Tiafenacil is a new non-selective protoporphyrinogen IX oxidase (PPO)-inhibiting herbicide with both grass and broadleaf activity labeled for preplant application to corn (Zea mays L.), cotton (Gossypium hirsutum L.), soybean [Glycine max (L.) Merr.], and wheat (Triticum aestivum L.). Early-season soybean emergence and growth often coincide in the U.S. Midsouth with preplant herbicide application in later-planted cotton and soybean, thereby increasing opportunity for off-target herbicide movement from adjacent fields. Field studies were conducted in 2022 to identify any deleterious impacts of reduced rates of tiafenacil (12.5% to 0.4% of the lowest labeled application rate of 24.64 g ai ha−1) applied to 1- to 2-leaf soybean. Visual injury at 1 wk after treatment (WAT) with 1/8×, 1/16×, 1/32×, and 1/64× rate of tiafenacil was 80%, 61%, 39%, and 21%, while at 4 WAT, these respective rates resulted in visual injury of 67%, 33%, 14%, and 4%. Tiafenacil at these respective rates reduced soybean height 55% to 2% and 53% to 5% at 1 and 4 WAT and soybean yield 53%, 24%, 5%, and 1%. Application of tiafenacil directly adjacent to soybean in early vegetative growth should be avoided, as severe visual injury will occur. In cases where off-target movement does occur, impacted soybean should not be expected to fully recover, and negative impact on growth and yield will be observed.

Mounting cases of herbicide-resistant waterhemp [Amaranthus tuberculatus (Moq.) Sauer] in the U.S. Midwest have renewed the interest in nonchemical weed management strategies. Field experiments were conducted in 2021 and 2022 to quantify the effectiveness of a commercial combine equipped with a seed impact mill in preventing A. tuberculatus seed return to the soil seedbank in soybean [Glycine max (L.) Merr.]. Amaranthus tuberculatus seed shattering before crop harvest was quantified. Amaranthus tuberculatus started shattering seeds during the last week of August in both years. Overall, 51% of A. tuberculatus seeds were retained on the plant at harvest on October 23, 2021, compared with 61% at harvest on October 7, 2022. Viability of shattered A. tuberculatus seeds ranged from 84% to 94%. Additional seed shattering occurred when plants were disturbed by the combine header during soybean harvest, which caused 15% and 9% shattering in 2021 and 2022, respectively. Amaranthus tuberculatus seeds passed through the impact mill were grouped in three categories: no damage, moderate damage, and severe damage. In 2021, A. tuberculatus seeds with moderate damage had 26% lower germination and viability than seeds with no visible damage. In 2022, seed germination and viability of no-damage seeds did not differ from seeds with a moderate level of damage. No severely damaged seed germinated or tested viable in either year. Altogether, impact mill treatment reduced the number of germinable seeds by 87% compared with the no–impact mill treatment. These results indicate that seed impact mills can be a useful tool in Iowa soybean production to help manage multiple herbicide–resistant A. tuberculatus populations. However, A. tuberculatus seed shattering before crop harvest reduces the overall effectiveness of seed impact mills in preventing seedbank replenishments.

New agronomic practices are emerging in the green cane system to utilize sugarcane (Saccharum officinarum L.) straw for energy cogeneration, which necessitates its removal from the soil surface. This study has three main objectives: (1) evaluate the population dynamics and composition of Jamaican crabgrass (Digitaria horizontalis Willd.) and large crabgrass [Digitaria sanguinalis (L.) Scop.] under different sugarcane straw amounts, with and without herbicide treatment; (2) assess the development of sugarcane under different straw amounts; and (3) determine the amount of sugarcane straw that should be kept on the soil surface after harvest to ensure that it does not compromise the chemical control for Digitaria spp. in ratoon cane in a green cane system. We conducted this research at two experimental sites, one at the beginning and the other during the middle of the harvest season, over a span of 2 yr. Our primary treatments consisted of different amounts of sugarcane straw after harvest on the soil surface (0, 5, 10, and 15 Mg ha−1), while secondary treatments included the herbicide application (sulfentrazone + tebuthiuron for the beginning of harvest season and isoxaflutole + tebuthiuron for the middle of harvest season). The Digitaria spp. exhibited higher density (four times more) and dry matter (two times more) in scenarios with a lower sugarcane straw amount (5 Mg ha−1) on the soil surface and no herbicide application. However, a higher straw amount (15 Mg ha−1) contributed to reduced Digitaria spp. infestation and to improved sugarcane yield. According to this research, it is essential to maintain at least 10 Mg ha−1 of sugarcane straw on the soil surface and remove only 5 Mg ha−1 for energy cogeneration.

Herbicide drift to sensitive crops can result in significant injury, yield loss, and even crop destruction. When pesticide drift is reported to the Georgia Department of Agriculture (GDA), tissue samples are collected and analyzed for residues. Seven field studies were conducted in 2020 and 2021 in cooperation with the GDA to evaluate the effect of (1) time interval between simulated drift event and sampling, (2) low-dose herbicide rates, and (3) the sample collection methods on detecting herbicide residues in cotton (Gossypium hirsutum L.) foliage. Simulated drift rates of 2,4-D, dicamba, and imazapyr were applied to non-tolerant cotton in the 8- to 9-leaf stage with plant samples collected at 7 or 21 d after treatment (DAT). During collection, plant sampling consisted of removing entire plants or removing new growth occurring after the 7-leaf stage. Visual cotton injury from 2,4-D reached 43% to 75% at 0.001 and 0.004 kg ae ha−1, respectively; for dicamba, it was 9% to 41% at 0.003 or 0.014 kg ae ha−1, respectively; and for imazapyr, it was 1% to 74% with 0.004 and 0.03 kg ae ha−1 rates, respectively. Yield loss was observed with both rates of 2,4-D (11% to 51%) and with the high rate of imazapyr (52%); dicamba did not influence yield. Herbicide residues were detected in 88%, 88%, and 69% of samples collected from plants treated with 2,4-D, dicamba, and imazapyr, respectively, at 7 DAT compared with 25%, 16%, and 22% when samples were collected at 21 DAT, highlighting the importance of sampling quickly after a drift event. Although the interval between drift event and sampling, drift rate, and sampling method can all influence residue detection for 2,4-D, dicamba, and imazapyr, the factor with the greatest influence is the amount of time between drift and sample collection.

Machine vision–based herbicide applications relying on object detection or image classification deep convolutional neural networks (DCNNs) demand high memory and computational resources, resulting in lengthy inference times. To tackle these challenges, this study assessed the effectiveness of three teacher models, each trained on datasets of varying sizes, including D-20k (comprising 10,000 true-positive and true-negative images) and D-10k (comprising 5,000 true-positive and true-negative images). Additionally, knowledge distillation was performed on their corresponding student models across a range of temperature settings. After the process of student–teacher learning, the parameters of all student models were reduced. ResNet18 not only achieved higher accuracy (ACC ≥ 0.989) but also maintained higher frames per second (FPS ≥ 742.9) under its optimal temperature condition (T = 1). Overall, the results suggest that employing knowledge distillation in the machine vision models enabled accurate and reliable weed detection in turf while reducing the need for extensive computational resources, thereby facilitating real-time weed detection and contributing to the development of smart, machine vision–based sprayers.

Cereal rye (Secale cereale L.) cover crop and preemergence herbicides are important components of an integrated weed management program for waterhemp [Amaranthus tuberculatus (Moq.) Sauer] and Palmer amaranth (Amaranthus palmeri S. Watson) management in soybean [Glycine max (L.) Merr.]. Accumulating adequate cereal rye biomass for effective suppression of Amaranthus spp. can be challenging in the upper Midwest due to the short window for cereal rye growth in a corn–soybean rotation. Farmers are adopting the planting green system to optimize cereal rye biomass production and weed suppression. This study aimed to evaluate the feasibility of planting soybean green when integrated with preemergence herbicides for the control of Amaranthus spp. under two soybean planting time frames. The study was conducted across 19 site-years in the United States over the 2021 and 2022 growing seasons. Factors included cover crop management practices (“no-till,” “cereal rye early-term,” and “cereal rye plant-green”), soybean planting times (“early” and “late”), and use of preemergence herbicides (“NO PRE” and “YES PRE”). Planting soybean green increased cereal rye biomass production by 33% compared with early termination. Greater cereal rye biomass production when planting green provided a 44% reduction in Amaranthus spp. density compared with no-till. The use of preemergence herbicides also resulted in a 68% reduction in Amaranthus spp. density compared with NO PRE. Greater cereal rye biomass produced when planting green reduced soybean stand, which directly reduced soybean yield in some site-years. Planting soybean green is a feasible management practice to optimize cereal rye biomass production, which, combined with preemergence herbicides, provided effective Amaranthus spp. management. Soybean stand was a key factor in maintaining soybean yields compared with no-till when planting green. Farmers should follow best management recommendations for proper planter and equipment setup to ensure effective soybean establishment under high levels of cereal rye biomass when planting green.

Effective pesticide application is dependent on precise and sufficient delivery of active ingredients to targeted pests. Water-sensitive papers (WSPs) have been used to estimate the stain coverage, droplet density, droplet size, total spray volume, and other spray-quality metrics by analyzing deposit stains using image analysis software. However, because WSPs are expensive, they are typically distributed along unidimensional transects at intervals of 0.5 m or more, which comprises 0.5% or less of the total treated area. This might limit the ability to accurately represent the deposition of agricultural sprayers with irregular patterns, such as agricultural drone sprayers in the early developmental stage. This study introduces a novel approach utilizing white Kraft paper and a blue colorant proxy for assessing spray deposition. A custom Python-based image analysis tool, SprayDAT (Spray Droplet Analysis Tool), was developed and compared with traditional image analysis software, DepositScan. Both models showed increased accuracy in detecting larger objects, with SprayDAT generally performing better for smaller droplets. DepositScan underestimated the total deposited spray volume by up to 2.7 times less compared with the colorant extraction assessed via spectrophotometry and the predicted output based on flow rate, coverage, and speed. Accuracy of software-estimated spray volume declined with increasing total stain coverage, likely due to overlapping stain objects. Droplet density exhibited a Gaussian trend, with peak density at approximately 22% stain cover, offering evidence for overlapped stains for both DepositScan and SprayDAT as stain cover increased. Both models showed exponential growth in volumetric median diameter (VMD) with increasing stain cover. SprayDAT is freely accessible through an online repository. It features a user-friendly interface for batch processing large sets of scanned images and offers versatility for customization to meet individual needs, such as adjusting spread factor, updating the standard curve for spray volume estimation, or modifying the stain detection threshold.

Soybean [Glycine max (L.) Merr.] that lack resistance to auxin herbicides [i.e., not genetically modified for resistance] have well-documented responses to those particular herbicides, with yield loss being probable. When a soybean field is injured by auxin herbicides, regulatory authorities often collect a plant sample from that field. This research attempted to simulate soybean exposures due to accidental mixing of incorrect herbicides, tank contamination, or particle drift. This research examined whether analytical testing of herbicide residues on soybean to aminocyclopyrachlor (ACP), aminopyralid, 2,4-D, or dicamba would be related to the visual observations and yield responses from these herbicides. ACP and aminopyralid were applied to R1 soybean at 0.1, 1, and 10 g ae ha−1; 2,4-D and dicamba were applied at 1, 10, and 100 g ae ha−1. Visual evaluations and plant sample collections were undertaken at 1, 3, 7, 14, and 21 d after treatment (DAT), and yield was measured. The conservative limits of detection for the four herbicides in this project were 5, 10, 5, and 5 ng g−1 fresh weight of soybean for ACP, aminopyralid, 2,4-D, and dicamba, respectively. Many of the plant samples were non-detects, especially at lower application dosages. All herbicide concentrations rapidly declined soon after application, and many reached nondetectable limits by 14 DAT. All herbicide treatments caused soybean injury, although the response to 2,4-D was markedly lower than the responses to the other three herbicides. There was no apparent correlation between herbicide concentrations (which were declining over time) and the observed soybean injury (which was increasing over time or staying the same). This research indicated that plant samples should be collected as soon as possible after soybean exposure to auxin herbicides.

Agricultural spray drone (ASD) use in managed turfgrass has been given limited attention in the scientific literature. Further, deposition patterns of ASD spray have been obscured in previous research by ambient wind, crop canopy interference, and limited sampling resolution. Using a continuous sampling method involving blue colorant and water sprayed over white Kraft paper that was assessed via digital image analysis of stain objects and referenced spectrophotometric analysis of extractant, deposition metrics were estimated across a 29.3-m transect perpendicular to an ASD or ground-sprayer spray swath. The ASD applies very fine droplets that are highly concentrated with herbicide, similar to ultra–low volume treatments, that improved smooth crabgrass [Digitaria ischaemum (Schreb.) Schreb. ex Muhl.] control compared with a ground sprayer when the ASD was operated 2 m above the turf. Unfortunately, these very fine droplets also drift, leading to four times greater droplet density at distance of almost 12 m away from the targeted spray swath following an operational height of 10 m compared with 2 m. As ASD operational height increases, drift and effective swath width at 30% coefficient of variation uniformity increases, while effective application rate, total deposition, and D. ischaemum control by quinclorac herbicide decreased. Total deposition decreased 6% for each meter increase in ASD operational height, likely due to evaporation. The potential losses due to evaporation are a serious consideration for ASD use that has received little attention in the scientific literature. Our data suggest that ASD operational height should be as low as possible, but modification of spray systems may be needed to improve homogeneity of spray pattern.

Downy brome (Bromus tectorum L.) is a difficult species to control in the dryland wheat (Triticum aestivum L.) production areas of northeastern Oregon. The selection of herbicide-resistant B. tectorum populations has further complicated B. tectorum management. A survey of wheat growers was conducted in 2021 and 2022 to understand B. tectorum management practices. The survey included four questions based on the growing seasons from 2017 to 2022 related to crop rotation, tillage versus no-tillage, irrigation versus dryland, and herbicide programs. To determine the extent of herbicide resistance, seeds were collected from 49 B. tectorum populations in wheat fields in northeastern Oregon and tested for resistance. Herbicides tested were clethodim, glyphosate, imazamox, mesosulfuron, metribuzin, propoxycarbazone, pyroxasulfone, pyroxsulam, quizalofop, and sulfosulfuron. Winter wheat–summer fallow rotation was the most predominant cropping system in the region. Most of the fields were in no-tillage systems, and none were irrigated. Pyroxasulfone applied preemergence and acetolactate synthase (ALS) inhibitors applied postemergence were the most often used herbicides for B. tectorum control in winter wheat. Glyphosate was the most frequently used herbicide for B. tectorum control in summer fallow. Resistance screenings confirmed that 46 of the 49 B. tectorum populations were resistant to ALS inhibitors with different cross-resistance patterns. Two populations were resistant to metribuzin and exhibited multiple resistance to ALS inhibitors. All populations were susceptible to clethodim, glyphosate, pyroxasulfone, and quizalofop. The widespread occurrence of ALS inhibitor–resistant B. tectorum populations limits effective postemergence herbicide options in winter wheat.

Purple witchweed [Striga hermonthica (Delile) Benth.], a highly destructive parasitic weed, poses a significant threat to sorghum [Sorghum bicolor (L.) Moench] cultivation. This hemiparasitic plant intrudes its root system into the host plant, leading to substantial yield losses, particularly in susceptible genotypes. In the pursuit of eco-friendly solutions, the biocontrol approach has gained attention as a potential management strategy for Striga. In this study, 13 bacterial strains belonging to the genera Bacillus, Gluconobacter, Pseudomonas, and Streptomyces were investigated in vitro for their efficiency in controlling the early-stage development of Striga. Among the tested strains, Streptomyces morookaensis NRRL B-12429 demonstrated significant inhibition of Striga seed germination and radicle elongation at 54.36% and 61.84%, respectively, when applied to preconditioned seeds with a synthetic germination stimulant. The effect of S. morookaensis on the inhibition of Striga seed germination was more pronounced in the presence of the host plant, sorghum, at 62.35%. However, biopriming of sorghum seeds with S. morookaensis did not enhance the inhibitory effects on Striga seed germination but resulted in a greater reduction in radicle elongation at 74.64% compared with non-primed seeds. Additionally, the biopriming with S. morookaensis promoted the growth of shoots and roots of germinating sorghum, regardless of the presence of Striga seeds. These findings highlight the potential of S. morookaensis strain NRRL B-12429 as a viable candidate for biocontrol agent applications in sorghum cultivation. Further exploration and investigation of its biocontrol capabilities can provide valuable insights for sustainable management practices against Striga infestations.