using the empirical method\u003c/b\u003e: Idso et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) introduced the empirical method, which has been widely adopted for calculating CWSI (Han et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003eb; DeJonge et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Irmak et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This method is known as \"CWSI-EB,\" where \"EB\" signifies the empirical determination of the lower baseline. The empirical lower baseline (Tc-Ta) L is established through a linear regression between the temperature difference (Tc \u0026ndash; Ta) and VPD, as illustrated in Eq.\u0026nbsp;\u003cspan refid=\"Equ9\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b). Here, Tc is obtained from a fully watered, non-stressed crop. The upper baseline ((Tc-Ta)U can be identified using two approaches: (1) by collecting Tc from a stressed crop (e.g., rainfed treatment) across a broad VPD range, with the maximum observed (Tc \u0026ndash; Ta) serving as the upper limit (DeJonge et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and (2) by employing Eq.\u0026nbsp;(1d), which differs from Eq.\u0026nbsp;(1c) by utilizing VPG instead of VPD (Han et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003ea; Yuan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The first method, which uses the upper limit with Eq.\u0026nbsp;(1c), is termed CWSI-EB1, while the second method, using Eq.\u0026nbsp;(2d), is called CWSI-EB2.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:CWSI=\\frac{{{(T}_{c}-{T}_{a})}_{a}-({{T}_{c}-{T}_{a})}_{L}}{{{(T}_{c}-{T}_{a})}_{u}-({{T}_{c}-{T}_{a})}_{L}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1a\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{{(T}_{c}-{T}_{a})}_{L}=A+B\\times\\:VPD$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1b\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{{(T}_{c}-{T}_{a})}_{U}=\\text{m}\\text{a}\\text{x}\\left({T}_{c}-{T}_{a}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1c\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{{(T}_{c}-{T}_{a})}_{U}=A+B\\times\\:\\:VPD$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1d\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eComputation of CWSI through theoretical models (Th1 and Th2)\u003c/h3\u003e\n\u003cp\u003eThe upper and lower boundaries, as determined by the theoretical framework of Jackson et al. (1981) and Jackson et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), were calculated using Eq.\u0026nbsp;(2a) and Eq.\u0026nbsp;(2b), respectively. This method is referred to as \u0026ldquo;CWSI-Th\u0026rdquo; in the following discussion. The upper boundary of (Tc \u0026ndash; Ta) is derived when rc is allowed to become infinitely large, leading to Eq.\u0026nbsp;(2a). Conversely, the lower boundary of (Tc \u0026ndash; Ta) is determined by assuming that the canopy resistance matches the canopy resistance at potential transpiration for a well-watered canopy surface (rc\u0026thinsp;=\u0026thinsp;rcp), resulting in Eq.\u0026nbsp;(2b). There are, however, two distinct methods for calculating the theoretical CWSI, which depend on different approaches to computing both aerodynamic and canopy resistances (Han et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003ea). The first method (CWSI-Th1) involves using Eq.\u0026nbsp;(2c) to calculate aerodynamic resistance (Jackson et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) and assuming that potential canopy resistance is zero (i.e., rcp\u0026thinsp;=\u0026thinsp;0), where canopy transpiration is considered equivalent to the evaporation of a free water surface (Jackson et al., 1981, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). The second method (CWSI-Th2) employs seasonal averages of ra (ra) (Eq.\u0026nbsp;2d) and rcp (rcp) (Eq.\u0026nbsp;2e) as recommended by O\u0026rsquo;Toole and Real (1986). CWSI-Th2 has been effectively utilized and demonstrated satisfactory performance, as noted by Han et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003ea) and Clawson et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:({{\\text{T}}_{\\text{c}}-{\\text{T}}_{\\text{a}})}_{\\text{U}}=\\frac{{\\text{r}}_{\\text{a}}\\left({\\text{R}}_{\\text{n}}-\\text{G}\\right)}{{{\\rho\\:}\\text{C}}_{\\text{p}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2a\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:\\left(({{\\text{T}}_{\\text{c}}-{\\text{T}}_{\\text{a}})}_{\\text{L}}=\\left\\{\\left[\\frac{{\\text{r}}_{\\text{a}}\\left({\\text{R}}_{\\text{n}}-\\text{G}\\right)}{{{\\rho\\:}\\text{C}}_{\\text{p}}}\\right]\\times\\:{\\gamma\\:}\\frac{\\left(1+\\frac{{\\text{r}}_{\\text{c}\\text{p}}}{{\\text{r}}_{\\text{a}}}\\right)}{\\varDelta\\:+{\\gamma\\:}\\left(1+\\frac{{\\text{r}}_{\\text{c}\\text{p}}}{{\\text{r}}_{\\text{a}}}\\right)}\\right]\\right\\}-\\left[\\frac{\\text{V}\\text{P}\\text{D}}{\\varDelta\\:+{\\gamma\\:}\\left(1+\\frac{{\\text{r}}_{\\text{c}\\text{p}}}{{\\text{r}}_{\\text{a}}}\\right)}\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2b\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhere\u003c/b\u003e,(Tc-Ta)u and (Tc \u0026ndash; Ta)L are upper and lower limits, respectively, Tc (◦C) is the canopy temperature, Ta (◦C) is the air temperature, \u003cem\u003eρ\u003c/em\u003e (kg/m3) is air density, Cp (J/kg/C) is the heat capacity of air, \u003cem\u003eγ\u003c/em\u003e (Pa\u0026deg;C\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)is psychrometric constant, r\u003csub\u003ecp\u003c/sub\u003e (sm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) is canopy resistance at full canopy transpiration, Δ (Pa\u0026deg;C\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) is the slope of the saturated vapor pressure, Rn (W m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) is the net radiation computed based on Stefan-Boltzmann relationship following Ortega-Farias et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Ben-Gal et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) (i.e., \u003cem\u003eRn\u003c/em\u003e = (1- \u0026prop;)\u003cem\u003eRs\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eεaσT\u003c/em\u003e\u003csup\u003e4\u003c/sup\u003e\u003cem\u003ea\u003c/em\u003e- \u003cem\u003eεcσT\u003c/em\u003e\u003csup\u003e4\u003c/sup\u003e\u003cem\u003ec\u003c/em\u003e, where Rs is the incoming solar radiation (W m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), α (0.23) is the surface albedo, \u003cem\u003eσ\u003c/em\u003e (5.67 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e W m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e K) is Stefan-Boltzmann constant, \u003cem\u003eεa\u003c/em\u003e is hemispherical longwave atmospheric emissivity (s m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and \u003cem\u003eεc\u003c/em\u003e is canopy surface emissivity (0.98)), VPD (kPa) is the vapor pressure deficit which was computed following Allen et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)d is the soil heat flux assumed to be 10% of Rn (Han et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003ea).\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:{\\text{r}}_{\\text{a}}=\\frac{4.7[{\\text{L}\\text{N}(\\text{Z}-\\text{D})/{\\text{Z}}_{0}]}^{2}}{1+0.54\\text{u}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2c\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\:{\\overline{\\text{r}}}_{\\text{a}}=\\frac{{{\\rho\\:}\\text{C}}_{\\text{p}}\\times\\:\\text{A}}{{\\overline{\\text{R}}}_{\\text{n}}\\times\\:\\text{B}(\\overline{\\varDelta\\:}+\\frac{1}{\\text{B}})}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2d\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$\\:{\\overline{\\text{r}}}_{\\text{c}\\text{p}}=-{\\overline{\\text{r}}}_{\\text{a}}[\\frac{\\overline{\\varDelta\\:}+\\frac{1}{\\text{B}}}{{\\gamma\\:}}+1]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2e\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere, \u003cem\u003eRn\u003c/em\u003e (W m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) is the seasonal average net radiation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\overline{\\varDelta\\:}\\)\u003c/span\u003e\u003c/span\u003e (Pa ◦C-\u003csup\u003e1\u003c/sup\u003e) is the seasonal average of Δ, A and B are parameters or coefficients from Eq.\u0026nbsp;(2b) of the developed lower empirical baseline, z (m) is the reference measurement height, d (m) is the displacement height (d\u0026thinsp;=\u0026thinsp;0.63 h), h (m) is the canopy height, zo (m) is the roughness length (zo\u0026thinsp;=\u0026thinsp;0.13 h), and u (m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) is the wind speed at height z. Both \u003cem\u003eRn\u003c/em\u003e and Δ were computed considering only the mid-day hours (1200\u0026ndash;1600 h, central day time) (Han et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003ea).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEmpirical method based on canopy reference surfaces (CWSI-EN, CWSI-EA)\u003c/h2\u003e\u003cp\u003eTo create simple techniques for establishing limits, given the challenges in developing Idso\u0026rsquo;s empirical method and the inaccuracies in Jackson\u0026rsquo;s theoretical approach, the canopy temperature (Tc) of both well-watered and stressed crops, along with the temperatures of dry and wet artificial canopy reference surfaces, can be employed (Apolo-Apolo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Maes et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Meron et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). When utilizing the actual canopy surface, the upper and lower limits are determined as temperature differences using Tc from a non-water-stressed canopy at potential transpiration (Tcl) and a water-stressed canopy without transpiration (Tcu), respectively (Eqs.\u0026nbsp;4a, 4b). This approach is similar to the derivation of other stress indices like Degrees Above Non-Stress canopy (DANS) (DeJonge et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) or temperature stress day (TSD) (Gardner et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Alternatively, artificial canopy surfaces can be used by employing the temperatures of wet soaked fabric (Twet) and dry fabric (Tdry) made from green styrofoam. The upper and lower limits are calculated as the differences between Ta and the temperatures of wet and dry styrofoam surfaces, respectively (Eqs.\u0026nbsp;3c and 3d). However, the CWSI can be simplified as shown in Eq.\u0026nbsp;(3e) after substituting Eqs.\u0026nbsp;(3a\u0026ndash;3d).\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$$\\:{{(\\varvec{T}}_{\\varvec{c}}-{\\varvec{T}}_{\\varvec{a}})}_{\\varvec{L}}=({\\varvec{T}}_{\\varvec{c}\\varvec{l}}-{\\varvec{T}}_{\\varvec{a}})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3a\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$$\\:{{(T}_{c}-{T}_{a})}_{U}=({T}_{cu}-{T}_{a})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3b\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e\n$$\\:{{(T}_{c}-{T}_{a})}_{L}=({T}_{wet}-{T}_{a})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3c\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ13\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ13\" name=\"EquationSource\"\u003e\n$$\\:{{(T}_{c}-{T}_{a})}_{U}=({T}_{dry}-{T}_{a})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3d\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CWSI=\\frac{{T}_{c}-{T}_{cl}}{{T}_{cu}-{T}_{cl}}\\:\\text{o}\\text{r}\\:CWSI=\\frac{{T}_{c}-{T}_{wet}}{{T}_{dry}-{T}_{wet}}\\:\\left(3\\text{e}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere, Tcu (◦C) is the temperature of stressed canopy (upper limit), Tcl is temperature of non-stressed canopy (lower limit), Twet (◦C), and Tdry (◦C) is the temperature of wet and dry artificial canopy reference surfaces, respectively.\u003c/p\u003e\u003cp\u003eThe CWSI is crucial for overseeing irrigation scheduling across various supplemental and traditional irrigation methods. Implementing strategies at different crop growth phases can enhance water management, boost water use efficiency, and increase farmers' earnings. This is especially important as the agricultural sector and water security in northern India are expected to face numerous environmental challenges, such as rising temperatures, reduced rainfall, and more frequent droughts (FAO 2016).\u003c/p\u003e\u003cp\u003eThis study aims to reevaluate some of the current perspectives on wheat crop irrigation and assess the crop water stress index (CWSI) of wheat under various treatments. The presentation will cover the materials and methods, as well as the evaluation of CWSI across different irrigation levels. Following this, relationships between wheat yield and CWSI, as well as water use efficiency and CWSI, will be established to determine the CWSI that results in optimal water use efficiency.\u003c/p\u003e\u003c/div\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eField experiments were carried out in the crop years 2018-19 and 2019-20 in a village close to Roorkee, Uttarakhand, India. Roorkee is situated at an elevation of 900 meters above sea level, with coordinates of 29.8543\u0026deg; N and 77.8880\u0026deg; E. The region's climate is sub-humid subtropical, receiving an average annual rainfall of 1170 mm. Approximately 75% of this rainfall occurs during the monsoon season, from June to September, and is unevenly distributed. The rest of the rainfall happens outside the monsoon period. The soil at the experimental location is sandy-loam and alluvial in nature. It has a low water retention capacity and a high infiltration rate.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExperiments\u003c/h3\u003e\n\u003cp\u003eField experiments were carried out to establish a connection between the crop water stress index and yield. Wheat (Triticum aestivum L.) was cultivated during the non-monsoon seasons of 2018-19 and 2019-20. Typically, the wheat crop requires 120\u0026ndash;125 days to complete its growth cycle in the winter. The experiments included five plots, each measuring 4 m \u0026times; 3 m, with a 1-meter buffer, and were organized using a randomized block design (RBD) across different treatments. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the experimental layout.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eExperimental details\u003c/h3\u003e\n\u003cp\u003eThe experiments conducted in this study took place in the experimental fields of the Institute Research Farm, situated within a 1 km radius of the institute. Seeds of the HD2967 variety were acquired from a local vendor, and necessary permissions were obtained from both local and national authorities to carry out the experiments. Throughout the experiments, guidelines provided by the institution at both national and international levels were followed.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eInstruments used\u003c/h2\u003e\u003cp\u003eAn infrared small handheld self-contained camera was employed to gather data on air and canopy temperatures, with measurements taken between midnight and 2 PM under clear skies to minimize radiation effects. Each reading was captured from four different directions. The canopy temperature was determined by averaging five readings from each plot. Measurements included canopy temperature, ambient air temperature, and both dry and wet bulb temperatures. The experimental procedure followed was akin to that described by previous researchers (Kumar et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The mean air temperature (Ta) was derived from the average of dry-bulb temperature readings during the measurement period. The mean VPD was calculated as the average of the instantaneous VPD, determined using the corresponding instantaneous wet and dry-bulb temperatures and the standard psychomotor equation (Brakke et al.1995). The CWSI was computed using the upper and lower limit estimates, following the method of (Idso et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIrrigation Scheduling\u003c/h3\u003e\n\u003cp\u003eThe maximum allowable depletion (MAD) of the available soil storage was set at 10%, 30%, and 50% for treatments T3, T4, and T5, respectively. Treatment T1 experienced no water stress, with frequent irrigation to keep soil moisture close to field capacity. In treatment T2, irrigation was only provided at sowing, with additional water applied once during the season solely for crop survival. Treatments T3, T4, and T5 were chosen to identify the optimal MAD level of Available Soil Water (ASW) for wheat irrigation in the area and to monitor the canopy-air temperature difference (Tc-Ta) under varying vapor pressure deficits to develop a crop water stress index. Treatments T1 and T2 were used to establish the minimum stress baseline (lower baseline) and maximum stress baseline (upper baseline), respectively, for the relationship between canopy-air temperature difference (Tc-Ta) and vapor pressure deficit (VPD) in creating the crop water stress index (CWSI). The percentage depletion of available soil moisture within the effective root zone was determined using the equation provided by Martin et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003cdiv id=\"Equ14\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ14\" name=\"EquationSource\"\u003e\n$$\\:Depletion\\:\\left(\\%\\right)=100\\times\\:\\frac{1}{n}\\sum\\:_{i=1}^{n}\\frac{{FC}_{i}-{\\theta\\:}_{i}}{{FC}_{i}-{PWP}_{i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere n is the number of layers of the effective rooting depth used for soil moisture sampling; FC\u003csub\u003ei\u003c/sub\u003e is the soil moisture at field capacity of the i\u003csup\u003eth\u003c/sup\u003e layer; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the volumetric soil moisture in the ith layer before irrigation; and PWP\u003csub\u003ei\u003c/sub\u003e is the soil moisture before permanent wilting point. The depth of irrigation water, d, to be applied at a particular MAD is given by Eq.\u0026nbsp;\u003cspan refid=\"Equ9\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Michael et al. 1978).\u003cdiv id=\"Equ15\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ15\" name=\"EquationSource\"\u003e\n$$\\:d=\\frac{1}{IRE}\\times\\:\\sum\\:_{i=1}^{n}\\frac{MAD({FC}_{i}-{PWP}_{i})\\times\\:{R}_{zi}}{100}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere d is the depth of irrigation water to be applied; Rzi is the depth of the i\u003csup\u003eth\u003c/sup\u003e soil layer within the effective rooting depth; and IRE is the irrigation efficiency. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows water applied to treatment in cropping seasons 2018-19 and 2019-20. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows soil properties at different depths. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the variation of soil moisture during the crop period for various treatments. Under treatments T\u003csub\u003e2\u003c/sub\u003e, T\u003csub\u003e4\u003c/sub\u003e, and T\u003csub\u003e5\u003c/sub\u003e, soil moisture exhibited a zigzag behaviour.\u003c/p\u003e\n\u003ch3\u003eCalculation of CWSI baseline\u003c/h3\u003e\n\u003cp\u003eThe non-water stress baseline essentially describes the correlation between Tc-Ta and VPD under conditions of potential evapotranspiration for a specific crop in a given area (Gardner et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Idso et al. 1982). Various methods, such as diurnal and seasonal, can be used to calculate the non-water stress index baseline equation (Gardner et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). In this study, the seasonal method was chosen over the diurnal one to determine the non-water stress index baseline equation, as it accounts for seasonal variations in canopy structure and transpiration and reflects a broad spectrum of environmental conditions. Canopy temperature measurements commenced when the canopy covered 80% of the area, starting 20 DAS (Days After Sowing). Data collection occurred weekly between 12 PM and 2 PM under clear sky conditions. Observations for the non-water stress index baseline equation were made after each irrigation during various wheat growth stages. A graph was plotted with (Tc-Ta) against VPD, resulting in the baseline equation. A linear relationship was established in the following form (Idso, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1982\u003c/span\u003e):\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{T}_{c}-{T}_{a}=m\\left(VPD\\right)+C\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(6\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere m\u0026thinsp;=\u0026thinsp;the slope of the linear regression equation, and C\u0026thinsp;=\u0026thinsp;the intercept of the line on the Tc-Ta axis.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCWSI value calculation\u003c/h2\u003e\u003cp\u003eEquation \u003cspan refid=\"Equ14\" class=\"InternalRef\"\u003e4\u003c/span\u003e was utilized to accomplish this. The recorded canopy and air temperatures were employed for this analysis. The CWSI value must remain within the range defined by the extremes of maximum water stress and no water stress as per the baseline.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{C}\\text{W}\\text{S}\\text{I}=\\left[\\frac{{(\\text{T}}_{\\text{c}}-{\\text{T}}_{\\text{a}})-({{\\text{T}}_{\\text{c}}-{\\text{T}}_{\\text{a}})}_{\\text{L}\\text{o}\\text{w}\\text{e}\\text{r}\\:\\text{L}\\text{i}\\text{m}\\text{i}\\text{t}}}{({{\\text{T}}_{\\text{c}}-{\\text{T}}_{\\text{a}})}_{\\text{U}\\text{p}\\text{p}\\text{e}\\text{r}\\:\\text{L}\\text{i}\\text{m}\\text{i}\\text{t}}-({{\\text{T}}_{\\text{c}}-{\\text{T}}_{\\text{a}})}_{\\text{L}\\text{o}\\text{w}\\text{e}\\text{r}\\:\\text{L}\\text{i}\\text{m}\\text{i}\\text{t}}}\\right]\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(7\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere Tc\u0026thinsp;=\u0026thinsp;the canopy temperature, and Ta\u0026thinsp;=\u0026thinsp;the air temperature.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eBaseline development of CWSI for wheat in India\u003c/h2\u003e\u003cp\u003eEquations for the maximum and minimum water stress baselines for each year during the pre-flowering and post-flowering stages were derived from observations in plot T2. In the non-irrigated plot T2, a peak value of 1.9\u0026deg;C was recorded as the highest difference between canopy and air temperatures (Tc-Ta). Consequently, this study used this canopy-air temperature difference as the maximum water stress baseline for wheat in India.\u003c/p\u003e\u003cp\u003eUsing data from plot T1, a regression equation was formulated based on the observed differences between Tc and Ta, along with the corresponding Vapour Pressure Deficit (VPD). For the cropping seasons of 2018-19 and 2019-20, the R2 values were determined for both the pre-flowering and post-flowering phases, resulting in values of 0.95, 0.88, and 0.99, and 0.96, respectively. These R2 values indicated that the regression equation was effective for calculating the CWSI in the study area.\u003c/p\u003e\u003cp\u003eSimilarly, regression equations were developed for non-water stress baseline for pre-flowering and post-flowering stages of the cropping season as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{c}-{T}_{a}=-2.3715\\left(VPD\\right)-1.659\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{c}-{T}_{a}=-1.8952\\left(VPD\\right)-2.3202\\)\u003c/span\u003e\u003c/span\u003e, respectively, and for the year 2019-20 were \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{c}-{T}_{a}=-1.7184\\left(VPD\\right)-2.3009\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{c}-{T}_{a}=-1.813\\left(VPD\\right)-1.9176\\)\u003c/span\u003e\u003c/span\u003e, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Figures\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show the clear difference between pre-flowering and post-flowering in non-water stress conditions. It is observed that the slope during the post-flowering phase is more gradual compared to the pre-flowering phase. This finding suggests that the flowering heads of wheat in India significantly diminish the relative transpiration cooling at the canopy surface. A similar study11 demonstrated a reduction in the slope of the non-water stressed baseline after the formation of seed heads (post-heading stage) compared to the pre-heading stage.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCrop Water Stress Index evaluation for wheat in India\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the variations in the Crop Water Stress Index (CWSI) over the crop cycle for different plots, with values ranging from 0 to 1.0. These CWSI readings were subsequently utilized to infer water use efficiency, as detailed below. The figure also presents the relationship between the difference in canopy and air temperatures (Tc \u0026ndash; Ta) and the vapor pressure deficit (VPD) in wheat during the 2019\u0026ndash;20 growing season, concentrating on the stages before and after heading. A significant negative linear correlation was identified in both stages, indicating that Tc \u0026ndash; Ta consistently diminished as VPD rose. Regression analysis revealed slopes of \u0026minus;\u0026thinsp;1.7184 (R\u0026sup2; = 0.9972) for the pre-heading stage and \u0026minus;\u0026thinsp;1.8137 (R\u0026sup2; = 0.9682) for the post-heading stage, demonstrating a very strong association. Unlike the previous season, the differences between the two stages were minimal, with nearly parallel regression lines, suggesting that the crop canopy responded similarly to evaporative demand in both stages. The high R\u0026sup2; values further confirm the reliability of Tc \u0026ndash; Ta as an indicator of plant\u0026ndash;atmosphere interactions. These findings highlight the potential of using canopy temperature-based indices to monitor crop water status, especially under varying atmospheric conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eApplied water distribution across plots in the 2018\u0026ndash;19 and 2019\u0026ndash;20 crop years.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrop Year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIrrigation (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIrrigation Events\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e2018-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e375.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25,43,72,93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e337.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25,45,73,95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e262.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21,41,43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e187.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26,45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e2019-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e390.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25,46,75,91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e351.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24,45,72,95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e272.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23,41,43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e195.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25,45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAssessment of water application, seed yield, and water use efficiency\u003c/h2\u003e\u003cp\u003eThe regression equation for the 2018-19 period, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, was formulated for both the pre-heading and post-heading phases, with R2 values of 0.95 and 0.8755, respectively. It is understood that the wheat canopy begins to open as the sun rises in the morning, continuing until the leaves reach a critical point when sunlight provides peak radiation around noon. The model developed suggests that the canopy temperature (Tc) gradually increases in relation to the air temperature (Ta) as the sun rises, until the leaves reach potential evapotranspiration. Analysis of the data revealed a notable shift in the baseline as the wheat crop moved from the vegetative to the reproductive phase, i.e., from pre-heading to post-heading. As a result, these baselines were calculated separately for each phase. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the variation in soil moisture content under different irrigation treatments during the 2018\u0026ndash;19 cropping year. A consistent decrease in soil moisture was observed with increasing days after sowing (DAS) across all treatments. Among the treatments, T1 (non-water stress) and T2 (maximum water stress) maintained relatively higher soil moisture levels throughout the growing period, highlighting the effectiveness of adequate irrigation in sustaining soil water availability. In contrast, T3 (10% depletion), T4 (30% depletion), and particularly T5 (50% depletion) exhibited a sharp decline in soil moisture, with T5 falling below 0.05 cm\u0026sup3; cm⁻\u0026sup3; by 100 DAS. This pattern clearly demonstrates the adverse impact of higher depletion levels on soil water status, which can potentially impede crop growth and yield if not properly managed. The findings emphasize the importance of maintaining optimal irrigation levels to prevent moisture stress during the crop growth cycle.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the foundational equation for the wheat crop's phases before and after heading during the 2018-19 and 2019-20 growing seasons. The regression model calculations identified 20\u0026deg;C as the maximum canopy-air temperature difference (Tc-Ta) for non-irrigated wheat throughout the growing season. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the relationship between the canopy-air temperature difference (Tc \u0026ndash; Ta) and vapor pressure deficit (VPD) for wheat in the 2018\u0026ndash;19 season, encompassing both pre-heading and post-heading stages. A pronounced inverse linear correlation was observed in both stages, showing that Tc \u0026ndash; Ta decreases as VPD increases. During the pre-heading stage, the regression line had a steeper slope (\u0026ndash;2.3715) and a higher coefficient of determination (R\u0026sup2; = 0.9502), indicating a greater sensitivity of canopy temperature to atmospheric demand compared to the post-heading stage. In contrast, the post-heading stage showed a milder slope (\u0026ndash;1.8952) and a lower R\u0026sup2; (0.8755), suggesting reduced sensitivity, likely due to changes in canopy structure and physiological activity after heading. These results highlight that indices based on canopy temperature can effectively assess crop water status, particularly during the pre-heading stage when plants are more responsive to evaporative demand.\u003c/p\u003e\u003cp\u003eThe R2 values for all regression equations for both wheat crop seasons during the 2018-19 and 2019-20 cropping periods are 0.9502 and 0.8755 for the pre-harvesting and post-harvesting phases of 2018-19, respectively, and 0.9973 and 0.9682 for 2019-20. These statistical parameters suggest that the developed regression equations are highly suitable for further calculating the crop water stress index (CWSI).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e (A) CWSI variation under different irrigation schedules and DAS during the 2018\u0026ndash;2019 crop season\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariation of soil physical properties across depths.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil depth\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBulk Density\u003c/p\u003e\u003cp\u003e(g/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSand\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSilt\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClay\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eParticle Density\u003c/p\u003e\u003cp\u003e(g/c.c)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKs\u003c/p\u003e\u003cp\u003e(cm/hr)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFC\u003c/p\u003e\u003cp\u003e(cm\u003csup\u003e3\u003c/sup\u003e/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePWP\u003c/p\u003e\u003cp\u003e(cm\u003csup\u003e3\u003c/sup\u003e/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAvailable Water\u003c/p\u003e\u003cp\u003e(cm\u003csup\u003e3\u003c/sup\u003e/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0-0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.3\u0026ndash;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.6\u0026ndash;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.9\u0026ndash;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.2\u0026ndash;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.13\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\u003ewhere Ks\u0026thinsp;=\u0026thinsp;the saturated hydraulic conductivity (cm/hr), FC\u0026thinsp;=\u0026thinsp;the field capacity (cm\u003csup\u003e3\u003c/sup\u003e/cm\u003csup\u003e3\u003c/sup\u003e), and PWP\u0026thinsp;=\u0026thinsp;the permanent wilting point (cm\u003csup\u003e3\u003c/sup\u003e/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eYield productivity\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the grain yield for each plot during the 2018-19 and 2019-20 seasons. Plot T3 achieved the highest yield at 2375 Kg/ha, whereas plot T2 had the lowest yield at 350 Kg/ha. The findings indicated that soil moisture stress was less in plot T2 compared to plot T3. A higher CWSI value causes plants to lose turgidity, resulting in reduced grain yield (Kumawat et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Plot T2's yield was 84% lower than that of plot T1. These outcomes were consistent with the research of other scholars (Chauhan et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The grain yield in plot T1 was slightly less than in plot T3. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the correlation between seed yield and mean CWSI across various irrigation treatments for wheat in India, with an R2 of 0.96. The derived relationship is presented as follows:\u003c/p\u003e\u003cp\u003eYield Model\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:Yield\\left(\\frac{Kg}{ha}\\right)=-2155.8\\times\\:CWSI+2608.8\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(8\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eWater application\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the amount of water distributed to different plots, which is closely associated with the average CWSI. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows a linear relationship between the average CWSI and the irrigation water used, with an R2 value of 0.96. This linear connection was established from the water applied to wheat crops to determine the link between average CWSI and water usage during the cropping period, as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A regression equation was developed with an R2 value of 0.9641, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The equation in relationship 6, which has a negative slope, suggests a decrease in water application as the crop water stress index (CWSI) rises. A similar trend was noted in the relationship between yield and average CWSI. The linear relationship was derived in Eq.\u0026nbsp;6. The strong negative correlation between wheat yield and average CWSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e) is consistent with the canopy temperature\u0026ndash;VPD relationships depicted in Figures Y and Z. Both analyses consistently highlight the significance of crop water status in determining productivity. The regression of Tc \u0026ndash; Ta against VPD demonstrated that canopy temperature is highly responsive to atmospheric demand, particularly during pre-heading stages when crop transpiration is most active. This sensitivity acts as an indirect yet reliable indicator of water stress, further confirmed by the yield\u0026ndash;CWSI relationship, where yield significantly decreased with increasing stress levels. Collectively, these findings suggest that canopy temperature-based indices like Tc \u0026ndash; Ta and derived CWSI values can be effectively used not only for real-time stress detection but also as predictive tools for yield estimation. From a management perspective, integrating canopy temperature monitoring with irrigation scheduling could help farmers apply water at critical growth stages, reducing yield losses and enhancing water-use efficiency under limited irrigation conditions.\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:Water\\:Applied=-237.69\\times\\:CWSI+385.28\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(9\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eWater use efficiency\u003c/h2\u003e\u003cp\u003eEvaluating wheat yields across various plots may not necessarily reflect the best irrigation practices (Gontia et al.2008). For instance, in the 2018-19 season, plot T1 achieved a yield of 2250 Kg/ha, requiring 375 mm of water. In contrast, plot T4 also produced a yield of 2250 kg/ha but only needed 262.5 mm of water. The grain yield difference between plot T1 and plot T4 was 8.8%. However, the extra water used in plot T1 to achieve this yield increase was significantly higher, at 30%. Water use efficiency (WUE) is determined by the ratio of yield to the water applied (Howell et al.2003). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the WUE for various plots during the wheat cropping seasons. In plot T3, the highest yield was recorded with WUE values of 2.71 and 2.65 Kg/ha/mm for the years 2018-19 and 2019-20, respectively. A 10% reduction in soil moisture in plot T1 did not impact the yield due to similar WUE. From these irrigation experiments, plot T4 emerged as the optimal treatment plot. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e(A\u0026amp;B) illustrates the regression equation developed between WUE and mean CWSI for different irrigation treatments, showing an R2 value of 0.98. The WUE increased with the crop water stress index (CWSI), peaking before declining as the CWSI value rose. The non-linear relationship between WUE and CWSI is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e (A\u0026amp;B):\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:WUE={-4.0728\\times\\:CWSI}^{2}+2.6844\\times\\:CWSI+2.3116,\\:\\:\\:{R}^{2}=0.98\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(10\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe examination of the relationship between irrigation water usage and the average CWSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) revealed a clear negative linear trend, suggesting that as crop water stress intensified, the requirement for irrigation diminished. The regression equation (y = \u0026minus;\u0026thinsp;237.69x\u0026thinsp;+\u0026thinsp;385.28) indicated that for every unit increase in CWSI, irrigation water decreased by roughly 238 mm. When CWSI values were near zero, irrigation levels were approximately 380 mm, reflecting minimal stress due to frequent watering. In contrast, as CWSI approached one, irrigation reduced to about 150 mm, indicating significant water scarcity for the crops. This decline underscores CWSI's potential in optimizing water use, though its effect on yield becomes evident when compared to the yield\u0026ndash;CWSI relationship (Figure Y). As previously demonstrated, wheat yield dropped considerably with increasing CWSI, decreasing from over 2300 kg ha⁻\u0026sup1; at low stress levels (\u0026lt;\u0026thinsp;0.2) to below 500 kg ha⁻\u0026sup1; at high stress (\u0026ge;\u0026thinsp;1.0). These results highlight the inherent trade-off between conserving water and maintaining yield: while reduced irrigation conserves water, it also imposes stress that significantly impacts productivity. The consistency of these linear relationships underscores CWSI's value as a tool for irrigation scheduling. However, the findings suggest that maintaining wheat at moderate CWSI levels might offer an optimal balance\u0026mdash;ensuring efficient water use while minimizing yield losses. This approach would be particularly beneficial in water-scarce regions like the Indo-Gangetic plains, where careful irrigation management is essential for sustaining wheat production amid increasing resource constraints.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eIrrigation scheduling with CWSI\u003c/h2\u003e\u003cp\u003eIn this research, an empirical Crop Water Stress Index (CWSI) method was introduced for scheduling wheat irrigation in India. The findings indicated that plot T4 achieved the highest water use efficiency, aligning with a 30% soil moisture deficit. Plot T4 conserved 30% more water compared to plot T1. The best yield and water use efficiency (WUE) were observed when the CWSI reached 0.3. The results demonstrated that the water applied to plot T4 (375 mm) was nearly equivalent to the wheat crop's water requirement in India (around 400 mm). Therefore, this plot can be seen as a model for effective wheat irrigation scheduling. The regression equations developed in equations (\u003cspan refid=\"Equ15\" class=\"InternalRef\"\u003e5\u003c/span\u003e) to (7) were effective in estimating wheat yield, water use efficiency, and the water needs of the wheat crop. The comprehensive evaluation of irrigation water usage, crop yield, and WUE in relation to the average CWSI provides a detailed understanding of how crops respond to varying water stress levels. As shown in Figure X, the amount of irrigation water applied decreases linearly as CWSI increases, starting from about 380 mm at low stress (CWSI\u0026thinsp;\u0026asymp;\u0026thinsp;0.0) and reducing to approximately 150 mm at high stress (CWSI\u0026thinsp;\u0026asymp;\u0026thinsp;1.0), indicating a gradual reduction in water availability for the crops. This reduction in irrigation directly affects yield (Figure Y), which significantly decreases as stress levels rise. When CWSI values are below 0.2, yields exceed 2300 kg ha⁻\u0026sup1;, but at higher stress levels (\u0026ge;\u0026thinsp;1.0), yields drop below 500 kg ha⁻\u0026sup1;, highlighting the trade-off between water conservation and productivity. Notably, the WUE\u0026ndash;CWSI relationship (Figure Z) follows a quadratic trend, peaking at moderate stress levels (CWSI\u0026thinsp;\u0026asymp;\u0026thinsp;0.3\u0026ndash;0.4) where WUE surpasses 2.6 kg ha⁻\u0026sup1; mm⁻\u0026sup1;. This suggests that a slight level of water stress can improve resource-use efficiency, as plants optimize water uptake and biomass production relative to the water applied. However, beyond this point, WUE declines sharply with increasing stress, falling below 1.5 kg ha⁻\u0026sup1; mm⁻\u0026sup1; under severe water shortages (CWSI\u0026thinsp;\u0026ge;\u0026thinsp;1.0). This indicates that excessive water restriction not only reduces yield but also diminishes water use efficiency, affecting both productivity and sustainability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, the Q\u0026ndash;Q plot and histogram for Applied Water reveal that the data closely resemble a normal distribution, with points aligning well with the theoretical line and a symmetric histogram centered around the mean (\u0026micro;\u0026thinsp;=\u0026thinsp;267.5 mm, σ\u0026thinsp;=\u0026thinsp;88.2 mm). This suggests consistent irrigation application and balanced experimental conditions across treatments. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows that the Yield data exhibit slight left skewness and significant variability (\u0026micro;\u0026thinsp;=\u0026thinsp;1692.5 kg ha⁻\u0026sup1;, σ\u0026thinsp;=\u0026thinsp;742.6), indicating notable differences in yield performance among treatments due to varying irrigation levels and water stress.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e illustrates that the Water Use Efficiency (WUE) slightly deviates from normality, with a mean of 2.25 kg ha⁻\u0026sup1; mm⁻\u0026sup1; and σ\u0026thinsp;=\u0026thinsp;0.68, suggesting that moderate water stress improved efficiency, while excessive or insufficient irrigation diminished it. In Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e, the Crop Water Stress Index (CWSI) shows a departure from normality with a bimodal distribution (\u0026micro;\u0026thinsp;=\u0026thinsp;0.49, σ\u0026thinsp;=\u0026thinsp;0.36), reflecting a broad spectrum of water stress conditions from well-watered to highly stressed, confirming its effectiveness as an indicator of crop water status.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eSoil Moisture Dynamics\u003c/h2\u003e\u003cp\u003eThroughout the 2018\u0026ndash;19 cropping season, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, soil moisture levels consistently decreased as the number of days after sowing (DAS) increased across all treatments. Treatments T1 (non-water stress) and T2 (maximum water stress) maintained relatively higher soil moisture, whereas T3 (10% depletion), T4 (30% depletion), and T5 (50% depletion) experienced more pronounced declines. By the time 100 DAS was reached, the soil moisture in T5 had nearly reached the permanent wilting point (~\u0026thinsp;0.05 cm\u0026sup3; cm⁻\u0026sup3;), underscoring the potential for growth limitations at higher depletion levels. These findings underscore the importance of effective irrigation management to ensure optimal soil water conditions for wheat cultivation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eCanopy Temperature and VPD Relationship\u003c/h2\u003e\u003cp\u003eThe correlation between the difference in canopy and air temperatures (Tc \u0026ndash; Ta) and vapor pressure deficit (VPD) offered further understanding of the water status in crops. During the 2018\u0026ndash;19 period, Tc \u0026ndash; Ta showed a more pronounced negative sensitivity to VPD before heading (slope = \u0026minus;\u0026thinsp;2.3715, R\u0026sup2; = 0.9502) than after heading (slope = \u0026minus;\u0026thinsp;1.8952, R\u0026sup2; = 0.8755), indicating that crops were more reactive to atmospheric demand in the early growth stages. Conversely, in 2019\u0026ndash;20, the regression lines for both pre-heading and post-heading phases were nearly parallel (slopes = \u0026minus;\u0026thinsp;1.7184 and \u0026minus;\u0026thinsp;1.8137, R\u0026sup2; = 0.9972 and 0.9682, respectively), suggesting a more consistent crop response throughout the stages. These consistently strong correlations confirm that differences in canopy temperature are reliable indicators of water stress in wheat and can be used to monitor stress dynamics in real time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eYield Response to CWSI\u003c/h2\u003e\u003cp\u003eThe relationship between yield and CWSI, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, further confirmed the importance of canopy-based indices in forecasting crop outcomes. A pronounced negative linear correlation (y = \u0026minus;\u0026thinsp;2155.8x\u0026thinsp;+\u0026thinsp;2608.8) indicated that yields decreased from over 2300 kg ha⁻\u0026sup1; when stress was minimal (CWSI\u0026thinsp;\u0026lt;\u0026thinsp;0.2) to less than 500 kg ha⁻\u0026sup1; under severe stress conditions (CWSI\u0026thinsp;\u0026ge;\u0026thinsp;1.0). This finding highlights that increased water stress greatly diminishes yield potential, emphasizing the necessity of strategic irrigation management to prevent high-stress situations during critical growth phases.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eIrrigation Water and CWSI\u003c/h2\u003e\u003cp\u003eThe connection between the amount of irrigation water used and the average CWSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e) revealed a negative linear pattern. As irrigation decreased from nearly 380 mm at low stress levels (CWSI\u0026thinsp;\u0026asymp;\u0026thinsp;0.0) to around 150 mm at high stress levels (CWSI\u0026thinsp;\u0026asymp;\u0026thinsp;1.0), it indicated potential for water conservation. However, this reduction also led to yield losses at higher stress levels. This highlights the need to balance water conservation with productivity, suggesting that irrigation planning should aim to achieve both goals rather than solely focusing on reducing water usage.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eWater Use Efficiency under Stress\u003c/h2\u003e\u003cp\u003eThe WUE\u0026ndash;CWSI curve depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e demonstrated a quadratic pattern, with WUE rising from approximately 2.3 kg ha⁻\u0026sup1; mm⁻\u0026sup1; under minimal stress to peak values exceeding 2.6 kg ha⁻\u0026sup1; mm⁻\u0026sup1; at moderate stress levels (CWSI around 0.3\u0026ndash;0.4). This suggests that slight water stress can enhance resource-use efficiency by facilitating more effective allocation of water to biomass and yield. Nevertheless, WUE decreased sharply at higher stress levels, falling below 1.5 kg ha⁻\u0026sup1; mm⁻\u0026sup1; when CWSI surpassed 1.0, underscoring the inefficiency associated with severe water shortages.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eIntegrated Perspective\u003c/h2\u003e\u003cp\u003eThe integration of soil moisture dynamics, canopy temperature\u0026ndash;VPD relationships, yield response, irrigation application, and WUE trends underscores the pivotal role of CWSI as a comprehensive indicator of crop water stress. Soil moisture data reveal the gradual reduction of available water, while the Tc \u0026ndash; Ta versus VPD relationships highlight the canopy's sensitivity to evaporative demand. These physiological reactions are directly linked to yield declines at elevated stress levels, as evidenced by the yield\u0026ndash;CWSI regression. The irrigation\u0026ndash;CWSI relationship illustrates how water inputs influence stress dynamics, and the WUE\u0026ndash;CWSI curve indicates an optimal stress threshold (CWSI\u0026thinsp;\u0026asymp;\u0026thinsp;0.3\u0026ndash;0.4) where water productivity is maximized without significant yield losses. Together, these findings emphasize that maintaining wheat under moderate stress conditions provides the best balance between conserving irrigation water and sustaining yield, especially in the Indo-Gangetic plains where water scarcity is increasingly problematic. Canopy temperature-based indices like Tc \u0026ndash; Ta and CWSI offer practical tools for irrigation scheduling, allowing farmers to enhance water-use efficiency while minimizing productivity losses under limited water availability.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis research illustrated that the Crop Water Stress Index (CWSI), which is based on the relationship between canopy temperature and vapor pressure deficit (VPD), serves as a reliable measure for assessing water stress in wheat and informing irrigation strategies. The findings revealed strong linear and quadratic correlations between CWSI, crop yield, irrigation levels, and water use efficiency (WUE), underscoring its reliability. Optimal results were observed under moderate water stress conditions (CWSI\u0026thinsp;\u0026asymp;\u0026thinsp;0.3\u0026ndash;0.4), where the highest WUE (2.7 kg ha⁻\u0026sup1; mm⁻\u0026sup1;) was achieved without notable reductions in yield. Conversely, excessive stress (CWSI\u0026thinsp;\u0026ge;\u0026thinsp;1.0) resulted in a significant decrease in yield (\u0026le;\u0026thinsp;500 kg ha⁻\u0026sup1;), emphasizing the importance of balanced irrigation management.\u003c/p\u003e\u003cp\u003eFrom a managerial standpoint, utilizing irrigation strategies based on CWSI can enable farmers to optimize water usage, potentially reducing irrigation water by up to 30% compared to full supply while maintaining yields around 2300\u0026ndash;2375 kg ha⁻\u0026sup1;. This method is especially pertinent for the Indo-Gangetic plains, where water scarcity and competing demands are becoming more pronounced. By incorporating CWSI into decision-making processes, irrigation scheduling can be enhanced to achieve improved water productivity, increased resilience to water stress, and sustainable wheat production in resource-constrained environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003e\u003cb\u003eCompeting interests\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003cp\u003eCorrespondence and requests for materials should be addressed to Anuj Kumar Dwivedi\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNot Applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAKD conducted the experiment, analysed data and prepared the whole manuscripts. helped during the preparation of the manuscript and supervised.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNA\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data generated or analysed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlderfasi, A. 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Water-use efficiency and transpiration efficiency of wheat under rain-fed conditions and supplemental irrigation in a Mediterranean-type environment. \u003cem\u003ePlant and Soil\u003c/em\u003e, \u003cem\u003e201\u003c/em\u003e(2), 295-305.\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":"Wheat, Crop Water Stress Index (CWSI), Irrigation Scheduling, Water Use Efficiency (WUE), Canopy Temperature / VPD","lastPublishedDoi":"10.21203/rs.3.rs-8008539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8008539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research explores the use of the Crop Water Stress Index (CWSI) for planning irrigation in wheat (Triticum aestivum L.) under field conditions in Roorkee, India, during the 2018\u0026ndash;19 and 2019\u0026ndash;20 growing seasons. Five different irrigation treatments were applied: T1 (no water stress), T2 (maximum stress), T3 (10% soil moisture depletion), T4 (30% depletion), and T5 (50% depletion). The difference between canopy and air temperature (Tc \u0026ndash; Ta) and the vapor pressure deficit (VPD) were utilized to create baselines for estimating CWSI. Regression analysis showed strong negative correlations, with R\u0026sup2; values of 0.95 and 0.88 for the pre- and post-heading stages in 2018\u0026ndash;19, and 0.99 and 0.96 in 2019\u0026ndash;20, respectively. Average CWSI values ranged from 0.07 (T1) to 1.00 (T2). Grain yield varied significantly, with a peak of 2375 kg ha⁻\u0026sup1; (T3) and a low of 325\u0026ndash;375 kg ha⁻\u0026sup1; (T2), while the amount of irrigation applied ranged from 390.5 mm (T1) to 150.5 mm (T2). The highest water use efficiency (WUE) of 2.71 and 2.65 kg ha⁻\u0026sup1; mm⁻\u0026sup1; was recorded under T3 in both years. A quadratic relationship between WUE and CWSI suggested an optimal range at CWSI\u0026thinsp;\u0026asymp;\u0026thinsp;0.3\u0026ndash;0.4, where yield sustainability and water productivity were maximized. Beyond this point, both yield and WUE decreased sharply. These findings confirm that CWSI is an effective tool for monitoring crop stress and can be used to optimize irrigation scheduling for wheat, allowing for water savings of up to 30% without significant yield losses.\u003c/p\u003e","manuscriptTitle":"Evaluation of Crop Water Stress Index (CWSI) for Irrigation Scheduling of Wheat in Sub- Humid Conditions of India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 16:58:57","doi":"10.21203/rs.3.rs-8008539/v1","editorialEvents":[{"type":"communityComments","content":0}],"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":"040796bc-3e95-403a-aad6-1605faa0fef7","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58090733,"name":"Earth and environmental sciences/Climate sciences"},{"id":58090734,"name":"Earth and environmental sciences/Environmental sciences"},{"id":58090735,"name":"Biological sciences/Physiology"},{"id":58090736,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-02-02T05:54:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 16:58:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8008539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8008539","identity":"rs-8008539","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}