Introduction
Volunteer corn (Zea mays L.) is a troublesome weed in corn-based crop rotations. Alms et al. (Reference Alms, Moechnig, Vos and Clay2016) inferred that volunteer corn is equally or more competitive than some common grass weeds such as barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.], giant foxtail (Setaria faberi Herrm.), and green foxtail [Setaria viridis (L.) P. Beauv.]. Similar to other weeds, volunteer corn competes for moisture, nutrients, space, and light, causing yield loss in row crops. A plethora of literature exists reporting yield losses due to the interference of volunteer corn in corn (Marquardt et al. Reference Marquardt, Terry, Krupke and Johnson2012b) and soybean [Glycine max (L.) Merr.] (Alms et al. Reference Alms, Moechnig, Vos and Clay2016; Marquardt et al. Reference Marquardt, Krupke and Johnson2012a). Crop yield losses are greater when clumps of volunteer corn occur in the field compared with individual volunteer plants (Andersen et al. Reference Andersen, Ford and Lueschen1982; Piasecki and Rizzardi Reference Piasecki and Rizzardi2019).
The yield loss potential of volunteer corn is likely attributable to its wider leaves, deeper roots, and prolific growth, especially in short-stature crops such as soybean and dry bean (Phaseolus vulgaris L.). With these superior growth characteristics, volunteer corn can grow above the crop canopy and capture resources such as light that may be used by the crop instead. Holman et al. (Reference Holman, Schlegel, Olson and Maxwell2011) reported that volunteer corn infestation of 0.6 plants m−2 depleted available soil water by 2.5 cm at wheat (Triticum aestivum L.) planting at 8 out of 9 site-years and reduced wheat yield by 7% to 14% at half of the site-years. This study was conducted under dryland crop rotation in a semiarid region where weed growth during the fallow period was expected to induce water stress for subsequent crops as they rely on moisture conservation during the fallow period (Fernandez et al. Reference Fernandez, Quiroga, Noellemeyer, Funaro, Montoya, Hitzmann and Peinemann2008).
Crop–weed competition studies primarily focused on evaluating the effect of weed density on crop yield to determine economic losses (Zimdahl Reference Zimdahl2004). As the consumptive use of weeds in crops is not well studied, it may go unnoticed in the farm-level profitability calculations, especially in irrigated agriculture, where water is a considerable input cost. Norris (Reference Norris1996) estimated that uncontrolled weeds can add more than US$50 ha−1 in direct irrigation cost, which will vary depending on water-pumping cost and cropping system. Although cost estimates from Norris (Reference Norris1996) ignored complex trade-offs of resources in crop–weed mixtures and were simply based on the product of weed biomass, their water use efficiency (WUE), and irrigation cost, implications remain true that weeds may add extra cost while negatively affecting crop growth and yield. Only a few studies have characterized the water use of weeds (Singh et al. Reference Singh, Kukal, Irmak and Jhala2022a), and comparatively few have tracked or quantified water use of crop–weed mixtures at the systems level (Barnes et al. Reference Barnes, Jhala, Knezevic, Sikkema and Lindquist2018; Berger et al. Reference Berger, McDonald and Riha2010, Reference Berger, Ferrell, Rowland and Webster2015; Massinga et al. Reference Massinga, Currie and Trooien2003; Sadeghi et al. Reference Sadeghi, Starr, Teasdale, Rosecrance and Rowland2007; Vaughn et al. Reference Vaughn, Lindquist and Bernards2009). Interference of weeds in crops can create a soil water deficit during the growing season, ultimately reducing the crop yield (Sadeghi et al. Reference Sadeghi, Starr, Teasdale, Rosecrance and Rowland2007; Vaughn et al. Reference Vaughn, Lindquist and Bernards2009). Research is required to track the soil water balance of weeds in agricultural systems that include crops and weeds in proximity under different irrigation systems and/or levels. Knowing when and how much water is used by actual evapotranspiration (ETa) in the crop–weed mixture could help improve crop WUE and support planning for sustainable weed management solutions, especially in irrigated cropping systems (Kaur et al. Reference Kaur, Kaur and Chauhan2018; Singh et al. Reference Singh, Kukal, Irmak and Jhala2022a).
A center-pivot irrigation (CPI) system is a pipe structure equipped with overhead sprinklers that move around a pivot to water the crop (Waller and Yitayew Reference Waller, Yitayew, Waller and Yitayew2016). The CPI system is convenient, flexible, and versatile, as it can distribute water uniformly in large areas of various topographical characteristics with low operational cost, and hence, it is widely adopted throughout the world (New and Fipps Reference New and Fipps2000; Rogers et al. Reference Rogers, Aguilar, Kisekka and Lamm2017). For example, about 91% of the irrigated area (2.8 out of 3.1 million ha) is watered through CPI in Nebraska (USDA-NASS 2018), which has the highest irrigated cropland (15%) in the United States (USDA-ERS 2023). Apart from CPI, high-efficiency drip systems are also used for irrigation. For example, a subsurface drip irrigation (SDI) system delivers water directly to crop roots through buried driplines (Payero et al. Reference Payero, Yonts, Irmak and Tarkalson2005b; Reich et al. Reference Reich, Godin, Chávez and Broner2009) at higher efficiency than CPI (95% vs. 85%; Payero et al. Reference Payero, Yonts, Irmak and Tarkalson2005b). Among drip irrigation systems, SDI is adopted in 89% of the area (21,088 out of 23,771 ha) irrigated through drips in Nebraska (USDA-NASS 2018).
Crop–weed competition may be impacted by the timing and technique of irrigation management, as weeds respond to changes in soil water availability with irrigation (de Freitas Souza et al. Reference de Freitas Souza, Silva, dos Santos, Carneiro, Reginaldo, Bandeira, dos Santos, Pavão, de Negreiros and Silva2020, Reference de Freitas Souza, Lins, de Mesquita, da Silva Teófilo, Reginaldo, Pereira, Grangeiro and Silva2021). To develop irrigation management strategies as per available soil water, information is required on how crops and weeds respond to water availability, which can be gathered through modeling that links biomass assimilation to ETa (Paredes et al. Reference Paredes, Rodrigues, Alves and Pereira2014). Although empirical models of crop–weed competition have proven valuable (Renton and Chauhan Reference Renton and Chauhan2017), they need to be refined for predicting crop–weed–water interactions under different irrigation systems. Integrating characteristics such as ETa and total soil water in mechanistic models to examine how irrigation type affects competitive interactions of crops and weeds can provide useful information for developing crop–weed–water use dynamics. The impact of crop ETa rates on grain yield is widely established; however, the impact of crop–weed mixture ETa on weed morphological traits, such as biomass, leaf area, and plant height, is not well understood. The objective of this research was to characterize the growth and ETa of volunteer corn in corn, soybean, sorghum [Sorghum bicolor (L.) Moench], and fallow systems under CPI and SDI systems in climate, crop, soil, and water management conditions of south-central Nebraska.
Materials and Methods
Site Description, Experimental Design, and Agronomic Management
Field experiments were set up in 2021 and 2022 growing seasons at the South-Central Agricultural Laboratory of the University of Nebraska–Lincoln, near Clay Center, NE (40.58°N, 98.13°W). The research site is located 552 m above the mean sea level with a climatic zone falling between subhumid and semiarid (Irmak Reference Irmak2015; Irmak et al. Reference Irmak, Mohammed and Kranz2019). The long-term average annual precipitation is approximately 680 mm, and the magnitude and time of precipitation events vary considerably within a year as well as within cropping seasons (Irmak Reference Irmak2015; Irmak et al. Reference Irmak, Mohammed and Kranz2019). A large experimental field at Irmak Research Laboratory’s advanced irrigation engineering, evapotranspiration, crop physiology, and climate science research facility was subdivided into separate fields including CPI and SDI systems. The CPI system (T-L Irrigation, Hastings, NE, USA) had four spans that were hydrostatically powered to move and irrigate continuously with 90.8 L h−1 (Irmak et al. Reference Irmak, Mohammed and Kukal2022). The SDI system had drip laterals running at 0.4-m soil depth in the middle of two crop rows, spaced 1.5 m to cover alternate crop rows. The drip emitters were spaced at 0.2 m and had a flow rate of 3.8 L h−1 (Irmak et al. Reference Irmak, Mohammed and Kukal2022). The field topography, soil texture, water holding capacity, and other soil physical and chemical properties of both CPI and SDI fields were similar. Both fields had Hastings silt loam soil (fine, montmorillonitic, mesic Udic Argiustolls) with 15% sand, 20% clay, 65% silt, and 2.5% organic matter. The field had a 0.5% slope in the north-south direction. The permanent wilting point, field capacity, and saturation of this field were 0.14, 0.34, and 0.53 m3 m−3, respectively (Irmak Reference Irmak2015).
The experiment was conducted in a split-plot design with irrigation as the main plot factor with two levels: CPI and SDI. At the subplot level, each irrigation system had eight treatments. Treatments included corn, soybean, sorghum, and fallow with and without volunteer corn as described in Mausbach et al. (Reference Mausbach, Irmak, Kukal, Karnik, Sarangi and Jhala2024). A fallow treatment with volunteer corn simulating no crop competition was included for comparison. An individual subplot was 3 m by 9 m and was replicated four times. Before planting crops, a starter fertilizer 11-52-0 (N-P-K) was broadcast at 168 kg ha−1 during both years, and 82-0-0 (N-P-K) was injected in-furrow at 246 kg ha−1 in 2021 and at 224 kg ha−1 in 2022 in the entire experimental area. Corn, soybean, and sorghum subplots included four crop rows spaced at 0.76 m, which is typical row spacing in the region (McDonald et al. Reference McDonald, Sarangi, Rees and Jhala2023). Corn (Dekalb® ‘DKC 60-87 RIB’, Bayer, https://www.cropscience.bayer.us/brands/dekalb) was planted at 85,750 seeds ha−1, soybean (Asgrow® ‘AG27XF1’, Bayer, https://www.cropscience.bayer.us/brands/asgrow) at 308,750 seeds ha−1, and sorghum (Dekalb® ‘DKS37-07’) at 284,225 seeds ha−1. The crops were planted on May 14, 2021, and May 16, 2022.
Plots with volunteer corn had one row of volunteer corn in the middle of two crop rows and the center of fallow plots. On the day of planting crops, bin-run corn seeds were planted in the greenhouse to mimic volunteer corn, and plants were transplanted approximately 15 d afterward at 0.25-m spacing in the center of two middle crop rows. Glyphosate/glufosinate-resistant corn seeds (Dekalb® ‘DKC-60-67’) were harvested from the previous year (2020 and 2021) and planted in the greenhouse on the same day as crops were planted in the field. Volunteer corn plants around the V2 growth stage (10- to 12-cm tall) were transplanted at the plant-to-plant distance of 0.25 m (36 plants per row) on June 1, 2021, and May 31, 2022.
Preemergence herbicides were applied within 2 d after planting crops: atrazine/bicyclopyrone/mesotrione/S-metolachlor (Acuron®, Syngenta Crop Protection, Greensboro, NC, USA) at 2,438 g ai ha−1 in corn, imazethapyr/pyroxasulfone/saflufenacil (Zidua® PRO, BASF, Research Triangle Park, NC, USA) at 181 g ai ha−1 in soybean, and atrazine/mesotrione/S-metolachlor (Lexar® EZ, Syngenta Crop Protection) at 3,174 g ai ha−1 in sorghum to provide residual weed control. Glyphosate (Roundup® PowerMax, Bayer Crop Science, St Louis, MO, USA) at 1,260 g ae ha−1 was added along with preemergence herbicides for control of existing weeds. In-season weed pressure was managed through hoeing.
Soil Moisture Sensors and Irrigation Scheduling
Watermark Granular Matrix sensors (Irrometer Company, Riverside, CA, USA) were installed at three depths of 0 to 0.30 m, 0.30 to 0.60 m, and 0.60 to 0.90 m (Mausbach et al. Reference Mausbach, Irmak, Kukal, Karnik, Sarangi and Jhala2024). The Watermark Monitor Model 900M data loggers (Irrometer Company) connected to sensors collected hourly data on soil matric potential (SMP). A total of 32 sensors were installed in both irrigation systems, which were functional from June 15, 2021, and June 20, 2022, until September 24, 2021, and October 20, 2022, respectively. Sensors were installed in crop rows in the plots without volunteer corn and in the center of volunteer corn and crop rows in plots with volunteer corn. Soil moisture retention curves that were developed for the research field by Irmak et al. (Reference Irmak, Mohammed and Kranz2019) were used to convert SMP values to volumetric soil water content (VWC) as follows:
$${{\rm{\theta }}_{\rm{v}}} = a\; \times \;{\rm{SM}}{{\rm{P}}^{ - b}}$$
where θv represents VWC or volumetric soil water content (% vol or m3 m−3), SMP represents soil matric potential (kPa), a and b are constants whose values vary with soil depth. The values for a were 92.19, 77.82, and 80.82, while the values for b were 0.29, 0.21, and 0.27 for soil depths of 0 to 0.30 m, 0.30 to 0.60 m, and 0.60 to 0.90 m, respectively (Irmak et al. Reference Irmak, Mohammed and Kranz2019). The VWC was added across three depths and multiplied by a conversion factor of 304.8 (ft to mm) to get total soil water (TSW, mm) in the soil profile (0 to 0.90 m). The TSW represents total soil moisture added over incremental depths of 0 to 0.30 m, 0.30 to 0.60 m, and 0.60 to 0.90 m in the crop root zone or monitored soil profile of 0 to 0.90 m. The TSW was subtracted from the total field capacity of the monitored soil profile (329 mm per 0.90 m) to convert it to soil water depletion (SWD, mm) (Mausbach et al. Reference Mausbach, Irmak, Kukal, Karnik, Sarangi and Jhala2024).
Sensor data were used to estimate the ETa of the treatments and decide the time for irrigation. To avoid water stress, irrigation was triggered when the average SMP was greater than or equal to 90 kPa, which translates to maintaining soil water near 40% of the maximum allowable depletion of total available water in the root zone (0 to 0.90 m). The SMP values were averaged for the top 0.60 m of soil before reproductive growth stages and for the top 0.90 m after vegetative growth stages. Irrigation was applied in a nonlimiting fashion, meaning if one plot and/or treatment needed irrigation, all plots were irrigated. Thirty-two millimeters of irrigation was applied six times in 2021 and eight times in 2022 (Table 1). Each 32-mm irrigation in SDI was split approximately over a week.
Table 1. Dates of each irrigation applied to the experimental site at Clay Center, NE, during the 2021 and 2022 growing seasons a .
a Each irrigation was 32 mm.
Seasonal ET a Using Soil Water Balance
Seasonal ETa was computed using the soil water balance method (units, mm) (Irmak Reference Irmak2015) as follows:
$$P + I + U + \;{R_{{\rm{on}}}} = \;{R_{{\rm{off}}}} + D \pm \;\Delta {\rm{SWS}} + {\rm{E}}{{\rm{T}}_{\rm{a}}}$$
where P stands for precipitation, I stands for irrigation, U stands for upward soil moisture flux, R on stands for surface run-on within the individual subplot, R off stands for surface runoff from the individual subplot, D stands for deep percolation below the crop root zone, and ∆SWS represents change in soil water storage in the soil profile at the start and end of the season.
Volunteer Corn Growth, Crop Yield, and WUE
Plant height, leaf area, and biomass of volunteer corn plants were recorded at 15, 30, 45, and 60 d after transplanting (DATr). The height of five volunteer corn plants was determined by measuring the distance from the base of the stem to the tip of the longest leaf pre-tasseling, and to the tip of the tassel post-tasseling (Irmak et al. Reference Irmak, Mohammed and Kukal2022). Three volunteer corn plants were destructively sampled for measuring leaf area using the LI-3100C Area Meter (Li-Cor Biosciences, Lincoln, NE, USA). After leaf area measurements were taken, individual samples from each subplot were bagged and oven-dried at 70 C to constant weight to record dry biomass. For each destructive sampling, volunteer corn plants were selected to avoid edge effects from the previous sampling, leaving the remainder of volunteer corn plants scattered along the entire plot length. At crop maturity, two middle rows, each 9-m long, were harvested from each subplot with a small plot combine to determine crop yield. Corn, soybean, and sorghum yields were adjusted to the standard moisture content of 15.5%, 13%, and 14%, respectively. Before running the combine, the cobs from volunteer corn plants were manually plucked to avoid mixing corn kernels with crop grains and overestimation of actual crop yield.
Crop WUE was calculated as the ratio of crop yield and seasonal total ETa following Irmak et al. (Reference Irmak, Mohammed and Kranz2019):
$${\rm{WUE}} = \;{{{\rm{Crop\;yield}}} \over {{\rm{E}}{{\rm{T}}_{\rm{a}}}}}\; \times 1,000$$
where WUE is expressed as quantity of grain per unit volume of water (kg m−3), crop yield as kg m−2, and crop evapotranspiration (ETa) as mm.
Statistical Analysis
Data were analyzed using R software v. 4.3.1 (R Core Team 2024). ANOVA was conducted with irrigation and crop as fixed factors and irrigation by year and year as random factors to calculate whole-plot and subplot errors. In this analysis, year was considered as a replication, because four replicates of each subplot had restricted randomization, making them pseudo-replicates or subsamples whose variation was accounted as random in the model (Chaves Reference Chaves2010). Assumptions of normality and equal variances were checked visually using residual plots. Nonnormal data (ET, biomass, and leaf area) were analyzed considering gamma distribution using the log link function (Mausbach et al. Reference Mausbach, Irmak, Kukal, Karnik, Sarangi and Jhala2024; Stroup Reference Stroup2015). The models for normal and nonnormal data were fit using lmer and glmmTMB packages, respectively (Bates et al. Reference Bates, Maechler, Bolker, Walker, Christensen, Singmann, Dai, Scheipl, Grothendieck, Green, Fox, Bauer and Krivitsky2023; Brooks et al. Reference Brooks, Bolker, Kristensen, Maechler, Magnusson, McGillycuddy, Skaug, Nielsen, Berg, Bentham, Sadat, Lüdecke, Lenth, O’Brien and Geyer2023). The covariance structure AR(1) was fit to growth data to account for correlation due to repeated measurements taken on the same experimental plots across the growing season (Piepho et al. Reference Piepho, Büchse and Emrich2003). Type III fixed-effects ANOVA was calculated with treatment means separated using Tukey’s method for P-value adjustment.
Results and Discussion
Weather
Daily average air temperature during the growing season of 2021 and 2022 usually followed the long-term (1991 to 2020) temperature averages (Figure 1A). Accumulated precipitation had a deficit of 80 mm in 2021 and 118 mm in 2022 compared with the long-term seasonal total precipitation (453 mm; Figure 1B). The biggest precipitation events of 41 mm and 46 mm occurred on August 20, 2021, and July 16, 2022, respectively. The other major precipitation events (≥25 mm) occurred later in the season (two events after mid-September) in 2021 and earlier in the season (four events before mid-September) in 2022. The rain events during October 2021 delayed harvesting to November 2 (172 d after planting [DAP]), however, in 2022, the crop was harvested on October 20 (157 DAP). The 2021 growing season was slightly warmer than 2022 with differences of 1.5, 0.5, and 1.0 C in minimum (T min), maximum (T max), and average (T avg) air temperature, respectively (Table 2). The 2022 growing season (64%) had 7% less relative humidity than 2021 (71%). May, September, and October 2022 had 7%, 10%, and 15% lower relative humidity (RH) than 2021, and 5%, 10%, and 14% lower RH than the long-term averages, respectively. Both growing seasons had similar (2.7 ms−1) average wind speed, which was 18% slower than the long-term average (3.3 ms−1). The in-season variability of wind speed between 2021 and 2022 translated to variability in RH, for early 2021 (May to June) with lower wind speeds resulting in higher RH. The incoming solar radiation (Rs) during the 2021 growing season was similar (18.3 MJ m−2 d−1) to the long-term mean Rs but was slightly higher (3%; 18.9 MJ m−2 d−1) in 2022. The evaporative demand as estimated by vapor pressure deficit (VPD) was 16% higher in the 2022 (0.79 kPa) growing season compared with 2021 (0.68 kPa). The last irrigation in 2022 was applied on September 10 compared with August 31 in 2021, because the first week of September had 74% more evaporative demand (0.80 vs. 0.46 kPa; data not shown). Higher evaporative demand (0.79 vs. 0.68 kPa) and lower RH (64% vs. 71%) and precipitation (335 vs. 373 mm) in 2022 required more irrigation (256 vs. 192 mm) and led to comparatively higher ETa in 2022 compared with 2021.
Figure 1. Daily average air temperature (C) and precipitation (mm) during crop growing seasons in 2021 and 2022 at Clay Center, NE, and their 30-yr long-term averages (1991–2020). The weather data were sourced from the Automated Weather Data Network (AWDN) of the High Plains Regional Climate Center (HPRCC) accessible at https://hprcc.unl.edu/awdn .
Table 2. Monthly means of air and soil temperatures, relative humidity (RH), wind speed (u), incoming solar radiation (Rs), vapor pressure deficit (VPD), and total precipitation during the 2021 and 2022 growing seasons along with their long-term (1991–2020) averages at the experimental site a .
a Long-term monthly averages (1991–2020) are added for comparison. T min, daily minimum air temperature; T max, daily maximum air temperature; T avg, daily average air temperature.
Volunteer Corn Growth
Volunteer corn height (P = 0.005; Figure 2A) and biomass (P < 0.001; Figure 2C) differed with time among crop types. Height was significantly shorter in corn (161 cm) than fallow (171 cm) at 60 DATr. The biomass was lower in corn than fallow at 30 DATr (6.5 vs. 10.0 g plant−1), 45 DATr (19.0 vs. 43.8 g plant−1), and 60 DATr (26.8 vs. 93.8 g plant−1). The leaf area of volunteer corn followed a similar trend, with lower leaf area in corn than fallow at 45 DATr (2,280 vs. 3,335 cm2 plant−1) and 60 DATr (2,611 vs. 3,866 cm2 plant−1) (P < 0.001; Table 3). As expected, volunteer corn in fallow had no crop competition; therefore, it had better growth than volunteer corn in corn. The corn overshadowed volunteer corn, suppressing its growth, and differences started to appear in biomass, leaf area, and plant height at 30, 45, and 60 DATr, respectively. Sorghum suppressed volunteer corn biomass (64.1 g plant−1) by 32% compared with volunteer corn in fallow (93.8 g plant−1) at 60 DATr. In 2021, grasshoppers (Melanoplus spp.) attacked sorghum and volunteer corn in sorghum plots, consuming their foliage and contributing to these differences. Traoré et al. (Reference Traoré, Mason, Martin, Mortensen and Spotanski2003) reported that sorghum usually does not compete well early in the season, with maximum growth forming a dense canopy around anthesis. This may be a reason for no suppression of volunteer corn before 60 DATr in sorghum. The growth of volunteer corn in soybean was no different from fallow; therefore, the competitiveness of crops is ranked as corn > sorghum > soybean. The observed differences in weed-suppression ability of crops align with their differences in canopy architecture such as plant height and leaf area distribution. For example, corn was the tallest crop—giving it a competitive advantage, such as allowing it to directly compete for light with wider leaves above the soybean canopy when occurring as volunteers. Caratti et al. (Reference Caratti, Lamego, Silva, Garcia and Agostinetto2016) reported that soybean competing with F2 corn hybrid plants (or volunteers) was unable to suppress the leaf area and root biomass of corn, and conversely suffered competition from corn manifested as a reduction in growth.
Figure 2. Volunteer corn height (A and B) and biomass (C and D) as affected by crop and irrigation type at the experimental site near Clay Center, NE, in 2021 and 2022. The error bars represent standard errors of the mean estimates. Different alphabetical letters indicate treatment means are significantly different within the given sampling date (P ≤ 0.05).
Table 3. Leaf area (cm2 plant−1) of volunteer corn in corn, soybean, sorghum, and fallow under center-pivot (CPI) and subsurface drip (SDI) irrigation systems averaged across 2021 and 2022 field experiments near Clay Center, NE.
a Treatment means having the same alphabetical letters within each column do not differ significantly as per Tukey’s method of P-value adjustment. DATr, days after transplanting.
b P-value: NS stands for nonsignificant, indicating treatment means within a specific time do not differ from one another. Otherwise, means differ at P-values of <0.001 and 0.034 for crop and irrigation, respectively.
Irrigation type did not affect volunteer corn growth at 15 to 45 DATr (Figure 2; Table 3). The differences in volunteer corn height (P = 0.003; Figure 2B), biomass (P = 0.026; Figure 2D), and leaf area (P = 0.034; Table 3) between irrigation systems appeared later in the season at 60 DATr. For example, volunteer corn height (159 vs. 174 cm), leaf area (2,961 vs. 3,665 cm2 plant−1), and biomass (49.9 vs. 69.2 g plant−1) in CPI was 9%, 19%, and 28% less than in SDI, respectively. Grasshopper (Melanoplus spp.) infestation starting around 45 DATr damaged volunteer corn plants, especially in sorghum plots in the CPI field in 2021, and was an important factor for observed differences in volunteer corn growth between CPI and SDI at 60 DATr. Other likely reasons might be the efficient use of water (Mitchell-McCallister et al. Reference Mitchell-McCallister, Cano and West2020) and lower interrow weed pressure (although weeds were managed from time to time through hoeing; Supplementary Figure S1) under SDI than CPI. A total of five irrigations were applied before 60 DATr across 2021 and 2022 (Table 1) when SDI showed superior growth of volunteer corn, suggesting low evaporative losses (Evett et al. Reference Evett, Marek, Colaizzi, Brauer and O’Shaughnessy2019), higher transpiration to evapotranspiration ratio (Odhiambo and Irmak Reference Odhiambo and Irmak2015), and low weed pressure (Hollingsworth et al. Reference Hollingsworth, Mitchell, Munk, Roberts and Shrestha2014; Mohammed and Irmak Reference Mohammed and Irmak2022a) in SDI may have played a role in observed differences.
SWD and Evapotranspiration
Seasonal mean SWD was similar across corn, soybean, and sorghum treatments with or without volunteer corn irrespective of the irrigation system (Figure 3A). While subseasonal SWD variation was observed among treatments (Supplementary Figure S2), these were mostly compensated for when considering seasonal means. Despite subseasonal differences, soil water in the root zone was not limiting (based on management allowable depletion) because of optimal irrigation scheduling. Consequently, ETa was similar across corn, soybean, and sorghum with or without volunteer corn irrespective of the irrigation system, implying minimal water competition in crop–volunteer corn mixtures (Figure 3B). These findings are similar to existing research reporting no change in soil water use between weed-free crops and crops infested with different weeds under nonlimiting water environment (Massinga et al. Reference Massinga, Currie and Trooien2003; Young et al. Reference Young, Wyse and Jones1983). Massinga et al. (Reference Massinga, Currie and Trooien2003) noticed similar soil water content (SWC) in the soil profile of corn and corn plus Palmer amaranth (Amaranthus palmeri S. Watson) (0.5, 1, 2, 4, and 8 plants m−1 of row), indicating that competition for water was negated by irrigation to maintain SWC between 80% and 90% of field capacity in 4 site-years of a study in Kansas. Similarly, Mausbach et al. (Reference Mausbach, Irmak, Kukal, Karnik, Sarangi and Jhala2024) observed that 1 A. palmeri plant m−2 did not influence the mean seasonal TSW in corn and soybean under fully irrigated conditions in Nebraska. Young et al. (Reference Young, Wyse and Jones1983) reported that quackgrass [Elymus repens (L.) Gould] interference in soybean did not induce water stress, as SWC and leaf water potential was similar to weed-free soybean under irrigated conditions in a 2-yr study in Minnesota.
Figure 3. The interaction effects of irrigation and crop type on (A) mean soil water depletion (mm) and (B) total seasonal evapotranspiration (mm) at the experimental site near Clay Center, NE, in 2021 and 2022. No VC, plots without volunteer corn; VC, plots with volunteer corn. The error bars represent standard error of the mean estimates. Different alphabetical letters indicate treatment means are significantly different (P ≤ 0.05).
The SWD was slightly greater in soybean and sorghum with volunteer corn than without volunteer corn under SDI but not in CPI (Figure 3A). This is likely due to the nature of irrigation events under SDI, characterized by high frequency, low application depths, and longer duration per irrigation event. Comparing irrigation systems, SDI had lower ETa than CPI in fallow with (306 vs. 524 mm) and without volunteer corn (65 vs. 337 mm) (P < 0.001; Figure 3B). This is because SDI does not cause wetting of canopy and soil surfaces, leading to minimal evaporation during and after irrigation events, unlike CPI (Irmak et al. Reference Irmak, Mohammed and Kranz2019). The ETa is higher by a magnitude of 272 mm in the fallow in CPI compared with SDI, and the difference is reduced to 218 mm in fallow with volunteer corn with lower evaporative surface available for radiative energy to interact with. Water savings through evaporation in SDI are reported to be no more than 9% under row crops in south-central Nebraska, and some of these savings can be compensated via greater transpiration in SDI, lowering the ETa differences between SDI and CPI (Odhiambo and Irmak Reference Odhiambo and Irmak2015). This observation is confirmed by the results of this study, as observed ETa differences in crops between CPI and SDI were statistically similar. CPI had a somewhat higher ETa of 8%, 3% to 5%, and 6% in corn (617 to 623 mm vs. 571 to 578 mm), soybean (623 to 630 mm vs. 597 to 605 mm), and sorghum (617 to 622 mm vs. 583 to 585 mm) with or without volunteer corn than SDI, respectively. The results are comparable to the literature; for example, various researchers reported corn and soybean ETa of 581 mm (Irmak et al. Reference Irmak, Djaman and Rudnick2016) and 591 mm (Kukal and Irmak Reference Kukal and Irmak2020) for fully irrigated conditions under SDI, respectively. Similarly, for CPI, researchers have reported corn and soybean ETa of 612 mm (Mohammed and Irmak Reference Mohammed and Irmak2022a) and 617 to 641 mm (Payero et al. Reference Payero, Melvin and Irmak2005a) at the same or similar research sites in Nebraska, respectively.
Crop Yield and WUE
Crop yield was affected by crop type (P < 0.001; Table 4) but not by irrigation (P = 0.265; Table 4). Volunteer corn interference caused a yield loss of 27% in soybean (4.8 vs. 3.5 Mg ha−1). The observed yield loss in soybean at similar densities of volunteer corn (5 plants m−2) was closer to the 34% reported in the literature (Marquardt et al. Reference Marquardt, Krupke and Johnson2012a). Volunteer corn was taller than soybean canopy, to which observed yield loss could be attributed; for example, it was two to three times taller than soybean at 15 (34 cm vs. 13 cm), 30 (63 cm vs. 21cm), 45 (118 cm vs. 40 cm), and 60 DATr (169 cm vs 67 cm) in 2022. For corn, Piasecki and Rizzardi (Reference Piasecki and Rizzardi2019) reported 17% to 20% yield loss at similar densities of volunteer corn (4 to 5 plants m−2). However, corn yield was not affected due to volunteer corn in this study. This might be because volunteer corn was not early seeded and established in this study, and was suppressed by corn due to late transplanting (Figure 2A and 2C). Marquardt et al. (Reference Marquardt, Terry, Krupke and Johnson2012b) concluded that later-emerging volunteer corn after the V4 growth stage of corn was not sufficiently competitive to cause significant yield loss. Although volunteer corn in this study was not transplanted that late, its transplanting at around 15 DAP of corn did not allow much competition, remaining shaded by corn for most of the season. To the best of our knowledge, there is no study reporting sorghum yield loss from volunteer corn competition, although some reports of volunteer corn control in sorghum have been published recently (Currie and Geier Reference Currie and Geier2022; Currie et al. Reference Currie, Geier and Lancaster2023). Sorghum yield was excluded from the analysis in this study, as CPI (4.0 to 4.5 Mg ha−1) had only about 60% of the yield in SDI (6.3 to 7.4 Mg ha−1), mostly because of an insect pressure that severely damaged sorghum in the CPI field in 2021. Yield loss estimates from this study may not accurately reflect actual yield loss due to volunteer corn infestation in the crop fields, as volunteer corn was transplanted, which is not the case in real-world scenarios. Some volunteer corn plants (<10%) suffered transplant shock, but they were replaced immediately in this study.
Table 4. The effect of crop and irrigation on crop yield averaged across 2021 and 2022 field experiments near Clay Center, NE.
a VC, volunteer corn. Fallow + VC denotes grain yield of volunteer corn.
b Treatment means having the same alphabetical letters within each column do not differ significantly as per Tukey’s method of P-value adjustment.
WUE was affected by crop type (P < 0.001; Figure 4A). WUE of corn (2.46 to 2.56 kg m−3) was greater than that of sorghum (0.87 to 0.98 kg m−3) and soybean (0.59-0.79 kg m−3). However, the differences between crop treatments with and without volunteer corn were similar. The observed corn and soybean WUE values were comparable to those reported in the literature for this environment. Irmak et al. (Reference Irmak, Djaman and Rudnick2016) reported corn WUE of 2.4 kg m−3 pooled across a 4-yr study (2005 to 2008) under SDI at Clay Center, NE. Irmak et al. (Reference Irmak, Specht, Odhiambo, Rees and Cassman2014) reported a WUE of 0.77 to 0.85 kg m−3 of fully irrigated soybean through SDI in a 2-yr study (2007 to 2008) at Clay Center, NE. At the same research site, approximately similar WUE values were reported for fully irrigated corn (2.44 to 2.50 kg m−3; Djaman and Irmak Reference Djaman and Irmak2012) and soybean (0.95 kg m−3; Kukal and Irmak Reference Kukal and Irmak2020) through CPI. Irrigation did not affect WUE (P = 0.118; Figure 4B); however, SDI (1.43 kg m−3) had numerically higher WUE than CPI (1.10 kg m−3). Similarly, Mohammed and Irmak (Reference Mohammed and Irmak2022b) observed slightly higher WUE of fully irrigated corn under SDI (2.88 kg m−3 in 2016; 3.04 kg m−3 in 2017) than CPI (2.71 kg m−3 in 2016; 3.00 kg m−3 in 2017) in south-central Nebraska. This indicates that slightly more water may have been consumed for transpiration rather than evaporation leading to slightly better crop productivity under SDI compared with CPI (Evett et al. Reference Evett, Marek, Colaizzi, Brauer and O’Shaughnessy2019; Howell Reference Howell2001).
Figure 4. Water use efficiency (WUE) as affected by the main effects of (A) crop and (B) irrigation at the experimental site near Clay Center, NE, in 2021 and 2022. No VC, plots without volunteer corn; VC, plots with volunteer corn. The error bars represent standard error of the mean estimates. Different alphabetical letters indicate treatment means are significantly different (P ≤ 0.05).
Interference of volunteer corn depends on several factors, including the crop grown in rotation and the availability of soil moisture. This is the first study to characterize the growth and ETa of volunteer corn in corn, soybean, and sorghum under two irrigation systems (CPI and SDI). Soybean did not compete with volunteer corn, as the plant height, leaf area, and biomass of volunteer corn were no different from in fallow (Figure 2; Table 3). Corn was the most competitive, as volunteer corn had the lowest biomass (6.5 vs. 10.0 g plant−1), leaf area (2,280 vs. 3,335 cm2 plant−1), and plant height (161 vs. 171 cm) compared with the fallow at 30, 45, and 60 DATr, respectively. Sorghum had lower volunteer corn biomass (64.1 vs. 93.8 g plant−1) compared with fallow at 60 DATr. Therefore, crop competitiveness is ranked as corn > sorghum > soybean. This relative competitiveness is likely a consequence of differences in crop canopy characteristics such as plant height, leaf area and distribution, and rooting structure. Volunteer corn interference decreased soybean yield by 27% (Table 4). Volunteer corn caused yield loss and lowered crop WUE (Figure 4) without depleting additional soil water, indicating that it might be competing for factors other than water, such as radiation (Barnes et al. Reference Barnes, Jhala, Knezevic, Sikkema and Lindquist2018; Berger et al. Reference Berger, McDonald and Riha2010, Reference Berger, Ferrell, Rowland and Webster2015). Thus, it will be useful to measure canopy radiation interception, as evidence suggests that crop–weed mixtures may compete for water when their light interception is higher than crop monoculture (Berger et al. Reference Berger, McDonald and Riha2010). Additionally, weed presence near crop plants may downregulate genes related to photosynthetic processes and/or etiolate their stems (Horvath et al. Reference Horvath, Bruggeman, Moriles-Miller, Anderson, Dogramaci, Scheffler, Hernandez, Foley and Clay2018, Reference Horvath, Horvath, Clay, Bruggeman, Anderson, Chao and Yeater2019; Moriles et al. Reference Moriles, Hansen, Horvath, Reicks, Clay and Clay2012), which may influence crop yield and WUE, highlighting the importance of considering these factors in future studies. Weed biomass should be measured at crop harvest to understand the overall interference of crop with weeds; those data were not collected in this study.
Volunteer corn was taller (174 vs. 159 cm) and had greater leaf area (3,665 vs. 2,961 cm−2 plant−1) and biomass (69.2 vs. 49.9 g plant−1) in SDI compared with CPI at 60 DATr (Figure 2; Table 3). These differences likely occurred due to insect infestation in volunteer corn in sorghum plots in 2021 and appeared after irrigation was applied during the crop season. The observations from this study suggest that weed growth may differ across irrigation systems (Supplementary Figure S1). Therefore, the consequences of season-long crop–weed competition may vary with the choice of irrigation system. The SDI system is being considered as an irrigation adaptation in sprinkler-irrigated fields under increased water shortages and pressures in semiarid/arid regions, and crop–weed dynamics are not actively viewed as a criterion. This research underscores the need for crop–weed competition to be considered as an additional criterion for irrigation system upgrades, in addition to traditional factors such as cropping system, irrigation efficiency, investment cost, and field size (Jacques et al. Reference Jacques, Fox and White2018; Lamm et al. Reference Lamm, O’Brien and Rogers2015; O’Brien et al. Reference O’Brien, Rogers, Lamm and Clark1998; Ramaswamy et al. Reference Ramaswamy, Stoecker, Jones, Warren and Taghvaeian2017).
No differences in water use (SWD or ETa) were observed between crop and crop–weed treatments under both irrigation systems, as water was nonlimiting in this study (Figure 3). This suggests that weeds may not compete for water when optimal soil water is maintained either through irrigation (Massinga et al. Reference Massinga, Currie and Trooien2003) or rainfall (Barnes et al. Reference Barnes, Jhala, Knezevic, Sikkema and Lindquist2018; Berger et al. Reference Berger, McDonald and Riha2010). Therefore, a generalized statement that weeds compete for water and induce crop water stress may not be true under all circumstances (Berger et al. Reference Berger, McDonald and Riha2010), especially when managed for full irrigation where irrigation minimizes the water competition among weeds and crops (Massinga et al. Reference Massinga, Currie and Trooien2003). In the future, it will be valuable to study soil moisture dynamics of economically important weeds under low rainfall or water-limited conditions where differences in soil water uptake are more likely to appear, which will improve understanding of the relative competitiveness of weeds to crops (Sadeghi et al. Reference Sadeghi, Starr, Teasdale, Rosecrance and Rowland2007; Singh et al. Reference Singh, Thapa, Kukal, Irmak, Mirsky and Jhala2022b). It is expected that soil water stress will develop under these circumstances, introducing disparities in water availability between crops and weeds. The studies conducted under fully irrigated conditions should be supplemented with no- or deficit-irrigation treatment(s) for relative comparison of the soil water profiles to characterize the intensity of water competition. Further, it will be worth exploring whether weeds induce water stress to crops in their proximity and/or impact their WUE (McKenzie-Gopsill et al. Reference McKenzie-Gopsill, Fillmore and Swanton2020). For this, measurements of crop growth, leaf water potential, stomatal conductance, and other relevant parameters should be recorded multiple times during the growing season. Soil moisture extraction is a function of root characteristics such as density, distribution, growth rate, and structure (Davis et al. Reference Davis, Wiese and Pafford1965; Dunbabin Reference Dunbabin2007; Yu et al. Reference Yu, Zhuang, Nakayama and Jin2007), which were not measured in this study but should be explored in the future to elucidate their role in water competition. Crop–weed competition studies with a holistic approach would help enhance the practical utility of crop–weed competition models for weed management, as accurate estimation of water use and economic evaluation of crop management choices, including irrigation, are essential to make better-informed decisions with these models. Mechanistic crop–weed competition models account for water competition by limiting the potential dry matter assimilation of competing species to available soil moisture (Spitters and Aerts Reference Spitters and Aerts1983) and simulating processes related to soil water balance (Kropff Reference Kropff1993). Robust data on soil moisture dynamics and water use from crop–weed competition studies under diverse environments, including water-limited conditions, can help validate and/or refine the predictive capabilities of the mechanistic models (Singh et al. Reference Singh, Deb, Slaughter, Singh, Ritchie, Guo and Saini2023).
Supplementary material
To view supplementary material for this article, please visit https://doi.org/10.1017/wsc.2024.50
Acknowledgments
The center-pivot and subsurface drip irrigation fields, soil moisture sensors, data loggers, and all other associated components for the field research were made available by the senior author, SI. We are very thankful to research technician and MS student Matthew Drudik for his assistance in the calibration and installation of soil moisture sensors, irrigation scheduling, record-keeping, and fieldwork. We thank Adam Leise, Irvin Schleufer, Jasmine Mausbach, Ramandeep Kaur, Shawn McDonald, Shorooq Al Hikmani, Trey Stephens, and Will Neels for their help in the fieldwork. We thank Ian Rogers for editing the manuscript. We thank Kelsey Karnik for her guidance in the data analysis.
Funding statement
This research received no specific grant from any funding agency or the commercial or not-for-profit sectors.
Competing interests
The authors declare no conflicts of interest.
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