Weed Science
www.cambridge.org/wsc
Dicamba air concentrations in eastern Arkansas
and impact on soybean
Maria Leticia Zaccaro-Gruener1 , Jason K. Norsworthy2 , Chad B. Brabham3 ,
Research Article
Cite this article: Zaccaro-Gruener ML,
Norsworthy JK, Brabham CB, Barber LT,
Roberts TL, Mauromoustakos A, Mueller TC
(2023) Dicamba air concentrations in eastern
Arkansas and impact on soybean. Weed Sci. 71:
265–277. doi: 10.1017/wsc.2023.22
Received: 23 November 2022
Revised: 31 March 2023
Accepted: 12 April 2023
First published online: 28 April 2023
Associate Editor:
Prashant Jha, Iowa State University
Keywords:
Air sampling; environments; off-target
movement; symptomology; volatility
Corresponding author:
Maria Leticia Zaccaro-Gruener, 1354 W.
Altheimer Drive, Fayetteville, AR 72704.
(Email: mzaccaro@uark.edu)
L. Tom Barber4 , Trenton L. Roberts5 , Andy Mauromoustakos6
and
7
Thomas C. Mueller
1
Graduate Research Assistant, University of Arkansas System Division of Agriculture, Fayetteville, AR, USA;
Distinguished Professor and Elms Farming Chair of Weed Science, University of Arkansas System Division of
Agriculture, Fayetteville, AR, USA; 3Former Postdoctoral Associate, University of Arkansas System Division of
Agriculture, Fayetteville, AR, USA; 4Professor and Extension Weed Scientist, University of Arkansas System
Division of Agriculture, Lonoke, AR, USA; 5Professor of Soil Fertility/Soil Testing, University of Arkansas System
Division of Agriculture, Fayetteville, AR, USA; 6Professor, Agricultural Statistics Laboratory, University of
Arkansas, Fayetteville, AR, USA and 7Professor, Department of Plant Sciences, University of Tennessee,
Knoxville, TN, USA
2
Abstract
Damage to non–dicamba resistant (non-DR) soybean [Glycine max (L.) Merr.] has been
frequent in geographies where dicamba-resistant (DR) soybean and cotton (Gossypium
hirsutum L.) have been grown and sprayed with the herbicide in recent years. Off-target movement field trials were conducted in northwest Arkansas to determine the relationship between
dicamba concentration in the air and the extent of symptomology on non-DR soybean.
Additionally, the frequency and concentration of dicamba in air samples at two locations in
eastern Arkansas and environmental conditions that impacted the detection of the herbicide
in air samples were evaluated. Treatment applications included dicamba at 560 g ae ha−1
(1X rate), glyphosate at 860 g ae ha−1, and particle drift retardant at 1% v/v applied to
0.37-ha fields with varying degrees of vegetation. The relationship between dicamba concentration in air samples and non-DR soybean response to the herbicide was more predictive with
visible injury (generalized R2 = 0.82) than height reduction (generalized R2 = 0.43). The
predicted dicamba air concentration resulting in 10% injury to soybean was 1.60 ng m−3 d−1
for a single exposure. The predicted concentration from a single exposure to dicamba resulting
in a 10% height reduction was 3.78 ng m−3 d−1. Dicamba was frequently detected in eastern
Arkansas, and daily detections above 1.60 ng m−3 occurred 17 times in the period sampled.
The maximum concentration of dicamba recorded was 7.96 ng m−3 d−1, while dicamba concentrations at Marianna and Keiser, AR, were ≥1 ng m−3 d−1 in six samples collected in 2020 and
22 samples in 2021. Dicamba was detected consistently in air samples collected, indicating high
usage in the region and the potential for soybean damage over an extended period. More
research is needed to quantify the plant absorption rate of volatile dicamba and to evaluate
the impact of multiple exposures of gaseous dicamba on non-targeted plant species.
Introduction
© The Author(s), 2023. Published by Cambridge
University Press on behalf of the Weed Science
Society of America. This is an Open Access
article, distributed under the terms of the
Creative Commons Attribution licence (http://
creativecommons.org/licenses/by/4.0/), which
permits unrestricted re-use, distribution and
reproduction, provided the original article is
properly cited.
Dicamba-resistant (DR) cotton (Gossypium hirsutum L.) and soybean [Glycine max (L.) Merr.]
were rapidly adopted following the commercial introduction of the technology in 2015 and
2016, respectively (Werle et al. 2018). In 2017, new dicamba formulations were approved by
the U.S. Environmental Protection Agency (USEPA 2020) to be used exclusively for overthe-top applications on DR crops. According to survey results of the 2017 season, approximately
50% and 85% of the soybean and cotton, respectively, produced in Arkansas, Missouri,
Mississippi, and Tennessee were DR cultivars (Steckel et al. 2017). The adoption of DR technology could be attributed to the expansion of weeds with resistance to multiple herbicide
modes of action, particularly Palmer amaranth (Amaranthus palmeri S. Watson); meanwhile,
several weed populations remained susceptible to dicamba despite the fact the herbicide has
been commercialized for decades (Behrens et al. 2007; Heap 2023). Before the development
of DR cultivars, the herbicide was primarily applied in preplant burndown and postemergence
weed management on cereal crops (Shaner 2014).
The expansion of DR technology increased the use of dicamba products in postemergence
applications and shifted the off-target movement toward the growing season, impacting sensitive plants (Jones et al. 2019a; McCown et al. 2018; Werle et al. 2018). Reports of damage to nontargeted sensitive vegetation, including non-DR soybean, have occurred in the past (Auch and
Arnold 1978) but not to the magnitude that followed the commercialization of dicamba in DR
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
266
crops (Bradley 2017, 2018; Hager 2017; Hartzler and Jha 2020;
Steckel 2018, 2019). For instance, according to state authorities,
2,708 complaints were recorded, accounting for approximately
1.46 million ha of damaged soybean impacted by the off-target
movement of dicamba in 2017 (Bradley 2017). Additionally,
growers reported a 10% to 20% yield loss due to multiple exposures
to dicamba off-target movement (Steckel et al. 2017).
The off-target movement of pesticides and atmospheric loading
have been reported and were attributed to agricultural areas associated with heavy use of these chemicals (Waite et al. 2005).
Atmospheric loading of pesticides is an emerging hypothesis to
explain the impact of the off-target movement on a large scale;
however, more research is needed to differentiate this mechanism
from others, elucidating the fate of these chemicals. Other pesticides, such as glyphosate and atrazine, have been detected in the
atmosphere and rainfall (Alonso et al. 2018; Chang et al. 2011;
Hill et al. 2002). Research has shown off-target dicamba movement
to occur via primary drift, secondary movement, and tank
contamination (Cundiff et al. 2017; Jones et al. 2019b; Maybank
et al. 1978; Teske et al. 2002). Research has shown that one of
the main sources of the secondary movement of dicamba is volatility (Castner et al. 2022; Egan and Mortensen 2012; Jones et al.
2019b; Mueller and Steckel 2019b, 2021; Oseland et al. 2020b;
Soltani et al. 2020; Zaccaro-Gruener et al. 2022), and characteristics
of the compound are important to consider for this type of
transport.
Important physicochemical characteristics concerning the offtarget movement of dicamba are the higher vapor pressure
(VP = 4,500 μPa) and a coefficient of dissociation (pKa) corresponding to 1.87 (Shaner 2014). In comparison to the VP of
dicamba, glyphosate and atrazine have VP at least 100 times lower,
indicating a low tendency to become volatile. Considering the pKa
of dicamba is approximately 2, and according to the acid–base
equilibria described by Henderson-Hasselbalch (Aronson 1983),
a dicamba solution at pH = 2 has a 50:50 equilibrium of ionic
(acidic) to anionic molecules. Therefore, changes in the pH of
the solution with dicamba could impact the dissociation of formulated dicamba and the formation of dicamba acid prone to volatilization (Mueller and Steckel 2019b; Riter et al. 2021). Under the
acid–base equilibrium, dicamba solutions at pH 4 have 99% undissociated to 1% acid molecules; at pH 5, 0.1% of the molecules
would be in the acid form and 99.9% non-dissociated. Other
factors may exacerbate dicamba volatility, such as spray tank partners and meteorological conditions following the application. For
instance, growers want to add glyphosate to dicamba to increase
the weed control spectrum from a single application; however, this
mixture (glyphosate with dicamba) reduces solution pH and
increases the volatility potential of dicamba (Mueller and Steckel
2019b). Early research considering dicamba volatility reported that
high temperatures and low relative humidity, typical in the
summer, allow the dissociation of dicamba acid, increasing volatility (Behrens and Lueschen 1979). A recent study reported that
average and low air temperature and wind speed on the day of
application and the following day were factors associated with
significant off-target movement of dicamba during temperature
inversions (Oseland et al. 2020b). Moreover, stable air and temperature inversions during and following application with dicamba
were found to impact secondary movement (Bish et al. 2019a,
2019b). However, environmental conditions are dynamic, and
their impact on volatility is complex.
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
Zaccaro-Gruener et al.: Dicamba impact on soybean
Researchers have attempted to quantify dicamba volatility using
laboratory and field methods. According to early studies, 13% of
radiolabeled dicamba acid volatilized from planchets containing
sandy loam soil incubated at 35 C for 7 d (Burnside and Lavy
1966). Later, researchers used soybean as a bioindicator of dicamba
emissions after dimethylamine (DMA) salt formulation was
applied in the field and laboratory studies, including enclosed
chambers (Behrens and Lueschen 1979). In recent field trials,
the diglycolamine (DGA) salt of dicamba further reduced volatility
compared with the DMA salt formulation (Egan and Mortensen
2012). Most recently, research comparing the secondary movement of DGA salt to N,N-bis-(3-aminopropyl) methylamine salt
(BAPMA) of dicamba reported further reduction but not the elimination of dicamba volatility (Anonymous 2022a; Jones et al.
2019b). One attempt to reduce dicamba volatility involved the
addition of acetic acid:acetate solution (VaporGrip®), to the
DGA salt formulation, which inhibits the formation of dicamba
acid (Anonymous 2022b, 2022c; MacInnes 2017). Typically,
solutions containing the above formulations have a pH greater
than 5.0, which minimizes the formation of the volatile dicamba
acid (Mueller and Steckel 2019b). Federal regulations only allow salt
formulations of dicamba with BAPMA or DGA with VaporGrip® for
in-season applications on DR crops (Anonymous 2022a, 2022b,
2022c). Furthermore, a 2020 modification of federal labels required
that every application of dicamba in DR technology have the
addition of a volatility reduction agent (VRA) to help stabilize higher
pH in the tank solution, reducing volatility potential (USEPA 2022).
The latest research has focused on using air-sampling techniques to
examine and model factors that impact the secondary movement
of dicamba, as these formulations did not prevent landscape damage
in several locations in the United States (Bish et al. 2021).
A research method was published to quantify the flux of
dicamba volatility in field conditions by employing small-volume
air samplers (3 L min−1) (Riter et al. 2020). Other research used air
samplers to measure volatility potential in humidomes and acrylic
chambers (Mueller and Steckel 2019a; Ouse et al. 2018). The main
benefit of controlled environment experiments is that they allow
evaluations using different treatment combinations. However,
these conditions do not represent field environments where
dicamba applications would be made and impacted by multiple
interacting factors.
Dicamba is a synthetic auxin mimic herbicide (Group 4), where
an overproduction of auxins in susceptible broadleaf plants can
culminate in plant death (Grossmann 2010). Exposure to low doses
of dicamba by susceptible plants results in epinasty, leaf crinkling,
cupping, and malformation, which could be severe (Behrens and
Lueschen 1979; Wax et al. 1969; Weidenhamer et al. 1989).
Studies associated with injury to non-DR soybean from exposure
to dicamba are abundant (Auch and Arnold 1978; Behrens and
Lueschen 1979; Griffin et al. 2013; Jones et al. 2019a; McCown
et al. 2018; Robinson et al. 2013; Sciumbato et al. 2004;
Solomon and Bradley 2014; Wax et al. 1969). Treatments as low
as 0.028 g ae ha−1 resulted in visible injury and height reduction
of non-DR soybean treated at vegetative and blooming growth
stages (Solomon and Bradley 2014). However, most field studies
involving the effect of dicamba on non-DR soybean were
conducted by direct foliar applications of the herbicide over a
range of doses.
Meanwhile, the impact of indirect exposure by volatile dicamba
is unclear, as is the effect on non-DR soybean by the amount of
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Weed Science
Table 1. Location, soil series, time of application, weather conditions, and solution pH of the herbicide treatment applied in 16 site-years during the 2018 and 2019
growing seasons.
Yeartrial
Location
Soil series
2018-1
Fayetteville, AR
2018-2
Fayetteville, AR
2018-3
Fayetteville, AR
2018-4
2018-5
2018-6
2018-7
Fayetteville,
Fayetteville,
Fayetteville,
Fayetteville,
AR
AR
AR
AR
Pembroke silt
loam
Pembroke silt
loam
Pembroke silt
loam
Pickwick silt loam
Captina silt loam
Captina silt loam
Captina silt loam
2018-8
Fayetteville, AR
Pickwick silt loam
2019-1
2019-2
2019-3
2019-4
2019-5
2019-6
2019-7
2019-8
Prairie
Prairie
Prairie
Prairie
Prairie
Prairie
Prairie
Prairie
Summit silty clay
Cherokee silt loam
Summit silty clay
Cherokee silt loam
Summit silty clay
Cherokee silt loam
Summit silty clay
Cherokee silt loam
Grove,
Grove,
Grove,
Grove,
Grove,
Grove,
Grove,
Grove,
AR
AR
AR
AR
AR
AR
AR
AR
Initiation date
and time
Solution
pHa
Air
temperature
Soil
temperature
Relative
humidity
Wind speedb
—%—
79
—km h−1—
0.69
May 7, 2018, 9:55 AM
—
————C————
19.4
17.2
May 21, 2018, 9:35 AM
—
20.6
16.1
92
2.40
May 28, 2018, 10:45 AM
—
29.4
25.0
64
5.26
June 4, 2018, 11:04 AM
June 11, 2018, 11:00 AM
July 31, 2018, 8:40 AM
September 4, 2018,
10:50 AM
September 11, 2018,
1:55 PM
May 14, 2019, 1:15 PM
May 27, 2019, 9:19 AM
June 3, 2019, 11:49 AM
June 13, 2019, 8:39 AM
June 18, 2019, 11:50 AM
June 24, 2019, 3:05 PM
July 3, 2019, 5:00 PM
July 18, 2019, 9:50 AM
4.75 (7.11)
4.79 (7.36)
4.86 (7.26)
5.01 (8.20)
26.7
27.2
21.7
27.2
23.9
23.9
22.8
25.6
51
64
80
72
6.33
6.78
0.25
10.23
4.99 (7.83)
26.7
25.6
40
4.32
4.89 (7.93)
4.93 (7.92)
4.89 (8.08)
4.81 (7.84)
4.75 (7.10)
4.70 (7.20)
4.72 (7.08)
4.78 (7.94)
23.9
23.9
26.1
16.5
25.6
27.8
31.7
28.9
21.1
22.8
22.2
21.1
23.9
26.1
28.3
26.7
60
71
68
80
70
59
58
74
5.94
5.10
8.43
3.81
3.95
6.59
6.56
7.98
a
Treatment solution contained 560 g ae ha−1 dicamba þ 860 g ae ha−1 glyphosate þ 1% v/v Intact™ (drift reduction adjuvant). The number in parentheses represents the pH of water before the
mixture with herbicides and adjuvant. No treatment solution samples were taken from trials 2018-1, 2018-2, and 2018-3.
b
Wind speed for a labeled application is 4.8 to 16 km h−1.
gaseous herbicide in the air, particularly under field conditions.
More research is necessary to evaluate the influence of volatilized
dicamba on soybean response and how environmental factors
impact dicamba detection in air samples. Field research was
conducted with the following objectives: (1) to understand the
influence of dicamba volatilization (concentration in air samples)
on the response of soybean, (2) to determine which main environmental factors impact the dicamba concentration in air samples,
and (3) to determine the frequency of detection and concentration
of dicamba in eastern Arkansas during the summer months.
Material and Methods
Dicamba Application in Field and Herbicide Deposition
Sixteen trials were conducted on fields located at the Milo J. Shult
Agricultural Research and Extension Center of the University of
Arkansas, near Fayetteville, AR (36.098889°N, 94.179167°W)
and at a producer field located near Prairie Grove, AR
(35.969167°N, 94.297222°W) during the 2018 and 2019 growing
seasons. The fields at the Fayetteville location had different soil
classifications: Captina silt loam (fine-silty, siliceous, active, mesic
Typic Fragiudults; 32.2% sand, 55.3% silt and 12.5% clay, 1.16%
organic matter, and pH 6.8), Pembroke silt loam (fine-silty, mixed,
mesic Ultic Paleudalfs; 11.3% sand, 67.7% silt and 21% clay, 2.4%
organic matter and pH 6.6), and Pickwick silt loam (fine-silty,
mixed, semiactive, thermic Typic Paleudults; 14% sand, 69% silt
and 17% clay, 1.75% organic matter and pH 5.7). The soils at
Prairie Grove were a Summit silty clay loam (fine, smectitic,
thermic Oxyaquic Vertic Argiudolls; 8.7% sand, 62.1% silt and
29.2% clay, 4.78% organic matter, and pH 5.8) and Cherokee silt
loam (fine, mixed, active, thermic Typic Albaqualfs; 27.1% sand,
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
54.4% silt, and 18.5% clay, 1.25% organic matter, and pH 6.2)
(soil analysis for both locations at the University of Arkansas
Agricultural Diagnostic Laboratory, Fayetteville, AR). Table 1
features the field location and soil texture for each trial.
An area equivalent to 0.37 ha (60.6 m by 60.6 m) was treated
with 560 g ae ha−1 of dicamba (XtendiMax® with VaporGrip®
Technology, Bayer, St Louis, MO) plus glyphosate at 860 g ae ha−1
(Roundup PowerMax®, Bayer) with a drift-reducing adjuvant
(DRA) at 1% v/v (Intact™, Precision Laboratories, Waukegan, IL).
Current label requirements include the addition of a volatility
reduction agent (VRA) with in-crop dicamba applications
(Anonymous 2022c); however, no VRAs were included in these
experiments. The applications were made using a Mudmaster
tractor-mounted sprayer (Bowman Manufacturing, Newport, AR)
utilizing TTI 11003 VS nozzles (TeeJet® Spraying Systems,
Wheaton, IL) calibrated to deliver 140 L ha−1. A 50-ml sample of
each treatment solution was collected before application for pH
measurement. The pH of each treatment solution was noted once
the measurement was constant for 3 min (HI 2211 pH Meter,
Hanna Instruments, Woonsocket, RI). Detailed information about
the pH of the treatment solution, the date, and the weather at trial
initiation is included in Table 1. Applications were made to the field
in distinct stages of production, including preplant (after soil cultivation) or vegetative soybean growth stages. A DR soybean cultivar
(AG 47X6 RR2X, Asgrow Seed, Creve Coeur, MO) was planted at
350,000 seeds ha−1 at some field sites in Fayetteville on a 91-cm-wide
row spacing and in a 19-cm-wide row at Prairie Grove. Conditions
of groundcover composition and percentage of living vegetation at
the target area at the application were recorded. The treated area of
the experimental replications included nine weed-free and tilled
replicates, six replicates with DR soybean, and one that was noncropped and weedy (Supplementary Table S1). Trial applications
268
Zaccaro-Gruener et al.: Dicamba impact on soybean
Figure 1. (A–C) High-volume air samplers and soybean bioindicators placed inside the treated area during different volatility experiments. (D) A close-up of an air sampler during
collection and the filtering media used to trap volatile dicamba (glass-fiber filter paper and polyurethane foam, top and bottom right, respectively).
were separated by at least 5 d, and a minimum of 3 mm of rainfall
occurred between each replication, except for two trials in 2018
(2018-4 and 2018-6; data not shown).
Before application, four filter papers measuring 110 mm
in diameter (VWR International, Radnor, PA) were fixed to a
150 mm by 150 mm cardboard sheet placed horizontally at the
target level (weedy or weed-free soil or soybean canopy) in the
treated area to determine the concentration of dicamba applied.
Wind speed data were collected every 3 s using a handheld
anemometer device during each application. At 30 min following
spraying, the filter papers were collected and placed inside separate
50-ml tubes (VWR International) and stored inside labeled ziplock
bags to avoid cross-contamination. Samples were stored in coolers
for transport to a −20 C freezer before analysis. Environmental
conditions were evaluated while air sampling occurred. Air and
soil temperature, dew point temperature, rainfall, and relative
humidity were recorded using a weather station approximately
30 m from the treated area (Supplementary Figures S1–S3).
Wind speed and direction data were not reported due to sensor
failure. Weather sensors of air temperature, relative humidity,
and the dew point were at 1.6 m from the soil surface, and the soil
temperature was at 1-cm depth.
Analysis of Dicamba in Air Samples and Deposition within
the Treated Area
Previous research evaluated volatile herbicides following field
application utilizing air-sampling devices of different capacities,
sample extraction from filtering media, and laboratory analysis
using liquid chromatography and spectroscopy techniques
(Mueller et al. 2013; Soltani et al. 2020). Dicamba volatility was
evaluated using high-volume air samplers (Hi-Q Environmental
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
Products, San Diego, CA) placed inside the treated area. For each
replicated trial, three and two air samplers were placed inside the
treated area in 2018 and 2019, respectively. Each air sampler was
equipped with a glass-fiber filter paper of 102 mm in diameter
(Hi-Q Environmental Products) placed in series with a polyurethane foam (PUF) sorbent that measured 6 cm by 8 cm (diameter
and length) (cat. no. 22954, Restek, Lancaster, PA). At 30 min after
application, the air samplers were placed inside the treated area
(Figure 1). The air sampler had the flow rate programmed to
run constantly at 185 L min−1 to collect volatile dicamba emitted
from the treated field. The samplers displayed the cumulative
volume of air sampled and the time elapsed. Figure 1 illustrates
air samplers in the field and filtering media used to trap the volatile
herbicide. Previous research has shown that this experimental
setup effectively traps volatile dicamba (Zaccaro-Gruener et al.
2022). The sampling height was 60 cm above the target. An additional air sampler was established as a control measurement
approximately 1 km from the treated plot. Air sampler components were cleaned utilizing methanol before a trial and between
each collection. Filters and PUFs were collected at 24, 48, 72,
and 96 h after application (HAA) on trials replicated in 2018; in
2019, the trials ended at 48 HAA. The samples from each time
interval were placed in labeled plastic bags and kept at −20 C until
analysis.
Samples of application deposition (filter papers) and airsampler PUFs and filter papers samples were sent to the
University of Tennessee in Knoxville, TN. The method for dicamba
extraction was based on Mueller and Steckel (2019a). An aliquot of
400 ml of methanol was added to the PUF samples before homogenizing and transferring them into a 1-L bottle. The bottle was
secured to a shaker for an overnight extraction process. The filter
paper samples were extracted using 40 ml of methanol for 2 h on
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Weed Science
the same shaker. The extract solution was filtered and concentrated
before resuspension with 5 ml of methanol. The solution was
then filtered through a 0.45-μm filter before a 1-ml aliquot was
dispensed in a 2-ml vial compatible with the liquid chromatography–mass spectroscopy (LC-MS) instrument. Samples of herbicide deposition were extracted using 40 ml of methanol and diluted
1/20 times before LC-MS analysis. External standards of dicamba
acid dissolved in methanol were used to verify the dicamba concentration. Analysis was conducted by high-performance liquid chromatography using an Agilent Liquid Chromatograph (model 1290,
Agilent Technologies, Santa Clara, CA) in tandem with singlequad MS. Analytical results showed that the retention time of
dicamba acid was 5.0 min, and the minimum quantitation limit
was equivalent to 0.1 ng ml−1 of solvent. Quality control included
fortified samples, blank matrix, and duplicates to evaluate carryover after each injection. Recovery was approximately 90%, and
results were corrected for dilutions. Results of the average herbicide
deposited on filters (ng) placed on the target area are summarized
in Supplementary Table S1. The total dicamba detected (ng) in air
samples was found by adding the amount detected in the PUF to
the amount found on the filter papers that compose the airsampler filtering media analyzed. Results were converted to
concentration (in ng m−3) according to the air volume sampled
at each collection interval.
Non-DR Soybean Bioindicators
Non-DR soybean plants (Credenz® 4748 LL, BASF, Research
Triangle Park, NC) were grown in a greenhouse until the V1 or
V2 growth stage. At 30 min following application for each experiment, the bioindicator plants were placed inside the treated area.
The bioindicator plants were kept on trays to prevent contact with
any treated surface (see Figure 1). The bioindicator sampling intervals were 0.5 to 24 h, 0.5 to 48 h, 0.5 to 72 h, 0.5 to 96 h, 24 to 48 h,
48 to 72 h, and 72 to 96 h in 2018; in 2019, the experiments ran up
to 48 h. An additional set of plants was placed by the control air
sampler to be used as a nontreated reference. Each sampling
interval had 8 to 10 individual plants (pseudo-replicates).
Preventive measures were taken to prevent the potential for
cross-contamination with the treated surface and among the plant
sets. Bioindicator plants were returned to the greenhouse following
the predetermined exposure intervals and maintained until evaluation. Evaluations consisted of a dicamba injury score (0% = no
effect to 100% = complete plant death) as represented in
Figure 2, and height (cm) measured at 21 ± 2 d after treatment
(DAT). Aboveground biomass was collected at termination
(21 DAT) and weighed after drying at 60 C for 5 d until constant
mass. Average height and biomass data were converted to
percentage relative reductions to a nontreated control for each trial
to account for differences in growing conditions. Positive values
indicate a reduction of height and biomass, while negative reduction values indicate that plants were taller or heavier than the
nontreated reference.
Frequency of Dicamba Detection and Concentration Level
in Air in Eastern Arkansas
In 2020 and 2021, air samplers were placed in a covered area in the
vicinity of the main building of the Lon Mann Cotton Research
Station, near Marianna, AR (34.732778°N, 90.766389°W), and
the Northeast Research & Extension Center, in Keiser, AR
(35.674167°N, 90.086667°W). Commercial dicamba applications
within 1.6 km of both facilities are prohibited (Unglesbee 2021).
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
Figure 2. Representative pictures of the symptomology of non-dicamba-resistant
soybean seedlings and the resulting injury rating (%) at 21 d after being exposed to
volatilized dicamba emitted from a field treated with dicamba at 560 g ae ha−1, glyphosate at 860 g ae ha−1, and 1% v/v of a drift reduction adjuvant evaluated in 2018 and
2019 growing seasons.
Low-volume air pumps (model 220-5000TC, SKC, Eighty Four,
PA) were connected to a 76-mm sorbent PUF contained within
a 22 mm by 100 mm glass tube (cat. no. 226-92, SKC) using clear
15-cm plastic tubing (6.35-mm inner diameter, 9.53-mm outer
diameter). The pumps ran continuously, and the air samplers were
programmed to sample for a 24-h period (midnight to midnight).
The airflow setting of the samplers was 5 L min−1 (Check-mate
Calibrator, SKC). The morning following sample collection
completion, the PUF cartridge was collected and stored in a labeled
plastic bag and kept at −20 C until analysis. Air-sampling components were cleaned after each collection using methanol, and a new
cartridge was installed for subsequent sampling intervals. In 2020,
sample collection was performed from June 3 until July 21 near
Marianna and from June 6 until July 21 in Keiser, generating 37
and 38 samples in Keiser and Marianna, respectively. In 2021,
collections were performed from June 6 until July 30 in
Marianna and from June 10 until July 29 in Keiser, which generated 47 and 48 samples per location, respectively. Malfunction of
the pump occurred a few days each year, resulting in failure to
collect samples daily within this period. Samples were sent to
the Mississippi State Chemistry Laboratory (Starkville, MS) for
analysis of dicamba content in the PUF samples. Sample extraction
and quantification were conducted following Soltani et al. (2020).
The analytical quantitation limit for the small PUFs was equivalent
to 0.3 ng ml−1 of solvent. Analytical results were converted (to total
ng m−3 d−1) based on the constant airflow reported earlier.
Dicamba content results were displayed separately by location
and year. Additionally, environmental data were obtained from
weather stations located at each research station. Maximum air
temperature, average relative humidity, wind speed, and accumulated rainfall with dicamba detection were included by sampling
date, location, and year.
Statistical Analysis
The total dicamba concentration in air (ng m−3 d−1) detected by the
three air samplers was averaged in each sampling interval. Data
distributions were analyzed and selected based on the lowest
log-likelihood fit and the corrected Akaike information criterion
(AICc) using the distribution platform of JMP Pro v. 16.1 (SAS
270
Institute, Cary, NC). Dicamba concentration in air samples had
lognormal distribution. The field experiment had 16 unique environments over the 2018 and 2019 seasons, with data organized by
site-year. Statistical analysis was performed only considering data
from 0.5 to 48 HAA, as this period was replicated in both years.
Natural logarithmic transformation of dicamba detection was done
to improve the homogeneity of variance. The impact of groundcover type (vegetation or soil), percentage of groundcover from
living vegetation, and sampling intervals were tested, considering
site-years as a random variable. ANOVA was performed in JMP
using the Mixed models of the Fit Model platform, and the
least-squares means were compared using Fisher’s protected
LSD (α = 0.05) (SAS Institute Inc. 2022). Dicamba detection
results were back-transformed to simplify the interpretation of
results. The relationships between weather variables (averaged
24-h intervals) and dicamba detections were evaluated using
Spearman’s correlation coefficients. The relationship between relative humidity and sampling timing with dicamba detection was
investigated using Mixed models in the Fit Model platform
(α = 0.05). However, correlations were discussed because relative
humidity did not satisfy the requirements for inclusion in the
model to result in a generalized R2 ≥ 0.2 (data not shown).
Bioindicator results of soybean injury (%), relative height, and
biomass reductions (% of nontreated) were analyzed using JMP.
Injury followed a beta distribution, while relative height and
biomass reductions exhibited normal distributions. Results of
dicamba in air samples (ng m−3 d−1) were analyzed by collection
timing (up to 48 HAA) and trial. Relationships between injury,
height, and biomass reductions with dicamba in air samples were
explored using a Generalized Regression model in the Fit Model
platform with the Lasso estimation method and AICc as the validation method. The best model of the relationship between injury
and dicamba concentration was nonlinear. The prediction
model of injury by dicamba concentration had limitations below
0.2 ng m−3, because every air sample analyzed from the treated
area resulted in a detection (lowest = 0.1 ng m−3), and every
plant exposed to the treated plot showed symptomology. The
Generalized Regression analysis resulted in values of generalized
R2 to the relationships, in which a value closest to 1 indicates a
perfect relationship (Nagelkerke 1991; SAS Institute Inc. 2022).
Previous research also utilized nonlinear models to explain
soybean injury resulting from low-dose applications of dicamba
(Robinson et al. 2013). Regression models of height and biomass
impacted by dicamba detection followed linear relationships.
Analyses of predicted dicamba concentrations over a 24-h period
that would result in 10%, 15%, 20%, and 50% soybean injury and
5% and 10% height reduction were conducted.
Dicamba concentration data from Marianna and Keiser were
analyzed in JMP Pro v. 16.1. Statistical analysis only considered
data when dicamba was detected. ANOVA was performed to
assess the impact of location, year, and the interaction on dicamba
concentrations in air samples using Mixed models of the Fit Model
platform in JMP, considering the sampling date as a random variable. Natural logarithmic transformations were applied to improve
the homogeneity of variance of the quantification data. Weather
variables were analyzed in the Multivariate platform of JMP
against the results of dicamba concentration in air samples.
Data considered in this analysis included times when no herbicide
was detected to evaluate the impact of weather conditions on
dicamba detection. Relationships among these variables were
explored using Spearman’s correlation coefficients. Generalized
regression analysis of dicamba concentration in the air of
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
Zaccaro-Gruener et al.: Dicamba impact on soybean
Marianna and Keiser was performed considering maximum air
temperature, average relative humidity, and the frequency of rain
events; however, the poor generalized R2 value (<0.2) was deemed
not appropriate; therefore, only correlations were discussed.
Additional analyses included hypothesis tests (t-test) of dicamba
concentration contrasting days with measurable rain events versus
clear days and the concentration when more than 2 d elapsed since
a rain event versus fewer than 2 d.
Results and Discussion
Dicamba Detections after Application and the
Environmental Impact
Replications of the field experiment to evaluate the impact of volatile dicamba on susceptible soybean response were initiated from
May 7 to September 11, 2018, and May 14 to July 18, 2019, from
8:40 AM to 5 PM (Table 1). Air temperature during herbicide
applications ranged from 16.5 to 31.7 C; relative humidity ranged
from 40% to 92%; and wind speed peaked at 10.23 km h−1
(Table 1). The pH of solutions used on treatments was collected
before application and analyzed in all but three replicated trials
(2018-1, 2018-2, and 2018-3; see Table 1). The solutions containing
the herbicide treatments (dicamba plus glyphosate with a particle
DRA) were acidic, as pH measurements ranged from 4.75 to 5.01.
According to the Henderson-Hasselbalch acid–base equilibrium
(Aronson 1983), a solution pH between 4 and 5 has 1% to 0.1%
of dicamba molecules in the acid form, prone to volatilize, and
99% to 99.9% of the molecules in nonionized form. Previous
research has examined the impact of herbicide formulations and
additives on the stability of dicamba in solution and conversion
to the volatile acid form (Mueller and Steckel 2019b). The authors
established that adding potassium salt of glyphosate to dicamba
was a key contributor to reducing the solution pH up to 2.1 units.
They concluded that the increase in the formation of dicamba acid
occurs due to the pH reduction, increasing volatility potential. The
present research showed a smaller change in the solution pH than
the study mentioned, implying that the off-target movement
observed could be impacted not only by volatilization. It is possible
that suspended spray particles could have remained in the area
after application.
Results of dicamba deposited on filter papers positioned on the
treated plot (soil or vegetation canopy) during application ranged
from 205,982 to 523,372 ng of dicamba per filter paper, which
equated to 217 to 551 g ae ha−1 (Supplementary Table S1). This
variation could be expected, as coverage variability could occur
during applications over a small area of the filter paper (95
cm2), or reflect the impact of wind gusts during application,
reducing herbicide deposition on the filter papers positioned on
the target beneath the sprayer. However, the deposition results
were within acceptable limits for applications of 560 g ha−1 of
dicamba using a large-scale sprayer.
Results of dicamba concentration in air samples over the
treated field in 2018 ranged from 0.384 to 6.536 ng m−3 d−1 from
0.5 to 24 HAA, 0.13 to 2.581 ng m−3 d−1 from 24 to 48 HAA, 0.258
to 1.486 ng m−3 d−1 from 48 to 72 HAA, and from 0.1 to
1.045 ng m−3 d−1 from 72 to 96 HAA (Table 2). Most of the
herbicide collected in air samples occurred from 0.5 to 48 HAA
(56% to 87% of total herbicide detected over replicated trials).
Therefore, trials in 2019 lasted up to 48 HAA for logistic and
cost reasons; however, this sampling period still accounted for
the majority of potential dicamba emitted. As expected, most
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Table 2. Dicamba concentration in air samples at 24-h intervals, up to four
different timings starting at 0.5 to 24, 48, 72, and 96 h after application (HAA)
in the 2018 and 2019 growing seasons.
Table 3. Influence of fixed effects on dicamba concentration in air samples
(ng m−3) and probability values.
Effectsa
Hourly intervals of exposure after applicationa
0.5–24
24–48
48–72
72–96
—————————ng m−3——————————
1.276
1.186
0.824
0.608
1.721
2.581
1.486
0.757
3.803
1.882
0.466
0.575
3.045
0.688
0.981
0.766
6.536
0.130
0.389
0.627
2.632
1.183
0.922
1.045
1.767
0.315
0.258
0.100
0.384
0.321
0.455
0.100
1.369
0.29
—
—
3.012
0.506
—
—
1.475
0.25
—
—
8.598
9.42
—
—
7.716
1.342
—
—
0.679
0.156
—
—
0.811
1.706
—
—
5.705
1.617
—
—
Average dicamba
concentrationc
P-value
−3
Sampling
interval
Dicamba concentration in air
Trial code
2018-1
2018-2
2018-3
2018-4
2018-5
2018-6
2018-7
2018-8
2019-1
2019-2
2019-3
2019-4
2019-5
2019-6
2019-7
2019-8
Treatmentsb
0.5–24 HAAc
24–48 HAA
——ng m ——
2.27 a
0.67 b
0.0010*
a
The effects of groundcover composition and % living vegetation of groundcover were not
significant (P-value = 0.56 and 0.51, respectively) and are not shown. Statistical analysis
included data from 0.5 to 48 HAA and considered experimental runs a random variable.
Asterisk (*) indicates a significance of treatment effects at P = 0.05.
b
HAA, hours after application.
c
Dicamba concentration represents back-transformed values of least-squares means. Means
followed by a different letter differ according to Fisher’s protected LSD (α = 0.05).
Table 4. Spearman’s correlation coefficients associated with dicamba
concentration (ng m−3) in air samples collected across 16 experimental runs
between 2018 and 2019 and environmental factors.a
Parameter
a
Trials conducted in 2018 were terminated at 96 HAA, while trials in 2019 lasted up to 48 HAA.
Trial code equals year and replicate.
Correlations
Average air
temperature
Min.
atmospheric
relative
humidity
Rainfall
Max. dew
point
temperature
0.08
−0.39
0.14
−0.12
a
replicated trials showed that the concentration of dicamba in air
samples was the highest at the first sampling interval and decreased
with time, except for trials 2018-2, 2019-4, and 2019-7. These
results were consistent with previous research, which reported that
dicamba concentrations were the greatest following an application
and decreased until the last evaluation at 72 HAA (Bish et al.
2019a). For trials conducted in 2019, the range of dicamba concentrations was broad, ranging from 0.679 to 8.598 ng m−3 from 0.5 to
24 HAA and 0.156 to 9.42 ng m−3 from 24 to 48 HAA (Table 2).
Weather conditions could have impacted the variability in dicamba
concentration in air samples during each experimental run.
The impacts of sampling interval, groundcover type, and
percentage of living vegetation were analyzed, considering experimental runs as random (Table 3). Data from the second sampling
interval of the 2019-4 trial were considered to be an outlier and
excluded from statistical analysis. The treated area groundcover
varied considerably (Supplementary Table S1); therefore, comparisons were made between the type of treated surfaces as vegetated
(combining DR soybean and non-crop weedy cover) versus nonvegetated (bare soil) and the percentage of living vegetation on
these surfaces. Results showed no statistical differences in dicamba
concentration by surface type (P-value = 0.56; Table 3); dicamba
concentration averaged 2.71 and 1.90 ng m−3 on vegetated and soil
surfaces, respectively (data not shown). Previous studies reported
higher dicamba volatility after applications to vegetated surfaces
than soil (Mueller and Steckel 2021). Researchers hypothesized
that dicamba volatility was higher in vegetated surfaces due to
several factors, including a greater surface area to absorb and
emit herbicide, and that water transpiration could promote
herbicide emissions from plant surfaces. The percentage groundcover of living vegetation (P-value = 0.51; Table 3) did not
significantly impact dicamba concentration in air samples. The
groundcover composition of the present trials varied substantially,
particularly considering trials with vegetation present; the amount
of living vegetation varied from 5% to 99%, and no significant
differences could be observed regarding groundcover conditions
(Supplementary Table S1). Statistical results showed that site-years
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
Data included herbicide detections collected across experimental runs from 0.5 to 48 h after
application. Values nearest to 1 correspond to the strongest relationships. Correlation value
in bold is significant at P = 0.05. The average soil temperature at 1-cm depth is not shown,
as the correlation coefficient was not significant and below 0.10.
were not different for dicamba detections (P-value = 0.3197; data
not shown), and only the sampling interval affected dicamba
concentration in air (P-value = 0.0010). According to these results,
dicamba concentration in air samples was 2.27 ng m−3 from
0.5 to 24 HAA and decreased to 0.67 ng m−3 by 24 to 48 HAA
(Table 3).
The impact of weather on dicamba concentration detected was
explored by monitoring air and soil temperatures, relative
humidity, and dew point temperature reported by site-year
(Supplementary Figures S1 and S2) and the accumulated hourly
results of rainfall by replicated trial (Supplementary Figure S3).
Sensor failure prevented wind speed data collection during these
experiments; meanwhile, data gathered during application showed
that wind lower than 4.8 km h−1 occurred in 6 of 16 experimental
runs. Therefore, stable air conditions could have impacted these
field trials similarly to reports in which stable air during a temperature inversion exacerbated the secondary movement of dicamba
(Bish et al. 2019a). Previous studies reported that higher
atmospheric temperatures, lower relative humidity, or conditions
with temperature inversions and stable air had been associated
with dicamba detections (Behrens and Lueschen 1979; Bish
et al. 2019a; Egan and Mortensen 2012). The minimum relative
humidity was the only environmental variable correlated with
dicamba concentration in air samples across 16 experiments (coefficient = −0.39; Table 4). The relative humidity data ranged from
28% to 100% across site-years (Supplementary Figures S1 and S2),
and the average dicamba concentration was reduced as relative
humidity increased. Other research reported that low atmospheric
relative humidity increases dicamba volatility (Behrens and
Lueschen 1979; Gavrilescu 2005; Mueller and Steckel 2021;
Mueller et al. 2013); although researchers have hypothesized that
high humidity conditions can increase dicamba settlement on
plant or soil surfaces (Egan and Mortensen 2012).
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Zaccaro-Gruener et al.: Dicamba impact on soybean
Figure 3. Generalized regression curves fit dicamba concentration in air (ng m−3) and (A) visible injury (%), (B) height reduction (%), or (C) biomass reduction (%) of susceptible
soybean at 21 d after treatment across 13 experimental runs between 2018 and 2019. The analysis excluded three experimental runs for which no plants were available for
evaluation; data for soybean height and biomass reductions by dicamba concentration both followed normal distributions; meanwhile, soybean injury by dicamba concentration
followed a beta distribution.
Response of Non-DR Soybean to Dicamba by Indirect
Exposure
Visible injury, height, and biomass reduction were evaluated on
non-DR soybean seedlings at 21 d after a single exposure to the
field treated with dicamba. Soybean bioindicators from three of
16 trials (2019-6, 2019-7, and 2019-8; Supplementary Table S1)
suffered damage (feeding by insects or rodents) and were excluded
from these evaluations. Auxin injury symptomology was observed
in nearly all plants placed in the field. The set of plants positioned
by the control air sampler occasionally showed symptoms of
damage (visible injury ≤ 5%), and dicamba was detected at approximately 54 ng (± 24) over the entire intervals (96 h in 2018 and 48 h
in 2019), equivalent to 0.24 ng m−3 in 2018 and 0.14 ng m−3 in
2019. Other mechanisms could have impacted the off-target movement of dicamba from the treated area toward the control sampler
positioned 1 km away. The main symptoms observed were leaf
cupping, epinasty, and sometimes extreme malformation of the
apical meristem. Figure 2 shows representative plants with symptoms and ratings of visible injury attributed (ratings of visible
injury ranged from 1 to 52%; data not shown). Soybean plants
exposed to the field at 0.5 to 24 h, 0.5 to 48 h, 0.5 to 72 h, 0.5
to 96 h resulted in similar responses, but these differed from plants
placed on the field at 24 – 48 h, 48 – 72 h, and 72 – 96 h (data not
shown). The injury results indicate that the first 24 h following the
application had the highest concentration of dicamba in the air.
A general trend of lower symptoms was observed after 24 HAA,
but this reduction was not significant by exposure timing (data
not shown). Analysis of dicamba concentrations with plant
responses (injury, height, and biomass) only considered consecutive intervals, 0.5 to 24 and 24 to 48 HAA, collected in 2018 and
2019. According to raw data, all air samples collected resulted in
dicamba concentrations greater than 0.1 ng m−3 d−1 and elicited
injury on non-DR soybean placed in the treated field (data not
shown). The percent reduction results of height and biomass were
lower than the percent visible injury of soybean exposed to the
treated field.
According to regression results across site-years, soybean injury
and dicamba concentration in air samples followed a strong
positive relationship (generalized R2 = 0.82; Figure 3). According
to the model, the predicted concentrations of a single exposure
to volatile dicamba resulting in 10% and 20% injury were 1.60
and 3.94 ng m−3 d−1, respectively (Table 5). The level of volatilized
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
dicamba predicted to result in 50% injury to non-DR soybean was
7.17 to 8.96 ng m−3 d−1. According to research by Robinson et al.
(2013), a foliar application of dicamba at 0.20 to 0.5 g ha−1 injured
V2 soybean an estimated 10%. Similar research determined that
field application of dicamba at 56 g ha−1 resulted in an overall
9.3% injury (Sciumbato et al. 2004). It is challenging to compare
exposures by volatile herbicides to those of direct applications;
furthermore, exposure time is likely to influence plant responses.
The absorption of foliar-applied herbicides occurs primarily via
diffusion across the cuticle of the upper leaf surface (Zimdahl
2013); meanwhile, stomata penetration is an essential pathway
of absorption of volatile compounds (Currier and Dybing 1959;
Skoss 1955). Specific research is needed to quantify the absorption
rate of volatile dicamba through stomata.
The relationships between soybean height or biomass reductions by dicamba concentrations were not as strong as that with
visible injury (Figure 3). Results showed that dicamba detection
explained 43% of the height reduction data variability and 34%
of biomass reduction variability. Similar to visible injury, height
and biomass reductions were slightly lower on plants exposed to
the field after 24 HAA (data not shown). Previous research
reported that low-dose treatments containing dicamba foliar
applied to V3/V4 soybean resulted in a quadratic relationship with
soybean height (R2 = 0.42) (Griffin et al. 2013). According to the
present research, a 5% reduction in soybean height resulted from
a single exposure to a dicamba concentration of 1.94 ng m−3 d−1,
while exposure to dicamba at 3.78 ng m−3 d−1 reduced height by
10% (Table 5). According to research by Griffin et al. (2013), plant
height was reduced by 9% when sprayed directly with dicamba at
17.5 g ha−1. Due to the low generalized R2 value of the relationship,
no predictions were made between the reduction of soybean
biomass and dicamba detections (Figure 3). Even though this relationship was not strong, it indicated that increasing dicamba
concentration could reduce soybean growth. Auxin mimic herbicides may impact non-DR species by stimulating growth and internode expansion (Zimdahl 2013), which could explain the lower
R2 values for the biomass and height models.
Frequency and Concentration of Dicamba Detected within
Air in Eastern Arkansas
The daily concentration of dicamba in air samples was monitored
at two locations in eastern Arkansas from June 3 to July 21, 2020,
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Table 5. Predicted dicamba concentration in air and the lower and upper 95% confidence intervals (CI) resulted from relationships with
soybean injury (%) and height reductions from a onetime exposure.
Predicted dicamba in aira
Variables
Soybean injury (%)
10
15
20
50
Soybean height reduction (%)
5
10
Average
1.60
2.93
3.94
7.93
1.94
3.78
Lower 95% CI
———— ng m−3 ——————
1.08
2.53
3.55
7.17
0.97
2.84
Upper 95% CI
2.03
3.31
4.36
8.96
2.89
5.76
a
Predicted values resulted from an inverse prediction model using the Generalized Regression (see Figure 3). Data comprised dicamba concentration in air samples
and soybean response across experimental runs between 2018 and 2019.
Figure 4. Dicamba concentration in air (ng m−3 d−1) at Marianna, AR, and daily rainfall (mm) in 2020 (A) and 2021 (B). Dicamba was detected in 18 of 37 samples in 2020 and 33 of
47 samples in 2021. Yellow arrows indicate days for which no sample was collected.
and June 6 to July 30, 2021, near Marianna and from June 6 to July
21, 2020, and June 10 to July 29 of 2021 in Keiser. The maximum
concentration of dicamba in Marianna was 1.49 ng m−3 d−1 in 2020
and 2.79 ng m−3 d−1 in 2021 (Figure 4). The maximum daily
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
concentration of dicamba was greater in Keiser: 2.87 ng m−3 in
2020 and 7.96 ng m−3 d−1 in 2021 (Figure 5). The frequency at
which dicamba detection occurred in eastern Arkansas was
substantial, especially considering that the state prohibited
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Zaccaro-Gruener et al.: Dicamba impact on soybean
Figure 5. Dicamba concentration in air (ng m−3 d−1) at Keiser, AR, and daily rainfall (mm) in 2020 (A) and 2021 (B). Dicamba was detected in 31 of 38 samples in 2020 and 31 of 48
samples in 2021. Yellow arrows indicate days for which no sample was collected.
dicamba applications in soybean and cotton beyond the cutoff
dates of May 25, 2020, and June 30, 2021, and a 1.6-km no dicamba
spray buffer around the university research stations was established (Unglesbee 2021). Dicamba detections occurred in 18 of
37 samples (49%) and 33 of 47 samples (70%) at Marianna in
2020 and 2021, respectively (Figure 4). In Keiser, dicamba detections occurred in 31 of 38 samples (82%) in 2020 and 31 of 48
samples (65%) in 2021 (Figure 5). All detections that occurred
in 2020 were after the dicamba application cutoff date, while
45% and 70% of the detections in 2021 occurred after the cutoff
date in Keiser and Marianna, respectively, which suggests that
applications regularly occurred beyond the state-appointed
dicamba application cutoff and that volatile dicamba remained
in the atmosphere several days after applications. Susceptible
soybean in these locations exhibited dicamba injury symptomology (leaf and apical meristem malformation), as seen in
Figure 6, taken on June 28, 2021, at the Northeast Research and
Extension Center in Keiser, AR. The soybean damage shown in
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
the picture was likely caused by multiple exposures to low doses
of dicamba. Based on dicamba concentrations found in air samples
at the Keiser site where the photo was taken in 2021, there were at
least nine daily exposures to more than 1 ng m−3 d−1 of dicamba,
with one exposure of almost 8 ng m−3 d−1 (Figure 5).
ANOVA results of dicamba concentration in air samples
collected in eastern Arkansas were impacted only by year (P-value =
0.0463; Table 6). According to statistical analysis with data pooled
over locations, average dicamba concentration increased by nearly
50% when comparing samples collected in 2020 (0.51 ng m−3 d−1)
with those collected in 2021 (0.72 ng m−3 d−1). The increase in
dicamba concentrations in the air from year to year indicated that
dicamba usage in these locations in east Arkansas most likely
increased over time, which is not surprising considering the extension of the dicamba application cutoff date in 2021. Overall, the
average concentration of dicamba in air samples was 0.66 and
0.54 ng m−3 d−1 in Keiser and Marianna, respectively (Table 6).
Additionally, the sampling date impacted dicamba concentration
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Figure 6. Non–dicamba resistant soybean damage at the Northeast Research and Extension Center in Keiser, AR. This photo was taken on June 28, 2021.
in air samples (P-value = 0.0474; data not shown), which could be
attributed to weather variations each day that influenced the detection of herbicide.
Daily environmental conditions were monitored as air
sampling occurred in Marianna and Keiser and reported by location and year (Figures 4 and 5; Supplementary Tables S2–S5).
Spearman’s correlation was used to determine relationships
between environmental factors and dicamba air concentration
in eastern Arkansas. The correlation coefficients were significant
and moderate for the maximum air temperature, average relative
humidity, accumulated rainfall, and time interval since a rainfall
event (Table 7). The maximum air temperature and the time
since rain event were positively correlated with the concentration
of dicamba in air samples (coefficient = 0.24 and 0.32, respectively). Similarly, previous research reported that higher air
temperatures significantly increased dicamba volatility potential
(Bish et al. 2019a; Mueller and Steckel 2019a; Ouse et al. 2018).
Additionally, findings suggested an increase of dicamba concentration in the air as time elapsed following a rain event. Dicamba
concentration in air samples collected in eastern Arkansas was
negatively correlated with average relative humidity and accumulated rainfall (coefficient = −0.35 and −0.28, respectively; Table 7).
It may be possible that high relative humidity and rainfall promote
the settlement of dicamba vapors in soil and onto plant surfaces,
reducing concentration in air samples, which is consistent with
previous research (Behrens and Lueschen 1979; Egan and
Mortensen 2012; Mueller and Steckel 2021; Oseland et al.
2020a). Considering only the days that dicamba was not detected
across years and locations, 58% of these samples were collected the
day following a rain event (Figures 4 and 5). Additional analysis
showed that average dicamba concentration was reduced from
0.74 ng m−3 d−1 on clear days to 0.25 ng m−3 d−1 when rainfall
occurred (lowest measurable rain = 0.245 mm d−1; Table 8). It is
possible that detected dicamba was a result of rainfall events later
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
in the day; hence, the earlier detection. Furthermore, the average
dicamba concentration increased from 0.41 ng m−3 d−1 in less than
2 d from a rain event to 0.83 ng m−3 d−1 when more than 2 d
elapsed after rainfall (Table 8). These results indicated
that greater rainfall frequency reduced dicamba detection in the
atmosphere by wet herbicide deposition in the soil profile
(Gavrilescu 2005).
The experiments conducted throughout this research showed
a positive relationship between the concentration of dicamba in
air and soybean response, particularly for visible injury and
height reductions after a single exposure. For instance, a single
exposure to 1 ng m−3 d−1 of dicamba could result in 8.3% injury
and 2.5% height reduction (Figure 3). However, data collected in
eastern Arkansas at Mariana and Keiser from June and July
resulted in dicamba concentrations in air ≥1 ng m−3 d−1 in
6 samples collected in 2020 and 22 samples in 2021 (Figures 4
and 5).
According to previous research, the loading of pesticides in the
atmosphere is relative to the input of agricultural use (Waite et al.
2005). The atmospheric loading of dicamba could result in
consecutive exposures of soybean and other susceptible crops
grown surrounding DR technology. As shown in this research
(Figures 4 and 5) and previous reports, soybean damage resulting
from multiple exposures to dicamba could be economically detrimental for farmers not growing the DR technology (Steckel et al.
2017). It is important to note that the dicamba treatment evaluated
in this study, the DGA salt with VaporGrip® formulation plus
glyphosate and DRA once labeled for DR cropping systems, is
not currently a legal application, and it could represent a worstcase scenario. Recent federal and state restrictions on dicamba
applications, including limited tank-mix partners, required additional VRA, and establishment of a season cutoff (June 30 for
soybean and July 30 for cotton) aim to reduce off-target movement
of dicamba (Anonymous 2022c; USEPA 2020). Conditions that
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Table 6. Effect of year, location, and their interaction on dicamba
concentrations in air samples collected in eastern Arkansas.
Fixed effectsa
Dicamba concentration in airb
—ng m−3 d−1—
0.51 b
0.72 a
Year
2020
2021
Location
Keiser
Marianna
Location × year
Keiser × 2020
Keiser × 2021
Marianna × 2020
Marianna × 2021
0.66
0.54
0.55
0.81
0.46
0.63
a
Analysis performed using a Mixed model in JMP Pro v. 16.1, with natural logarithm–
transformed dicamba detection and considering sampling date as a random variable. ANOVA
for dicamba concentration was impacted by year (P-value = 0.0463) but not by location by
year interaction (P-value = 0.7856) or by location (P-value = 0.4689).
b
Dicamba concentration represented back-transformed values of least-squares means.
Means followed by distinct lowercase letters within the same effect category were different
according to Fisher’s protected LSD (α = 0.05).
Table 7. Spearman correlation coefficients associated with dicamba
concentration (ng m−3) in air samples collected at Marianna and Keiser, AR,
in 2020 and 2021 and environmental factors during sampling.a
Parameter
Correlations
Max. air
temperatureb
Avg.
relative
humidity
Rainfallb
Time since
rain event
0.24
−0.35
−0.28
0.32
a
Data included herbicide concentration across locations and years. Values nearest to
1 correspond to the strongest relationships. Correlation values in bold were significant at
P = 0.05. The average wind speed correlation was not shown as it was not significant.
b
Rainfall is daily rainfall accumulated.
Table 8. Means and t-test results contrasting dicamba concentration in air
(ng m−3 d−1) for comparisons of clear days vs. days with measurable rain or
more than 2 d since a rain event versus fewer than 2 d since a rain event.a
Contrast
Dicamba concentration
in air
−3
Clear days vs. days with
measurable rain
>2 d since rain vs. <2 d since
rain
P-value
−1
—ng m d —
0.74 vs. 0.25
0.83 vs. 0.41
<0.0001*
0.0059*
a
Data averaged over years and locations. The lowest amount of rain accumulated was 0.245
mm. Asterisk (*) indicates a significance of treatment effects at P = 0.05.
increase the potential for dicamba volatility should be avoided to
reduce the impacts of off-target movement, and additional
research should establish the consequences of multiple low-dose
exposures on non-targeted species.
Supplementary material. To view supplementary material for this article,
please visit https://doi.org/10.1017/wsc.2023.22
Acknowledgments. The authors are thankful for the technical support
provided by the Weed Science research associates and graduate students at
the University of Arkansas for the field components of these experiments.
The Arkansas State Plant Board provided partial funding for this research.
No conflicts of interest were declared.
https://doi.org/10.1017/wsc.2023.22 Published online by Cambridge University Press
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