Corn is the most cultivated crop in the United States, with an area of 32 million ha harvested for grain in 2022 (USDA-NASS 2023). The Midwest is the top-producing region, representing more than 85% of the harvested area and more than 88% of the corn produced in 2022 in the United States (USDA-NASS 2023). Weed management is a major challenge in corn production. U.S. corn growers rely primarily on herbicides and tillage for weed management (Dong et al. Reference Dong, Mitchell, Davis and Recker2017; Grint et al. Reference Grint, Arneson, Arriaga, DeWerff, Oliveira, Smith, Stoltenberg and Werle2022a). Herbicides are the most extensively used pesticide on corn crops, applied to >95% of planted corn hectares in the United States in 2021 (USDA-NASS 2022). The dependence on chemical weed control has led to the widespread occurrence of herbicide resistance, mainly to postemergence (POST) herbicides (Heap Reference Heap2022, Jha et al. Reference Jha, Kumar, Godara and Chauhan2017). An effective strategy to minimize the overreliance on POST herbicide applications is to apply soil residual preemergence (PRE) herbicides for early season weed control (Knezevic et al. Reference Knezevic, Pavlovic, Osipitan, Barnes, Beiermann, Oliveira, Lawrence, Scott and Jhala2019). The use of herbicides with effective soil residual activity applied PRE provides an extended period of early season weed control, protecting crop yields during their most vulnerable developmental stages from weed interference (Grint et al. Reference Grint, Proctor, DeWerff, Smith, Arneson, Arriaga, Stoltenberg and Werle2022b; Oliveira et al. Reference Oliveira, Feist, Eskelsen, Scott and Knezevic2017a). PRE herbicides can reduce the weed density and delay the time to POST applications, thus lowering the selection pressure for further resistance to POST herbicides (Faleco et al. Reference Faleco, Oliveira, Arneson, Renz, Stoltenberg and Werle2022a; Oliveira et al. Reference Oliveira, Jhala, Gaines, Irmak, Amundsen, Scott and Knezevic2017b). Including PRE herbicides as part of an integrated weed management program brings more diversity to effective sites of action (SOAs) and opportunities for broad-spectrum chemical weed control (Norsworthy et al. Reference Norsworthy, Ward, Shaw, Llewellyn, Nichols, Webster, Bradley, Frisvold, Powles, Burgos and Witt2012; Somerville et al. Reference Somerville, Powles, Walsh and Renton2017).
The residual weed control efficacy of a PRE herbicide depends on several variables, including environmental conditions (i.e., pattern and amount of rainfall following application, temperature), physicochemical properties of the herbicide (i.e., water solubility, vapor pressure, octanol-water coefficient, acid ionization constant), physicochemical properties of the soil (i.e., pH, organic matter, texture), and soil seedbank weed community composition (Varanasi et al. Reference Varanasi, Prasad and Jugulam2016; Zhao et al. Reference Zhao, Zuo, Li, Guo, Liu and Wang2017). Effective early season weed control can be achieved with PRE herbicides when these variables are favorable for a properly selected chemical program. However, when some of these conditions are not favorable, failure in early season weed control may occur (Hay et al. Reference Hay, Shoup and Peterson2018; Urach Ferreira et al. Reference Urach Ferreira, Ferguson, Reynolds, Kruger and Irby2020). For example, adequate rainfall following application increases the probability of effective waterhemp and common lambsquarters control with PRE herbicides (Landau et al. Reference Landau, Hager, Tranel, Davis, Martin and Williams2021a), which are two of the most troublesome weeds in Wisconsin corn production (Werle and Oliveira Reference Werle and Oliveira2018). Low residual weed control has been commonly reported under dry weather conditions due to the lack of rainfall to dissolve the herbicide into a soil solution (Bell et al. Reference Bell, Norsworthy, Scott and Popp2015; Jursik et al. Reference Jursik, Soukup, Holec, Andr and Hamouzová2015; Priess et al. Reference Priess, Norsworthy, Roberts and Gbur2020).
A practice recommended to lower the risk of early season weed control failure and herbicide resistance is the use of PRE herbicide premixes containing multiple effective SOAs (Striegel et al. Reference Striegel, DeWerff, Arneson, Oliveira and Werle2021a). PRE herbicides with multiple SOAs can expand the spectrum of weed control compared to a single SOA herbicide (Carneiro et al. Reference Carneiro, de Freitas Souza, Lins, das Chagas, Silva, da Silva Teófilo, Pavão, Grangeiro and Silva2020). Besides providing broader spectrum control, herbicides with multiple SOAs that simultaneously target the same weed spectrum reduce the selection intensity for the evolution of herbicide-resistant weed biotypes (Norsworthy et al. Reference Norsworthy, Ward, Shaw, Llewellyn, Nichols, Webster, Bradley, Frisvold, Powles, Burgos and Witt2012). Their effectiveness is also improved when the active ingredients have similar soil residual activity (Beckie and Harker Reference Beckie and Harker2017; Palma-Bautista et al. Reference Palma-Bautista, Cruz-Hipólito, Alcantara-de La Cruz, Vázquez-García, Yanniccari and De Prado2021). Jha et al. (Reference Jha, Kumar, Garcia and Reichard2015) reported high control (≥72%) of kochia (Kochia scoparia L.), common lambsquarters, and wild buckwheat (Polygonum convolvulus L.) with saflufenacil + dimethenamid-P and acetochlor + pendimethalin at 63 d after treatment (DAT) compared to these herbicides applied alone (≤47%). Other studies also demonstrated high efficacy of herbicide premixes (>90%) in controlling weeds in corn-soybean cropping systems (Oliveira et al. Reference Oliveira, Jhala, Gaines, Irmak, Amundsen, Scott and Knezevic2017b; Sarangi and Jhala Reference Sarangi and Jhala2018; Striegel et al. Reference Striegel, DeWerff, Arneson, Oliveira and Werle2021a).
PRE herbicide mixes tend to be more effective than the same active ingredients applied solo when weather conditions are not favorable, but the extension of this effect may vary according to water solubility and soil sorption of each herbicide in the premix (Janak and Grichar Reference Janak and Grichar2016; Landau et al. Reference Landau, Hager, Tranel, Davis, Martin and Williams2021a; Stewart et al. Reference Stewart, Nurse, Hamill and Sikkema2010). It is well known that each herbicide has a unique behavior in the soil depending on edaphoclimatic conditions. For instance, clopyralid and dicamba dissipate faster in moist soils with warm temperatures; in contrast, under dry soils and cold temperatures, their residual activity can persist longer (Cahoon et al. Reference Cahoon, York, Jordan, Everman, Seagroves, Culpepper and Eure2015a; Pik et al. Reference Pik, Peake, Strosher and Hodgson1977; Seefeldt et al. Reference Seefeldt, Boydston and Kaspari2014). As the weather becomes more variable across the corn-producing regions of the United States (Landau et al. Reference Landau, Hager and Williams2021b), PRE herbicide premixes with multiple SOAs may play a significant role for early season weed control, mainly for troublesome weeds with an extended emergence window such as giant ragweed and waterhemp (Striegel et al. Reference Striegel, Oliveira, DeWerff, Stoltenberg, Conley and Werle2021b). In this context, PRE herbicide premixes that contain multiple SOAs may provide more consistent early season weed control due to the widespread occurrence of herbicide resistance across the United States coupled with the more variable and extreme weather conditions. In this study, we evaluated a comprehensive list of labeled corn residual PRE herbicides (18 products containing one or multiple SOAs) including commonly used PRE herbicides in Wisconsin corn production, a novel premix herbicide (clopyralid + pyroxasulfone + mesotrione), and a herbicide premix (saflufenacil + dimethenamid-P) not commonly used on corn. Results from this study can support corn growers and those who influence their weed management decisions by providing them with a selection of more effective PRE herbicides based on key weed species present in their fields.
Materials and Methods
Field experiments were conducted in 2021 and 2022 at the Rock County Farm, in Janesville, Wisconsin (42.43°N, 89.01°W), and at the University of Wisconsin–Madison Lancaster Agricultural Research Station, near Lancaster, Wisconsin (42.83°N, 90.76°W), to evaluate the residual weed control efficacy of solo (single SOA) and premix (commercial products with two or more SOAs) herbicides applied PRE to conventional tillage corn. PRE herbicide rates used herein are commonly recommended by the industry and adopted by growers in Wisconsin (DeWerff et al. Reference DeWerff, Arneson and Werle2022; Table 1). The rates of the single active ingredient herbicide treatments did not necessarily match their rates when used as a premix (Table 1).
a The number in parentheses indicates the Group number as categorized by the Weed Science Society of America (WSSA).
c Located in St. Louis, MO.
d Located in Greensboro, NC.
e Located in Durham, NC.
f Located in Indianapolis, IN.
g Located in Walnut Creek, CA.
The experimental areas were managed in a soybean-corn rotation; thus, soybean was grown at all experimental sites in the previous growing season before the experiment was established. Before corn planting, the experimental area was tilled using a field cultivator. Corn was planted 5 cm deep in 76-cm row spacing at all experimental sites. Soil at the Janesville site was a Plano silt loam, and at Lancaster it was a Fayette silt loam. Soil properties, corn hybrid, seeding rate, and planting and herbicide application dates for each site-year are described in Table 2.
a Janesville-2021was fertilized with 200 kg ha−1 of nitrogen (46-0-0); Lancaster-2021: 128 kg ha−1 of nitrogen (46-0-0); 2022-Janesvile: 112 kg ha−1 of nitrogen (32-0-0) and 32 kg ha−1 of sulfur in the form of ammonium thiosulfate (12-0-0-26S); 2022-Lancaster: 55 kg ha−1 of phosphorus + 112 kg ha−1 of potassium nitrate (4-19-38) applied early spring, and 160 kg ha−1 of nitrogen (46-0-0).
b Brevant®, Indianapolis, IN 46268.
c Syngenta®, Greensboro, NC 27419.
d Pioneer®, Johnston, IA 50131.
The experiment was conducted as a randomized complete block design with four replications. The treatments consisted of 18 PRE herbicides plus a nontreated control (NTC; Table 1). The experimental units were 3 m wide (four corn rows) × 9 m long. Herbicides were applied within a day after corn planting (Table 2) using a CO2-pressurized backpack sprayer equipped with six TTI110015 flat-fan nozzles (Teejet, Springfield, IL) spaced 51 cm apart at a boom height of 50 cm from the soil surface. The sprayer was calibrated to deliver 140 L ha−1 of spray solution at 240 kPa at a speed of 4.8 km h−1.
Daily mean air temperature and total cumulative precipitation at each site-year were obtained from onsite weather stations (WatchDog 2700; Spectrum Technologies®, Aurora, IL; Figure 1). The density of the predominant weed species at each site-year was recorded from the NTC experimental units at 6 wk after treatment (WAT) for Janesville-2021, Lancaster-2021, and Lancaster-2022, and at 4 WAT for Janesville-2022. Weed control and weed aboveground biomass for Janesville-2021, Lancaster-2021, and Lancaster-2022 were assessed at 6 WAT. For Janesville-2022, visual weed control and aboveground weed biomass were assessed at 4 WAT because of the high population of giant ragweed (Ambrosia trifida L.) and its rapid growth. All response variables were assessed between the two center corn rows of each experimental unit. Weed control in each experimental unit was estimated using a visual scale (0 = no control, 100% = complete control). Weed aboveground biomass was collected using two quadrats (0.25 m−2) randomly placed between the center two rows of each experimental unit. Weeds were enumerated and harvested by species. Weed biomass for each species from both quadrats within an experimental unit was combined into a single paper bag. Weed biomass was dried at 60 C until a constant weight was achieved and then weighed. Weed biomass data were reported as percentage biomass reduction compared to the NTC:
where NTC is the mean weed biomass (in grams) of the NTC across replications within a specific site-year, and T is the weed biomass (in grams) of the experimental unit of interest.
All response variables (visual weed control [%] and biomass reduction [%]) were analyzed using R statistical software (version 4.2.1; R Core Team, 2022). A generalized linear mixed model with a beta distribution and logit family (glmmTMB package; Brooks et al. Reference Brooks, Kristensen, Van Benthem, Magnusson, Berg, Nielsen, Skaug, Machler and Bolker2017) was used to analyze both response variables. PRE herbicide efficacy is known to vary by year and location because of weather and soil conditions (Glaspie et al. Reference Glaspie, Jones, Penner, Pawlak and Everman2021; Landau et al. Reference Landau, Hager, Tranel, Davis, Martin and Williams2021a) and soil seedbank weed community composition (Striegel et al. Reference Striegel, DeWerff, Arneson, Oliveira and Werle2021a). Therefore, data were analyzed separately by weed species and site-year. Herbicide treatments were considered as fixed effects, while replications nested within site-year were treated as a random effect. ANOVA was performed for giant ragweed control and biomass reduction for Janesville-2021 and Janesville-2022. For Lancaster-2021, ANOVA was performed for waterhemp and common lambsquarters, whereas giant foxtail control and biomass reduction were also analyzed for Lancaster-2022 in addition to waterhemp and common lambsquarters. Evaluation of homogeneity of residual variance was carried out using Levene’s test (the car package; Fox and Weisberg Reference Fox and Weisberg2019). When ANOVA (the glmmTMB package) indicated a significant PRE herbicide treatment effect, means were compared using Fisher’s LSD test (P ≤ 0.05; emmeans package; Lenth Reference Lenth2022).
Pearson’s correlation was performed (the cor.test function) to estimate the linear correlation between visual weed control and weed biomass reduction. A linear mixed model was also performed to analyze weed control (%) according to the number of herbicide SOAs for each weed species and for weed species combined (combined across species, site-years pooled together). The number of herbicide SOA groups in each treatment (one, two, and three SOAs) were considered as fixed effects, and replications nested within site-years were included as a random effect. If ANOVA indicated a significant effect of number of PRE herbicide SOA groups (P ≤ 0.05), means were compared using Fisher’s protected LSD test.
Results and Discussion
Daily precipitation varied across site-years (Figure 1). At the Janesville-2021 site, the first rain occurred 6 DAT (30 mm), whereas 40 mm of rain fell within 15 DAT. At Janesville-2022, the first rain occurred 7 DAT (9 mm), accumulating 21 mm within 15 DAT. The average air temperature in the first week after treatment was lower in 2021 (15 C) compared with 2022 (22 C). At Lancaster-2021, the first rain occurred 1 DAT (2 mm), whereas 32 mm accumulated within 7 d and 35 mm within 15 DAT. At Lancaster-2022, the first rain occurred within 1 d (3 mm), accumulating 6 mm within 7 d and 42 mm within 15 DAT. The average air temperature in the first week after treatment was 15 C in 2021 and 18 C in 2022.
Weed Species Composition at Each Site-year
Giant ragweed was the predominant weed species observed at the Janesville location in both years (24 ± 2 plants m−2, average ± standard error from NTC, in 2021 [6 WAT] and 104 ± 4 plants m−2 in 2022 [4 WAT]). At the Lancaster location, common lambsquarters (109 ± 24 plants m−2) and waterhemp (41 ± 13 plants m−2) were the predominant weed species in 2021; and waterhemp (100 ± 18 plants m−2), common lambsquarters (37 ± 12 plants m−2), and giant foxtail (27 ± 9 plants m−2) were predominant in 2022. The weed species present at these site-years comprise some of the most common weeds in Wisconsin corn production (Werle and Oliveira Reference Werle and Oliveira2018).
Giant Ragweed Control
At the Janesville-2021 site, the PRE herbicide treatment effect was significant for giant ragweed control (P < 0.01) and biomass reduction (P < 0.01), and efficacy across treatments was low at 6 WAT (<75% of control; Figure 2). Giant ragweed control was higher with certain herbicide premixes containing two or more SOAs compared with herbicide treatments with a single SOA (Figures 2 and 3). For instance, premixes containing mesotrione (S-metolachlor + bicyclopyrone + mesotrione, atrazine + S-metolachlor + bicyclopyrone + mesotrione, and clopyralid + acetochlor + mesotrione) provided ≥72% control and ≥60% biomass reduction of giant ragweed (Figure 2). These premixes improved giant ragweed control and biomass reduction compared with acetochlor (58% and 27%), or S-metolachlor (8% and 3%; Figure 2), which have a single active ingredient. Acetochlor or S-metolachlor in premixes with atrazine (atrazine + acetochlor and atrazine + S-metolachlor) provided similar giant ragweed control (<50%) when compared to each active ingredient sprayed separately. The thiencarbazone-methyl + isoxaflutole premix also increased giant ragweed control (40%) compared to isoxaflutole applied alone (26%).
At Janesville-2022, the PRE herbicide treatment effect was significant for control (P < 0.01) and biomass reduction (P < 0.01), but none of the treatments provided ≥70% giant ragweed control and biomass reduction 4 WAT. Similar to Janesville-2021 results, the premix S-metolachlor + bicyclopyrone + mesotrione provided greater giant ragweed control (53%) and biomass reduction (56%) compared to S-metolachlor applied alone (<20%; Figure 2). The other herbicide premixes provided similar giant ragweed control compared to each active ingredient applied separately. Surprisingly, dicamba alone provided the greatest level of giant ragweed control (≥60%; Figure 2) compared to the NTC.
The relatively high level of giant ragweed control observed in 2021 for the PRE herbicide premixes (S-metolachlor + bicyclopyrone + mesotrione, atrazine + S-metolachlor + bicyclopyrone + mesotrione, and clopyralid + acetochlor + mesotrione) compared with acetochlor or S-metolachlor, which have a single active ingredient, may be a result of the different SOA combinations in these mixtures. These different SOAs can complement each other under a range of environmental conditions, thus providing more consistent weed control (Barbieri et al. Reference Barbieri, Young, Dayan, Streibig, Takano, Merotto Junior and Avila2022; Bollman et al. Reference Bollman, Kells and Penner2006). Striegel et al. (Reference Striegel, DeWerff, Arneson, Oliveira and Werle2021a) reported high (95%) giant ragweed control at this experimental location (Rock County Farm, Janesville, Wisconsin) using herbicide premixes that contain mesotrione (clopyralid + acetochlor + mesotrione and S-metolachlor + bicyclopyrone + mesotrione).
The large giant ragweed population and lack of timely rainfall for herbicide activation in the soil might be two of the factors that led to poor giant ragweed control (≤70%) at the Janesville location in 2022. The lower amount of accumulated rain during the first (9 mm) and second weeks (21 mm) after PRE herbicide application was probably not adequate to dissolve the herbicide into the soil-water solution that provides weed control (Figure 1). In 2022, the amount of rain between application and 15 DAT was only half the amount that fell in 2021 (21 mm vs. 40 mm; Figure 1). According to Landau et al. (Reference Landau, Hager, Tranel, Davis, Martin and Williams2021a), 50 to 100 mm of total rain in the first 15 d, depending on the herbicide and weed species, is typically required to prevent losses in control efficacy due to poor activation of PRE herbicides.
The greater control of giant ragweed in 2022 by dicamba confirms the extended residual activity of this herbicide when rain is limited (21 mm within 15 DAT; Figure 1). This would be associated with a reduction in microbial degradation under dry conditions and reduced leaching, which means that more dicamba is available to control sensitive broadleaf weeds such as giant ragweed (Cahoon et al. Reference Cahoon, York, Jordan, Everman, Seagroves, Braswell and Jennings2015b). Dicamba is highly soluble in water (4,500 mg L−1 at 25 C), thus the little rain that fell in 2022 can explain the greater dicamba residual activity 4 WAT (Shaner Reference Shaner2014). Although dicamba PRE activity did not result in giant ragweed control >70% in this study, the residual activity of dicamba appears to improve early season giant ragweed control in dry spring seasons. Mundt et al. (Reference Mundt, Carbonari, Moisinho, de Brito, Picoli Junior, Ovejero and Velini2022) also observed that residual weed control is extended if the rainfall accumulation is not enough to leach dicamba molecules through the crop residue and soil profile.
Another reason for reduced giant ragweed control in 2022 compared with 2021 was the high degree of weed seedling emergence (24 ± 2 plants m−2 in 2021 [6 WAT] compared with 104 ± 4 plants m−2 in 2022). A previous study also reported low giant ragweed control by PRE herbicides due to the high giant ragweed seedling density at the Janesville site in 2018 (Striegel et al. Reference Striegel, DeWerff, Arneson, Oliveira and Werle2021a). No cases of giant ragweed resistance have been documented in Wisconsin for the PRE herbicides tested in this study (Heap Reference Heap2022).
At the Lancaster-2021 site, the PRE herbicide treatment effect was significant for control (P < 0.01) and biomass reduction (P < 0.01); most PRE herbicides provided ≥90% waterhemp control and biomass reduction 6 WAT, other than atrazine, dicamba, and flumetsulam + clopyralid (<45%; Figure 4). At the Lancaster-2022 site, the PRE herbicide treatment effect was significant (P < 0.01), and the herbicides isoxaflutole, dicamba, atrazine, and simazine (all with a single SOA) and the premix flumetsulam + clopyralid (all with two SOAs) provided poor waterhemp control (≤70% control and biomass reduction; Figure 4). Atrazine + acetochlor, atrazine + S-metolachlor, and atrazine + S-metolachlor + bicyclopyrone + mesotrione premixes controlled waterhemp by 98%, 92%, and 80%, respectively, and reduced waterhemp biomass by 96%, 84%, and 87%, respectively, compared to atrazine alone (66% of control and 56% of biomass reduction) in 2022; however, waterhemp control and biomass reduction were similar to that achieved with acetochlor or S-metolachlor applied alone (Figure 4). Herbicide premixes with more than one SOA provided relatively better waterhemp control than herbicides with a single SOA (Figure 3).
The low degree of waterhemp control provided by the PRE herbicide premix flumetsulam + clopyralid and atrazine may be related to waterhemp resistance to herbicides that inhibit acetolactate synthase (ALS) (Faleco et al. Reference Faleco, Oliveira, Arneson, Renz, Stoltenberg and Werle2022b) and photosystem II (PS II), respectively (Faleco et al. Reference Faleco, Oliveira, Arneson, Renz, Stoltenberg and Werle2022a, Reference Faleco, Oliveira, Arneson, Renz, Stoltenberg and Werle2022b). ALS- and PS II-resistant waterhemp has been widely reported across the midwestern United States (Evans et al. Reference Evans, Strom, Riechers, Davis, Tranel and Hager2019; Heap Reference Heap2022; Vennapusa et al. Reference Vennapusa, Faleco, Vieira, Samuelson, Kruger, Werle and Jugulam2018). The ineffective waterhemp control provided by atrazine may also be a result of reduced residual activity caused by repeated use of atrazine over the years (Mueller et al Reference Mueller, Parker, Steckel, Clay, Owen, Curran, Currie, Scott, Sprague, Stephenson and Miller2017). According to previous studies, atrazine microbial degradation is enhanced in soils with repeated atrazine use compared with soils that have not been treated with the herbicide (Mueller et al Reference Mueller, Parker, Steckel, Clay, Owen, Curran, Currie, Scott, Sprague, Stephenson and Miller2017; Shaner and Henry Reference Shaner and Henry2007).
Reduced residual waterhemp control provided by simazine in 2022 may be due to the rapid dissipation of this herbicide. Although simazine is considered moderately persistent in soil with an average half-life of 60 d (Shaner Reference Shaner2014), persistence is affected by edaphoclimatic conditions and history of use with a wide half-life range (16 to 186 d). Abit et al. (Reference Abit, Rainbolt, Krutz, Shaner and Hanson2012) observed a range of simazine half-life of 21 to 158 d in California vineyards, and the residual weed control was reduced in the site-years where simazine had dissipated more quickly due to rapid microbial degradation.
The effective control of waterhemp in the premixes containing atrazine may be attributed to the shared active ingredients mesotrione (Group 27), acetochlor (Group 15), and S-metolachlor (Group 15), which are recommended for small-seeded broadleaf control (DeWerf et al. Reference Dewerf, Bick, Liesch, Nice, Renz, Smith and Werle2023). Therefore, despite considered the most troublesome weed in Wisconsin cropping systems and across U.S. corn production (Van Wychen Reference Van Wychen2020; Werle and Oliveira Reference Werle and Oliveira2018), multiple effective PRE herbicide options exist for waterhemp management.
Common Lambsquarters Control
At the Lancaster-2021 site, the PRE herbicide treatment effect was significant for control (P < 0.01) and biomass reduction (P < 0.01), and most PRE herbicides provided ≥90% control of common lambsquarters at 6 WAT. Dicamba provided the least control of common lambsquarters (≤64%). Acetochlor, dicamba, and saflufenacil + dimethenamid-P provided low biomass reduction (≤66%), and the remaining treatments resulted in ≥87% biomass reduction (Figure 5). In 2022, the PRE herbicide treatment effect was significant for control and biomass (P < 0.01) reduction (P < 0.01); acetochlor, saflufenacil + dimethenamid-P, isoxaflutole, and S-metolachlor resulted in ≤77% of common lambsquarters control and the remaining treatments provided effective control (≥90%). Isoxaflutole, dicamba, acetochlor, S-metolachlor, and saflufenacil + dimethenamid-P resulted in the lowest common lambsquarters biomass reduction (≤68%; Figure 5). The premixes atrazine + acetochlor, atrazine + S-metolachlor, atrazine + S-metolachlor + bicyclopyrone + mesotrione, clopyralid + acetochlor + mesotrione, S-metolachlor + bicyclopyrone + mesotrione, and flumetsulam + clopyralid + acetochlor controlled common lambsquarters to a similar degree when atrazine was applied alone, but control was greater than that provided by acetochlor or S-metolachlor (Figure 5). A similar trend was reported by Jha et al. (Reference Jha, Kumar, Garcia and Reichard2015) who noted that adding pendimethalin to acetochlor improved residual control of common lambsquarters by 40% and 36% at 21 and 35 DAT, respectively.
For the current study, the premixes, except saflufenacil + dimethenamid-P, resulted in effective control and biomass reduction of common lambsquarters (≥90%; Figures 3 and 5), similar to atrazine, simazine, and mesotrione when applied alone. Although ranked in the top five most problematic weeds in U.S. corn production (Van Wychen Reference Van Wychen2020), results of this study demonstrate that several PRE herbicides can be effective for common lambsquarters control.
Giant Foxtail Control
Giant foxtail control data were collected at the Lancaster-2022 site only (this species was not present at the Lancaster-2021 field study location). The PRE herbicide treatment effect was significant for control (P < 0.01) and biomass reduction (P < 0.01), and only atrazine + acetochlor resulted in >90% control of giant foxtail 6 WAT (Figure 6). The premixes performed better than the herbicides with a single SOA (Figures 3 and 6), except for acetochlor (≥87%) and S-metolachlor (≥74%), which provided an effective level of giant foxtail control, and the premix flumetsulam + clopyralid, which provided a low level of giant foxtail control (≤68%; Figure 6). The biomass reduction followed a similar trend: the PRE herbicides with a single active ingredient (simazine, isoxaflutole, dicamba, mesotrione, and atrazine) resulted in low levels of giant foxtail biomass reduction (≤48%), except S-metolachlor and acetochlor (≥75%), which both provided an effective reduction in biomass, similar to that of the premixes (Figure 6).
The premix flumetsulam + clopyralid and atrazine + S-metolachlor + bicyclopyrone + mesotrione provided low giant foxtail biomass reduction (≤42%). The low biomass reduction by atrazine + S-metolachlor + bicyclopyrone + mesotrione suggests that not all herbicide premixes with multiple SOAs may provide effective weed control. The lower rate of S-metolachlor applied in this premix (1,498 g ai ha−1) compared to S-metolachlor alone (1,791 g ai ha−1) may have contributed to the lower giant foxtail biomass reduction in the premix treatment. Thus, it is important to consider the application rate of each active ingredient in a premix and how that compares to the same herbicide applied alone. Besides containing multiple SOAs at appropriate rates, premixes or herbicide mixtures should contain active ingredients that have similar efficacy and persistence in soil to act simultaneously on the same spectrum of weeds (Norsworthy et al. Reference Norsworthy, Ward, Shaw, Llewellyn, Nichols, Webster, Bradley, Frisvold, Powles, Burgos and Witt2012).
Corroborating the visual control results, acetochlor and atrazine + acetochlor provided the greatest reduction in giant foxtail biomass (≥90%; Figure 6). The high degree (≥87%) of giant foxtail control with acetochlor, atrazine + acetochlor, and the relatively effective (≥70%) control with the other acetochlor premixes, S-metolachlor alone, and S-metolachlor premixes might be due to the action of herbicides that inhibit very-long-chain fatty acids (e.g., acetochlor or S-metolachlor) since the premixes did not provide a reduction in giant foxtail biomass that was different from that provided by acetochlor or S-metolachlor applied alone.
A strong positive correlation was detected between overall visual weed control and biomass reduction (R = 0.88; P <0.001; Figure 7). Despite the potential subjectivity of visual weed control ratings, the strong correlation detected herein indicates that such assessments can be a reliable assessment of weed control efficacy. Visual weed control and weed biomass reduction are important measurements in determining PRE herbicide efficacy, but often researchers will collect only visual weed control ratings. According to our results, high-quality visual weed control data can be used as indicators of PRE herbicide efficacy when biomass data are not available. In general, less work is required to collect visual weed control data, which allows for a rapid quantitative evaluation of herbicide efficacy. Despite that, biomass data are commonly required in the weed science literature to support weed control results and can be used to estimate weed seed production if such correlations (biomass and seed production) are available in the literature (Chauhan and Johnson Reference Chauhan and Johnson2010; Schwartz et al. Reference Schwartz, Norsworthy, Young, Bradley, Kruger, Davis, Steckel and Walsh2016; Wilson et al. Reference Wilson, Wright, Brain, Clements and Stephens1995).
Weed Control by the Number of Active Ingredients
The PRE herbicide comparison by the number of SOAs concluded that PRE herbicide premixes (two and three SOAs) tended to result in greater control of giant ragweed, waterhemp, common lambsquarters, and giant foxtail than herbicides with a single SOA (Figure 3). The overall weed control across site-years followed the same trend, wherein PRE herbicides with two (78%) and three (81%) SOAs provided greater weed control than PRE herbicides with a single SOA (68%) (Figure 3).
Supporting our weed control and biomass reduction findings, these results indicate that at least two SOAs are needed in a premix to achieve greater weed control with PRE herbicides; however, more SOAs may not further improve the weed control. In this study there was no difference in weed control efficacy between the premixes with two and three SOAs (Figure 3). Nevertheless, the strategic selection of premixes with at least two SOAs considering the weed seed bank community composition and the predicted environmental conditions following application can improve the diversity of the weed management program and may delay the evolution of resistance because of reduced selection pressure on single PRE and POST herbicides (Norsworthy et al. Reference Norsworthy, Ward, Shaw, Llewellyn, Nichols, Webster, Bradley, Frisvold, Powles, Burgos and Witt2012). With limited rainfall as occurred at the Janesville-2022 experimental site, the PRE herbicide premixes still performed better than most PRE herbicides with a single SOA (Figures 1 and 2).
Considering that more variable weather conditions and future weed resistance problems are likely to occur across the Midwest United States (Landau et al. Reference Landau, Hager, Tranel, Davis, Martin and Williams2021a; Westwood et al. Reference Westwood, Charudattan, Duke, Fennimore, Marrone, Slaughter, Swanton and Zollinger2018), the use of strategically selected herbicide premixes should become a standard management practice for more effective early season weed control in corn crops. Premixes add to the diversity of SOAs and contribute to a more sustainable and effective corn PRE herbicide program by offering a broader spectrum weed control and reducing the reliance on single PRE and POST herbicides.
The results of this study provide insight into PRE herbicide options to improve early season weed control in conventional tillage corn crops. PRE herbicide premixes containing at least two SOAs provided more reliable and improved weed control compared to herbicides with a single SOA, but dominant weed species and rainfall amount and pattern are still essential factors to be considered when selecting a PRE herbicide premix. These results will support Wisconsin farmers and others who must select or recommend PRE herbicides for weed control in corn production while also considering the soil seedbank weed community composition and anticipated environmental conditions.
We thank the University of Wisconsin–Madison Lancaster Agricultural Research Station and the Rock County Farm staff, and the University of Wisconsin–Madison Cropping Systems Weed Science members for their technical assistance in conducting this research. Tatiane Severo Silva’s graduate research assistantship was partially sponsored by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES), Finance Code 001. The authors acknowledge Drs. Alexandre Tonon Rosa (Bayer Crop Science), Luis Antonio de Avila (Mississippi State University), Ahmadreza Mobli (University of Wisconsin–Madison), Christopher A. Proctor (University of Nebraska–Lincoln), and John M. Wallace (Pennsylvania State University) for their contributions to the manuscript. No conflicts of interest have been declared. This research received no specific grant from any funding agency, commercial, or not-for-profit sectors.