Plant names carry a significant amount of information without providing a lengthy description. This is an efficient shorthand for scientists and stakeholders to communicate about a plant, but only when the name is based on a common understanding. It is standard to think of each plant having just two names, a common name and a scientific name, yet both names can be a source of confusion. There are often many common names that refer to the same plant, or a single common name that refers to multiple different species, and some plants have no common name at all. Scientific names are based upon international standards; however, when the taxonomy is not agreed upon, two scientific names may be used to describe the same species. Weed scientists and practitioners can easily memorize multiple plant names and know that they refer to the same species, but when we consider global communication and far-reaching databases, it becomes very relevant to consider two sides of this shift: (1) a need for greater standardization (due to database management and risk of lost data from dropped cross-referencing); and (2) the loss of local heritage, which provides useful meaning through various common names. In addition, weed scientists can be resistant to changing names that they learned or frequently use. The developments in online databases and reclassification of plant taxonomy by phylogenetic relationships have changed the accessibility and role of the list of standardized plant names compiled by the Weed Science Society of America (WSSA). As part of an attempt to reconcile WSSA and USDA common names for weedy plants, the WSSA Standardized Plant Names Committee recently concluded an extensive review of the Composite List of Weeds common names and had small changes approved to about 10% of the list of more than 2,800 distinct species.

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.

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.

Weed resistance to herbicides has increased exponentially during the past 30 to 40 yr, consequently reducing the number of effective products available to control certain species and populations. Future efforts should target not only the discovery of new protein binding sites and the development of new molecules, but also the revival of old molecules with reduced efficacy due to widespread herbicide resistance. The addition of herbicide synergists that inhibit metabolic pathways or enhance intrinsic plant stress is a possible solution to ameliorate the negative effects caused by the lack of new herbicide chemistries. Glutathione S-transferase (GST) enzymes are involved with numerous herbicide detoxification reactions and plant stress responses. This review approaches the potential use of natural and synthetic GST inhibitors to enhance herbicidal activity or induce crop safety to provide effective, sustainable weed management strategies in the future.

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.

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.

Isoproturon phytotoxicity to wheat (Triticum aestivum L.) is a worry for many farmers in chemical control of weeds in wheat fields, especially in subzero weather conditions. Iron chlorin e6 (ICe6), a new plant growth regulator, has been reported to enhance crop stress resistance to alleviate damage caused by stress; however, it is not clear whether ICe6 has an alleviative effect on isoproturon phytotoxicity to wheat. We determined the alleviative effect of ICe6 on isoproturon phytotoxicity to wheat, and 0.018 g ai ha−1 was the optimal dose. Meanwhile, we also studied the photosynthetic pigment content, photosynthetic parameters, oxidative stress indicators, and antioxidant enzyme activity of wheat treated with the three different treatments. We found that the photosynthetic pigment content, antioxidant enzyme activity, and photosynthesis of wheat damaged by isoproturon were significantly lower than those of the control, and the hydrogen peroxide (H2O2) and malondialdehyde (MDA) content increased. These results indicate that isoproturon stress significantly weakened the photosynthetic and antioxidant capacity of wheat. The photosynthetic pigment content, photosynthetic parameters (excluding intercellular CO2 concentration), and antioxidant enzyme activity of isoproturon+ICe6– treated wheat were significantly higher than those of isoproturon-treated wheat. The H2O2 and MDA content was significantly lower than that of isoproturon-treated wheat. These results indicate that ICe6 treatment maintained the photosynthetic pigment content of wheat and relatively improved photosynthetic capacity, allowing photosynthesis to proceed normally. ICe6 treatment also limits the decrease in antioxidant enzyme activity, effectively clearing excess reactive oxygen species and ultimately alleviating membrane lipid peroxidation damage. In summary, ICe6 not only enhances stress resistance and increases yield in crops such as soybean [Glycine max (L.) Merr.] and canola (Brassica napus L.), but also has an alleviating effect on the isoproturon phytotoxicity to wheat, which is manifested by the improvement of photosynthetic and antioxidant abilities, ultimately leading to an increase in wheat shoot height and shoot fresh weight.

Automatic detection and removal of weeds is a challenging task that requires precise sensors. While crops and weeds possess similar features in terms of appearance, they can be discriminated based on spectral information. This can be done because any object has its own specific spectral signature based on its physical structure and chemical contents. This study examined the use of wavelet transform and deep learning for discrimination of weeds from crops. A total of 626 spectral reflectances in the range of 380 to 1,000 nm were obtained for three crops (cucumber [Cucumis sativus L.], tomato [Solanum lycopersicum L.], and bell pepper [Capsicum annuum L.]) and five different weeds (bindweed [Convolvulus spp.], purple nutsedge [Cyperus rotundus L.], narrowleaf plantain [Plantago lanceolata L.], common cinquefoil [Potentilla simplex Michx.], and garden sorrel [Rumex acetosa L.]). Morse wavelet was employed to decompose the spectra and extract the scalograms, which are the RGB representations of the spectral data. Two deep convolutional neural networks (i.e., GoogLeNet and SqueezNet) were employed for the recognition of crops and weeds. In addition, six common classifiers, including linear discriminant analysis, quadratic discriminant analysis, linear support vector machine, quadratic support vector machine, artificial neural networks, and k-nearest neighbors classifier (KNN), were used for the task of crop/weed discrimination to build the comparison with the proposed method. The error of prediction gradually decreased, and a 100% correct classification was achieved after 258 iterations. Analysis showed that SqueezNet provided classification of 100% accuracy, while GoogLeNet’s accuracy was 97.8% for the test set. Among the common classifiers, KNN provided the highest accuracy (i.e., 100%). This study showed that the proposed method can be successfully utilized for classification of crops and weeds.

Using convolutional neural networks (CNNs) for image recognition is effective for early weed detection. However, the impact of training data curation, specifically concerning morphological changes during the early growth phases of weeds, on recognition robustness remains unclear. We focused on four weed species (giant ragweed [Ambrosia trifida L.], red morningglory [Ipomoea coccinea L.], pitted morningglory [Ipomoea lacunosa L.], and burcucumber [Sicyos angulatus L.]) with varying cotyledon and true leaf shapes. Creating 16 models in total, we employed four dataset patterns with different growth stage combinations, two image recognition algorithms (object detection: You Look Only Once [YOLO] v5 and image classification: Visual Geometry Group [VGG] 19), and two conditions regarding the number of species treated (four and two species). We evaluated the effects of growth stage training on weed recognition success using two datasets. One evaluation revealed superior results with a single class/species training dataset, achieving >90% average precision for detection and classification accuracy under most conditions. The other dataset revealed that merging different growth stages with different shapes as a class effectively prevented misrecognition among different species when using YOLOv5. Both results suggest that integrating different shapes in a plant species as a single class is effective for maintaining robust recognition success amid temporal morphological changes during the early growth stage. This finding not only enhances early detection of weed seedlings but also bolsters the robustness of general plant species identification.

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.

Common milkweed (Asclepias syriaca L.) is widely planted as part of monarch butterfly (Danaus plexippus) conservation efforts. Vegetative propagation is an alternative to planting A. syriaca from seed and offers advantages such as high emergence rates. The aim of this study was to determine the ideal planting depth and initial root segment length to vegetatively propagate A. syriaca. In a greenhouse trial with two runs, A. syriaca was grown from seed, and then 3-, 8-, and 15-cm segments were harvested. These segments were then planted at depths of 3, 8, or 15 cm. Planting depth did not impact A. syriaca growth, but an initial root segment length of 15 cm was associated with greater above- and belowground biomass and height in both runs of the experiment. Emergence rates were not impacted by either factor. Overall, A. syriaca was likely to establish regardless of the initial root segment length or planting depth, but plants grown from root segments of 15 cm were more vigorous than plants grown from shorter segments.

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.

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.

Slender Russian thistle (Salsola collina Pall.) is a troublesome weed distributed mainly in the cropping regions of northern China that produces heteromorphic seeds in the same plant. However, limited information is available on the germination ecology of heteromorphic seeds in S. collina. Thus, the present study was conducted to verify the effect of alternating temperature conditions, light conditions, winged perianth, salt concentrations, water stress, and burial depths on the seed germination or seedling emergence of S. collina. The results showed that S. collina produced two different types of fruits/seeds that significantly differed in seed size, seed color, external structure, and germination/dormancy behavior. The type A seeds (green seeds) were nondormant, and the germination percentage was >96% at all alternating day/night temperatures and light conditions; whereas type B seeds (yellow seeds) exhibited dormancy characteristics and poor germination (≤1%). Moreover, the winged perianth did not inhibit the germination of S. collina green seeds. The germination of green seeds declined rapidly when NaCl concentration exceeded 100 mM, and only 2.22% germination was observed at 600 mM NaCl. About 62.00% of green seeds germinated at −0.6 MPa, and 8.00% germination was obtained at −1.2 MPa. The seedling emergence declined with an increase of burial depth, and decreased sharply when the burial depth exceeded 1.0 cm. Only 8.33% seedling emergence occurred at a burial depth of 4.0 cm. The results gathered from present study will help to illustrate the ecological adaptation strategy of S. collina and indicate that shallow tillage can effectively minimize the seedling emergence of S. collina.

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.

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.

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.

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.

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.