Soil texture, a critical factor influencing plant growth, cultivation practices, hydraulic conductivity, and soil strength, was assessed using the hydrometer method. This widely accepted laboratory technique, originally introduced by Bouyoucos, 1927 and refined by Day (1965) and the American Society for Testing and Materials (1972), involves suspending a measured soil sample in water and measuring the suspension density over time as particles settle. The resulting data are used to calculate the percentage of each particle size class (sand, silt, and clay).

The electrical conductivity (EC) of the soil was assessed through the analysis of saturated soil paste extracts, adhering to the methodology outlined by van Reeuwijk (1992). Soil pH was determined potentiometrically using a 1:2.5 soil-to-water suspension, following established protocols. Organic carbon content was quantified using the wet combustion procedure described by Walkley and Black (1934). Consequently, the organic matter content was estimated by multiplying the measured organic carbon value by a conversion factor of 1.724.

Undisturbed soil samples were collected using a core sampler at three depths (0-30 cm, 30-60 cm, and 60-100 cm) within the experimental field. Soil water retention characteristics, including field capacity (FC) and permanent wilting point (PWP), were determined using a pressure plate and pressure membrane apparatus, following the methodology of Klute (1965). The total available soil water content was calculated as the difference between FC and PWP moisture contents (Hillel, 1982). Soil bulk density was determined as the ratio of oven-dried soil mass to the bulk volume of the soil core (Blake, 1986).

The moisture content at each soil water property (FC and PWP) was expressed on a gravimetric basis. The gravimetric water content was calculated using the standard equation, as outlined in the literature (FAO (Food and Agricultural Organization), 1989; Jury et al., 1991; Evans et al., 1996). The gravimetric water content was calculated using the following equation:

Measurements were obtained at physiological maturity from nine randomly selected plants in each plot. Leaf area was estimated by multiplying leaf length and maximum width, followed by the application of a correction factor of 0.75 (Francis et al., 1969) to account for non-planar leaf surfaces. LAI represents the total one-sided leaf area per unit ground area occupied by the crop canopy.

Plant height (cm) was measured at physiological maturity (end of March) from the base of the plant to the tip of the panicle using a meter tape on nine randomly selected plants within each plot.

Following harvest, destructive sampling was employed on nine randomly chosen plants from each plot. These plants were sun-dried for two weeks to determine aboveground biomass, grain yield, and 1000-grain weight.

The daily reference evapotranspiration (ET₀) was calculated using the FAO’s CROPWAT 8.0 software (Smith, 1992). The crop water requirement was subsequently determined by multiplying ET₀ by the corresponding crop coefficient (Kc). Following this, irrigation requirements were calculated. The actual irrigation depth was calculated as the difference between the crop water requirement and the effective precipitation depth.

River water was diverted from the main channel into an irrigation canal and subsequently distributed to individual furrows within treatment plots ( Table 1 ). The Water flow was carefully regulated to prevent over-irrigation. The volume of irrigation water applied to each plot was calculated using the following formula (Doorenbos and Pruitt, 1992):

Irrigation water discharge was measured using the float method (Bessembinder et al., 2005). A tennis ball was allowed to drift along a 20-meter straight section of the irrigation canal, and its travel time was recorded using a stopwatch. This measurement was repeated three times to ensure accuracy. The average velocity of the water flow was calculated and adjusted using a correction factor of 0.85 to account for channel irregularities. The width and depth of the irrigation canal were measured at ten points along the 20-meter section. The average values were used in the discharge calculation. The discharge was calculated using the following formulas (Bessembinder et al., 2005):

The time required to apply the desired water depth to each plot was calculated using the following relationship, as suggested by Jensen (1982):

The analysis of variance (ANOVA) revealed a statistically significant (p < 0.01) effect of irrigation level on plant height, as detailed in Table 5 . Plants receiving full irrigation (100% ETc) exhibited the greatest average height, followed by those under 85% Etc. Importantly, no significant difference in plant height was observed between these two treatments. Similarly, the 70% ETc treatment produced plants with heights statistically indistinguishable from the 85% ETc group. Conversely, the 55% irrigation level resulted in the lowest average plant height. These findings align with the established trend that plant height generally increases with greater water availability. This observation is corroborated by the works of Bozkurt et al. (2006); Cakir (2004); Istanbulluoglu et al. (2002); Otegui et al. (1995), and Pandey et al. (2000), who all reported that maize under full irrigation achieved the highest average heights. Further support for this notion comes from El-Noemani et al. (2009) and Admasu et al. (2017), who suggested a proportional relationship between plant growth and irrigation level.

However, it is important to acknowledge contrasting findings from Furgassa (2017) and Gebreigziabher (2020), who reported no significant impact of the irrigation level on maize plant height. These discrepancies indicate that the influence of irrigation on plant height may be contingent on additional factors beyond water availability, potentially including specific environmental conditions or the maize cultivar employed in the study.

A statistically significant difference (p < 0.01) was observed in aboveground biomass accumulation based on the water supplied ( Table 5 ). Plants subjected to full irrigation exhibited the highest biomass, followed by those receiving 85% of their crop water requirement. The lowest biomass was recorded in the treatment, receiving only 55% of its evapotranspiration (ETc) needs throughout the growing season. These findings suggest a direct correlation between the irrigation level and aboveground biomass production. These results align with previous research conducted by Yenesew and Tilahun (2009), who reported the highest biomass yield under 100% ETc irrigation throughout the growing season. Similarly, Ullah et al. (2003) confirmed that varying irrigation levels significantly affect biological yield, which is closely linked to aboveground biomass. Ayana (2011) further corroborated these findings by demonstrating that maximum biomass was achieved with 100% ETc irrigation. Additionally, Moser et al. (2006) reported a reduction in biomass under moisture stress conditions, further supporting the observed relationship.

A statistically significant difference was observed in the leaf area index (LAI) among the various moisture stress treatments applied. As shown in Table 5 , the 100% ETc irrigation level resulted in the highest LAI, while the lowest value was recorded at the 55% ETc stress level. This aligns with established literature, where maize LAI during the grain filling period typically falls within a range of 2-6 (Tollenaar, 1986). The findings of this study further support this established range, as evidenced by the data presented in ( Table 5 ). Similarly, previous research by Gonzalez et al. (2005) reported a maximum LAI range of 2.9-7.14, which aligns with the current study’s observations with the exception of the 55% ETc treatment ( Table 5 ). In corroborating these findings, Azarpanah et al. (2013) demonstrated a significant effect of irrigation regimes on LAI, highlighting a decrease in leaf surface area with reduced irrigation levels. Likewise, Greaves and Wang (2017) reported a statistically significant impact of irrigation treatments on maize leaf area index (LAI), with a mean value of 5.94.

This study investigated the relationship between irrigation water availability and grain yield. Results revealed a statistically significant (p < 0.01) difference in grain yield among moisture stress treatments ( Table 6 ). The control treatment (100% ETc), representing non-water-stressed conditions, produced the highest yield ( Table 6 ). Grain yield progressively decreased with increasing moisture stress levels (85% ETc, 70% ETc, and 55% ETc), with statistically significant differences observed between treatments ( Table 6 ).

These findings corroborate previous research by Mansouri-Far et al. (2010) who demonstrated a negative impact of the irrigation water reduction on grain yield. Similarly, Ullah et al. (2003) reported a significant positive correlation between irrigation level and grain yield. Our results further support the established body of knowledge documented by Nadanam and Morachan (1974); Hiraoka et al. (1976); Lazarov et al. (1976); Warrick and Gardner (1983); Karlen and Camp (1985), and van Averbeke and Marais (1992), all of whom observed a direct association between increased irrigation and enhanced grain yield.

A study investigating the influence of irrigation on corn yield revealed a statistically significant effect (p < 0.05)) on the number of ears produced per plant. While no significant differences were observed between most irrigation treatments, plants receiving only 55% of their potential evapotranspiration (ETc) exhibited a marked decrease in ear number compared to those receiving full irrigation (100% ETc). These findings support previous research by Cakir (2004); Karasu et al. (2015), and Pandey et al. (2000), who all demonstrated that water stress negatively impacts ear production in corn.

The number of grains per ear was significantly influenced by the irrigation level at (p<0.01). The maximum grain number per ear was achieved with a 100% ETc irrigation level, followed by 85% ETc, with no significant difference between them. Similarly, there was no significant difference between 85% ETc and 70% ETc moisture deficist ( Table 6 ). The minimum number of grains per ear was recorded at 55% Etc. This finding aligns with Ertek and Kara (2013), who reported that deficit irrigation reduced the number of grains per ear. Ullah et al. (2003) supported this result, concluding that varying irrigation levels significantly impacted the number of grains per ear. These findings have also been documented in other studies for maize (Aydinsakir et al., 2013; Karasu et al., 2015; Moosavi, 2012). Conversely, Elzubeir and Mohamed (2011) found that the amount of irrigation water did not affect the number of kernels per ear.

The level of irrigation had a highly significant impact on the ear length of maize at (p< 0.01) ( Table 6 ). These findings indicated that the ear length of maize was greater at 100% ETc and shorter at 55% ETc irrigation levels. Among the treatments, 85% ETc and 70% ETc showed no significant difference. This outcome is consistent with the findings of Ertek and Kara (2013), who demonstrated that ear length, was influenced by varying irrigation water levels and reported a decrease in ear length with reduced water application. Contrary to this result, Tabatabaei and Dadashi (2013) found that irrigation levels had no significant effect on ear length.

Irrigation significantly impacted maize 1000-grain weight. As shown in Table 6 , the highest 1000-grain weight was observed under 100% ETc irrigation, followed by 85% Etc. No significant difference was found between these treatments or between 85% ETc and 70% Etc. Conversely, the lowest 1000-grain weight was recorded under 55% ETc irrigation. These results corroborate the existing literature. Ullah et al. (2003) and Mansouri-Far et al. (2010) previously reported a positive correlation between irrigation levels and 1000-grain weight. Similarly, Aydinsakir et al. (2013); Cakir (2004), and Karam et al. (2003) observed a decrease in 1000-grain weight due to water deficits. However, some studies, such as those by Elzubeir and Mohamed (2011) and Yazar et al. (2009) did not find a significant association between irrigation water amounts and grain weight.

A highly significant effect (p<0.01) of the irrigation level on the maize harvest index was observed through an analysis of variance ( Table 7 ). Plants receiving 100% of their reference evapotranspiration (ETc) exhibited the greatest harvest index, while those under 55% ETc displayed the lowest. This progressive decrease in harvest index with decreasing irrigation level suggests a strong dependence of grain formation on soil moisture content. These findings align with those of Ullah et al. (2003) and Toor (1990), who reported significant impacts of irrigation levels on harvest index. However, Furgassa (2017) observed no significant difference in the harvest index under varying irrigation, suggesting the potential influence of additional factors in specific contexts.

Variance analysis indicated that the irrigation level had a highly significant (p<0.01) impact on the irrigation water use efficiency of maize ( Table 7 ). The maximum irrigation water use efficiency was achieved with the 70% ETc treatment, followed by 55% ETc, with no significant difference between the two. This finding suggests that the 70% ETc irrigation level is preferable over the 55% ETc, 85% ETc, and 100% ETc levels for water conservation without a notable yield reduction, allowing for additional land production. Conversely, the lowest water productivity was observed at 100% Etc. The results indicate that irrigation water use efficiency decreased with increasing water supply, except at the 70% ETc level. In support of this, Bozkurt and Yazar (2011) reported that irrigation water use efficiency values increased with decreasing seasonal irrigation amounts. These findings align with those of Yenesew and Tilahun (2009); Lee et al. (2011); Karrou et al. (2012); Narayanan and Seid (2015); Admasu et al. (2017), and Furgassa (2017), who demonstrated that irrigation water use efficiency significantly increased as irrigation levels were reduced. However, Payero et al. (2006) showed that applying deficit irrigation to boost water productivity might not be a beneficial strategy. In this context, limited irrigation of maize is not a viable practice. In this study, 55% ETc reduced yield but had lower irrigation water use efficiency compared to the 70% ETc level.

As shown in Table 8 , deficit irrigation at 85% ETc resulted in an 8% yield penalty to save 15% irrigation water, with a corresponding 8% increase in irrigation water use efficiency. Deficit irrigation at 70% ETc led to a 13.5% yield penalty, increasing irrigation water use efficiency by 23.4% and saving 30% water. Deficit irrigation at 55% ETc saved 45% water and increased irrigation water use efficiency by 21.9%, but resulted in a 33.1% yield loss. The study results indicate that among the deficit treatments, deficit irrigation at 70% ETc can save water and increase irrigation water use efficiency without significant yield reduction compared to the other treatments.