Herbicides have been placed in global Herbicide Resistance Action Committee (HRAC) herbicide groups based on their sites of action (e.g., acetolactate synthase–inhibiting herbicides are grouped in HRAC Group 2). A major driving force for this classification system is that growers have been encouraged to rotate or mix herbicides from different HRAC groups to delay the evolution of herbicide-resistant weeds, because in theory, all active ingredients within a herbicide group physiologically affect weeds similarly. Although herbicide resistance in weeds has been studied for decades, recent research on the biochemical and molecular basis for resistance has demonstrated that patterns of cross-resistance are usually quite complicated and much more complex than merely stating, for example, a certain weed population is Group 2-resistant. The objective of this review article is to highlight and describe the intricacies associated with the magnitude of herbicide resistance and cross-resistance patterns that have resulted from myriad target-site and non–target site resistance mechanisms in weeds, as well as environmental and application timing influences. Our hope is this review will provide opportunities for students, growers, agronomists, ag retailers, regulatory personnel, and research scientists to better understand and realize that herbicide resistance in weeds is far more complicated than previously considered when based solely on HRAC groups. Furthermore, a comprehensive understanding of cross-resistance patterns among weed species and populations may assist in managing herbicide-resistant biotypes in the short term by providing growers with previously unconsidered effective control options. This knowledge may also inform agrochemical company efforts aimed at developing new resistance-breaking chemistries and herbicide mixtures. However, in the long term, nonchemical management strategies, including cultural, mechanical, and biological weed management tactics, must also be implemented to prevent or delay increasingly problematic issues with weed resistance to current and future herbicides.

Knowledge of weed seeds present in the soil seedbank is important for understanding population dynamics and forecasting future weed infestations. Quantification of the weed seedbank has historically been laborious, and few studies have attempted to quantify seedbanks on the scale required to make management decisions. An accurate, efficient, and ideally automated method to identify weed seeds in field samples is needed. To achieve sufficient precision, we leveraged YOLOv8, a machine learning object detection to accurately identify and count weed seeds obtained from the soil seedbank and weed seed collection. The YOLOv8 model, trained and evaluated using high-quality images captured with a digital microscope, achieved an overall accuracy and precision exceeding 80% confidence in distinguishing various weed seed species in both images and real-time videos. Despite the challenges associated with species having similar seed morphology, the application of YOLOv8 will facilitate rapid and accurate identification of weed seeds for the assessment of future weed management strategies.

Opportunistic use of limited resources is often attributed to invasive species, and as a mature vine, old man’s beard (Clematis vitalba L.) is known to have devastating negative impacts on the trees it colonizes. No previous experimental studies have been published on how easily C. vitalba seedlings can colonize ground covered by other established vegetation. This species has had an increasing presence in forestry blocks and riparian zones in New Zealand, both of which usually maintain some grass cover. To determine the importance of vegetative ground cover for preventing ingress of new C. vitalba plants, this study looked at seedling emergence through the soil and establishment of C. vitalba within four different levels of grassy cover at three sites: (1) ground kept bare after vegetation removal; (2) ground bare at C. vitalba seed sowing, but thereafter allowed to recover; (3) vegetative cover trimmed to 4 cm high at C. vitalba sowing, and then allowed to recover; and (4) unmanaged vegetation. At the highest level of vegetation density (unmanaged vegetation), no C. vitalba seedlings were ever detected throughout a 1-yr monitoring period. At lower ground cover densities, poor seedling emergence was observed, with a maximum of 36% of seeds sown in bare plots producing a seedling. Also, seedlings did not survive past 1 yr, except in bare plots or in plots where vegetation grew sparsely. However, seedlings that did survive began producing multiple stems within 6 mo of emergence. These results indicate that obstacles to seedling emergence and poor development at the young seedling stage when vegetative cover is dense severely limit C. vitalba’s chances to invade new sites via seed. Yet some successful seedling recruitment does occur due to the magnitude of the propagule pressure on the landscape and the difficulty of maintaining high-density ground cover across large areas throughout the year.

Goosegrass [Eleusine indica (L.) Gaertn.] is one of the most problematic weeds in plasticulture strawberry [Fragaria × ananassa (Weston) Duchesne ex Rozier (pro sp.) [chiloenis × virginiana]] production systems in Florida. A 2-yr trial was implemented to evaluate the effects of different E. indica densities on strawberry growth and yield. Eleusine indica densities evaluated were 0, 0.4, 0.8, 1.9, 2.7, and 3.8 plants m−2 equally distributed on the plastic-mulched bed top within the strawberry transplant holes. Eleusine indica density did not affect E. indica height or biomass. However, E. indica seed production was positively correlated with E. indica density in Season 1 and negatively correlated with E. indica density in Season 2. A negative linear regression was observed between E. indica density and strawberry yield in both seasons. For each increase in E. indica plants per square meter, strawberry yield was reduced by 316 and 2,356 kg ha−1 for Seasons 1 and 2, respectively. Our results highlight the importance of achieving adequate E. indica management to minimize yield losses.

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.

The Amaranthus genus contains numerous agronomic weedy species that are widely distributed across the United States. The seeds of many Amaranthus species are morphologically indistinguishable. The Minnesota Department of Agriculture declared Palmer amaranth (Amaranthus palmeri S. Watson) a prohibited noxious weed seed in 2016. Any Amaranthus spp. seeds that are identified as contaminants during routine seed testing require genetic testing to determine whether A. palmeri is present. This research aimed to validate and optimize the molecular detection of A. palmeri in seed. We refined the DNA extraction from pools of Amaranthus spp. seeds ranging in size from 1 to 100 seeds to improve sample testing throughput. Real-time polymerase chain reaction (qPCR) using primers developed by Murphy and colleagues correctly identified the presence of A. palmeri genetics in 84 samples containing 0, 1, 2, 25, or 50 A. palmeri seeds in samples containing up to 100 seeds. The method specificity was examined using 17 Amaranthus species and 4 hybrids of unknown genetics. The Murphy regular qPCR cycle amplified Watson’s amaranth (Amaranthus watsonii Standl.), spleen amaranth (Amaranthus dubius Mart. ex Thell.), and spiny amaranth (Amaranthus spinosus L.), which would result in false-positive calls; however, the fast cycle only identified A. watsonii as a potential false positive. Examination of the Murphy primer binding site revealed an identical sequence for A. palmeri and A. watsonii. Additional markers were evaluated and optimized for use in qPCR to eliminate the risk of a false positive. The additional markers did eliminate the amplification of A. dubius and A. spinosus but did not eliminate the amplification for A. watsonii. Currently, A. watsonii is not known to be distributed outside of its limited native range and is not expected to be encountered in samples.

Pollinators risk exposure to insecticide residue when visiting weedy flowers in urban landscapes. Previous research shows that pollinators are routinely exposed to a variety of pesticides, but herbicides have exhibited minimal toxicity and did not contribute to the modeled risk quotients. Herbicides from different modes of action may deter pollinators from visiting turfgrass weeds, but their temporal influence on floral quality and pollinator foraging is unaddressed. Research experiments were conducted at Blacksburg, VA, in 2023 to assess the effect of four herbicides on floral morphology and ultraviolet (UV) reflectance of three different UV floral classes of weeds and associated pollinator foraging visits. Among 1,080 assessments per weed species, honeybees (Apis mellifera), bumble bees (Bombus spp.), solitary bees (Chelostoma florisomne), and flies (Diptera spp.) accounted for 94%, 2%, 3%, and 1%, respectively, of the total pollinator visitations on white clover (Trifolium repens L.) inflorescences; 71%, 2%, 0%, and 27%, respectively, on dandelion (Taraxacum officinale F.H. Wigg.) flowers; and 0%, 0%, 78%, and 22%, respectively, on bulbous buttercup (Ranunculus bulbosus L.) flowers. Pollinator visitation and floral quality were temporarily affected by herbicide application, with some herbicides eliminating food resources, while others transiently impacted floral quality and density. The combination of 2,4-D + dicamba + MCPP and topramezone eliminated pollinator foraging visits, but on differing temporal scales of 3 d for auxins and 14 d for topramezone. Halosulfuron and sulfentrazone transiently suppressed floral quality and density, with varying degrees of deterrence on pollinators depending on the weed species. All evaluated herbicides reduced radiometric UV reflectance of T. officinale petal apices, but only synthetic auxin and topramezone reduced digitally assessed floral UV-reflecting area. Petal UV reflectance appears to contribute but not solely influence pollinator foraging behavior. UV-absorbing and UV-reflecting flowers differed in UV-reflectance response to herbicides, but pollinators were similarly deterred. Results suggest that herbicides may offer a variety of management solutions to pollinator deterrence in areas slated for insecticide treatment, including long-term or transient deterrence with potential food-resource preservation.

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.

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.

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.

Following the application of MCPA/MCPB at 1.7 kg ae ha−1 at a field site near Dresden, ON, Canada, poor control (<50% visible control) of green pigweed (Amaranthus powellii S. Watson) was observed. Amaranthus powellii is a common weed in Ontario crop production, and its evolution of resistance to synthetic auxin herbicides (SAHs) could pose a risk to crop yields. The suspected resistant A. powellii population (R) was used in dose–response and field experiments to determine resistance to SAHs. The objective of these studies was to determine whether this population of A. powellii is resistant to MCPA and cross-resistant to other SAHs. The GR50 (herbicide dose that causes a 50% reduction in plant aboveground biomass) values were determined by fitting plant dry weight data, obtained following application with seven SAHs, to a four-parameter log-logistic equation and were compared between the suspected-resistant (R) population and a known susceptible (S) population of A. powellii. The field trial was conducted in 2017, 2018, 2019, and 2021 in corn (Zea mays L.) and consisted of 11 postemergence SAH treatments. The GR50 values differed between the R and S populations following application with MCPA, aminocyclopyrachlor, dichlorprop-p, and mecoprop, resulting in resistance factors of 4.4, 3.0, 2.5, and 2.4, respectively. In the field study, dicamba and MCPA ester controlled A. powellii 84% and 30%, respectively, at 8 wk after treatment application (WAA). The control of Amaranthus powellii with all SAHs applied POST in corn was poor (<90% visible control) at 8 WAA. Both studies confirmed resistance to SAHs in this population of A. powellii, which will create limitations for farmers aiming to control this weed.

The Northern Great Plains of the United States is a major production region for organic pulse crops that are prone to yield losses due to weeds. Weed management in organic systems relies on the integration of several tactics to stack additive effects and for redundancy to deal with variable efficacy of individual weed management practices. To address the need for effective, integrated weed management, we conducted a 2-yr trial that evaluated the effects of planting date, seeding rate, and preemergent weed control practices (shallow tillage and flame weeding) on weed biomass, crop density, and yield in organically managed chickpea (Cicer arietinum L.) in Montana. Stacking weed management practices increased yields. Early planting had the largest effects on yields, increasing them by 1.8- to 3.6-fold compared with planting 10 to 14 d later. Increasing the seeding rate from the standard rate (43 viable seeds m−2) by 50% increased yields by 47% from 889 to 1,304 kg ha−1. Both preemergent weed control practices increased yields by 40% to 50 % relative to the non-weeded controls. By integrating all three practices, yields of organic chickpea increased greater than 6-fold from 318 kg ha−1 in the controls to 2,006 kg ha−1. The effects of weed control treatments on midseason weed biomass were complex and variable. Although efficacy of the cultural (seeding rate and planting date) and physical (preemergent) treatments on weed biomass varied between years and when combined with other treatments, their full integration, that is, early planted, at higher seeding rate, and preemergent weed control, produced consistently lower weed biomass (84% reduction on average) compared with the standard grower practice (later planting, standard seeding rate, no preemergent weed control). The results lend support to the concept of integrating multiple weed management practices to achieve weed control and high yields in organically managed crops.

Palmer amaranth (Amaranthus palmeri S. Watson, AMAPA) is one of the most troublesome weeds in North America due to its rapid growth rate, substantial seed production, competitiveness and the evolution of herbicide-resistant populations. Though frequently encountered in the South, Midwest, and Mid-Atlantic regions of the United States, A. palmeri was recently identified in soybean [Glycine max (L.) Merr.] fields in Genesee, Orange, and Steuben counties, NY, where glyphosate was the primary herbicide for in-crop weed control. This research, conducted in 2023, aimed to (1) describe the dose response of three putative resistant NY A. palmeri populations to glyphosate, (2) determine their mechanisms of resistance, and (3) assess their sensitivity to other postemergence herbicides commonly used in NY crop production systems. Based on the effective dose necessary to reduce aboveground biomass by 50% (ED50), the NY populations were 42 to 67 times more resistant to glyphosate compared with a glyphosate-susceptible population. Additionally, the NY populations had elevated EPSPS gene copy numbers ranging from 25 to 135 located within extrachromosomal circular DNA (eccDNA). Label rate applications of Weed Science Society of America (WSSA) Group 2 herbicides killed up to 42% of the NY populations of A. palmeri. Some variability was observed among populations in response to WSSA Group 5 and 27 herbicides. All populations were effectively controlled by labeled rates of herbicides belonging to WSSA Groups 4, 10, 14, and 22. Additional research is warranted to confirm whether NY populations have evolved multiple resistance to herbicides within other WSSA groups and to develop effective A. palmeri management strategies suitable for NY crop production.

Sourgrass [Digitaria insularis (L.) Mez ex Ekman] is considered the most troublesome weed in agronomic crops in South America. Overreliance on glyphosate has selected for resistant populations, although the resistance mechanisms remain unknown. Recently, populations were identified that exhibited multiple resistance to 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) and acetyl-CoA carboxylase (ACCase) inhibitors, posing a significant challenge due to the lack of alternative control options. This project aimed to identify the resistance patterns and levels to glyphosate and ACCase inhibitors of three suspected resistant populations (P1, P2, and P3), and elucidate the resistance mechanisms. We performed dose–response experiments with clethodim, fluazifop-P-butyl, glyphosate, and pinoxaden to identify the possibility of cross- and multiple resistance and to quantify the resistance levels. We sequenced the ACCase and EPSPS genes to test the hypothesis that target-site mutations were involved in the resistance mechanisms, given the resistance patterns observed. Our results indicated that two of the tested populations, P1 and P2, were multiple resistant to glyphosate and all ACCase-inhibitor classes, while P3 was resistant to glyphosate only. Resistance levels varied by herbicide, with resistance indices ranging from 2.7- to nearly 2,000-fold. We identified an amino acid substitution in ACCase at position 2078 (Asp-2078-Gly), homozygous for both P1 and P2, corroborating the resistance patterns observed. Interestingly, EPSPS sequencing identified multiple heterozygous DNA polymorphisms that resulted in amino acid substitutions at positions 106 (P1 and P2) or at both 102 and 106 (P3), indicating multiple evolutionary origins of glyphosate-resistance evolution. We show for the first time the genetic mechanisms of multiple resistance to glyphosate and ACCase in D. insularis, and provide a thorough discussion of the evolutionary and management implications of our work.

Volunteer corn (Zea mays L.) is a competitive weed in corn-based cropping systems. Scientific literature does not exist about the water use of volunteer corn grown in different crops and irrigation systems. The objectives of this study were to characterize the growth and evapotranspiration (ETa) of volunteer corn in corn, soybean [Glycine max (L). Merr.], and sorghum [Sorghum bicolor (L.) Moench] under center-pivot irrigation (CPI) and subsurface drip irrigation (SDI) systems. Field experiments were conducted in south-central Nebraska in 2021 and 2022. Soil moisture sensors were installed at depths of 0 to 0.30, 0.30 to 0.60, and 0.60 to 0.90 m to track soil water balance and quantify seasonal total ETa. Corn was the most competitive, as volunteer corn had the lowest biomass, leaf area, and plant height compared with the fallow. Soybean was the least competitive with volunteer corn, as the plant height, biomass, and leaf area of volunteer corn in soybean were similar to fallow at 15, 30, 45, and 60 d after transplanting (DATr). Averaged across crop treatments, irrigation type did not affect volunteer corn growth at 15 to 45 DATr. Soil water depletion and ETa were similar across crop treatments with and without volunteer corn, as water was not a limiting factor in this study. The ETa of volunteer corn was the highest in soybean (623 mm), followed by sorghum (622 mm), and corn (617 mm) under CPI. The SDI had higher irrigation efficiency, because without affecting crop yield, it had 3%, 6%, and 8% lower ETa in soybean (605 mm), sorghum (585 mm), and corn (571 mm), respectively. Although soil water use did not differ with volunteer corn infestation, a soybean yield loss of 27% was observed, which suggests that volunteer corn may not compete for moisture under fully irrigated conditions; however, it can impact the crop yield potential due to competition for factors other than soil moisture.

Cover crops are becoming an increasingly important tool for weed suppression. Biomass production in cover crops is one of the most important predictors of weed suppressive ability. A significant challenge for growers is that cover crop growth can be patchy within fields, making biomass estimation difficult. This study tested ground-based structure-from-motion (SfM) for estimating and mapping cereal rye (Secale cereale L.) biomass. SfM generated 3D point clouds from red, green, and blue (RGB) videos collected by a handheld GoPro camera over five fields in North Carolina during the 2022 to 2023 winter season. A model for predicting biomass was generated by relating measured biomass at termination using a density–height index (DH) from point cloud pixel density multiplied by crop height. Overall biomass ranged from 320 to 9,200 kg ha−1, and crop height ranged from 10 to 120 cm. Measured biomass at termination was linearly related to DH (r2 = 0.813) through levels of 9,000 kg ha−1. Based on independent data validation, predicted biomass and measured biomass were linearly related (r2 = 0.713). In the field maps generated by kriging, measured biomass data were autocorrelated at a range of 5.4 to 42.2 m, and predicted biomass data were autocorrelated at a range of 3.4 to 12.0 m. However, the spatial arrangement of high- and low-performing areas was similar for predicted and measured biomass, particularly in fields with greatest patchiness and spatial correlation in biomass values. This study provides proof-of-concept that ground-based SfM can potentially be used to nondestructively estimate and map cover crop biomass production and identify low-performing areas at higher risk for weed pressure and escapes.

Information regarding the prevalence and distribution of herbicide-resistant waterhemp [Amaranthus tuberculatus (Moq.) Sauer] in Minnesota is limited. Whole-plant bioassays were conducted in the greenhouse on 90 A. tuberculatus populations collected from 47 counties in Minnesota. Eight postemergence herbicides, 2,4-D, atrazine, dicamba, fomesafen, glufosinate, glyphosate, imazamox, and mesotrione, were applied at 1× and 3× the labeled doses. Based on their responses, populations were classified into highly resistant (≥40 % survival at 3× the labeled dose), moderately resistant (<40% survival at 3× the labeled dose but ≥40% survival at 1× the labeled dose), less sensitive (10% to 39% survival at 1× the labeled dose), and susceptible (<10% survival at 1× the labeled dose) categories. All 90 populations were resistant to imazamox, while 89% were resistant to glyphosate. Atrazine, fomesafen, and mesotrione resistance was observed in 47%, 31%, and 22% of all populations, respectively. Ten percent of the populations were resistant to 2,4-D, and 2 of 90 populations exhibited >40% survival following dicamba application at the labeled dose. No population was confirmed to be resistant to glufosinate. However, 22% of all populations were classified as less sensitive to glufosinate. Eighty-two populations were found to be multiple-herbicide resistant. Among these, 15 populations exhibited resistance to four different herbicide sites of action (SOAs); 7 and 4 populations were resistant to five and six SOAs, respectively. All six-way-resistant populations were from southwest Minnesota. Two populations, one from Lincoln County and the other from Lyon County, were resistant to 2,4-D, atrazine, dicamba, fomesafen, glyphosate, imazamox, and mesotrione, leaving only glufosinate as a postemergence control option for these populations in corn (Zea mays L.) and soybean [Glycine max (L.) Merr.]. Diversified management tactics, including nonchemical control measures along with herbicide applications from effective SOAs, should be implemented to slow down the evolution and spread of herbicide-resistant A. tuberculatus populations.

During the past 30 yr an impasse has developed in the discovery and commercialization of synthetic herbicides with new molecular targets and novel chemistries. Similarly, there has been little success with bioherbicides, both microbial and chemical. These bioherbicides are needed to combat fast-growing herbicide resistance and to fulfill the need for more environmentally and toxicologically safe herbicides. In response to this substantial and growing opportunity, numerous start-up companies are utilizing novel approaches to provide new tools for weed management. These diverse new tools broaden the scope of discovery, encompassing advanced computational, bioinformatic, and imaging platforms; plant genome–editing and targeted protein degradation technologies; and machine learning and artificial intelligence (AI)-based strategies. This review contains summaries of the presentations of 10 such companies that took part in a symposium held at the WSSA annual meeting in 2024. Four of the companies are developing microbial bioherbicides or natural product–based herbicides, and the other six are using advanced technologies, such as AI, to accelerate the discovery of herbicides with novel molecular target sites or to develop non-GMO, herbicide-resistant crops.

Peanut (Arachis hypogaea L.) and maize (Zea mays L.) are essential crops for Ghana’s economy and food security, but weed infestation poses a significant threat to their cultivation. Crop rotations influence weed communities, but little is known about these processes in peanut-cropping systems in West Africa. This study investigated the impact of different crop rotations and input levels on weed communities in Ghana over 3 yr. Results showed that low inputs (absence of herbicide and fertilization) favored species richness, while higher input levels (weed control with herbicides and fertilizer use) reduced it. Diversity and evenness were also affected by inputs, with varying patterns across locations and seasons. Weed population growth rates (λ) varied significantly by location and treatment; all management programs resulted in increasing weed populations. Principal component analysis revealed distinct associations between weed species and crop management. The majority of weed species exhibited a generalist behavior and did not associate with a particular management. However, billygoat weed (Ageratum conyzoides L.) and Benghal dayflower (Commelina benghalensis L.) were positively associated with high-input systems, while purple nutsedge (Cyperus rotundus L.) exhibited strong associations with low and medium inputs. The weed–crop rotation dynamics described here demonstrate how management drives the selection of weed species that are more pervasive and interfere with important food crops in Ghanaian agriculture.

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.