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The past 50 yr of advances in weed recognition technologies have poised site-specific weed control (SSWC) on the cusp of requisite performance for large-scale production systems. The technology offers improved management of diverse weed morphology over highly variable background environments. SSWC enables the use of nonselective weed control options, such as lasers and electrical weeding, as feasible in-crop selective alternatives to herbicides by targeting individual weeds. This review looks at the progress made over this half-century of research and its implications for future weed recognition and control efforts; summarizing advances in computer vision techniques and the most recent deep convolutional neural network (CNN) approaches to weed recognition. The first use of CNNs for plant identification in 2015 began an era of rapid improvement in algorithm performance on larger and more diverse datasets. These performance gains and subsequent research have shown that the variability of large-scale cropping systems is best managed by deep learning for in-crop weed recognition. The benefits of deep learning and improved accessibility to open-source software and hardware tools has been evident in the adoption of these tools by weed researchers and the increased popularity of CNN-based weed recognition research. The field of machine learning holds substantial promise for weed control, especially the implementation of truly integrated weed management strategies. Whereas previous approaches sought to reduce environmental variability or manage it with advanced algorithms, research in deep learning architectures suggests that large-scale, multi-modal approaches are the future for weed recognition.

Seed retention, and ultimately seed shatter, are extremely important for the efficacy of harvest weed seed control (HWSC) and are likely influenced by various agroecological and environmental factors. Field studies investigated seed-shattering phenology of 22 weed species across three soybean [Glycine max (L.) Merr.]-producing regions in the United States. We further evaluated the potential drivers of seed shatter in terms of weather conditions, growing degree days, and plant biomass. Based on the results, weather conditions had no consistent impact on weed seed shatter. However, there was a positive correlation between individual weed plant biomass and delayed weed seed–shattering rates during harvest. This work demonstrates that HWSC can potentially reduce weed seedbank inputs of plants that have escaped early-season management practices and retained seed through harvest. However, smaller individuals of plants within the same population that shatter seed before harvest pose a risk of escaping early-season management and HWSC.

The objective of this paper was to review the reproductive biology, herbicide-resistant (HR) biotypes, pollen-mediated gene flow (PMGF), and potential for transfer of alleles from HR to herbicide-susceptible grass weeds including barnyardgrass, creeping bentgrass, Italian ryegrass, johnsongrass, rigid (annual) ryegrass, and wild oats. The widespread occurrence of HR grass weeds is at least partly due to PMGF, particularly in obligate outcrossing species such as rigid ryegrass. Creeping bentgrass, a wind-pollinated turfgrass species, can efficiently disseminate herbicide resistance alleles via PMGF and movement of seeds and stolons. The genus Agrostis contains about 200 species, many of which are sexually compatible and produce naturally occurring hybrids and hybrids with species in the genus Polypogon. The self-incompatibility, extremely high outcrossing rate, and wind pollination in Italian ryegrass clearly point to PMGF as a major mechanism by which herbicide resistance alleles can spread across agricultural landscapes, resulting in abundant genetic variation within populations and low genetic differentiation among populations. Italian ryegrass can readily hybridize with perennial ryegrass and rigid ryegrass due to their similarity in chromosome numbers (2n = 14), resulting in interspecific gene exchange. Johnsongrass, barnyardgrass, and wild oats are self-pollinated species, so the potential for PMGF is relatively low and limited to short distances; however, seeds can easily shatter upon maturity before crop harvest, leading to wider dispersal. The occurrence of PMGF in reviewed grass weed species, even at a low rate, is greater than that of spontaneous mutations conferring herbicide resistance in weeds and thus can contribute to the spread of herbicide resistance alleles. This review indicates that the transfer of herbicide resistance alleles occurs under field conditions at varying levels depending on the grass weed species.

Recent innovations in 3D imaging technology have created unprecedented potential for better understanding weed responses to management tactics. Although traditional 2D imaging methods for mapping weed populations can be limited in the field by factors such as shadows and tissue overlap, 3D imaging mitigates these challenges by using depth data to create accurate plant models. Three-dimensional imaging can be used to generate spatiotemporal maps of weed populations in the field and target weeds for site-specific weed management, including automated precision weed control. This technology will also help growers monitor cover crop performance for weed suppression and detect late-season weed escapes for timely control, thereby reducing seedbank persistence and slowing the evolution of herbicide resistance. In addition to its many applications in weed management, 3D imaging offers weed researchers new tools for understanding spatial and temporal heterogeneity in weed responses to integrated weed management tactics, including weed–crop competition and weed community dynamics. This technology will provide simple and low-cost tools for growers and researchers alike to better understand weed responses in diverse agronomic contexts, which will aid in reducing herbicide use, mitigating herbicide-resistance evolution, and improving environmental health.

Due to complex interactions between social and ecological systems, herbicide resistance has classic features of a “wicked problem”. Herbicide-resistant (HR) Poa annua poses a risk to sustainably managing U.S. turfgrass systems, but there is scant knowledge to guide its management. Six focus groups were conducted throughout the United States to gain understanding of socio-economic barriers to adopting herbicide-resistance management practices. Professionals from major turfgrass sectors (golf courses, sports fields, lawn care, and seed/sod production) were recruited as focus-group participants. Discussions emphasized challenges of the weed management of turfgrass systems as compared to agronomic crops. This included greater time constraints for managing weeds and more limited chemical control options. Lack of understanding about the proper use of compounds with different modes of action was identified as a threat to sustainable weed management. There were significant regional differences in perceptions of the existence, geographic scope, and social and ecological causes of HR in managing Poa annua. Effective resistance management will require tailoring chemical and non-chemical practices to the specific conditions of different turfgrass sectors and regions. Some participants thought it would be helpful to have multi-year resistance management programs that are both sector- and species-specific. Published version

The presence of a soil seedbank facilitates the persistence of annual weed species in arable fields. Soil weed seedbank is replenished by many sources, but the largest one is the seeds produced by uncontrolled late-season weed escapes. The estimation of weed seed production potential from late-season escapes may allow farmers to make appropriate management decisions to minimize seedbank replenishment. The objective of this research was to evaluate the feasibility of using unmanned aerial vehicle–based RGB and multispectral imagery for estimating seed rain potential in late-season weed escapes in crop fields. Three case studies were used to capture images of weed escapes before crop harvest: common waterhemp [Amaranthus tuberculatus (Moq.) Sauer] in soybean [Glycine max (L.) Merr.], Palmer amaranth [Amaranthus palmeri (S.) Watson] in cotton (Gossypium hirsutum L.), and johnsongrass [Sorghum halepense (L.) Pers.] in soybean. Randomly selected quadrats with different density gradients of weed escapes were sampled at the time of crop maturity. High-resolution RGB and multispectral images of the experimental area were collected using drones immediately before ground sample collection. Normalized difference vegetation index (NDVI), excess green index (ExG), and canopy volume estimates derived from canopy height models were used to obtain weed biological measurements (biomass and seed production). Among the indices investigated, NDVI and ExG had very strong correlations (0.71 to 0.97) with weed biomass. No specific remote sensing variable was ideal across the three cases examined here, suggesting that a generalized remote sensing approach may not offer robust estimations and case-specific applications are imperative. Nonetheless, drone imagery is a powerful tool for estimating seed production from uncontrolled weed escapes and assisting with management decision making.

Aims: Field studies were conducted to determine sesame response to the pre-emergence herbicides (acetochlor at 1.7 kg ai ha-1; S-metolachlor at 0.72, 1.43, and 2.86 kg ai ha-1; dimethenamid-P at 0.84 kg ai ha-1; pethoxamid at 0.22 kg ai ha-1; pyroxasulfone at 0.09 kg ai ha-1and bicyclopyrone at 0.12 and 0.24 kg ai ha-1) applied 3 or 6 days after 50% emergence. Study Design: Randomized complete block design with 3-4 reps depending on location. Place and Duration of Study: Sesame growing areas of Alabama, Mississippi, and Texas during the 2016 through 2018 growing seasons. Methodology: Treatments consisted of a factorial arrangement of herbicide treatments at two early POST application timings. A non-treated control was included for comparison. Crop oil concentrate (Agridex®, Helena, Collierville, TN 38017) at 1.0% v/v was added to all herbicide treatments. Plot size was either five rows (76 cm apart) by 9.1 m or four rows (101 cm apart) by 7.3 m depending on location. Only the two middle rows were sprayed and the other rows were untreated and served as buffers. Sesame cultivars were seeded approximately 1.0 to 2.0 cm deep at 9 kg/ha at all locations. Injury was evaluated early-season, 7 to 27 days after herbicide application (DAA), and later, 28 to 147 DAA, based on a scale of 0 (no sesame injury) to 100 (complete sesame death). Injury consisted of stuntingand leaf chlorosis and/or necrosis. Results: All herbicides tested resulted in significant injury to sesame at some location and application timing. None of the herbicides evaluated are safe to use early POST on sesame without causing significant injury. Conclusion: The ability of sesame to recover from significant injury and compensate for injury led to no yield loss in many instances. However, levels of injury observed are not acceptable by growers and will not allow the use of these herbicides soon after sesame emergence.

Ryegrass (Lolium spp.) is a troublesome weed in major wheat (Triticum aestivum L.) production regions in the United States. High diversity and adaptive potential are known to contribute to its success as a weed species and also create difficulties in correct species identification in fields. The objective of this research was to characterize diversity for 16 different morphological traits among 56 Lolium populations collected from wheat production fields across the Texas Blackland Prairies region and identify Lolium species based on taxonomic characteristics. Populations were highly diverse (both at inter- and intrapopulation levels) for the traits studied, and a taxonomic comparison with USDA-GRIN reference samples revealed that all the populations were variants of Italian ryegrass [Lolium perenne L. ssp. multiflorum (Lam.) Husnot] with a few offtypes of perennial ryegrass (Lolium perenne L.) or probable hybrids between the two species. Hierarchical clustering grouped the populations into six clusters based on their similarities for the morphological traits investigated. Principal component analysis showed that the variability for yield traits greatly contributed to the total diversity. Pre-flowering plant height (stage 10 on Feekes scale) was positively correlated with tiller count, shoot biomass, and spike count, but not with total seed count per plant, whereas plant height at maturity (stage 11.3 to 11.4 on Feekes scale) was highly correlated with total seeds per plant. Further, basal node color was positively correlated with plant growth habit, regrowth rate, and leaf color. Leaf blade width was positively correlated with survival to pinoxaden and multiple herbicides, whereas, spike count was negatively correlated with survival to mesosulfuron. The high levels of intra- as well as interpopulation variability documented in this study indicate the potential of this species to rapidly adapt to herbicides and emphasize the need for implementing diverse management tactics, including the integration of harvest weed seed control.

Potential effectiveness of harvest weed seed control (HWSC) systems depends upon seed shatter of the target weed species at crop maturity, enabling its collection and processing at crop harvest. However, seed retention likely is influenced by agroecological and environmental factors. In 2016 and 2017, we assessed seed-shatter phenology in 13 economically important broadleaf weed species in soybean [Glycine max (L.) Merr.] from crop physiological maturity to 4 wk after physiological maturity at multiple sites spread across 14 states in the southern, northern, and mid-Atlantic United States. Greater proportions of seeds were retained by weeds in southern latitudes and shatter rate increased at northern latitudes. Amaranthus spp. seed shatter was low (0% to 2%), whereas shatter varied widely in common ragweed (Ambrosia artemisiifolia L.) (2% to 90%) over the weeks following soybean physiological maturity. Overall, the broadleaf species studied shattered less than 10% of their seeds by soybean harvest. Our results suggest that some of the broadleaf species with greater seed retention rates in the weeks following soybean physiological maturity may be good candidates for HWSC. U.S. Department of Agriculture, Agricultural Research Service Areawide program; HATCH Program of the National Institute of Food and Agriculture; U.S. Department of AgricultureUnited States Department of Agriculture (USDA) Published version We would like to thank the staff and students at each university for helping conduct this research, specifically Kreshnik Bejleri, Sheri Heard, John Sanders, Barbara Scott, Annie Klodd, Zach Schaefer, Russ Garetson, Vitor Damiao, Matheus Martins, Camille Werner, Bruno Flaibam, and Camila Grassmann, and each institute's research and experimental stations. The authors would also like to thank the U.S. Department of Agriculture, Agricultural Research Service Areawide program for funding and the HATCH Program of the National Institute of Food and Agriculture and the U.S. Department of Agriculture for providing partial funding. No conflicts of interest have been declared.

Seed shatter is an important weediness trait on which the efficacy of harvest weed seed control (HWSC) depends. The level of seed shatter in a species is likely influenced by agroecological and environmental factors. In 2016 and 2017, we assessed seed shatter of eight economically important grass weed species in soybean [Glycine max (L.) Merr.] from crop physiological maturity to 4 wk after maturity at multiple sites spread across 11 states in the southern, northern, and mid-Atlantic United States. From soybean maturity to 4 wk after maturity, cumulative percent seed shatter was lowest in the southern U.S. regions and increased moving north through the states. At soybean maturity, the percent of seed shatter ranged from 1% to 70%. That range had shifted to 5% to 100% (mean: 42%) by 25 d after soybean maturity. There were considerable differences in seed-shatter onset and rate of progression between sites and years in some species that could impact their susceptibility to HWSC. Our results suggest that many summer annual grass species are likely not ideal candidates for HWSC, although HWSC could substantially reduce their seed output during certain years. U.S. Department of Agriculture, Agricultural Research Service Areawide program; HATCH Program of the National Institute of Food and Agriculture; U.S. Department of AgricultureUnited States Department of Agriculture (USDA) Published version We would like to thank the staff and students at each university for helping conduct this research, specifically Kreshnik Bejleri, Sheri Heard, John Sanders, Barbara Scott, Annie Klodd, Zach Schaefer, Russ Garetson, Vitor Damiao, Matheus Martins, Camille Werner, Bruno Flaibam, and Camila Grassmann, and each institute's research and experimental stations. The authors would also like to thank the U.S. Department of Agriculture, Agricultural Research Service Areawide program for funding and the HATCH Program of the National Institute of Food and Agriculture and the U.S. Department of Agriculture for providing partial funding for this work. No conflicts of interest have been declared.

Herbicide-resistant weeds pose a severe threat to sustainable vegetation management in various production systems worldwide. The majority of the herbicide resistance cases reported thus far originate from agronomic production systems where herbicide use is intensive, especially in industrialized countries. Another notable sector with heavy reliance on herbicides for weed control is managed turfgrass systems, particularly golf courses and athletic fields. Intensive use of herbicides, coupled with a lack of tillage and other mechanical tools that are options in agronomic systems, increases the risk of herbicide-resistant weeds evolving in managed turfgrass systems. Among the notable weed species at high risk for evolving resistance under managed turf systems in the United States are annual bluegrass, goosegrass, and crabgrasses. The evolution and spread of multiple herbicide resistance, an emerging threat facing the turfgrass industry, should be addressed with the use of diversified management tools. Target-site resistance has been reported commonly as a mechanism of resistance for many herbicide groups, though non–target site resistance is an emerging concern. Despite the anecdotal evidence of the mounting weed resistance issues in managed turf systems, the lack of systematic and periodic surveys at regional and national scales means that confirmed reports are very limited and sparse. Furthermore, currently available information is widely scattered in the literature. This review provides a concise summary of the current status of herbicide-resistant weeds in managed turfgrass systems in the United States and highlights key emerging threats.Nomenclature: Annual bluegrass, Poa annua L.; crabgrass, Digitaria spp.; goosegrass, Eleusine indica (L.) Gaertn

The genus Echinochloa constitutes some of the most prominent weed species found in rice (Oryza sativa L.) production worldwide. The taxonomy of Echinochloa is complex due to its morphological variations. The morphophysiological diversity and taxonomic characteristics of Echinochloa ecotypes infesting rice fields in Texas are unknown. A total of 54 Echinochloa ecotypes collected during late-season field surveys in 2015 and 2016 were characterized in a common garden in 2017. Plants were characterized for 14 morphophysiological traits, including stem angle; stem color; plant height; leaf color; leaf texture; flag leaf length, width, and angle; days to flowering; panicle length; plant biomass; seed shattering; seed yield; and seed dormancy. Principal component analysis indicated that 4 (plant height, flag leaf length, seed shattering, and seed germination) of the 14 phenological traits characterized here had significantly contributed to the overall morphological diversity of Echinochloa spp. Results showed wide interpopulation diversity for the measured traits among the E. colona ecotypes, as well as diverse intrapopulation variability in all three Echinochloa species studied, including barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.], junglerice [Echinochloa colona (L.) Link], and rough barnyardgrass [Echinochloa muricata (P. Beauv.) Fernald]. Taxonomical classification revealed that the collection consisted of three Echinochloa species, with E. colona being the most dominant (96%), followed by E. crus-galli (2%), and E. muricata (2%). Correlation analysis of morphophysiological traits and resistance status to commonly used preemergence (clomazone, quinclorac) and postemergence herbicides (propanil, quinclorac, imazethapyr, and fenoxaprop-ethyl) failed to show any significant association. Findings from this study provided novel insights into the morphophysiological characteristics of Echinochloa ecotypes in rice production in Texas. The morphological diversity currently present in Echinochloa ecotypes could contribute to their adaptation to selection pressure imposed by different management tools, emphasizing the need for a diversified management approach to effectively control this weed species.

Abiotic stresses such as drought, extreme temperature, and salinity can negatively impact seed germination and plant growth and have become major limitations to crop production. Most crops are vulnerable to abiotic stress factors during their early growth phase, especially during seed germination and seedling emergence. Rapid crop seed germination and seedling establishment is known to provide competitive advantages over weeds and improve yields. Seed osmopriming is defined as a pre-sowing treatment in which seeds are soaked in osmotic solutions to undergo the first stage of germination, but radicle protrusion has not occurred. The process of osmopriming involves prior exposure of seeds in low-water-potential solutions. Osmopriming can generate a series of pre-germination metabolic activities, increase the antioxidant system activities, and prepare the seed for radicle protrusion. Polyethylene glycol (PEG) is a popular osmopriming agent that can alleviate the negative impacts of abiotic stresses. This review summarizes research findings on crop responses to seed priming with PEG under abiotic stresses. The challenges, limitations, and opportunities of using PEG for crop seed priming are discussed with the goal of providing insights into future research towards effective application of seed priming in crop production.

Tetraploid johnsongrass [Sorghum halepense (L.) Pers.] is a sexually-compatible weedy relative of diploid sorghum [Sorghum bicolor (L.) Moench]. To determine the extent of interspecific hybridization between male sterile grain sorghum and johnsongrass and the ploidy of their progeny, cytoplasmic (CMS), genetic (GMS) and chemically induced male sterile lines of Tx623 and Tx631 were pollinated with johnsongrass pollen. At maturity 1% and 0.07% of the developing seeds of Tx623 and Tx631 respectively were recovered. Ninety-one percent of recovered hybrids were tetraploid and two percent were triploid, the tetraploids resulting from 2n gametes present in the sorghum female parent. Their formation appears to be genotype dependent as more tetraploids were recovered from Tx623 than Tx631. Because a tetraploid sorghum x johnsongrass hybrid has a balanced genome, they are male and female fertile providing opportunities for gene flow between the two species. Given the differences in 2n gamete formation among Tx623 and Tx631, seed parent selection may be one way of reducing the likelihood of gene flow. These studies were conducted in controlled and optimum conditions; the actual outcrossing rate in natural conditions is expected to be much lower. More studies are needed to assess the rates of hybridization, fitness, and fertility of the progeny under field conditions.

To enable robotic weed control, we develop algorithms to detect nutsedge weed from bermudagrass turf. Due to the similarity between the weed and the background turf, manual data labeling is expensive and error-prone. Consequently, directly applying deep learning methods for object detection cannot generate satisfactory results. Building on an instance detection approach (i.e. Mask R-CNN), we combine synthetic data with raw data to train the network. We propose an algorithm to generate high fidelity synthetic data, adopting different levels of annotations to reduce labeling cost. Moreover, we construct a nutsedge skeleton-based probabilistic map (NSPM) as the neural network input to reduce the reliance on pixel-wise precise labeling. We also modify loss function from cross entropy to Kullback-Leibler divergence which accommodates uncertainty in the labeling process. We implement the proposed algorithm and compare it with both Faster R-CNN and Mask R-CNN. The results show that our design can effectively overcome the impact of imprecise and insufficient training sample issues and significantly outperform the Faster R-CNN counterpart with a false negative rate of only 0.4%. In particular, our approach also reduces labeling time by 95% while achieving better performance if comparing with the original Mask R-CNN approach.

Seed shattering refers to the natural shedding of seeds when they ripe, a phenomenon typically observed in wild and weedy plant species. The timing and extent of this phenomenon varies considerably among plant species. Seed shattering is primarily a genetically controlled trait; however, it is significantly influenced by environmental conditions, management practices and their interactions, especially in agro-ecosystems. This trait is undesirable in domesticated crops where consistent efforts have been made to minimize it through conventional and molecular breeding approaches. However, this evolutionary trait serves as an important fitness and survival mechanism for most weeds that utilize it to ensure efficient dispersal of their seeds, paving the way for persistent soil seedbank development and sustained future populations. Weeds have continuously evolved variations in seed shattering as an adaptation under changing management regimes. High seed retention is common in many cropping weeds where weed maturity coincides with crop harvest, facilitating seed dispersal through harvesting operations, though some weeds have notoriously high seed shattering before crop harvest. However, high seed retention in some of the most problematic agricultural weed species such as annual ryegrass (Lolium rigidum), wild radish (Raphanus raphanistrum), and weedy amaranths (Amaranthus spp.) provides an opportunity to implement innovative weed management approaches such as harvest weed seed control, which aims at capturing and destroying weed seeds retained at crop harvest. The integration of such management options with other practices is important to avoid the rapid evolution of high seed shattering in target weed species. Advances in genetics and molecular biology have shown promise for reducing seed shattering in important crops, which could be exploited for manipulating seed shattering in weed species. Future research should focus on developing a better understanding of various seed shattering mechanisms in plants in relation to changing climatic and management regimes.

Amaranthus palmeri, ranked as the most prolific and troublesome weed in North America, has evolved resistance to several herbicide sites of action. Repeated use of any one herbicide, especially at lower than recommended doses, can lead to evolution of weed resistance, and, therefore, a better understanding of the process of resistance evolution is essential for the management of A. palmeri and other difficult-to-control weed species. Amaranthus palmeri rapidly developed resistance to 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors such as mesotrione. The objective of this study was to test the potential for low-dose applications of mesotrione to select for reduced susceptibility over multiple generations in an A. palmeri population collected from an agricultural field in 2001. F0 plants from the population were initially treated with sub-lethal mesotrione rates and evaluated for survival three weeks after treatment. All F0 plants were controlled at the 1× rate (x = 105 g ai ha−1). However, 2.5% of the F0 plants survived the 0.5× treatment. The recurrent selection process using plants surviving various mesotrione rates was continued until the F4 generation was reached. Based on the GR50 values, the sensitivity index was determined to be 1.7 for the F4 generation. Compared to F0, HPPD gene expression level in the F3 population increased. Results indicate that after several rounds of recurrent selection, the successive generations of A. palmeri became less responsive to mesotrione, which may explain the reduced sensitivity of this weed to HPPD-inhibiting herbicides. The results have significance in light of the recently released soybean and soon to be released cotton varieties with resistance to HPPD inhibitors.

Recent computer vision techniques based on convolutional neural networks (CNNs) are considered state-of-the-art tools in weed mapping. However, their performance has been shown to be sensitive to image quality degradation. Variation in lighting conditions adds another level of complexity to weed mapping. We focus on determining the influence of image quality and light consistency on the performance of CNNs in weed mapping by simulating the image formation pipeline. Faster Region-based CNN (R-CNN) and Mask R-CNN were used as CNN examples for object detection and instance segmentation, respectively, while semantic segmentation was represented by Deeplab-v3. The degradations simulated in this study included resolution reduction, overexposure, Gaussian blur, motion blur, and noise. The results showed that the CNN performance was most impacted by resolution, regardless of plant size. When the training and testing images had the same quality, Faster R-CNN and Mask R-CNN were moderately tolerant to low levels of overexposure, Gaussian blur, motion blur, and noise. Deeplab-v3, on the other hand, tolerated overexposure, motion blur, and noise at all tested levels. In most cases, quality inconsistency between the training and testing images reduced CNN performance. However, CNN models trained on low-quality images were more tolerant against quality inconsistency than those trained by high-quality images. Light inconsistency also reduced CNN performance. Increasing the diversity of lighting conditions in the training images may alleviate the performance reduction but does not provide the same benefit from the number increase of images with the same lighting condition. These results provide insights into the impact of image quality and light consistency on CNN performance. The quality threshold established in this study can be used to guide the selection of camera parameters in future weed mapping applications.

Harvest weed seed control (HWSC) comprises a set of tools and tactics that prevents the addition of weed seed to the soil seed bank, attenuating weed infestations and providing a method to combat the development and spread of herbicide-resistant weed populations. Initial HWSC research efforts in North America are summarized and, combined with the vast area of crops suitable for HWSC, clearly indicate strong potential for this technology. However, potential limitations exist that are not present in Australian cropping systems where HWSC was developed. These include rotations with crops that are not currently amenable to HWSC (e.g. corn), high moisture content at harvest, untimely harvest, and others. Concerns about weeds becoming resistant to HWSC (i.e. adapting) exist, as do shifts in weed species composition, particularly with the diversity of weeds in North America. Currently the potential of HWSC vastly outweighs any drawbacks, necessitating further research. Such expanded efforts should foremost include chaff lining and impact mill commercial scale evaluation, as this will address potential limitations as well as economics. Growers must be integrated into large-scale, on-farm research and development activities aimed at alleviating the problems of using HWSC systems in North America and drive greater adoption subsequently. (c) 2020 Society of Chemical Industry Public domain – authored by a U.S. government employee

This article belongs to the Special Issue The Application of Models for Weed Management in Cropping Systems., In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making., Muthukumar V. Bagavathiannan’s research program is supported by state and federal funds appropriated to Texas A&M AgriLife Research. Gayle J. Somerville was supported by the UKRI project of evaluating people-environment trade-offs through low-tech intensification of livestock management in communal grazing systems in South Africa (BB/S014683/1) whilst working at Rothamsted. Brian J. Schutte is supported by state and federal funds appropriated to the New Mexico Agricultural Experiment Station. JLG-A was funded by FEDER (European Regional Development Funds) and the Spanish Ministry of Economy and Competitiveness grants (projects AGL2015-64130-R). Guillermo R. Chantre received funding from Agencia Nacional de Promoción Científica y Tecnológica MINCyT (PICT-2016-1575) and Universidad Nacional del Sur of Argentina (PGI 24/A225).

This study was designed to determine whether a remotely piloted aerial application system (RPAAS) could be used in lieu of a backpack sprayer for post-emergence herbicide application. Consequent to this objective, a spray mixture of tap water and fluorescent dye was applied on Palmer amaranth and ivyleaf morningglory using an RPAAS at 18.7 and 37.4 L&middot, ha&minus, 1 and a CO2-pressurized backpack sprayer at a 140 L&middot, 1 spray application rate. Spray efficiency (the proportion of applied spray collected on an artificial sampler) for the RPAAS treatments was comparable to that for the backpack sprayer. Fluorescent spray droplet density was significantly higher on the adaxial surface for the backpack sprayer treatment than that for the RPAAS platforms. The percent of spray droplets on the abaxial surface for the RPAAS aircraft at 37.4 L&middot, 1 was 4-fold greater than that for the backpack sprayer at 140 L&middot, 1. The increased spray deposition on the abaxial leaf surfaces was likely caused by rotor downwash and wind turbulence generated by the RPAAS which caused leaf fluttering. This improved spray deposition may help increase the efficacy of contact herbicides. Test results indicated that RPAASs may be used for herbicide application in lieu of conventional backpack sprayers.

In recent years, Unmanned Aerial Systems (UAS) have emerged as an innovative technology to provide spatio-temporal information about weed species in crop fields. Such information is a critical input for any site-specific weed management program. A multi-rotor UAS (Phantom 4) equipped with an RGB sensor was used to collect imagery in three bands (Red, Green, and Blue, 0.8 cm/pixel resolution) with the objectives of (a) mapping weeds in cotton and (b) determining the relationship between image-based weed coverage and ground-based weed densities. For weed mapping, three different weed density levels (high, medium, and low) were established for a mix of different weed species, with three replications. To determine weed densities through ground truthing, five quadrats (1 m &times, 1 m) were laid out in each plot. The aerial imageries were preprocessed and subjected to Hough transformation to delineate cotton rows. Following the separation of inter-row vegetation from crop rows, a multi-level classification coupled with machine learning algorithms were used to distinguish intra-row weeds from cotton. Overall, accuracy levels of 89.16%, 85.83%, and 83.33% and kappa values of 0.84, 0.79, and 0.75 were achieved for detecting weed occurrence in high, medium, and low density plots, respectively. Further, ground-truthing based overall weed density values were fairly correlated (r2 = 0.80) with image-based weed coverage assessments. Among the specific weed species evaluated, Palmer amaranth (Amaranthus palmeri S. Watson) showed the highest correlation (r2 = 0.91) followed by red sprangletop (Leptochloa mucronata Michx) (r2 = 0.88). The results highlight the utility of UAS-borne RGB imagery for weed mapping and density estimation in cotton for precision weed management.

Despite the best weed control efforts, weed escapes are often present in large production fields prior to harvest, contributing to seed rain and species persistence. Late-season surveys were conducted in cotton (Gossypium hirsutum L.) fields in Texas in 2016 and 2017 to identify common weed species present as escapes and estimate seed rain potential of Palmer amaranth (Amaranthus palmeri S. Watson) and waterhemp [A. tuberculatus (Moq.) J.D. Sauer], two troublesome weed species with high fecundity. A total of 400 cotton fields across four major cotton-producing regions in Texas [High Plains (HP), Gulf Coast (GC), Central Texas, and Blacklands] were surveyed. Results have revealed that A. palmeri, Texas millet [Urochloa texana (Buckley) R. Webster], A. tuberculatus, ragweed parthenium (Parthenium hysterophorus L.), and barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.] were the top five weed escapes present in cotton fields. Amaranthus palmeri was the most prevalent weed in the HP and Lower GC regions, whereas A. tuberculatus escapes were predominantly observed in the Upper GC and Blacklands regions. On average, 9.4% of an individual field was infested with A. palmeri escapes in the Lower GC region; however, it ranged between 5.1 and 8.1% in the HP region. Average A. palmeri density ranged from 405 (Central Texas) to 3,543 plants ha−1 (Lower GC). The greatest seed rain potential by A. palmeri escapes was observed in the upper HP region (13.9 million seeds ha−1), whereas the seed rain potential of A. tuberculatus escapes was the greatest in the Blacklands (12.9 million seeds ha−1) and the upper GC regions (9.8 million seeds ha−1). Results indicated that seed rain from late-season A. palmeri and A. tuberculatus escapes are significant in Texas cotton, and effective management of these escapes is imperative for minimizing seedbank inputs and impacting species persistence.

Knowledge of the effects of burial depth and burial duration on seed viability and, consequently, seedbank persistence of Palmer amaranth (Amaranthus palmeriS. Watson) and waterhemp [Amaranthus tuberculatus(Moq.) J. D. Sauer] ecotypes can be used for the development of efficient weed management programs. This is of particular interest, given the great fecundity of both species and, consequently, their high seedbank replenishment potential. Seeds of both species collected from five different locations across the United States were investigated in seven states (sites) with different soil and climatic conditions. Seeds were placed at two depths (0 and 15 cm) for 3 yr. Each year, seeds were retrieved, and seed damage (shrunken, malformed, or broken) plus losses (deteriorated and futile germination) and viability were evaluated. Greater seed damage plus loss averaged across seed origin, burial depth, and year was recorded for lots tested at Illinois (51.3% and 51.8%) followed by Tennessee (40.5% and 45.1%) and Missouri (39.2% and 42%) forA. palmeriandA. tuberculatus, respectively. The site differences for seed persistence were probably due to higher volumetric water content at these sites. Rates of seed demise were directly proportional to burial depth (α=0.001), whereas the percentage of viable seeds recovered after 36 mo on the soil surface ranged from 4.1% to 4.3% compared with 5% to 5.3% at the 15-cm depth forA. palmeriandA. tuberculatus, respectively. Seed viability loss was greater in the seeds placed on the soil surface compared with the buried seeds. The greatest influences on seed viability were burial conditions and time and site-specific soil conditions, more so than geographical location. Thus, management of these weed species should focus on reducing seed shattering, enhancing seed removal from the soil surface, or adjusting tillage systems.

Gene flow between crops and their weedy or wild relatives can be problematic in modern agricultural systems, especially if it endows novel adaptive genes that confer tolerance to abiotic an...

There is great value in quantifying and reporting weed seed production as a component of herbicide efficacy evaluations for two reasons. First, visual weed control ratings and associated measurements such as weed density and biomass are not sufficient indicators of fecundity. Second, knowledge of fecundity associated with herbicide treatments can guide the development of effective management programs that impact long-term weed population dynamics and reduce the risk of herbicide resistance.

Unmanned aerial vehicles (UAVs), commonly called ‘drones’, have enormous potential for technological advances in many sectors including agriculture. The recent revision in UAV policy by the Directorate General of Civil Aviation (DGCA), India, can impact the pace of research and development in machine vision capabilities in the country. Several other countries that have framed UAV policy previously, are continuously bringing changes to the existing framework to make it more user friendly. India can learn from those changes and bring out a comprehensive update to foster a broader application of these tools in agriculture. This policy review provides suggestions and solutions for increasing licensing centres, limiting UAV speed and weight for safer flights and including aerial pesticide applications in UAV permits to revolutionize the multibillion-dollar agriculture industry. This article has also examines the current UAV regulations in four other countries. Published online (Publication status)

Italian ryegrass (Lolium perenne ssp. multiflorum (Lam) Husnot) is a troublesome weed species in wheat (Triticum aestivum) production in the United States, severely affecting grain yields. Spatial mapping of ryegrass infestation in wheat fields and early prediction of its impact on yield can assist management decision making. In this study, unmanned aerial systems (UAS)-based red, green and blue (RGB) imageries acquired at an early wheat growth stage in two different experimental sites were used for developing predictive models. Deep neural networks (DNNs) coupled with an extensive feature selection method were used to detect ryegrass in wheat and estimate ryegrass canopy coverage. Predictive models were developed by regressing early-season ryegrass canopy coverage (%) with end-of-season (at wheat maturity) biomass and seed yield of ryegrass, as well as biomass and grain yield reduction (%) of wheat. Italian ryegrass was detected with high accuracy (precision = 95.44 ± 4.27%, recall = 95.48 ± 5.05%, F-score = 95.56 ± 4.11%) using the best model which included four features: hue, saturation, excess green index, and visible atmospheric resistant index. End-of-season ryegrass biomass was predicted with high accuracy (R2 = 0.87), whereas the other variables had moderate to high accuracy levels (R2 values of 0.74 for ryegrass seed yield, 0.73 for wheat biomass reduction, and 0.69 for wheat grain yield reduction). The methodology demonstrated in the current study shows great potential for mapping and quantifying ryegrass infestation and predicting its competitive response in wheat, allowing for timely management decisions. Published version

Weeds pose severe threats to agricultural and natural landscapes worldwide. One major reason for the failure to effectively manage weeds at landscape scales is that current Best Management Practice guidelines, and research on how to improve such guidelines, focus too narrowly on property-level management decisions. Insufficiently considered are the aggregate effects of individual actions to determine landscape-scale outcomes, or whether there are collective practices that would improve weed management outcomes. Here, we frame landscape-scale weed management as a social dilemma, where trade-offs occur between individual and collective interests. We apply a transdisciplinary system approach—integrating the perspectives of ecologists, evolutionary biologists and agronomists into a social science theory of social dilemmas—to four landscape-scale weed management challenges: (i) achieving plant biosecurity, (ii) preventing weed seed contamination, (iii) maintaining herbicide susceptibility and (iv) sustainably using biological control. We describe how these four challenges exhibit characteristics of ‘public good problems’, wherein effective weed management requires the active contributions of multiple actors, while benefits are not restricted to these contributors. Adequate solutions to address these public good challenges often involve a subset of the eight design principles developed by Elinor Ostrom for ‘common pool social dilemmas’, together with design principles that reflect the public good nature of the problems. This paper is a call to action for scholars and practitioners to broaden our conceptualization and approaches to weed management problems. Such progress begins by evaluating the public good characteristics of specific weed management challenges and applying context-specific design principles to realize successful and sustainable weed management. The severe threat of weeds to agriculture and ecosystems necessitates management practices on the scale of landscapes. This Perspective uses four case studies to examine the best practices for this collective dilemma.

Weeds, which have been the bane of agriculture since the beginning of civilization, are managed manually, mechanically, and, more recently, by chemicals. However, chemical control options are rapidly shrinking due to the recent rise in the number of herbicide-resistant weeds in crop fields, with few alternatives on the horizon. Therefore, there is an urgent need for alternative weed suppression systems to sustain crop productivity while reducing our dependence on herbicides and tillage. Such a development will also allay some of the negative perceptions associated with the use of herbicide-resistance genes and heavy dependence on herbicides. Transgenic plants expressing the bacterial

Acetolactate synthase (ALS) –inhibitor resistance has been recently documented in a yellow nutsedge biotype in Arkansas rice production, with a target-site mutation resulting in an amino acid substitution from Trp574 to Leu. Preliminary observations have indicated that the resistant biotype showed distinct phenotypic characteristics. Two greenhouse experiments were conducted on the resistant biotype in comparison with three susceptible standards (1) to understand differential growth habit and spatial distribution, and (2) to characterize shoot emergence pattern and seedling vigor. The resistant biotype exhibited a drastically different growth habit with secondary and tertiary basal bulbs emerging away from the parent shoot, resulting in a wider spatial distribution and ground coverage compared to the very compact growth habit of susceptible biotypes. Unlike the susceptible biotypes, the rhizomes developing into tubers were not often connected to the primary basal bulb, but were originating randomly from daughter shoots. The resistant biotype produced an extensive subterranean network of rhizomes and basal bulbs, with wider root spread and distribution compared to the susceptible biotypes. The growth habit of the resistant biotype appeared to be intermediate between yellow and purple nutsedges. Further, the resistant biotype showed a considerably delayed emergence pattern with relatively high levels of tuber dormancy. Although the resistant plants exhibited low early-growth seedling vigor and biomass production compared to the susceptible biotypes (perhaps because of smaller tubers), final aboveground biomass production was greater than that of susceptible biotypes. The overall growth habit and phenotype of the resistant biotype may provide a competitive advantage over adjacent species through the ability to occupy niches and gain improved access to critical resources. The distinct growth pattern may also mean that tillage should not be relied upon for control because it can assist further spread by disconnecting and displacing the chains of rhizomes.

Herbicide-resistant barnyardgrass has become widespread in the rice production systems of the midsouthern United States, leaving few effective herbicide options for controlling this weed. The acetolactate synthase (ALS)- and acetyl-CoA carboxylase (ACCase)-inhibiting herbicides remain largely effective in Clearfield®rice production, but strategies need to be developed to protect the long-term utility of these options. A two-trait model was developed to understand simultaneous evolution of resistance in barnyardgrass to the ALS- and ACCase-inhibiting herbicides in Clearfield rice. The model was used to predict resistance under a number of common weed management scenarios across 1,000 hypothetical rice fields in the Mississippi Delta region and answer some key management questions. Under an ALS inhibitor–only program consisting of three annual applications of imidazolinone herbicides (imazethapyr or imazamox) in continuous Clearfield rice, resistance was predicted within 4 yr with 80% risk by year 30. Weed management programs that consisted of ALS- and ACCase-inhibiting herbicides such as fenoxaprop and cyhalofop greatly reduced the risk of ALS-inhibiting herbicide resistance (12% risk by year 30), but there was a considerable risk for ACCase resistance (evolving by year 14 with 13% risk by year 30) and multiple resistance (evolving by year 16 with 11% risk by year 30) to both of these mechanisms of action. A unique insight was that failure to stop using a herbicide soon after resistance evolution can accelerate resistance to the subsequent herbicide option. Further, a strong emphasis on minimizing seedbank size is vital for any successful weed management strategy. Results also demonstrated that diversifying management options is not just adequate, but diversity combined with timely herbicide applications aimed at achieving high efficacy levels possible is imperative.

Glyphosate-resistant (GR) weeds have been a prime challenge to the sustainability of GR cotton-based production systems of the midsouthern United States. Barnyardgrass is known to be a high-risk species for evolving herbicide resistance, and a simulation model was developed for understanding the likelihood of glyphosate resistance evolution in this species in cotton-based systems. Under a worst-case scenario of five glyphosate applications in monoculture GR cotton, the model predicts resistance evolution in about 9 yr of continuous glyphosate use, with about 47% risk by year 15. A unique insight from this model is that management in response to GR Palmer amaranth in this system (a reactive response) provided a proactive means to greatly reduce the risks of glyphosate resistance evolution in barnyardgrass. Subsequent model analysis revealed that the risk of resistance is high in fields characterized by high barnyardgrass seedbank levels, seedling emergence, and seed production per square meter, whereas the risk is low in fields with high levels of postdispersal seed loss and annual seedbank loss. The initial frequency of resistance alleles was a high determinant of resistance evolution (e.g., 47% risk at year 15 at an initial frequency of 5e−8vs. 4% risk at 5e−10). Monte Carlo simulations were performed to understand the influence of various glyphosate use patterns and production practices in reducing the rate and risk of glyphosate resistance evolution in barnyardgrass. Early planting and interrow cultivation are useful tools. Crop rotation is effective, but the diversity of weed management options practiced in the rotational crop is more important. Diversifying weed management options is the key, yet application timing and the choice of management option is critical. Model analyses illustrate the relative effectiveness of a number of diversified glyphosate use strategies in preventing resistance evolution and preserving the long-term utility of glyphosate in midsouthern U.S. cotton-based production systems.

Global warming leads to the concurrence of a number of abiotic and biotic stresses, thus affecting agricultural productivity. Occurrence of abiotic stresses can alter plant–pest interactions by enhancing host plant susceptibility to pathogenic organisms, insects, and by reducing competitive ability with weeds. On the contrary, some pests may alter plant response to abiotic stress factors. Therefore, systematic studies are pivotal to understand the effect of concurrent abiotic and biotic stress conditions on crop productivity. However, to date, a collective database on the occurrence of various stress combinations in agriculturally prominent areas is not available. This review attempts to assemble published information on this topic, with a particular focus on the impact of combined drought and pathogen stresses on crop productivity. In doing so, this review highlights some agriculturally important morpho-physiological traits that can be utilized to identify genotypes with combined stress tolerance. In addition, this review outlines potential role of recent genomic tools in deciphering combined stress tolerance in plants. This review will, therefore, be helpful for agronomists and field pathologists in assessing the impact of the interactions between drought and plant-pathogens on crop performance. Further, the review will be helpful for physiologists and molecular biologists to design agronomically relevant strategies for the development of broad spectrum stress tolerant crops.

Late-season weed escapes are often ignored because they rarely cause crop yield penalty. Traditional weed management recommendations are based on the economic threshold (ET) approach, wherein management is required if the predicted current-season yield loss is greater than the cost of control interventions. While ET-based weed management can reduce current-season production costs and promote farmland biodiversity, it does not consider the long-term biological and economic consequences associated with late-season weed seed production. An important concern is that late-season weed seed production will replenish the soil seedbank, ensuring future weed problems. In the context of herbicide resistance evolution, allowing late-season weed seed production can be problematic because the probabilities of occurrence of resistant mutants rise with increases in seed production. A key component of herbicide resistance mitigation and management is preventing seed production and buildup of the soil seedbank. Late-season weed management efforts constitute additional expenses to growers, which cannot be recouped in that growing season, but any such investment must be weighed against the perceived long-term benefits. It appears that management of late-season weed escapes is valuable in a number of situations, and the degree to which management interventions should be employed can be case-specific. Adoption of economic optimum thresholds (EOTs), which can be established using bio-economic models, will be useful for making management decisions for late-season weed escapes. In systems vulnerable to herbicide resistance evolution, bio-economic resistance thresholds (BERTs) will be appropriate and bio-economic resistance models (BERMs) will be helpful for establishing such thresholds for specific production scenarios. Management considerations for late-season weed escapes are discussed, and knowledge gaps for future research are identified.

Whether season-long weed control can be achieved in a furrow-irrigated rice system with similar herbicide inputs to that of a flooded system is not known. Field experiments were conducted in 2007 and 2008 at Pine Tree, AR to evaluate different herbicide programs on the weed control efficacy and rice grain yield in furrow-irrigated and flooded rice production systems. Six herbicide programs were evaluated with and without additional late-season “as-needed” herbicide treatments. Minor injury to rice was noted for quinclorac plus propanil. However, the injury was transient and the plants fully recovered. Overall weed control was greater in the flooded system compared with the furrow-irrigated system (up to 20% greater), because flooding effectively prevented the emergence of most terrestrial weeds. In addition, rice grain yields were 13 to 14% greater in flooded compared with furrow-irrigated plots. Irrespective of the irrigation system, herbicide programs that contained a PRE-applied herbicide provided greater weed control and resulted in greater yield compared with those that did not contain PRE-applied herbicide, indicative of the importance of early-season weed control in achieving higher grain yields. On the basis of weed control, yield, and weed treatment cost, the herbicide program with clomazone PRE followed by propanil at four- to five-leaf rice was more efficient than other programs evaluated in both irrigation systems. However, furrow-irrigated plots required as-needed herbicide applications, which were applied after the four- to five-leaf rice stage when two or more plots within a program exhibited ≤ 80% control for any of the weed species. This suggests that furrow-irrigated rice production demands additional weed management efforts and thereby increases production costs. There is also a possibility for substantial yield reduction in the furrow-irrigated system compared with the flooded system. Nevertheless, furrow-irrigated rice production can still be a viable option under water-limiting situations and under certain topographic conditions.

Johnsongrass is one of the most troublesome weeds of the world and is listed as a noxious weed in Arkansas. Reduced johnsongrass control with the recommended application rate of glyphosate (840 g ae ha−1) was reported in a continuous soybean field near West Memphis, AR, in the fall of 2007. A greenhouse study was conducted (1) to confirm and characterize glyphosate resistance in the johnsongrass biotype from West Memphis and (2) to determine whether resistant and susceptible biotypes have differential glyphosate absorption or translocation. Dose–response studies revealed that the resistant biotype was five- to seven-fold less sensitive to glyphosate than the susceptible biotype. Glyphosate absorption was similar in resistant and susceptible biotypes at 72 h after treatment (HAT). However, the treated leaf of the resistant biotype retained 28 percentage points more absorbed14C glyphosate compared to the susceptible biotype at 72 HAT. Additionally, the resistant biotype had less14C glyphosate translocated to the aboveground tissue below the treated leaf and to roots compared to the susceptible biotype at 24 and 72 HAT. Reduced translocation and increased retention of glyphosate in treated leaves is a probable mechanism of resistance in this glyphosate-resistant johnsongrass biotype.

Alfalfa is an important forage crop in North America, and it can also be found as a roadside weed in alfalfa-growing regions. Weediness and invasiveness are greatly facilitated by establishment ability, yet little is known about the ability of alfalfa to establish in competitive environments such as roadsides. The primary objective of this study was to estimate the degree of alfalfa establishment without managed cultivation under different seed-dispersal times and disturbance regimes. The study had a split-plot design with two main plots (spring and fall seed dispersal) and five subplots (mowing, soil disturbance, herbicide spray, seedbed, and undisturbed control). The study examined establishment, growth attributes, and reproductive output of alfalfa in response to these treatments. Alfalfa establishment in the undisturbed grass swards ranged between 0.5 and 9.7% (out of the total number of seeds dispersed) across the dispersal times. The density of alfalfa in fall-seeded plots was about 82% lower than in spring-seeded plots. Soil disturbance reduced the density of alfalfa to < 50% of the initial density. Generally, low plant densities were compensated over time by increased numbers of shoots and reproductive units (racemes and pods) per plant. Herbicide application (2,4-D + dicamba) effectively controlled all emerged alfalfa plants, but in some cases, seedling recruitment was observed in the years following herbicide application. Although mowing did not kill alfalfa plants, mowed plants did not produce mature seeds, and as such, mowing may be useful in restricting the reproductive success and population growth of alfalfa. Overall, it is evident that alfalfa is capable of establishing in competitive environments (such as roadside habitats) and rapidly recovering from moderate disturbances. The results of this study have implications for managing roadside alfalfa and for designing novel trait-confinement protocols for alfalfa.

Barnyardgrass is one of the most problematic weeds in Arkansas, and with the documentation of herbicide-resistant biotypes, there is a need to gain a detailed understanding of its ecology. In particular, knowledge on barnyardgrass seedbank size and emergence pattern is vital. An extensive seedbank survey was carried out in 2008 in 12 counties in eastern Arkansas to determine barnyardgrass seedbank size across the region. There was a great variability in seedbank size with a maximum of 215,000 seeds m−2. Among the fields surveyed, barnyardgrass seedbank was found only in 7% of the cotton fields, while it was 22 and 20%, respectively, for rice and soybean. To examine the emergence pattern of barnyardgrass, experiments were conducted in Rohwer (two sites), Stuttgart (one site), and Fayetteville (one site), Arkansas in 2008 and 2009. In each site, barnyardgrass emergence was quantified from naturally occurring seedbanks. Barnyardgrass exhibited an extended period of emergence with days to 100% emergence ranging from 99 to 165 across sites and years. Nevertheless, effective management may be achieved by targeting the peak emergence periods, which range from mid-April to mid-June in Arkansas. The four-parameter Weibull model provided a better fit to the cumulative emergence data. However, the thermal time (growing degree days, GDDs) or hydrothermal time (HTT) models did not predict barnyardgrass emergence any better than calendar days, perhaps because of the inherent variations associated with natural seedbanks. This study establishes seedbank size and general emergence pattern for barnyardgrass in Arkansas. Additionally, these results will be useful for parameterizing herbicide-resistance simulation models for barnyardgrass.