The water flowed by way of gravity into a 125 mL beaker, leaving debris behind. Filter papers were changed periodically as needed. Then, 50 mL were allocated from the filtered sample and placed in storage at -20° C until analysis. A standard for pendimethalin, ACS-grade hexane and MS-grade acetonitrile were obtained from Fisher Scientific. Liquid-liquid extraction methods were modified from USEPA . High pressure liquid chromatography tandem mass spectrometry was employed to analyze for residue in water samples. Fifteen mL samples were extracted three times with 3 mL of hexane and placed on a rotary platform shaker for 5 minutes, then set aside for 15 minutes. Hexane extracts were pooled and 3 mL were then dried under a nitrogen gas stream. Then, volumes of 500 µL acetonitrile were added to the dried sample and vortexed. Volumes of 500 µL 0.4% formic acid was then added and vortexed for a final concentration factor of 15. A Shimadzu LCMS-8040 triple quadrupole mass spectrometer was used equipped with electrospray ionization on positive mode. The desolvation line temperature and heat block temperature were 250° and 400° C, respectively. Nebulizing gas and drying gas were set at a flow of 3 L min-1 and 15 L min-1 , respectively. The mobile phase flow rate was 0.4 mL min-1 and an injection volume of 10 µL. The C18 column was Phenomenex Kinetex polar, 100 by 3.0 mm and 2.6 µm particle size. The multiple reaction monitoring ion transitions for the quantifier ionwere 282.0 > 212.1 m/z in a dwell time of 10 ms and for the qualifier ions were 282.2 > 43.1 m/z and 282.2 > 194 m/z in a dwell time of 5 ms. The limit of detection was 0.006 µg L-1 and the limit of quantification was 0.008 µg L-1. Multiple calibration curves were implemented for the low concentration range and for the high concentration range using Shimadzu LabSolutions and MacCoss Skyline software for small molecules. Method recovery was performed by spiking five non-treated collected water samples with 0.20 µg L-1 of pendimethalin before extraction . A low concentration of pendimethalin below 0.05 µg L-1 was present in the collected non-treated samples, therefore, cannabis dryingn racks the peak areas of the control samples without standard spiking were subtracted from the spiked samples.

The recovery in water samples was on average 79%.A significant increase in sweet potato [Ipomoea batatas Lam.] production area in the southeastern United States has occurred in the past decade, increasing from 33,548 ha in 2007 to 51,800 ha in 2017 . Sweetpotato has proven to be a valuable crop with a national farm gate value of $705.7 million in 2016, up from $298.4 million in 2006 . North Carolina is the largest sweetpotato-producing state, accounting for 54% of U.S. production . North Carolina, California, Mississippi, and Louisiana account for 94% of sweetpotato production in the United States . Unfortunately, due to its prostrate growth habit and relatively slow growth, sweetpotato does not compete well with problematic weeds, resulting in reduced yields . Palmer amaranth and large crabgrass [Digitaria sanguinalis Scop.] are among the top five most common weeds in North Carolina sweetpotato, with A. palmeri being identified as the most troublesome weed . Amaranthus palmeri has been reported to be taller, to have a faster growth rate and greater leaf area, and to produce more overall biomass when compared with other Amaranthus species . Season-long A. palmeri interference is seen in vegetable crops, with reduced yield of 94% in bell pepper , 67% in tomato , 36% to 81% in sweetpotato , with the greater yield losses associated with higher A. palmeri densities. Limited herbicide options exist for use in sweetpotato . Growers rely on PRE herbicides, which do not always provide efficacious weed control and require rainfall for activation. POST herbicide options for A. palmeri control in sweetpotato are limited to between-row applications of carfentrazone or glyphosate . The lack of POST herbicides forces growers to use tillage for control of weeds until row closure, at which time growers have no additional control options for dicotyledonous weeds other than mowing weeds above the cropcanopy and hand weeding, which is a costly control measure . Digitaria sanguinalis is commonly found in fruit and vegetable crops but has not been highly ranked as a problematic weed due to efficacious POST herbicides such as clethodim, fluazifop, or sethoxydim . Although these graminicides can be effective, grasses escaping herbicide application or sprayed after substantial establishment may continue to compete with the crop and reduce yields.

Furthermore, herbicide resistance management for D. sanguinalis should be considered, as resistance to acetyl-CoA carboxylase herbicides, including those registered for use in sweetpotato has been reported . While its impact on sweetpotato has not been reported, season-long, D. sanguinalis reduced yield in bell pepper by 46% , snap bean by 47% to 50% , and watermelon [Citrullus lanatus Matsum. & Nakai] by 82% . A better understanding of the interactions of A. palmeri and D. sanguinalis with sweetpotato would allow for better decision making regarding their control. Thus, the objectives of this study were to determine the effect of five densities of A. palmeri and D. sanguinalis on sweetpotato biomass and storage root yield and quality, the intraspecific response of A. palmeri and D. sanguinalis across five densities with and without sweetpotato, and the effect of sweetpotato on growth of A. palmeri and D. sanguinalis.Field studies were conducted with ‘Covington’ sweetpotato at the Horticultural Crops Research Station near Clinton, NC on a Norfolk loamy sand with humic matter 0.31% and pH 5.9 in 2016 and an Orangeburg loamy sand with humic matter 0.47% and pH 5.9 in 2017. Nonrooted ‘Covington’ sweetpotato 20- to 30-cm-long cuttings were mechanically planted approximately 7.6-cm deep into ridged rows 1 m apart in the entire study at an in-row spacing of approximately 30 cm on June 9, 2016, and June 12, 2017. At 1 d after transplanting, sweetpotato plants were removed by hand in the no-sweetpotato treatments. On the same day, treatment rows assigned A. palmeri or D. sanguinalis were broadcast seeded on the soil surface and lightly raked to a depth of approximately 1.0 cm. After weed seeding, the entire study was irrigated with 1.3 cm of water using overhead irrigation to aid in weed seed establishment. No additional irrigation was applied, in either year, after the initial irrigation event. Treatments consisted of a single weed species at five weed densities grown with and without sweetpotato arranged in a randomized complete block design with three replications . Amaranthus palmeri and D. sanguinalis were hand thinned to treatment densities of 0 , 1, 2, 4, and 8 and 0 , 1, 2, 4, and 16 plants m−1 of row, respectively, when A. palmeri was approximately 8 cm tall, and D. sanguinalis had two expanded leaves. At the time of weed thinning, sweetpotato averaged one to two newly expanded leaves on each plant. Densities of A. palmeri and D. sanguinalis were based on those used in previous research . Plots consisted of two bedded rows, each 1-m wide by 5-m long, with the first row being a weed-free buffer row planted to sweetpotato and the second row a treatment row. Treatment rows were maintained at specific weed treatment densities, and border rows were maintained free of weeds season-long by weekly removal by hand. Season-long rainfall and growing degree day data are presented in Table 1. Two days before sweetpotato harvest, 5 sweetpotato plants and 5 plants of each weed species were randomly harvested at the soil level from each plot to determine aboveground biomass. Samples were placed in 2-ply paper yard waste bags measuring 40 by 30 by 89 cm and fresh biomass was recorded. Samples were then placed in a propane-heated, forced-air drier for 96 h at 80 C. Once dry, samples were removed and weighed immediately to determine dry biomass. To determine fresh and dry sweetpotato and weed biomass on a per plant basis, total sweetpotato or weed biomass within a treatment and replication was divided by the number of plants harvested. To determine dry biomass per meter of row, individual weed biomass was multiplied by sweetpotato plant and/or weed number in 1 m of row, respectively.

Sweetpotato storage roots were harvested at 113 d after transplanting in 2016 and at 107 DAT in 2017. In both years storage roots were harvested with a tractor-mounted two-row chain digger and hand sorted into jumbo , no. 1 , and canner grades and weighed. Total marketable yield was calculated as the sum of jumbo and no. 1 grades. Data for crop biomass, vertical growing weed individual weed biomass, weed biomass per meter of row, yield, and quality were subjected to ANOVA using PROC MIXED in SAS . Treatment, year, and treatment by year were considered fixed effects, while replication within year was treated as a random effect. Year was treated as a fixed effect to further evaluate components of the year by treatment interaction, such as year by weed density and year by crop presence or absence. If the treatment by year interaction was not significant, a contrast statement was used to test for a linear trend for dependent variables with increasing weed density, calculated separately for each weed species. All response variables, except canner yield, were square-root transformed to reduce both data skewness and variance heterogeneity before carrying out the mixed model ANOVA.Marketable yield decreased as the density of A. palmeri or D. sanguinalis increased. No treatment by year interaction for sweetpotato yield was observed ; therefore, data were combined over years. Marketable yield loss associated with A. palmeri density ranged from 50% with 1 A. palmeri plant m −1 of row to 79% with 8 plants m−1 of row, respectively, when compared with the weed-free check . Marketable yield reduction by D. sanguinalis was similar to marketable yield reduction caused by A. palmeri but at higher weed densities. Marketable yield was reduced by 35% and 76% with 1 and 16 D. sanguinalis plants m−1 of row, respectively . Loss of jumbo yield is a significant contributor to overall marketable yield loss at weed densities as low as 1 plant of either species m−1 . Jumbo grade had greater yield loss with 1 plant m−1 for A. palmeri and D. sanguinalis than the no. 1 gradefor both weed species at the same density . Results for estimated marketable yield loss per weed as weed density approaches zero for A. palmeri and D. sanguinalis were 119% and 61%, respectively. The higher estimated marketable yield loss as weed density approaches zero for A. palmeri relative to D. sanguinalis indicated higher competitive capacity of A. palmeri at low densities. These results for A. palmeri are consistent with another study in sweetpotato but higher than in soybean [Glycine max Merr.] , peanut , and corn . Estimated yield loss as weed density approaches zero in the present study indicates that A. palmeri and D. sanguinalis, even at low densities, can greatly reduce sweetpotato marketable yield. The initial yield loss as weed density approaches zero for D. sanguinalis was less than A. palmeri at lower densities. However, sweetpotato yield loss from interference byD. sanguinalis was higher than yield loss reported in snap bean . For parameter A, the asymptote of the regression model estimating the maximum yield loss due to weed density was 87% for A. palmeri and 83% for D. sanguinalis. Meyers et al. estimated a maximum marketable yield loss of 90% at A. palmeri densities of 6.5 plants m−1 of sweetpotato row. Findings from our study further support the findings of Meyers et al. , who also reported the highly competitive nature of A. palmeri with sweetpotato. To reduce interference of A. palmeri and D. sanguinalis, which are commonly reported in sweetpotato, growers should use a combination of efficacious PRE herbicides, as outlined by Meyers et al. , in combination with tillage, hand removal, and mowing . Although POST herbicides for A. palmeri are limited, POST herbicide options for selective grass control in sweetpotato are available and should be used when D. sanguinalis is less than 10 cm to minimize yield loss. If D. sanguinalis resistance is suspected, then alternative methods should be analyzed for control. Growers should not dismiss the impact of either weed, as a single A. palmeri or D. sanguinalis per meter of row reduced marketable yield by 50% and 35%, respectively . Reduction in marketable yield loss was due to a decrease in weight of no. 1 and jumbo sweetpotato grades.

Visual percent rice injury assessments were carried out at 20 DAT and 40 DAT by observing present symptomology, which included stand reduction and stunting, and compared to the non-treated, on a scale of 0 to 100, where 0=no injury and 100=plant death. Rice tiller counts were conducted at 75 DAS by sampling twice within 30-cm by 30-cm quadrat in each plot and data scaled to a meter squared area for presentation. Rice grain was hand harvested from two 1-m2 quadrats in each plot and mechanically threshed . Grain was then cleaned and weighed, and adjusted to 14% moisture.An experiment to compare rice cultivar response to pendimethalin was conducted at the Rice Experiment Station greenhouse in Biggs, CA. A factorial arrangement of treatments in a completely randomized design was implemented. The factors were five cultivars, two formulations, two timings and two rates. The rice cultivars consisted of ‘S-102,’ ‘M-105,’ ‘M- 205,’ ‘M-206,’ and ‘M-209.’ These rice cultivars represent common short-grain and mediumgrain cultivars produced in California. CS and GR formulations were applied at 5 and 10 DAS at1.1 kg ai ha-1 and 2.3 kg ai ha-1 . Three experimental runs were conducted separated by time. The first run was seeded on January 15, 2021, the second run on March 7, 2021 and the third run on April 20, 2021. Field soil with similar characteristics to the field site soil above, was used to fill 34-cm by 12-cm by 12-cm plastic containers, with drainage openings on the bottom, and placed inside larger 58-cm by 41-cm by 31-cm plastic containers, vertical racking with no drainage. Seeds were pregerminated by placing the different cultivar seeds inside cloth bags and in five-gallon buckets completely submerged underwater for 24 h, and then seeds were air dried before sowing.

Twenty seeds were sown in each smaller container by placing the seed on the soil surface in a shallow flood onto the soil surface. The larger containers were immediately filled with water up to 10-cm above the soil level and maintained at that level throughout the study. Starting after the day of seeding, each smaller container was treated as a plot and was set in a completely randomized placement and rerandomized every seven days. Copper sulfate crystals were applied by hand at 13 kg ha-1 three DAS for control of algae in each container for each run. The emerged rice seeds were counted before the pendimethalin applications and at 21 DAT to calculate the percent rice stand survival. At 20 DAT, plant height was measured from the soil surface to the far most extended leaf end in each plot. At 21 DAT, above ground biomass was harvested from each plot and dry biomass was recorded. The greenhouse was maintained at 33/25 ± 2C day/night temperature. A 16-hr photoperiod was provided and natural light was supplemented with metal halide lamps at 400 µ mol m-2 sec-1 photosynthetic photon flux. The CS formulation was applied using a track-sprayer at 187 L ha-1 with a single 8001EVS nozzle by placing container inside the spray chamber with a height of 43 cm from the surface of the floodwater to the spray nozzle. The GR formulation was spread by hand in each respective tub, calculated by the area of the larger plastic container.All statistical analysis was conducted on R with the use of the LMERTEST and EMMEANS packages . Data was subjected to linear mixed effects regression models and mean separation, when appropriate, with Tukey’s HSD at α=0.05. In the field study, the model consisted of the three formulations, three rates, three application timings as fixed factors, and assessment dates as repeated measure, while replications were set as random separately each year. In the greenhouse study, the model consisted of two formulations, two rates, two application timings, and five cultivars as fixed factors, while experimental runs were treated as random.

Normality of distribution were visually examined with quantile-quantile plots and linearity were visually examined by plotting residuals.There was interaction by year for Echinochloa spp. control . In 2020, 330 ± 8 Echinochloa spp. plants m-2 was observed in the non-treated, while in 2021, 180 ± 2 Echinochloa spp. plants m-2 was observed by 56 DAT . The field site previously recorded variations in weed species populations by year caused by differences in weather conditions and soil seedbank . The cyhalofop and propanil application influenced the grass control levels observed in 2020. Interaction effect across formulation with timing were observed for Echinochloa control both years . The interaction of formulations with timings in 2020 demonstrated a reduction in Echinochloa control as application timing was delayed from 5 to 15 DAS with theEC formulation; however, the differences were not observed after application of GR and CS formulations . In 2021, the interaction of formulations with timings demonstrated a decrease in Echinochloa control as application timing was delayed from 5 to 15 DAS with the EC and CS formulation, but again not with the GR formulation . Application rates impacted grass control across timings in 2020 and across formulation in 2021 Interaction of rate with timing in 2020 and rate with formulation in 2021 were observed . The Echinochloa control results are not consistent with Ahmed and Chauhan findings who repeatedly demonstrated an increase in grass control with an increase in pendimethalin rates in a dry-seeded rice system. In the water-seeded rice system, pendimethalin degradation will be increased compared to a dry-seeded system ; therefore, greater pendimethalin rates may be necessary to observe an effect. Transformations on the sprangletop control data did not help meet the assumptions of normality of distribution; therefore, the data is presented as if normality was met. Only pendimethalin timing and rate appeared to affect sprangletop control .

The bearded sprangletop population is minimal and previously observed by Brim-DeForest et al. . Therefore, the control results from pendimethalin may not be comparable to fields with greater sprangletop pressure and because of the population differences each year control levels are unclear. In this study, the flood was continuous and pendimethalin application was into the water. The flood may have also been a factor in suppression of sprangletop .There was treatment interaction by year for visual rice injury but not across assessment dates . Injury differed across formulation, rate and timing . Rice treated at the 15 DAS timing had the lowest injury levels, but differed across formulations . Theresults demonstrate that different formulations resulted in varying rice injury levels, which is similar to the results of Hatzinikolaou et al. who evaluated pendimethalin injury on various grass crop species. In 2020, tiller counts ranged from 30 to 200 tillers m-2 . In 2021, however, tiller counts were higher, ranging from 200 to 500 m-2 . After a GR and CS application, rice tillers were similar across timings; however, after EC application at 15 DAS tillers was higher. The rice treated at 15 DAS produced similar tiller numbers when treated at 10 DAS but not when treated at 5 DAS with pendimethalin applied at the 2.3 and 3.4 kg ha-1 from the EC formulations . Differences in formulations by application timings was evident and resulted in varying injury levels effected by the formulation. The greater weedy grass pressure in 2020 may have been a factor in the increase on visual rice injury and decrease in rice stands compared to 2021. Rice treated with pendimethalin showed increased injury with increasing rates when applied at the 5 and 10 DAS; however, at 15 DAS, injury was similar across rates, which suggests that after rice reaches the 3- to 4-leaf stage, rolling benches pendimethalin injury may not impact rice development. Absorption of pendimethalin can cause greater growth disturbance at earlier seedling stages when the grass seedling coleoptile is emerging at the surface of the soil and comes in contact with the herbicide as demonstrated by Knake and Wax with the grass weed, giant foxtail. Pendimethalin remains on the upper soil surface due to its physico-chemical properties ; therefore, once the seedling growing points are further above the soil surface there is a potential to overcome pendimethalin injury.An interaction in year was observed for grain yield. Interaction effect by formulation with timings were observed for grain yield . In both years, rice grain yield was similar across timings with the GR and CS formulations, but not with the EC formulation . Timing was most influential on grain yield with the EC. Overall, similar grain yield was achieved from rice treated with the GR across all rates and timings in 2020 and similarly in 2021 . The GR is formulated as a slow-release of the active ingredient which results in a reduction of crop injury . These characteristics of the GR may have allowed more rice seedlings to establish by not being exposed to high concentrated levels of the active ingredient at once. There was a rate by timing interaction for grain yield . Rice treated with 1.1 kg ha-1 at all timings produced similar grain yield in both years, which were similar to yield in plots when treated with 2.3 kg ha-1 at 10 and 15 DAS, and with 3.4 kg ha-1 at 15 DAS . Pendimethalin applied to rice at 3.4 kg ha-1 at 15 DAS timing had greater yield by 3,014 kg ha-1 of grain in both years when compared to the 5 and 10 DAS timings at 3.4 kg ha-1 .

The results demonstrate that formulation, rate and timing are important factors affecting grain yield in water-seeded rice with use of pendimethalin. An application of pendimethalin in dry-seeded rice in Bangladesh decreased grain yields by 44% to 50% when pendimethalin was applied 2 DAS compared to the weed-free check . Application timing or soil saturation timing is an important influence on rice injury after a pendimethalin application in dry-seeded systems . In the water-seeded system, application timing is the important factor. While not included in the analysis, the grain yields of the non-treated plots in 2020 were extremely weedy and attempts to harvest failed and yield was recorded as zero. In 2021, thenon-treated plots averaged yields of 2,450 ± 340 kg ha-1 . The yields recorded in this study after pendimethalin treatment were low compared to statewide average yields and potentially affected by the pendimethalin application.Stand reduction was influenced by cultivar, formulation, rate and timing . In general, rice treated at 5 DAS resulted up to 68% stand reduction across cultivars for both CS and GR formulations . At 5 DAS, stand was reduced after application of both formulations for ‘M-105’, ‘M-205’, ‘M-206’ and ‘M-209’ . Only ‘S-102’ at the 5 DAS timing resulted in less than 54% reduction . At 10 DAS, ‘S-102’ and ‘M-206’ did not show stand loss across rates, while ‘M-105’ resulted up to 21% decrease in stand after a 2.3 kg ha-1 application compared to a 1.1 kg ha-1 . However, stand reduction after 10 DAS applications were zero to 29% for all cultivars . Koger et al. observed differential cultivar response from pendimethalin applications on long grain rice in a dry-seeded system. Relative tolerance was attributed to mesocotyl length of seedling rice which may vary by cultivar; however, planting depth is also an important factor in dry-seeded rice for achieving pendimethalin tolerance . In water-seeded rice, a mesocotyl is very short on seedlings because the seeds are placed on the soil surface; however, differences in seedling vigor can be important for relative tolerance to pendimethalin. Ceseski and Al-Khatib observed ‘M-205’ and ‘M-209’ to have greater seedling vigor when compared to ‘M-105’ and ‘M-206’, when drill-seeded in a high clay soil. The cultivar vigor characteristic differences can help understand the observed relative tolerance to pendimethalin across cultivars in this Rice biomass was affected by pendimethalin rate and timing . The higher rate was an important factor in decreasing biomass for ‘S-102’ at the 5 DAS from CS and GR applications at 2.3 kg ha-1 . Dry biomass was reduced by 77% at 5 DAS compared to the 10 DAS timing averaged across formulations, rates, and cultivars. However, biomass reduction was minimal and not significant at 10 DAS, except for ‘M-205’ at 2.3 kg ha-1 GR formulation . Awan et al. observed a decrease in rice seedling biomass in dry-seeded rice when pendimethalin was applied at 2.0 kg ha-1 , but not at 1.0 kg ha-1 . Similarly, in this study biomass reduction was rate-dependent for ‘M-205’. Plant height was no different among treatments and were similar to the non-treated by time of biomass harvest .