IntroductionA large share of arid and semiarid regions’ freshwater resources is used in irrigated agriculture (Postel et al. 1996; Stephenson et al. 2004; Vaishali et al. 2017). The agriculture sector in these regions is facing severe water scarcities and challenges posed by increased water demands coupled with a growing population (Zwart and Bastiaanssen 2004), limited water resources (Dubois 2011; Margat et al. 2005), widely used traditional irrigation techniques (Cirelli et al. 2009; Finley 2016), and climate change–induced precipitation variability (Padakandla 2016). This situation has increased the need to shift toward real-time smart irrigation practices to optimize water use in agriculture (Adeyemi et al. 2018). Smart irrigation is a promising tool for enhancing crop yield and water productivity, improving irrigation scheduling, and decreasing farming costs while sustaining the environment (Dobbs et al. 2014; Paredes et al. 2018; Pereira et al. 2015; Rosa et al. 2012; Seidel et al. 2016; Zotarelli et al. 2011).Through smart irrigation management, various irrigation scheduling methods have been adopted, such as evapotranspiration and water balance (ET-WB), soil moisture status, and plant water status approaches (Fereres and Soriano 2007; Jones 2004; Scherer et al. 1996). A combination of different irrigation scheduling methods has been practiced and endorsed as a means of improving the irrigation scheduling of crops and preventing water wastage (Deb et al. 2013; Navarro-Hellín et al. 2016; Torres Sánchez et al. 2016).Several research works have reported the use of either evapotranspiration (ET), soil moisture sensors, or plant-based smart water irrigation technologies in scheduling irrigation events by providing site and crop-specific water requirements while considering weather factors, soil moisture conditions, or plant water status, respectively (Cardenas-Lailhacar and Dukes 2010; Dursun and Ozden 2011; McCready et al. 2009; Migliaccio et al. 2010; Sun et al. 2018). Many studies have been carried out on scheduling irrigation based on soil moisture status, such as in papayas (Migliaccio et al. 2010), tomatoes (Zotarelli et al. 2009), and chile peppers (Sharma et al. 2017), among many others. The advantage of soil moisture–based methods is the ease in practice and automation with some commercially available systems. Major drawbacks to the soil moisture sensor–based scheduling method are the spatial soil moisture heterogeneity, errors in sensor installation, the difficulty in the representation of the entire root zone, the need for sensor calibration, and the inaccuracy of measurements when gravel exists (Evett et al. 2011; Jones 2004). Examples of field studies demonstrating the use of the ET-based irrigation scheduling include those conducted by Jaafar et al. (2017) on biblical hyssop, Ertek and Kara (2013) and Garcia y Garcia et al. (2009) on sweet corn, and Di Paolo and Rinaldi (2008) and Irmak et al. (2016) on maize. However, ET-based irrigation methods strongly rely on the estimation of local climatic data. ET-based irrigation methods also depend on crop coefficients that are local-specific and may be inaccurate. Cumulative errors may occur with calculated ET-based scheduling approaches, and that is why field-based measurements are generally needed to correct or reset ET-based irrigation suggestions (Masmoudi et al. 2011; Pauwels and Samson 2006).Plant-based irrigation methods can be based on either direct or indirect measurements of plant water status or plant physiological responses to drought. They can be based on measurements such as sap flow and stomatal conductance (Jones 2004; Padilla-Díaz et al. 2016). Sap flow measurements have been used in irrigation management in maize (Jiang et al. 2016), olives and apples (Fernández et al. 2008), and soybeans (Gerdes et al. 1994). The strength of these methods is their sensitivity to moisture deficit. However, plant-based methods require sophisticated instrumentation and expertise (Gu et al. 2020; Jones 2004).In this paper, field experiments were conducted on sweet corn using two different irrigation scheduling methods: ET-based and soil moisture sensor–based methods, at three thresholds for each experiment. The objectives of this work were to compare ET-based and soil moisture–based irrigation scheduling methods and evaluate their impact on sweet corn morphometric parameters, water use, and water productivity under different treatments.Materials and MethodsStudy SiteThe study site was located at the American University of Beirut’s Agricultural Research and Education Centre (AREC) in the Beqaa, Lebanon (33°55’83” N, 36°04’18” E; 990 AMSL) (Fig. 1). The sweet corn (Zea mays L. var. merkur) used in this field experiment is typically planted in the summer, requiring warm soil temperatures (20°C–30°C) for favorable growth. Sweet corn is generally cultivated over an extended period in order to provide a continuous supply of fresh corn. Varieties of sweet corn take 70 to 100 days to mature from the day after planting. A proper irrigation management would achieve maximum yield (beyond 20,000  kg/ha) (Garcia y Garcia et al. 2009) in addition to enhanced water use efficiency with minimum water losses; conversely, poor irrigation practices with insufficient water provided to sweet corn would lead to low yield and, hence, economic loss (Archana et al. 2016; Dukes and Scholberg 2005; Mubarak 2020).The field plots on which the experiment was carried out were flat and had no clear slope. A semiarid climate characterizes the experimental site, with dry hot summers from May to September and cold winters throughout the rest of the year. The average rainfall is around 500 mm per year. Daily crop evapotranspiration (ETc) was calculated as a factor of grass-reference crop evapotranspiration (ETref) and crop coefficient (Kc) using the Ref-ET version 3.1 software (Allen 2009; Annandale et al. 2002) based on the ASCE standardized Penman-Monteith equation from weather data obtained from the AREC weather station (Campbell Scientific, Logan, Utah) (Fig. 2) within 200 m of the field. The soil of the experimental site was shallow, gravelly clay, with 19% sand, 36% silt, and 45% clay. The soil had a pH of 7.89, an electrical conductivity of 0.4  dS/m, CaCO3 of 32.5%, and an organic matter content of 2.48% (Jaafar et al. 2017). The available nutrient supply of phosphorus, potassium, and nitrogen was 19.9, 530, and 30 ppm, respectively. Soil water characteristics were estimated using the model described by Saxton and Rawls (2006) (permanent wilting point was at approximately 22%, field capacity at 40%, and saturation at 49.7%). Bulk density was determined in this experiment by using a cylinder method and was found to be 1.33  g/cm3.Cultural Practices for the Sweet Corn ExperimentsSowing was carried out on June 6, 2018, with hybrid sweet corn seeds (Zea mays L. var. merkur) having a germination ratio of 90. Seeds were planted every 20 cm down the rows and with 75 cm between the rows. Each plot had a size of 13.5  m2 with four rows. Seeds were sown at a depth of 5–6 cm. Nitrogen, phosphorus, and potassium (15:15:15 NPK ration) fertilizer was applied at a rate of 250  kg/ha. To control weeds, periodic hand weeding and 2,4-D herbicide was applied 40 days after planting (DAP) at a concentration of 50 cm3 in 12 L of water (4.58 cc/L of water) for the whole field. All experimental plots were irrigated with the same amount of water until emergence to ensure uniform plant establishment. After the emergence of the sweet corn seedlings, irrigation was performed according to the prescribed irrigation treatments.Experimental DesignThe two experiments consisted of three replicates for each of the three treatments in the soil moisture sensor–based and evapotranspiration-based methods, making a total of 18 experimental plots of 6 m by 2.25 m and 1.5 m apart. Water was sourced from a well penetrating an underlying marl limestone aquifer with total dissolved solids of 320  mg/L. A tapping was taken from one of the risers to feed the secondary network through a 32-mm pipe connecting to the six solenoid valves, each corresponding to a different irrigation treatment. The ET treatments (60%, 90%, and 120%) were operated using an automated control system. The total water flow for each treatment was measured with a flowmeter installed downstream of each solenoid valve. Irrigation was scheduled at an average interval of 2 days during initial crop growth stages and 3 days afterward. Drip emitters had an average discharge rate of 3.7  L/h at a pressure of 100 kPa (1 bar) as an average of 20 samples. A layout of the experimental plots is shown in Fig. 3.In the ET-based irrigation system, ETref is multiplied by the crop coefficients (Kc) of sweet corn to get crop evapotranspiration (ETc). In the Mediterranean region, Kc is 0.3, 1.15, and 1.05 for Kc initial, Kc mid, and Kc end, respectively, with the initial period taking 20 days, the middle period taking 50 days, and the end period taking 20 days (Allen et al. 1998c). Irrigation run times varied according to ETc, which is a factor of crop growth stage, and prevailing meteorological phenomena.The soil moisture sensor–based irrigation had three irrigation treatments (25%, 30%, and 35%) representing the thresholds of soil volumetric water content at which irrigation was scheduled. Values of 0.3, 0.8, and 0.13  m/m of available water, respectively, out of the full potential available water of 0.15  cm/cm were maintained in each of those treatments. The valves were operated to start an irrigation event based on the thresholds in each treatment and were closed when the soil volumetric water content (VWC) exceeded field capacity at the sensor depth.Field MeasurementsSap Flow MeasurementsSap flow was measured using a sap flow meter (SFM1) (Burgess and Downey 2014) with readings taken every 10 min from August 28 to September 4, 2018. The sap flow meter consisted of a set of three measurement probes and an integrated standalone data logger. The three probes were designed with two thermistors located 7.5 and 22.5 mm from the tip. The heater probe had a high resistance filament that produced a high and efficient amount of heat. The SFM1 sap flow meter measured the sap velocity and flow using the heat-ratio method (HRM). This method calculates the magnitude and direction of water flux by measuring the ratio of heat transported between two symmetrically spaced temperature sensors. The heat pulse velocity was calculated as per Eq. (1) (Barrett et al. 1995): (1) where Vh = heat pulse velocity (cm/h); k = thermal diffusivity of fresh plant tissue=2.5×10−3  cm2/s; x = distance (cm) between the heater and either temperature probe = 0.6 cm; and v1 and v2 = increase in temperature (from initial temperatures) at equidistant points downstream and upstream, respectively, x cm from the heater. Sap flow meters were installed on stems of random sweet corn in the three soil moisture irrigation regimes. The installation of an SFM1 was conducted via the following steps. First, measurements of the corn stem circumference (ranging from 6 to 6.5 cm) and diameter (average of 2 cm) were conducted. Based on these measurements, three parallel probes were inserted.Measurements from the sap flow meter include corrected sap velocity, flow, and needle temperature (°C). Because the data generated from the sap flow meters were in hourly intervals, we used the Sap Flow Tool software version 1.4.1 to convert the corrected sap flow to daily accumulated sap volume (cm3) and sap flow rates (cm3/h) by inputting the correction factors for the stem and sensor properties (Steppe et al. 2009) as in Table 1.Table 1. Correction factors and the values made in the sap flow tool settingsTable 1. Correction factors and the values made in the sap flow tool settingsStem and sensor propertiesCorrectionsStem circumference6.9 cmStem diameter2 cmBark thickness0.004 cmXylem radius1 cmSapwood depth0.45 cmThermal diffusivity0.0025  cm2/sSapwood fresh weight1 gSapwood dry weight0.63 gProbe spacing0.5 cmFirst thermistor depth1.5 cmWound diameter17 mSoil Moisture Measurements and Sensor CalibrationCS655 soil moisture sensors (Campbell Scientific) were calibrated and installed in each of the experimental plots, ensuring good sensor–soil contact. Soil moisture data (1-min increments) were collected using a CR850 Campbell Scientific data logger. The soil volumetric water content was computed based on the Topp equation [Eq. (2)], representing only C0 and C1 (Campbell Scientific 2017): (2) Qv(Ka)=C0+C1Ka×C2Ka2+⋯+CnKanwhere Qv = volumetric water content (% or m3/m3); Ka = bulk dielectric permittivity (unit-less) of the soil; and Cn = calibration coefficients.Fig. 4 shows the equation that was derived from calibration. Soil water at a depth of 30 cm was measured throughout the growing season in every treatment using 16 soil moisture sensors. One soil moisture sensor was installed in two of the three replicates of the ET-based treatments, and two sensors were installed in irrigation treatment ET60%. Average sensor data from the subplots representing the same treatment were assumed to be representative of the soil moisture content in the entire treatment. For soil moisture irrigation regimes, the soil moisture readings from the three replicates were averaged to schedule the irrigation events (Jones 2004) considering the variability of soil moisture in the different plots of the same treatment (Dabach et al. 2015). The sensors were installed at a depth of 30 cm to represent the active root zone of corn, which has been observed to reach a depth of 30–40 cm (Wiesler and Horst 1994).Measured Parameters and Statistical AnalysisThe measured parameters from the six regimes’ harvests were as follows: shoot height and ear length, aboveground fresh and dry biomass, cob fresh and dry weight, root biomass, and grain yield. Shoot height and cob length were directly measured on the day of harvest, 90 DAP for 10 corn plants from the middle two rows within each treatment. The fresh aboveground weight was immediately weighed, oven-dried at 65°C to a constant weight and weighed. The fresh cob weight was measured right after harvest and oven-dried under the same conditions of the aboveground biomass. Root biomass was measured by uprooting the sweet corn using a shovel, and washing the roots of soil under running water before taking their weights. Grain yield of the sweet corn was measured after drying the cobs in the oven. Subsequently, the grains were removed from the cob and weighed. The calculated variables were as follows: harvest index (HI), grains-to-cob ratio, and water productivity (WP). Grains-to-cob ratio represents the weight of grains divided by the total weight of the cob. HI was calculated as the ratio of fresh weight of cobs to fresh aboveground biomass. WP is a measure of the biophysical gain in terms of consumed water. WP is calculated as the ratio of fresh and dry biomass of cob yield to cumulative ETc or total water use (WU) (Fereres and Soriano 2006). 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