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Pre- and Post-anthesis Deficit Irrigation of Corn in the West Central Great Plains— Working with Less Water

Alan Schlegel1,*, Freddie Lamm2, Yared Assefa3,*

Published in Applied Engineering in Agriculture 38(5): 763-776 (doi: 10.13031/aea.14838). Copyright 2022 American Society of Agricultural and Biological Engineers.

1    Kansas State University, Tribune, USA.

2    Kansas State University, Colby, USA.

3    Kansas State University, Manhattan, USA.

*    Correspondence: schlegel@ksu.edu and yareda@ksu.edu

The authors have paid for open access for this article. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License https://creative commons.org/licenses/by-nc-nd/4.0/

Submitted for review on 7 September 2021 as manuscript number NRES 14838; approved for publication as a Research Article by Associate Editor Dr. Kendall DeJonge and Community Editor Dr. Kyle Mankin of the Natural Resources & Environmental Systems Community of ASABE on 9 August 2022.

Highlights

Abstract. A three-year study (2018 to 2020) was conducted in west central and northwestern Kansas on silt loam soils to determine corn grain yield and yield component response, water use, and crop water productivity as affected by irrigation capacity and timing, corn hybrid, and seeding rate. A range of four irrigation treatments concentrated greater application amounts in either the pre-anthesis or post-anthesis period. The Pre-38 treatment applied 38 mm weekly pre-anthesis as needed (limited by weather-based scheduling) and only applied 38 mm every two weeks during the post-anthesis period. The Pre-25 treatment applied 25 mm weekly as needed pre-anthesis, followed by 25 mm applications every two weeks post anthesis. Post-38 and Post-25 treatments had application amounts similar to Pre-38 and Pre-25, respectively, but had the weekly applications concentrated during the post anthesis period and less frequent (every two weeks) applications pre-anthesis. Two corn hybrids (Pioneer 1197 and Pioneer 0801) were planted at seeding rates of 62 and 74 thousand seeds ha-1. Averaged across location and year, the Pre-25 irrigation treatment had approximately 5% less yield than the other three treatments but received approximately 28% less irrigation. Overall, when averaged across all irrigation, hybrid and seeding rate treatments, the yield components of kernels ear-1 and kernel mass varied approximately 2% and 3%, respectively and were responsible for nearly all of the grain yield variation. The Pre-25 and Post-25 treatment had the greatest water productivity and generally had similar yields to the other deficit irrigation treatments and thus should be considered as effective strategies to reduce irrigation in this region. Seeding corn at 74 thousand seeds ha-1 increased yield only 2% compared with 62 thousand seeds ha-1 indicating that a considerable range of seeding rates produce similar yields when using deficit irrigation strategies in this region. Hybrid effects were not consistent across years and locations.

Keywords.Crop water productivity, Evapotranspiration, Irrigation scheduling, Limited irrigation, Maize.

Corn seasonal water requirement ranges from 600 to 790 mm in the western U.S. Great Plains region, but annual precipitation averages approximately 400 to 500 mm and is temporally sporadic during the cropping season (Musick and Dusek, 1980; Lamm et al., 2009; Lamm, 2017; Trout and DeJonge, 2017). Additional water required for corn development is often provided by irrigation to minimize the effect of water deficit on growth and productivity. However, irrigation to the full required amount of water contributes to the depletion of the groundwater in the Ogallala Aquifer, and threatens future availability of irrigation water and sustained productivity (Araya et al., 2017).

Deficit irrigation is generally defined as a deliberate irrigation strategy that allows the crop to experience some level of water stress and/or yield reduction that increases some index of productivity [e.g., overall economics or water productivity (English et al., 1990)]. It is also sometimes referred to as regulated deficit irrigation (Howell and Lamm, 2007) or managed deficit irrigation (Trout et al., 2020) to distinguish the term from unintentional deficit irrigation which may occur due to insufficient irrigation capacity or due to irrigation system malfunction. In this article, deficit irrigation refers to deliberate strategies. Another term in the literature that is also applicable to this study is limited irrigation where water is timed to specific critical growth stages (Howell and Lamm, 2007). Generally, deficit and limited irrigation strategies are most applicable in regions that are not arid (i.e., arid regions do not have sufficient precipitation to replenish soil profiles) with the exception of cases where deficit irrigation is used to elicit a crop quality aspect (e.g., wine grapes, cotton maturation). A recent comprehensive review of deficit irrigation for corn in the High Plains region was provided by Rudnick et al. (2019). The reader is encouraged to examine this particle for the myriad strategies by which deficit irrigation has been used and how it can vary from location to location (e.g., water laws and policies, climatic conditions, soils).

When application of the full amount of irrigation water is not feasible or desirable, application of the limited available irrigation water to the most responsive crop growth stages minimizes the effect of water deficit on productivity (Kirda, 2002). Klocke et al. (2004) reported only a 7% and 16% corn yield reduction when irrigation was reduced by 24% and 43% from the full requirement, respectively. The reproductive stage is generally the most sensitive stage to plant water stress and water limitation at that stage can greatly reduce productivity for corn and many other plants. Cakir (2004) reported a 40% yield reduction from omitting a single irrigation for corn during anthesis and ear formation. Robins and Domingo (1953) reported a 22% yield reduction for corn with soil water content near wilting point for only two days during the pollination period and 50% yield reduction if the same water stress continued for five to six days. A recent study by Lamm (2017) indicated modern corn hybrids yielded greater when watered during the pre-anthesis stage than during the post-anthesis stage, which suggests that establishing sufficient numbers of kernels is very important. Comas et al. (2019) reported that corn yield is more sensitive to deficit during grain fill than vegetative stage, and they stated that application of greater deficit during the late vegetative state with full or nearly full ET during the rest of the season consistently resulted in yield similar to full ET treatments while saving approximately 15% to 17% of ET.

Distributing the right amount of the limited irrigation water across the sensitive stages of corn growth is another crucial irrigation management decision (Martin et al., 1990). The right amount of irrigation water depends primarily on weather, soil water, and the growth stage of the plant, and these factors are the basic information required for most scientific irrigation scheduling techniques (van Bavel, 1956; Rogers and Alam, 2007). ET-based (a.k.a., weather-based) irrigation scheduling has been promoted in the central Great Plains for many years, however, existing ET-based scientific irrigation scheduling techniques are generally based on full irrigation requirements (Lamm and Roger, 2015). When total irrigation is limited by availability or institutional constraints, strategies to allocate deficit irrigation amounts during the season would be useful in optimizing production and economics.

In addition to deficit irrigation, other crop management factors such as seeding rate and hybrid can impact crop water use without appreciably affecting productivity. Plant density (i.e., the number of plants per unit area) can affect crop water use (Assefa et al., 2016; Licht et al., 2017). However, the change in water use with a change in corn plant density is not a linear relationship (Yao and Shaw, 1964). As plant density increases to a sufficient level to more fully utilize the incoming energy, crop water use will approach an asymptote and not increase further.

Corn hybrids can differ in their water requirement and response to water deficit due to the number of required days to maturity or other phenological, morphological, and physiological differences. For example, some research studies have reported that early maturing hybrids are less affected by water deficit than late maturing corn hybrids (Alessi and Power, 1976). Generally, short season hybrids may use less water overall due to a shorter season, but correspondingly also yield less (Williams et al., 2020). In a study at Garden City, Kansas, on a silt loam soil, Trooien et al. (1999) reported no significant differences in water use and water productivity as affected by corn hybrid maturity. They concluded that based on water productivity, there would be no justification to choose earlier maturing hybrids. Development of modern drought tolerant corn hybrids is another reason to consider hybrid selection under deficit irrigation conditions. In a study in the Texas High Plains, a three-year average water use of 730 and 811 mm was reported for legacy and drought-tolerant corn hybrids, respectively (Marek et al., 2020). The increased water use for the drought-tolerant hybrid was partially attributed to a rapid increase in the crop coefficient (Kc) during the mid-developmental period and to a greater overall grain yield.

The objectives of this study were to quantify corn grain yield, yield components, water use, and crop water productivity responses to deficit irrigation amount and timing, hybrid, and plant density treatments.

Materials and Methods

This study was conducted from 2018 through 2020 at the Northwest Research-Extension Center at Colby, Kansas (39° 23’ 32”N, 101° 2’ 51”W, 963 m asl) and the Southwest Research-Extension Center at Tribune, Kansas (38° 28’ 05” N, 101°46’ 37” W, 1108 m asl). Average annual precipitation is 480 mm at Colby and 455 mm at Tribune (Kansas State University weather data library). Monthly total precipitation for the study period is presented in figure 1. The soil at Colby is medium textured, deep, well drained, loessial Keith silt loam soil (Aridic Argiustoll; fine silty, mixed, mesic) and the soil at Tribune is a deep silt loam (Ulysses silt loam, fine-silty, mixed superactive mesic AridicHaplustoll).

The experimental design was a randomized complete block design with a split plot arrangement. Four irrigation treatments were the main plot and the two corn hybrid and two plant density treatments were the subplots with each whole plot replicated four times. Main plot size at Colby was 30 m long × 20 m wide with subplots 30 m long × 3 m wide (4 plant rows). Main plot size at Tribune was 40 m long × 20 m wide with subplots 12 m long × 3 m wide (4 plant rows). The growing season (planting to maturity) was divided into two periods, i.e., pre- and post-anthesis of the crop. The four irrigation treatments were designed to supply deficit irrigation applications either in the pre- or post-anthesis periods with the complementary period (i.e., post- and pre-anthesis) receiving less irrigation. Treatment plots varied every year, so deficits didn’t accumulate over years. The four treatments were:

  1. Pre-38, application of 38 mm of irrigation weekly during the pre-anthesis period and 38 mm every two weeks during the post-anthesis period.
  2. Post-38, application of 38 mm of irrigation every two weeks during the pre-anthesis period and 38 mm weekly during the post-anthesis period.
  3. Pre-25, application of 25.4 mm of irrigation weekly during the pre-anthesis period and 25.4 mm every two weeks during the post-anthesis period.
  4. Post-25, application of 25 mm of irrigation every two weeks during the pre-anthesis period and 25.4 mm weekly during the post-anthesis period.

For comparison purposes, an irrigation capacity of 25.4 mm every 4 days (44.5 mm per week) would be considered nearly full irrigation in this region (Lamm and Rogers, 2015). Irrigations were scheduled only as needed according to calculated weather-based water budgets, and limited by the specific deficit irrigation treatments. An irrigation event was postponed/skipped when there was insufficient deficit as indicated by the water budget (i.e., being unneeded) and a new irrigation need was assessed at the next decision point (1 or 2 weeks). The weather-based water budgets were constructed using data collected from Kansas Mesonet weather stations located on the research centers. The reference evapotranspiration (ETr) was calculated using a modified Penman combination equation using procedures outlined by Kincaid and Heermann (1974). The specifics of the ETr calculations used in this study are fully described by Lamm et al. (1987). A 2-year (2005 and 2006) comparison using weather data from Colby, Kansas, of this estimation method to the ASCE standardized reference evapotranspiration equation [short (grass) reference] which is based on FAO-56 (Allen et al., 1998) indicates that the Modified-Penman values are approximately 1.5% to 2.8% lower. This is well within the accuracy of the resultant scheduling and irrigation application procedures. Crop coefficients were generated using FAO-56 (Allen et al., 1998) as a guide with periods adjusted to western Kansas growing period lengths. No adjustments in Kc were made for hybrid maturity differences in this study as maturity differences were only a few days. Crop evapotranspiration (ETc) was calculated as the product of Kc and ETr (Lamm and Rogers, 1983, 1985). In constructing the irrigation schedules, no attempt was made to modify ETc with respect to soil evaporation losses or soil water availability as outlined by Kincaid and Heermann (1974). Alfalfa-based ETr is considered to give better estimates than short-grass ETo in this region (Terry Howell, Sr., USDA-ARS, personal communication, 2007). Irrigation was applied through lateral move sprinkler irrigation systems on the research centers. The systems are equipped with spray nozzles at the mid-elevation height (2 m) at Colby at the low elevation height (0.6 m) at Tribune at 1.5 m spacing.

Two corn hybrids, Pioneer 1197 (P1197, 111-day relative maturity rating) and Pioneer 0801 (P0801, 108-day relative maturity rating), were planted at seeding rates of 62 and 74 thousand seeds ha-1. The hybrids were selected as being high yielding for the region. Pioneer 0801 was selected because it handles drought better while Pioneer 1197 yields better under favorable conditions. The seeding rates were based on typical producer practices for corn grown on marginal capacity irrigation systems. Planting dates were 27, 23, and 25 April at Colby and 10 May, 29 April, and 13 May at Tribune, Kansas, in 2018, 2019, and 2020, respectively. Corn was planted at both locations with John Deere 1700 planters (Moline, Ill.) in 76 cm rows. Urea ammonium nitrate (UAN) was broadcast applied at 179 kg N ha-1 at Colby and band applied at 269 kg ha-1 at Tribune prior to planting. At Tribune, liquid starter fertilizer (ammonia polyphosphate, 10-34-0) was banded on soil surface to supply additional 10 kg ha-1 of N and 34 kg ha-1 of P2O5 at planting. At Colby, an additional 89 kg N ha-1 and 50 kg ha-1 of P2O5 were applied in fall strip tillage operations in all years using UAN and ammonia polyphosphate.

Harvest dates were 21, 25, and 18 September at Colby and 2 October, 9 October, and 30 September at Tribune, Kansas, in 2018, 2019, and 2020, respectively. At Colby, a 6 m section of one row of the center portion of all plots was hand harvested and threshed after drying in the laboratory. At Tribune, the center portion of all plots (two row × 11.2 m) was machine harvested (Kincaid 8XP plot combine, Haven, Kan.). All grain yields were adjusted to 155 g kg-1 moisture content (wet basis). Plant and ear numbers were counted in the harvested area, average kernel mass was measured in the laboratory, and average kernels per ear were calculated using algebraic closure of the grain yield with the other measured yield components. There was no attempt to adjust grain yield for location based on the harvesting technique.

Volumetric soil water content was measured periodically in the complete root zone (2.4 m) for each 0.3 m increment with a neutron moisture meter model 503DR (CPN International Inc., Martinez, Calif.) throughout the season to help quantify periods of water stress and to determine crop water use.

Crop water use was calculated by summing seasonal soil water depletion (soil water near emergence less soil water at harvest) plus in-season irrigation and precipitation. Deep percolation and runoff were not measured and were considered negligible as precipitation and irrigation events did not exceed the soil water deficit. Crop water productivity (WP) was calculated by dividing grain yield (kg ha-1) by total crop water use (mm).

Statistical analysis of data was conducted using SAS PROC MIXED procedure (SAS Institute, Cary, N.C.). Initially, data analysis was conducted separately for each year and location and later, summarized across location and years. A type 3 test of fixed effects (treatments) on grain yield, yield components, water use, water productivity, and yield/irrigation ratio was conducted for each location by year. The response variables were then modeled against fixed variables deficit irrigation, seeding rate, hybrid, and their interactions, while replication and replication × irrigation interactions were random variables. For across years within a location analysis, data was sorted only by location and year, replication, and replication × irrigation were in the random statement. For across site-year averages, the whole data set was used with location added to the random statement with year, replication, and replication × irrigation.

For a type 3 test of fixed effects (treatments) on total profile available soil water at planting (ASWP) and available soil water at harvest (ASWH) by location, the response variables were modeled against fixed variables deficit irrigation, seeding rate, hybrids, location and their interactions while year, replication, and replication × irrigation were in the random statement. Mean separation tests for fixed variables that showed significant differences in all analyses (P < 0.05) were conducted using Tukey’s Honest Significant Difference (HSD) test.

Results and Discussion

Weather and Irrigation Needs

Growing season precipitation (May through September) was 337, 445, and 254 mm at Colby in 2018, 2019, and 2020 respectively compared to a long-term average (1972-2021) of approximately 300 mm (fig. 1). At Tribune, growing season precipitation was 330, 324, and 333 mm for the three respective years and compares to a long-term average (1981-2010) of approximately 300 mm. Calculated weather-based evapotranspiration (ETc) was near normal in 2018 and 2020 at approximately 580 mm at Colby, but was approximately only 500 mm in 2019. Tribune ETc was greater than at Colby in 2018 and 2020 with Tribune values at 621 and 670 mm, respectively. Overall, the weather conditions of the experimental years were typical of west central and northwestern Kansas in this semi-arid region with summer-pattern rainfall.

Irrigation amounts varied by year, location, and of course by treatment as would be anticipated (table 1; fig. 1). In general, Tribune irrigation amounts were greater than Colby, except in 2020. The Post-38 treatment which concentrated more irrigation post-anthesis (i.e., 38 mm weekly post-anthesis and 38 mm every two weeks pre-anthesis) generally had the greatest total seasonal amount except for an anomaly at Tribune in 2020 where timing of precipitation allowed the Pre-38 to exceed the Post-38 treatment. The general result is as anticipated, since the crop is at full canopy during the post-anthesis period thus having greater ET and also because precipitation generally decreases during this latter summer period in this region. Conversely, the least amount of irrigation water was applied for the Pre-25 treatment which concentrated its weekly applications during the pre-anthesis period. Overall average irrigation amounts varied by as much as 62%. The importance of these irrigation differences will be discussed in more detail later.

Grain Yield

Average yields across years, locations and treatments ranged from 12.51 to 13.27 Mg ha-1, so yields were reasonably good and not greatly affected by irrigation strategy (table 2). Even the lowest average yield (11.1 Mg ha-1 at Tribune for Pre-25) could be considered good in that the average combined irrigation and rainfed corn yield for Kansas was 8.4 Mg ha-1 in 2020 (NASS, 2020). There were no statistically significant differences (P<0.05) in corn grain yield attributable to irrigation treatment by specific year or by location (table 2). However, when averaged across years and locations, the Pre-25 mm had significantly lower yields than the Pre-38 and Post-38 treatments and numerically lower yield than the Post-25 treatment. These yield results coincide with the Pre-25 treatment also having the least applied irrigation (fig.1 and table 1). These results suggest that concentrating limited water, in greater amount or frequency, during the post-anthesis stage rather than during the pre-anthesis stage makes an important difference. A yield loss from post-anthesis water stress was reported by other researchers (Comas et al., 2019; Nasielski et al., 2019; Sah et al., 2020). However, the current study result is contrary to the finding of Lamm (2017) in an earlier study who concluded pre-anthesis water stress was more detrimental to grain yield than similar levels of post-anthesis stress at Colby, Kansas. In the earlier study, greater plant density (approximately 81,000 plants ha-1) and four different hybrids were used. And the number of kernels ear-1 was the component most responsible for grain yield variation contributing nearly 6% of the yield variation, which was approximately 7% overall across irrigation treatments. Because the potential and actual number of kernels ear-1 is set earlier (i.e. V9 and two weeks after R1 growth stages, respectively, Ritchie and Hanway, 1989) than grain filling and final yield, pre- and near-anthesis water stress conditions would have been very important.

Seeding rate significantly (P < 0.01) affected grain yield in 2018 at Colby and at Tribune in 2020 and when averaged across locations and years (table 2). Seeding at 74 thousand seeds ha-1 resulted in greater grain yield compared with seeding at 62 thousand seeds ha-1 on average increasing yield by 0.3 Mg ha-1 (2%). However, the economic value of this yield increase might not justify the additional seed cost. Under these irrigation strategies and climatic conditions, it appears this range of seeding rates is acceptable. Corn plant density to yield relationships are dependent on resource and environment and optimal plant density up to 92 thousand plant ha-1 was reported for very high corn yield environments which could support corn yield >13 Mg ha-1 (Assefa et al., 2016).

There were significant hybrid effects on yield in all three years at Colby and also for 2018 and 2019 at Tribune. However, hybrid effects were not consistent across years with Pioneer P0801 having greater yields across three site-years and Pioneer 1197 having greater yield in two site-years. In general, when averaged across location, the P0801 hybrid had slightly greater yields (13.13 vs. 12.91 Mg ha-1). It is thought that the yield differences are primarily related to general hybrid differences and not related to the relative maturity differences.

The interactions between irrigation × hybrid and seeding rate × hybrid were significant at Tribune in 2019 (table 2). Grain yield from irrigation treatment Post-38 of both hybrids were greater than grain yield of hybrid P1197 from irrigation treatment Pre-25 at Tribune in 2019 (fig. 2a). Pioneer 1197 appears to suffer yield losses when water is greatly restricted. Grain yield of hybrid P0801 at seeding rate of 74 thousand seeds ha-1 was greater than seeding the same hybrid at 62 thousand seeds ha-1 and from grain yield of P1197 at either of the two seeding rates in 2019 at Tribune (fig. 2b). Although the interaction was significant, the actual yield differences were small.

Figure 1. Cumulative evapotranspiration (ET) and precipitation and cumulative irrigation for the four irrigation treatments for the years 2018 through 2020 at Colby (left panel) and Tribune (right panel), Kansas. Symbols in panels a and b are used to denote the different years and are not data points. Anthesis dates were at about 204, 204, and 200 DOY at Colby and at 193, 203, and 205 DOY at Tribune in 2018, 2019, and 2020, respectively.

Yield Components

As would be anticipated, plant density at harvest was significantly affected in all years at both locations by the seeding rate treatment [i.e, greater seeding rate had greater plant density (table 3)]. Hybrid effect was also significant in all years at Tribune and hybrid P1197 had greater plant density at harvest compared with hybrid P0801 in all years (table 3), probably due to greater emergence. There was no significant hybrid effect on plant density at Colby in any year. There was also no irrigation treatment effect on plant density

Number of ears plant-1 was significantly affected at Colby only in 2018 and only by hybrid (table 3) with hybrid P0801 having greater number of ears plant-1 compared with hybrid P1197. At Tribune, seeding rate effect on ears plant-1 was significant in 2018 and 2020; and in both years, ears plant-1 was greater for 62 thousand seeds ha-1 compared with seeding at 74 thousand seeds ha-1. Hybrid effect was also significant in 2018 at Tribune with hybrid P1197 having greater number of ears plant-1 compared with hybrid P0801 (table 3). Across location and years, the number of ears plant-1 varied little with any treatment (0.99 vs. 1.00 ears plant-1).

Table 1. Total irrigation amounts for the years 2018-2020 at Colby and Tribune, Kansas, by treatment.
LocationYearIrrigation Treatments (mm)[a]
Pre-38Post-38Pre-25Post-25
PrAPoAPrAPoAPrAPoAPrAPoA
Colby20187611476229767676176
2019152102761781027751127
202019012816522915277102177
Mean1391151062121107776160
Tribune201819412119620316283137134
20191801231052831227874188
2020218821281421577297150
Mean19710914320914778103157
Overall Mean 1681121242111297790159

    [a]     PrA and PoA stands for the pre-anthesis and post anthesis irrigation     amounts.

Table 2. Main effects of irrigation, seeding rate, and hybrid on corn yield and type 3 test of fixed effects for 2018-2020 study at Colby and Tribune, Kansas.[a]
Grain Yield (Mg ha-1)
Across Location
and Year
ColbyTribune
Factors201820192020Mean201820192020MeanAverage
Irrigation (I)
Pre-3813.7614.5012.9113.7213.0512.0512.9212.67ab13.20a
Post-3812.5114.4612.9413.3013.8413.3512.5313.24a13.27a
Pre-2512.5614.0712.4613.0312.6911.0912.1911.99b12.51b
Post-2513.1214.1512.2613.1813.8312.1513.0813.02a 13.10ab
HSD[a] NS NS NS NS NS NS NS0.810.64
Seeding rate (S)
6212.42b[b]14.4012.6813.1713.3011.9712.42b12.5612.87b
7413.56a14.1912.6113.4513.4012.3512.94a12.8913.17a
HSD 0.61 NS NS NS NS NS 0.34 NS0.28
Hybrid (H)
P0801 12.53b 14.78a 13.30a 13.54a 13.11b 12.44a12.6212.7213.13
P1197 13.44a 13.81b 11.99b 13.08b 13.60a 11.88b12.7312.7412.91
HSD 0.61 0.65 0.42 0.42 0.42 0.44 NS NS NS
Type 3 statistical test
I0.05310.73000.11940.19090.21990.10960.32530.00490.0183
S0.00060.50720.74480.18550.65050.09600.00390.07480.0353
I*S0.69700.82760.59760.86120.10930.53240.45100.76040.9857
H0.00470.0047<0.00010.03390.02400.01470.51870.94420.1282
I*H0.11140.36350.55830.42070.81600.00200.41160.26480.234
S*H0.44430.75200.08490.43440.64840.03050.68890.23610.8577
I*S*H0.21270.36440.45020.75240.13970.47420.74570.71640.7358

    [a]    HSD is minimum difference between two treatments used to declare they are significantly different using Tukey’s Honest Significant Difference Test at p<0.05. Significant main or interaction effects are indicated in bold.

    [b]    Mean values in column and within a factor followed by the same letter or with no letter are not significantly different (P<0.05).

Figure 2. The effect of the interaction between (a) irrigation × hybrid and (b) seeding rate × hybrid interaction effect on corn yield in 2019 at Tribune, Kansas. Error bars are standard errors and bars with the same letter are not significantly different (P<0.05).

There were no significant differences in number of kernels ear-1 attributable to irrigation treatment in any year, nor at any location which suggests that irrigation treatments did not appreciably affect kernel initiation, pollination, and seed set. These results may be attributable to the deep silt loam soils with good water holding capacity which alleviated to some degree pre-anthesis plant water stress. These results might change in years when available soil water at planting is low. The fact that the number of kernels ear-1 were not affected by irrigation is probably the major reason corn grain yield was generally not affected (Ritchie and Wei, 2000). As discussed earlier, the number of kernels ear-1 had a profound effect of corn grain yield in an earlier study in northwest Kansas (Lamm, 2017), which had a greater plant density and different hybrids.

Number of kernels ear-1 was significantly affected by seeding rate and hybrid at Colby and Tribune in all years except 2018 at Colby where only the hybrid effect was significant (table 4). In all years and both locations, hybrid P0801 had greater number of kernels ear-1 compared with hybrid P1197 except in 2018 at Colby where the result was the opposite. Similarly, in all years and both locations, kernels ear-1 was greater for 62 thousand seeds ha-1 compared with seeding at 74 thousand seeds ha-1 except in 2018 at Colby where there was no significant effect by seeding rate. Greater plant densities will generally reduce kernels ear-1 and overall optimizing the intermediate yield component kernels per unit area (i.e., plant density × ears plant-1 × kernels ear-1) is thought to be responsible for most of the yield improvement in recent decades (Ritchie and Wei, 2000). Kernels ear-1 varied approximately 10% with both seeding rate and hybrid when averaged across years and locations.

Irrigation treatment significantly affected kernel mass only at Tribune in 2019 with irrigation treatment Pre-25 having lower values compared with other irrigation treatments (table 4). Kernel mass was significantly affected by hybrid at Colby and Tribune in all three years; in all cases, hybrid P1197 had greater kernel mass compared with hybrid P0801 (table 4). Seeding rate effect was significant only in 2019 at Colby, but was significant in all three years at Tribune; and in all significant cases, kernel mass was greater for 62 thousand seeds ha-1 compared with seeding at 74 thousand seeds ha-1. Kernel mass varied less than 3% with both seeding rate and hybrid when averaged across years and locations.

Overall, the only yield component that was affected by irrigation treatment in this study was kernel mass in 2019 at Tribune and when averaged across years for the same location. Similar to the overall grain yield results, average kernel mass for the years 2018 to 2020 at Tribune for the Pre-25 irrigation treatment irrigation was 4% to 5% less than the Pre-38 and Post-38 treatments. This is a confirmation that the reduced yield for irrigation treatment Pre-25 was primarily due to reduced kernel mass. Eck (1986) concluded grain yield reduction in their deficit irrigation treatment at grain fill was proportional to reduction in mass of the kernel, which is in line with our finding. Deficit irrigation prior to flowering impacts kernel numbers but has little effect on kernel mass (Eck, 1986).

Table 3. Main effects of irrigation, seeding rate, and hybrid on plant and ear density and type 3 test of fixed effects for 2018-2020 study at Colby and Tribune, Kansas.[a]
FactorsPlant Density
(1000 plants ha-1)		Ear Density (ears plant-1)
ColbyTribuneAcross
Location		ColbyTribuneAcross.
Location
201820192020Mean201820192020MeanAvg.201820192020Mean201820192020MeanAvg.
Irrigation (I)
Pre-3872.767.567.069.164.063.660.462.765.90.930.991.010.981.020.981.011.010.99
Post-3870.467.467.868.563.864.460.362.865.70.921.011.000.981.050.991.011.021.00
Pre-2570.965.967.868.263.264.362.063.265.70.911.011.000.971.030.971.011.000.99
Post-2571.066.767.068.363.964.562.363.665.90.931.011.000.981.050.971.011.011.00
HSD[b]NSNSNSNSNSNSNSNSNSNSNSNSNSNSNSNSNSNS
Seeding rate (S)
6269.3 b62.0 b62.2b64.5 b58.3 b58.1 b55.8 b57.4 b60.90.911.011.010.981.05 a0.991.02 a1.02 a1.00
7473.2 a71.8 a72.7a72.6 a69.2 a70.3 a66.6a68.7 a70.60.931.001.000.981.02 b0.971.00 b1.00 b0.99
HSD1.61.10.91.01.21.01.40.80.9NSNSNSNS0.02NS0.020.01NS
Hybrid (H)
P080171.066.567.168.262.3 b63.1 b59.7 b61.7 b64.90.94 a1.001.000.981.01 b0.971.011.000.99
P119771.567.367.768.865.2 a65.3 a62.8 a64.4 a66.60.91 b1.011.010.971.07 a0.981.011.021.00
HSDNSNSNSNS1.21.01.40.80.90.02NSNSNS0.02NSNS0.01NS
Type 3 statistical test
I0.29970.19020.58390.62970.77610.57460.26390.4510.9740.58450.55940.57620.92890.11450.29550.92880.40870.5563
S<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.00010.05270.79130.06510.66070.00050.08280.00180.00040.0895
I*S0.98550.87320.37880.93660.44000.09160.81580.92530.98410.47170.41540.11930.83920.58420.90250.71110.86060.8192
H0.49610.17050.24290.2329<0.0001<0.0001<.0001<0.00010.00010.02970.58070.26120.575<0.00010.11430.86680.00020.0827
I*H0.99620.29980.11590.8320.93820.53530.65020.77480.95190.45280.31860.04080.44040.00360.53190.29320.33680.9410
S*H0.30910.16980.55720.23250.38050.75110.2430.33370.80510.69870.37590.70580.5750.00070.53750.61550.11770.6487
I*S*H0.54140.81190.62720.88420.7820.93340.50850.98980.96940.65190.60230.03090.92740.32160.29340.63500.81910.9811

    [a]     Mean values in column and within a factor followed by the same letter or with no letter are not significantly different (P<0.05).

        Significant main or interaction effects are indicated in bold.

    [b]     HSD is minimum difference between two treatments used to declare they are significantly different using Tukey’s Honest Significant Difference Test at p<0.05.

Crop Water Use and Water Productivity

Crop water use was generally affected by irrigation treatment at both locations as would be anticipated (table 5). Averaged across locations and years, crop water use was in the order Post-38 = Pre-38 > Post-25 = Pre-25. There were no significant differences in crop water use attributable to seeding rate. At Tribune, the P1197 hybrid had statistically greater crop water use (approximately 3%) than P0801 in both 2018 and 2019, but there was no hybrid effect in 2020 (table 5).

Table 4. Main effects of irrigation, seeding rate, and hybrid on number of kernels ear-1 and kernel mass; and type 3 test of fixed effects for 2018-2020 study at Colby and Tribune, Kansas.[a]
Number of Kernels
(kernels ear-1)		Kernel Mass
(mg kernel-1)
ColbyTribuneAcross
Location		ColbyTribuneAcross
Location
Factors201820192020Mean201820192020MeanAvg.201820192020Mean201820192020MeanAvg.
Irrigation (I)
Pre-38572650617613558559621579596357337313336361350a345352 a344
Post-38530657631606576599598591598365326306332363353a348355 a344
Pre-25533657613601569542588566584366325303331349334b335339 b335
Post-25538647610598598560600586592370327303333350349a349350 ab341
HSD[b]NSNSNSNSNSNSNSNSNSNSNSNSNSNS14NSNSNS
Seeding rate (S)
62541694 a661 a632 a608 a595 a626 a610a621a365335a308336360 a354 a349 a354 a345 a
74545612 b574 b577 b542 b535 b577 b551b564b364323b305330352 b339 b340 b344 b337 b
HSDNS2420222022181413NS10NSNS66755
Hybrid (H)
P0801526 b703 a674 a634 a600 a613 a625 a613 a623a360 b318 b296 b324 b352 b334 b338 b341 b333 b
P1197561 a602 b561 b575 b550 b517 b579 b548 b562b370 a340 a316 a342 a359 a359 a351 a356 a349 a
HSD2524203220221814137106966755
Type 3 statistical test
I0.13640.90290.50590.82190.21860.30680.20420.15570.47140.10590.42770.14430.8980.18920.00670.34250.00620.1378
S0.7562<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.00010.78760.02210.3470.21950.0157<0.00010.011<0.00010.0023
I*S0.49460.48160.11350.56420.03720.85850.90100.67130.56920.69420.17000.20150.8860.63840.65010.97580.87990.7958
H0.0076<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.0001<0.00010.00480.0001<0.00010.00010.024<0.00010.0007<0.0001<0.0001
I*H0.02980.37170.52570.61150.39650.29690.11870.72920.50980.30120.99820.59530.93360.18810.54430.38010.85060.8537
S*H0.13010.60320.06130.18940.19850.80350.49430.84720.22740.19570.69110.10810.93180.57960.03880.66960.16960.4813
I*S*H0.72950.18830.65410.77010.13540.14090.37260.34320.40490.43880.83750.51510.84040.88390.1890.68210.69370.6535

    [a]    Mean values in column and within a factor followed by the same letter or with no letter are not significantly different (P<0.05).

        Significant main or interaction effects are indicated in bold.

    [b]    HSD is minimum difference between two treatments used to declare they are significantly different using Tukey’s Honest Significant Difference Test at p<0.05.

Table 5. Main effects of irrigation, seeding rate, and hybrid on crop water use and type 3 test of fixed effects for 2018-2020 study at Colby and Tribune, Kansas.[a]
Crop Water Use (mm)
ColbyTribuneAcross
Location
Factors201820192020Mean201820192020MeanAverage
Irrigation (I)
Pre-38603bc656a635a632b681ab705ab698695ab663a
Post-38651a653a659a654a721a756a665714a684a
Pre-25588c605b570c588d623b651b635636c612b
Post-25632ab604b600b612c646b669b652655bc634b
HSD[b]352927196862NS5825
Seeding rate (S)
62618627612619675689653673646
74619632619624661701671678651
HSDNSNSNSNSNSNSNSNSNS
Hybrid (H)
P0801618625619621656b687b664669645
P1197620634613622680a703a661681652
HSDNSNSNSNS1715NSNSNS
Type 3 test
I0.00130.0003<0.0001<0.00010.00720.00220.26510.0083<0.0001
S0.89320.24740.22680.28780.11510.13550.05740.42460.3288
I*S0.81700.45480.73940.75660.37450.50170.65220.86610.9612
H0.85590.06210.31970.68930.00940.04550.71020.06190.1704
I*H0.87510.42070.22420.46450.99310.76410.71280.98120.8791
S*H0.45490.06370.68170.93090.49980.32830.82560.97620.9848
I*S*H0.06180.82380.90250.42920.70310.91760.82660.71980.5718

    [a]    Mean values in column and within a factor followed by the same letter or with no letter are not significantly different (P<0.05).

        Significant main or interaction effects are indicated in bold.

    [b]    HSD is minimum difference between two treatments used to declare they are significantly different using Tukey’s Honest Significant Difference Test at p<0.05.

Water productivity was significantly affected by all irrigation treatment in 2 of 3 years at Colby but was not significantly affected at Tribune (table 6). Averaged across locations and years, water productivity was in the order, Post-25 = Pre-25 = Pre-38 > Post-38. The greater seeding rate (74 thousand seeds ha-1) had significantly greater water productivity at Colby in 2018, but had little effect in other years, nor at Tribune in any year. The hybrid effect on water productivity was not consistent across years or locations, but overall P0801 had 3% greater water productivity than P1197. At Tribune in 2019, there was an irrigation × hybrid interaction effect with water productivity of hybrid P0801 being greater than for hybrid P1197 when irrigation treatment of Pre-25 was used (fig. 3). This interaction and decrease in water productivity were attributable to the decrease in yield for P1197 that was discussed earlier.

Table 6. Main effects of irrigation, seeding rate, and hybrid on water productivity and type 3 test of fixed effects for 2018-2020 study at Colby and Tribune, Kansas.
Water Productivity [kg (ha-mm)-1]
ColbyTribuneAcross Location
Factors201820192020Mean201820192020MeanAverage
Irrigation (I)
Pre-3822.9a22.120.4ab21.8ab19.217.118.618.320.0ab
Post-3819.2b22.219.7b20.4b19.317.718.918.719.5b
Pre-2521.4ab23.221.9a22.2a20.417.119.418.920.5ab
Post-2520.8ab23.520.5ab21.6ab21.418.220.119.920.7a
HSD[b]2.4NS1.81.6NSNSNSNS1.2
Seeding rate (S)
6220.2b23.020.821.319.817.419.118.820.0
7422.0a22.520.421.620.417.619.419.120.4
HSD1.1NSNSNSNSNSNSNSNS
Hybrid (H)
P080120.3b23.7a21.6a21.9a20.118.1a19.119.120.5a
P119721.8a21.8b19.6b21.1b20.116.9b19.418.819.9b
HSD1.11.10.80.7NS0.8NSNS0.54
Type 3 test
I0.00710.20400.02340.02850.13320.45770.60520.05500.0451
S0.00220.31450.44450.38800.20670.47770.41210.23860.2198
I*S0.74400.82150.84670.96200.51770.69670.90510.96990.9972
H0.01130.0011<0.00010.02731.00000.00290.45140.31150.0447
I*H0.19160.46210.73870.66370.85090.00440.54650.34710.6500
S*H0.82240.65250.19370.39770.44010.19490.75340.31470.9819
I*S*H0.35820.28820.57050.61050.14090.50730.76550.43900.4898

    [a]    Mean values in column and within a factor followed by the same letter or with no letter are not significantly different (P<0.05).

    [b]    HSD is minimum difference between two treatments used to declare they are significantly different using Tukey’s Honest Significant Difference Test at p<0.05.

Figure 3. An irrigation × hybrid interaction at (a) Colby and (b) Tribune on water productivity of corn in 2019. Error bars are standard errors and bars with the same letter or no letters are not significantly different (P<0.05).

    Annual average crop water use for irrigation treatments ranged from 577 to 659 mm at Colby and 623 to 756 mm at Tribune. These crop water use values for all irrigation treatments indicated water was provided close to the regional water requirement of corn as reported by many researchers (Musick and Dusek, 1980; Lamm et al., 2009; Assefa et al., 2014; Lamm, 2017; Trout and DeJonge, 2017). In-season precipitation amounts in the range between 253 and 495 mm (fig. 1), availablesoil water at corn planting in the range of 280 to 380 mm (table 7), and additional irrigation amounts in the range from 150 to 400 mm made the years 2018 through 2020 less water deficit compared with some years in the region. Consistent with irrigation amounts and overall grain yield results, averaged across environments, water use was 4% to 11% less for the 25.4 mm irrigation treatments compared with 38 mm irrigation treatments. Conversely, water productivity was greater by 6% for the Post-25 treatment compared with Post-38 treatment (table 6). As irrigation water supply decreases and becomes more expensive, production per unit of water (water productivity) can become the metric for selecting management over production per unit area (Fereres and Soriano, 2007). Irrigation treatment Post-25 not only had the greatest water productivity, but also overall average grain yield was not significantly less than the other irrigation treatments.

Available Soil Water

There was little effect of treatment on available soil water at planting (ASWP) as would be anticipated (table 7). There were unexplained interactions on ASWP at Tribune in 2018, but they were small and of negligible practical significance (table 7). There were differences between years with 2019 having greater ASWP and between locations with Colby having greater ASWP.

Total available soil water at harvest (ASWH) was significantly affected by the two-way interactions of irrigation × hybrid and year × irrigation at Colby (table 7). Total ASWH at Colby for both hybrids was greatest for irrigation level Post-38 and least for irrigation level Pre-25 (fig. 5a). In 2018 and 2019 at Tribune, total ASWH was greater for hybrid P0801 compared with hybrid P1197, but there was no significant difference in 2020 (fig. 5b). At Tribune, there were no significant differences in ASWH at harvest due to irrigation treatment but the trend in results was similar to Colby in that Post-38 was greater than Pre-25. In 2018, total ASWH for the Pre-38 irrigation levels at 74 thousand seed ha-1 was the greatest at Tribune (fig. 4d).

Soil water profiles at Colby tended to be wetter than for Tribune at both planting and at harvest when averaged over the three-year period, 2018 to 2020 (fig. 6). Overall, there was a greater average total profile ASWH for the Post-38 treatment (fig. 7). A greater average ASWH for the Post-38 treatment is perhaps why Post-38 irrigation treatment had reduced water productivity (i.e., some deep percolation may have occurred for this wetter treatment).

Table 7. Main effects of irrigation, seeding rate, and hybrid onavailable soil water at planting and at harvest and type 3 test of fixed effects for 2018-2020 study at Colby and Tribune, Kansas.[a]
Colby
Total Available Soil Water
[mm (240 cm soil profile)-1]		Tribune
Total Available Soil Water
[mm (240 cm soil profile)-1]
FactorsPlantingHarvestPlantingHarvest
Irrigation
Pre-38372308b303191
Post-38362338a293208
Pre-25375285b301166
Post-25363302b296176
HSD[b]NS28NSNS
Seeding rate
62369311298187
74367305299183
HSDNSNSNSNS
Hybrid
P0801368310300193a
P1197368307296177b
HSDNSNSNS9
Year
2018340c287c283b175b
2019393a325a328a193a
2020371b313b283b188a
HSD1110713
Type 3 test
Irrigation (I)0.20620.00150.54160.3793
Seeding rate (S)0.56130.08490.71540.4225
I*S0.99440.66420.51670.7436
Hybrid (H)0.98830.45430.07200.0002
I*H0.13080.01590.92380.9598
S*H0.34740.51440.36960.5307
I*S*H0.95430.49190.22390.7749
Year (Y)<0.0001<0.0001<0.00010.0032
Y*I0.7987<0.0001<0.0001<0.0001
Y*S0.66450.21390.21490.0007
Y*I*S0.99500.69630.00800.0333
Y*H0.95140.16420.15630.0204
Y*I*H0.87710.35400.19680.2204
Y*S*H0.84740.38230.19740.9190
Y*I*S*H0.87770.07990.95920.9569

    [a]    Mean values in column and within a factor followed by the same letter or with no letter are not significantly different (P<0.05).

    [b]    HSD is minimum difference between two treatments used to declare they are significantly different using Tukey’s Honest Significant Difference Test at p<0.05.

Summary and Conclusions

Despite variation in the effects of treatments across years and locations, among the four irrigation treatments, Pre-25 and Post-25 had among the greatest water productivity and the least irrigation (35% less), while producing grain yields only slightly lower (3%) than the Pre-38 and Post-38 irrigation treatments. In environments similar to western Kansas, both the Post-25 and Pre-25 irrigation treatments were relatively effective and should be considered as practices to reducing irrigation for the Central Great Plains and similar

Figure 4. An irrigation × seeding rate × year interaction at (a, b) Colby and (c, d) Tribune total available soil water at planting and harvest. Error bars are standard errors and bars with the same letter or no letters are not significantly different (P<0.05).
Figure 5. An irrigation × hybrid interaction at (a) Colby and a hybrid x year interaction at (b) Tribune on total available soil water at harvest average for 2018-2020 or across four irrigation treatments. Error bars are standard errors and bars with the same letter or no letters are not significantly different (P<0.05).
Figure 6. Average (2018-2020) available soil water by depth at (a, b) Colby and at (c, d) Tribune, Kansas, at corn (a, c) planting and (b, d) harvest.
Figure 7. Time series data of total available soil water at Colby (top panel) and Tribune (bottom panel) from planting to harvest in 2018, 2019, and 2020 for the four irrigation treatments. Error bars are standard errors.

areas when irrigation must be constrained. However, it should be noted that all of this assumes that scientific irrigation scheduling, such as the weather-based scheduling used in this study, will be utilized to assess actual need for irrigation. Then irrigation limits can be imposed as desired.

In terms of the yield components, differences in kernel mass and to a lesser degree kernels ear-1 accounted for most of the yield variation across irrigation treatments. Hybrids effects were generally not significant or consistent across environments. Seeding corn at 74 thousand seeds ha-1 increased yields only 2% compared with 62 thousand seeds ha-1 indicating a wide range of acceptable corn plant densities under deficit irrigation in this region.

Acknowledgements

Support for this research was provided by the Kansas Corn Commission. This is Contribution 23-064-J, Kansas Agric. Exp. Station.

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