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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.

Lawns provide valuable ecological, economic and social services. Both the quantity and quality of the turfgrass affect the level of the services delivered. Unwanted weed infestations can degrade lawn quality and decrease the value of these services for lawn users and others. For example, Brosnan et al. reported that athletic fields with weed cover not only decreased aesthetic quality but also resulted in increased surface hardness and a concomitant increase in potential athlete injuries and soil erosion. Contemporary research pertaining to pest influences on lawn quality and management responses is sparse. We hope to begin filling that gap with findings from a recent study of U.S. lawn care operators about the challenges and opportunities omanaging Poa annua, an emergent threat to sustainable lawn management (USDA-NIFA Specialty Crops Research Initiative (SCRI) Grants Program (award #: 2018-51181-28436). Estimates for residential- and commercial-lawn cover in the United States (U.S.) range from 58,000 km2 to 120,000 km2 with total turfgrass cover (inclusive of golf courses, parks, schools, roadsides) estimated at 163,800 km2. The Federal Highway Administration (FHWA) estimated that there are 20,436 km2 of grassed rights of way (roadsides) in the U.S. The total U.S. urban area in 2010 was estimated at 802,053 km2. Together, we can estimate turf cover between 7 and 18% of U.S urban areas, including lawns as the primary turf surface along with parks, athletic fields, golf courses, and roadsides. The professional lawn care industry employed 295,000 workers and represented an $18.5 billion market value in 2002. Despite its size, the U.S. lawn care industry defies simple characterization in part due to its heterogeneity. Lawns may provide valuable ecosystem services, including wildlife habitat, water infiltration, and moderation of the urban heat-island effect as well as aesthetics and open space for recreation. Apart from offering economic and ecological services, lawns and their management can be an important component of community social character and culture. Effective weed management in lawns assures sustainable delivery of all of these services, such that lawns are uniform and free from potentially troublesome weed species, such as Poa annua which ranks as the #1 most troublesome weed in turfgrass systems.

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.

Wood vinegar, a product of pyrolysis, can induce phytotoxicity on plants when applied at an adequate rate and concentration. The objective of this research was to investigate wood vinegar obtained from the pyrolysis of apple tree branches for weed control in dormant zoysiagrass. In environment-controlled growth chambers, white clover visual injury and shoot mass reduction were evaluated and compared to the nontreated control after wood vinegar application at 1,000, 2,000, or 4,000 L ha−1 under 10 C or 30 C temperature conditions. Averaged across rates, wood vinegar rapidly desiccated white clover and caused 83% and 71% visual injury at 10 C and 30 C, respectively, at 1 d after treatment (DAT). Averaged across temperatures, wood vinegar at 1,000, 2,000, and 4,000 L ha−1 reduced white clover shoot mass by 56%, 81%, and 98% from the nontreated control at 10 DAT, respectively. In field experiments, weed control increased as wood vinegar rates increased from 1,000 to 5,000 L ha−1 in dormant zoysiagrass. The effective application dose of wood vinegar required to provide 90% control (ED90) of annual fleabane, Persian speedwell, and white clover was determined to be 2,450, 2,300, and 4,020 L ha−1, respectively, at 2 wk after treatment. Turf quality did not differ among the wood vinegar treatments and the nontreated control when zoysiagrass completely recovered from dormancy. Overall, results illustrate that wood vinegar resulting from the pyrolysis of apple tree branches can be used as a nonselective herbicide in dormant turfgrass, offering a new nonsynthetic herbicide option for weed control in managed turf.

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.

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.

Site-specific treatment of weeds in agricultural landscapes has been gaining importance in recent years due to economic savings and minimal impact on the environment. Different detection methods have been developed and tested for precision weed management systems, but recent developments in neural networks have offered great prospects. However, a major limitation with the neural network models is the requirement of high volumes of data for training. The current study aims at exploring an alternative approach to the use of real images to address this issue. In this study, synthetic images were generated with various strategies using plant instances clipped from UAV-borne real images. In addition, the Generative Adversarial Networks (GAN) technique was used to generate fake plant instances which were used in generating synthetic images. These images were used to train a powerful convolutional neural network (CNN) known as "Mask R-CNN" for weed detection and segmentation in a transfer learning mode. The study was conducted on morningglories (MG) and grass weeds (Grass) infested in cotton. The biomass for individual weeds was also collected in the field for biomass modeling using detection and segmentation results derived from model inference. Results showed a comparable performance between the real plant-based synthetic image (mean average precision for mask-mAPm: 0.60; mean average precision for bounding box-mAPb: 0.64) and real image datasets (mAPm: 0.80; mAPb: 0.81). However, the mixed dataset (real image + real plant instance-based synthetic image dataset) resulted in no performance gain for segmentation mask whereas a very small performance gain for bounding box (mAPm: 0.80; mAPb: 0.83). Around 40–50 plant instances were sufficient for generating synthetic images that resulted in optimal performance. Row orientation of cotton in the synthetic images was beneficial compared to random-orientation. Synthetic images generated with automatically-clipped plant instances performed similarly to the ones generated with manually-clipped instances. Generative Adversarial Networks-derived fake plant instances-based synthetic images did not perform as effectively as real plant instance-based synthetic images. The canopy mask area predicted weed biomass better than bounding box area with R2values of 0.66 and 0.46 for MG and Grass, respectively. The findings of this study offer valuable insights for guiding future endeavors oriented towards using synthetic images for weed detection and segmentation, and biomass estimation in row crops.

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.

Background Annual bluegrass is a troublesome weed in managed turf systems. A survey was conducted to evaluate the prevalence of herbicide resistance in golf course populations of annual bluegrass in eastern Texas. Screenings were conducted for two photosystem II (PS II)-inhibitor herbicides [simazine preemergence (PRE), amicarbazone postemergence (POST)], two acetolactate synthase (ALS) inhibitors (foramsulfuron POST, trifloxysulfuron POST) and one microtubule assembly inhibitor (pronamide PRE/POST). Results Ninety percent of the populations were found to be resistant to at least one of the tested herbicides. The TX15-14 population was >490-, 178-, 10-, 26-, 4.3- and 3.8-fold, and the TX15-27 population was >490-, 16-, 28-, 84-, 5.2- and 4.1-fold less sensitive to simazine, amicarbazone, foramsulfuron, trifloxysulfuron, pronamide POST and pronamide PRE, respectively, compared to the susceptible standard TX15-SUS. Populations resistant to pronamide POST were completely controlled by pronamide PRE at the label recommended rate. The ALS and psbA gene sequence analysis indicated the presence of target site mutations (Ser-264-Gly in the psbA gene of TX15-14 and Trp-574-Leu in the ALS gene of TX15-27). However, given the absence of any target-site mutation in the ALS gene of TX15-14, the psbA gene of TX15-27 and α-tubulin of both populations, nontarget site mechanisms of resistance are suspected. Conclusion This is the first case of multiple herbicide resistance in annual bluegrass populations to three herbicide modes of action. Results show the widespread occurrence of multiple herbicide resistance in golf course annual bluegrass populations in eastern Texas and emphasize the need for the development and implementation of effective resistance management practices. © 2020 Society of Chemical Industry.

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.

A paper-based survey was conducted from 2015 to 2017 among stakeholders of the Texas rice industry on current weed management challenges and factors influencing management decisions. A total of 108 survey questionnaires were completed by stakeholders at the rice Cooperative Extension meetings conducted in the rice-growing counties of Texas. In addition, late-season field surveys were conducted prior to harvest in 2015 and 2016 across the rice-growing counties to understand dominant weed escapes occurring in rice fields. Results from the questionnaire survey revealed that rice–fallow–rice was the most common rotation practiced in Texas rice production. Echinochloa spp., Leptochloa spp., and Cyperus spp. were the top three problematic weed issues faced by the respondents. Among the Leptochloa species, Nealley’s sprangletop, a relatively new species in rice fields, was indicated as an emerging concern. Clomazone was the most frequently used PRE herbicide, whereas quinclorac, propanil, imazethapyr, and cyhalofop were the popular POST herbicides. Most respondents (72%) made weed-control decisions on the basis of economic thresholds, whereas 63% made decisions on the basis of weed problems from previous years. Most respondents (88%) expressed moderate to high concern for herbicide-resistant weeds in their operations. Strategies to manage herbicide-resistant weeds and economical weed management practices were among the top suggested research needs. The field survey revealed that jungle rice, Nealley’s sprangletop, and hemp sesbania were the top three late-season weed escapes in rice production in Texas, with frequencies of occurrence of 28%, 19%, and 13%, respectively. Furthermore, average field area infested by a species was the greatest for jungle rice (13%), followed by hemp sesbania (11%) and weedy rice (11%). Findings from the stakeholder and field surveys help direct future research and outreach efforts for sustainable weed management in Texas rice.

Tillage regimes can influence weed population dynamics and, consequently, the choice of appropriate weed management practices. Studies were conducted in 2016 and 2017 in a long-term (36-yr) grain sorghum [Sorghum bicolor (L.) Moench ssp. bicolor] experiment at Texas A&M University, College Station, to determine the impact of long-term no-till (NT) and conventional till (CT) systems on weed species dynamics. Higher densities of johnsongrass [Sorghum halepense (L.) Pers.], prostrate spurge [Chamaesyce humistrata (Engelm. ex A. Gray) Small], waterhemp [Amaranthus tuberculatus (Moq.) Sauer], and henbit (Lamium amplexicaule L.) were recorded in the NT system compared with the CT system. Further, the NT system showed greater weed diversity (Shannon-Wiener index, H = 0.8) and species richness (S = 6.2), compared with the CT system (H = 0.6, S = 4.2). Seedling emergence of some dominant weed species was also delayed in the NT system. In the CT system, 50% emergence of S. halepense (8.5 C base temperature) and waterhemp (10 C base temperature) occurred at 59 and 63 growing degree days (GDD), respectively, whereas 68 and 75 GDD, respectively, were required in the NT system. Further, a greater proportion (61%) of the viable seedbank was present at the top 5 cm of the soil in the NT system compared with the CT system (46%). Overall, findings from this 36-yr-long tillage experiment have revealed that the NT system had greater weed densities (especially of the perennial weed S. halepense) and a high proportion of weed seeds (particularly small-seeded annuals) on the topsoil layer, corroborating some earlier reports that were based on short-term investigations. Findings indicate that growers transitioning to NT systems should be mindful of potential shifts in weed species dominance and develop appropriate management tactics.

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

Convolutional neural networks (CNNs) have revolutionized the weed detection process with tremendous improvements in precision and accuracy. However, training these models is time-consuming and computationally demanding; thus, training weed detection models for every crop-weed environment may not be feasible. It is imperative to evaluate how a CNN-based weed detection model trained for a specific crop may perform in other crops. In this study, a CNN model was trained to detect morningglories and grasses in cotton. Assessments were made to gauge the potential of the very model in detecting the same weed species in soybean and corn under two levels of detection complexity (levels 1 and 2). Two popular object detection frameworks, YOLOv4 and Faster R-CNN, were trained to detect weeds under two schemes: Detect_Weed (detecting at weed/crop level) and Detect_Species (detecting at weed species level). In addition, the main cotton dataset was supplemented with different amounts of non-cotton crop images to see if cross-crop applicability can be improved. Both frameworks achieved reasonably high accuracy levels for the cotton test datasets under both schemes (Average Precision-AP: 0.83–0.88 and Mean Average Precision-mAP: 0.65–0.79). The same models performed differently over other crops under both frameworks (AP: 0.33–0.83 and mAP: 0.40–0.85). In particular, relatively higher accuracies were observed for soybean than for corn, and also for complexity level 1 than for level 2. Significant improvements in cross-crop applicability were further observed when additional corn and soybean images were added to the model training. These findings provide valuable insights into improving global applicability of weed detection models.

Field surveys were conducted across the Blacklands region of Texas during 2016 and 2017 to document the distribution of herbicide-resistant Lolium spp. infesting winter wheat production fields in the region. A total of 68 populations (64 Italian ryegrass, four perennial ryegrass) were evaluated in a greenhouse for sensitivity to herbicides of three different modes of action: an acetolactate synthase (ALS) inhibitor (mesosulfuron-methyl), two acetyl-coenzyme-A carboxylase (ACCase) inhibitors (diclofop-methyl and pinoxaden), and a 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) inhibitor (glyphosate). Herbicides were applied at twice the label-recommended rates for mesosulfuron-methyl (29 g ai ha−1), diclofop-methyl (750 g ai ha−1), and pinoxaden (118 g ai ha−1); and at the recommended rate for glyphosate (868 g ae ha−1). The herbicide screenings were followed by dose-response assays of the most-resistant ryegrass population for each herbicide at eight rates (0.5, 1, 2, 4, 8, 16, 32, and 64×), compared with a susceptible population at six rates (0.0625, 0.125, 0.25, 0.5, 1, and 2×). The initial screening and dose-response experiments were conducted in a completely randomized design with three replications and two experimental runs. Survivors (

Experiments were conducted to evaluate the impact of 36 years of no-tillage (NT), compared to conventional-tillage (CT), on soil physical properties in a continuous grain sorghum experiment. Result...

We conducted a survey in the major row-crop production regions of Texas to determine the response of waterhemp to glyphosate (5-enolpyruvylshikimate-3-phosphate synthase [EPSPS] inhibitor), atrazine (photosystem II [PSII] inhibitor), pyrithiobac (acetolactate synthase [ALS] inhibitor), tembotrione (hydroxyphenylpyruvate dioxygenase [HPPD] inhibitor), fomesafen (protoporphyrinogen oxidase [PPO] inhibitor), and dicamba (synthetic auxin). We evaluated 127 accessions for these herbicides. Resistance was confirmed on the basis of plant survival within an accession, and the injury ratings of surviving plants were used to categorize each accession as resistant (

A state-level survey was conducted across major row-crop production regions of Texas to document the level of sensitivity of Palmer amaranth to glyphosate, atrazine, pyrithiobac, tembotrione, fomesafen, and dicamba. Between 137 and 161 Palmer amaranth populations were evaluated for sensitivity to the labelled field rate (1X), and rated as resistant (≤49% injury), less sensitive (50% to 89% injury), or susceptible (90% to 100% injury). For glyphosate, 62%, 19%, 13%, and 13% of the populations from the High Plains, Central Texas, Rio Grande Valley, and Lower Gulf Coast, respectively, were resistant. Resistance to atrazine was more common in Palmer amaranth populations from the High Plains than in other regions, with 16% of the populations resistant and 22% less sensitive. Approximately 90% of the populations from the High Plains that exhibited resistance to atrazine POST also were resistant to atrazine PRE. Of the 160 populations tested for pyrithiobac, approximately 99% were resistant or less sensitive, regardless of the region. No resistance was found to fomesafen, tembotrione, or dicamba. However, 22% of the populations from the High Plains were less sensitive to 1X (93 g ai ha-1) tembotrione, but were killed at 2X, illustrating the background variability in sensitivity to this herbicide. For dicamba, three populations, all from the High Plains, exhibited less sensitivity at the 1X rate (controlled at the 2X rate; 1X = 560 g ae ha-1). One population exhibited multiple resistance to three herbicides with distinct sites of action (SOAs) involving acetolactate synthase, 5-enolpyruvylshikimate-3-phosphate synthase, and photosystem II inhibitors. Palmer amaranth populations exhibited less sensitivity to approximately 15 combinations of herbicides involving up to five SOAs. Dose-response assays conducted on the populations most resistant to glyphosate, pyrithiobac, or atrazine indicated they were 30-, 32-, or 49-fold or more resistant to these herbicides, respectively, compared with a susceptible standard.Nomenclature: Atrazine; dicamba; fomesafen; glyphosate; pyrithiobac; tembotrione; Palmer amaranth, Amaranthus palmeri S. Watson

In-field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat.The object detection neural networks, including CenterNet, Faster R-CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels.CenterNet, Faster R-CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry.

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.

BACKGROUND Italian ryegrass (Lolium perenne ssp. multiflorum) is one of the major winter annual weeds worldwide. In this research, diversity for seed morpho-physiological traits such as seed weight, seed size, awnedness, dormancy, speed of germination, and seed vigor among Italian ryegrass populations collected from the Texas Blacklands region were assessed, and potential association with herbicide resistance was investigated. RESULTS A high degree of diversity was observed among the populations for 100-seed weight (125-256 mg), seed length (4.8-6.6 mm), awn length (0-6 mm), and total seedling length (9-14 cm at 21 days after seed germination). Inter-population range for seed dormancy was higher in the freshly harvested seed (31-85%), which reduced to 18 to 62% at 9 months after harvest. Populations with high initial seed dormancy (> 70% dormancy) released dormancy at a faster rate than the low dormancy group (

Vegetable producers are often concerned about potential herbicide carryover and injury to vegetables following the application of livestock manure/compost from animals fed with treated hay. A greenhouse study was conducted to evaluate the effect of activated charcoal on soil residual activity of the herbicides aminopyralid, aminocyclopyrachlor, and picloram, applied at recommended field rates of 120 g ae, 70 g ai, and 303 g ai ha−1 respectively, on tomato, okra, and cantaloupe. A commercial source of activated charcoal was used at four rates [0, 0.5X, 1X, and 2X (1X = 336 kg ha−1)]. When no activated charcoal was applied, aminopyralid caused the greatest injury to tomato and okra, leading to plant death at 4 weeks after emergence/transplanting (WAE/T). At the highest rate (2X) of activated charcoal, crop injuries due to aminopyralid were reduced by 72 and 78% for okra and cantaloupe, respectively at 4 WAE, while tomato did not respond to the 2X rate of activated charcoal. For aminocyclopyrachlor, even a much lower rate (0.5X) of activated charcoal was sufficient to reduce about 70% of the injury caused on cantaloupe and okra. For picloram, activated charcoal application at the 2X rate reduced injury by 78–94% across the three vegetable species investigated. Under low herbicide residue levels that usually occur under carryover situations, a much lower rate of activated charcoal (

Kochia [Bassia scoparia(L.) A. J. Scott] is a problematic weed species across the Great Plains, as it is spreading fast and has developed herbicide-resistant biotypes. It is imperative to understand key life-history stages that promote population expansion ofB. scopariaand control strategies that would provide effective control of these key stages, thereby reducing population growth. Diversifying weed control strategies has been widely recommended for the management of herbicide-resistant weeds. Therefore, the objectives of this study were to develop a simulation model to assess the population dynamics ofB. scopariaand to evaluate the effectiveness of diverse weed control strategies on long-term growth rates ofB. scopariapopulations. The model assumed the existence of a glyphosate-resistant (GR) biotype in theB. scopariapopulation, but at a very low proportion in a crop rotation that included glyphosate-tolerant corn (Zea maysL.) and soybean [Glycine max(L.) Merr.]. The parameter estimates used in the model were obtained from various ecological and management studies onB. scoparia. Model simulations indicated that seedling recruitment and survival to seed production were more important than seedbank persistence forB. scopariapopulation growth rate. Results showed that a diversified management program, including glyphosate, could provide excellent control ofB. scopariapopulations and potentially eliminate already evolved GRB. scopariabiotypes within a given location. The most successful scenario was a diverse control strategy that included one or two preplant tillage operations followed by preplant or PRE application of herbicides with residual activities and POST application of glyphosate; this strategy reduced seedling recruitment, survival, and seed production during the growing season, with tremendous negative impacts on long-term population growth and resistance risk inB. scoparia.

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.

More than 70 years after modern agriculture declared a 'war on weeds', they continue to thrive and suppress crop yields. Viewing weeds as an enemy that can be defeated if only a powerful enough technology can be deployed is a losing proposition. The latest evidence for the inadequacy of this approach, rampant evolution of multiple herbicide-resistant weed genotypes and dwindling options for chemical control in many production systems, should be seen as an urgent message to all those concerned with the science and practice of weed management: we need another way of thinking about the weed resistance issue. Fortunately, the theoretical and practical foundation of this alternative approach, ecological weed management, has been in development for decades. Here, we use Barry Commoner's laws of ecology as a conceptual frame for a review of some of the fundamental concepts of ecological weed management. © 2018 Society of Chemical Industry.

Research was conducted to determine whether resistance to glyphosate among Palmer amaranth (Amaranthus palmeriS. Watson) populations within the U.S. state of Arkansas was due solely to increasedEPSPSgene copy number and whether gene copy number is correlated with resistance level to glyphosate. One hundred and fifteenA. palmeriaccessions were treated with 840 g ae ha−1glyphosate. Twenty of these accessions, selected to represent a broad range of responses to glyphosate, underwent further testing. Seven of the accessions were controlled with this dose; the rest were resistant. The effective dose to cause 50% injury (ED50) for susceptible accessions ranged from 28 to 207 g ha−1. The glyphosate-resistant (GR) accessions had ED50values ranging from 494 to 1,355 g ha−1, a 3- to 48-fold resistance level compared with the susceptible standard (SS). The 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene relative copy number was determined for 20 accessions, 4 plants accession−1. Resistant plants from five GR accessions (38% of resistant plants tested) did not have increasedEPSPSgene copies. Resistant plants from the remaining eight GR accessions (62% of resistant plants tested) had 19 to 224 moreEPSPSgene copies than the SS. Among the accessions tested, injury declined 4% with every additionalEPSPScopy. ED50values were directly correlated withEPSPScopy number. The highly resistant accession MIS11-B had an ED50of 1,355 g ha−1and 150 gene copies. Partial sequences ofEPSPSfrom GR accessions withoutEPSPSamplification did not contain any of the known resistance-conferring mutations. Nearly 40% of GR accessions putatively harbor non–target site resistance mechanisms. Therefore, elevatedEPSPSgene copy number is associated with glyphosate resistance amongA. palmerifrom Arkansas.

Pruning trees in urban landscapes produces a large amount of pruning waste. Pyrolysis is a process that thermally converts lignocellulosic biomass to a number of useful products, including wood vinegar. The objectives of this research were to 1) identify the composition and property of wood vinegar resulting from the pyrolysis of elm (Ulmus spp.) tree pruning waste and 2) evaluate the efficacy of wood vinegar for control of several broadleaf weed species. Acids along with various other chemical groups including alcohols, aldehydes, carbohydrates, phenols, nitrides, and ketones were identified in wood vinegar. Water constituted the highest proportion of wood vinegar, accounted for 77 % by weight. In growth chamber experiments, wood vinegar at 4000 L ha−1 showed 95 % injury and 76 % fresh aboveground biomass reduction of perilla mint [Perilla frutescens (L.) Britt.] compared to the nontreated control at 7 d after treatment, whereas a 2000 L ha-1 application volume caused 80 % injury, with 60 % biomass reduction. The efficacy of 1000 or 2000 L ha−1 application volume were significantly lower. In field experiments, wood vinegar showed varying levels of control for perilla mint, creeping woodsorrel (Oxalis corniculata L.), and Carolina geranium (Geranium carolinianum L.). While wood vinegar at 4000 L ha-1 provided 100 % control of perilla mint and control of creeping woodsorrel and Carolina geranium were 82 % at best. Further, faster and greater control was achieved at low temperatures (10 °C) compared with high temperature conditions (20 or 30 °C). Overall, these findings suggest that wood vinegar derived from the pyrolysis of pruning waste can be used as a non-synthetic herbicide for control of broadleaf weeds.

Precise and efficient weed detection in agricultural fields is the key for robotic weed control. Recent developments in convolutional neural networks (CNNs) have achieved significant success in this regard. CNNs that simultaneously localize and classify objects in images are the predominant forms that have been widely used to detect crops and weeds. However, the use of CNNs in agriculture, particularly for weed detection, has been impeded by a lack of large training dataset with ground truth annotations. Cut-and-paste image synthesis approach and semi-supervised learning are popular methods to alleviate the training data deficiency problem. In this paper, we propose a novel image synthesis and semi-supervised learning pipeline to train site-specific weed detection models without the need for manually labeled images. The CNN models trained by this pipeline achieve performance levels close to that of the supervised models. We investigated the behavior of the proposed pipeline by varying several key components and showed that color match between the training and testing images, training-time color augmentation, and iterative semi-supervised learning largely improve the model performance. These promising results can be used to guide the construction of a weed image database applicable to different weed detection scenarios.

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...

Palmer amaranth is the most troublesome weed problem in mid-southern US crop production. Herbicides continue to be the most commonly employed method for managing Palmer amaranth, despite the weed’s widespread resistance to them. Therefore, farmers need research and extension efforts that promote the adoption of integrated weed management (IWM) techniques. Producers, crop consultants, educators, and researchers would be more likely to deploy diversified chemical and nonchemical weed management options if they are more informed about long-term biological and economic implications via user-friendly decision-support software. Described within is a recently developed software that demonstrates the effects of Palmer amaranth management practices on soil seedbank, risk of resistance evolution, and economics over a 10-year planning horizon. Aiding this objective is a point-and-click interface that provides feedback on resistance risk, yield potential, profitability, soil seedbank dynamics, and error checking of management options.

The process of wide hybridization refers to hybrids created through interspecific or intergeneric crosses of related species to extract useful and novel traits that protect or enhance the yield or quality of the domesticated crop. Sorghum contains approximately 25 recognized species that show significant variation in plant morphology, genetic and genomic diversity with an eightfold range in DNA content, and geographic distribution. Traits that increase the value of sorghum production have been reported in many of these species including resistance to sorghum midge, shootfly, and spotted stem borer. However, introgression of any traits has only been possible with species in the section Eusorghum due to pre- and post-fertilization barriers that isolate the other species. Now the creation of wide hybrids has been expanded beyond section Eusorghum. The Inhibition of Alien Pollen (Iap) gene that makes it possible to overcome pre-fertilization barriers by reducing adverse pollen–pistil interactions has been used to produce additional interspecific hybrids with species from sections Chaetosorghum, Parasorghum, and Stiposorghum. Post-fertilization barriers can be eliminated through embryo rescue techniques and the use of 2n gametes. Using 2n gametes as a vehicle to transfer genes by creating bridges that overcome ploidy and genomic differences between species is now being explored. With the chemical hybridizing agent trifluoromethanesulfonamide (TFMSA) the number of parental combinations and the number of florets that are emasculated are no longer limiting factors when developing strategies for creating wide hybrids. Accessing via wide hybridization novel traits that were previously unavailable is now possible.

Modern precision weed management relies on site-specific management tactics to maximize resource use efficiency and yield, while reducing unintended environmental impacts caused by herbicides. Scouting for weeds is an important activity to assist weed management decision making, and has been carried out by trained specialists through extensive and routine visual examination of the fields. Recent advancements in Unmanned Aircraft Systems (UAS)-based tools and geospatial information technology have created enormous applications for efficient and economical assessment of weed infestations as well as site-specific weed management. The utilization of UAS-based technologies for weed management applications is currently in its infancy, but this field has witnessed rapid growth in recent times in terms of aerial data acquisition and analysis. Challenges exist in UAS platform reliability, sensor capability and integration, image pre-processing, quantitative assessment and prediction, final product development, and product delivery. This review summarizes current knowledge on the utility of UAS platforms and remote sensing tools for weed scouting and precision weed management. Further, it critically examines potential opportunities and limitations to current UAS technologies, with particular emphasis on the lessons learned from UAS-based weed management research conducted at Texas A&M University.

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.

Background Weedy sunflower (Helianthus annuus L.) is a troublesome weed in row-crop production fields in South Texas. Populations with suspected resistance to glyphosate were evaluated with 1X and 4X rates (X = 868 g ae ha-1 ) of the herbicide, followed by a dose-response assay of the most resistant population. Molecular studies were conducted to determine if target-site mechanisms were responsible for resistance in these populations. Additionally, field experiments were conducted at two locations (Somerville and Granger, TX) to evaluate the effectiveness of different tank-mix combinations in controlling naturally infesting glyphosate-resistant (GR) weedy sunflower populations in GR corn. Results In a study conducted in the growth chamber, seven of the 11 tested populations survived up to the 4X rate of glyphosate. The most-resistant population (TX15-11) was 29-fold more resistant to glyphosate, compared to the susceptible standard. In resistant populations, 5-21 more copies of the EPSPS gene were observed compared to the susceptible standard. In the field studies, tank-mix applications of glyphosate + halosulfuron-methyl, glyphosate + prosulfuron, glyphosate + a premix of halosulfuron-methyl and dicamba or glyphosate + a premix of diflufenzopyr and dicamba effectively controlled GR weedy sunflower populations. Conclusion Glyphosate-resistance was observed in 81% of the putative resistant weedy sunflower populations tested in this study. Resistance in these populations was conferred primarily by amplification of the EPSPS gene. Effective control of GR weedy sunflower can be achieved by tank-mixes tested in the current study, which provides acceptable levels of crop safety. © 2020 Society of Chemical Industry.

BACKGROUND Dynamic optimization models are normative; they solve for what growers 'ought to do' to maximize some objective, such as long-run profits. While valuable for research, such models are difficult to solve computationally, limiting their applicability to grower resistance management education. While discussing properties of normative models in general, this study presents results of a specific positive model of herbicide resistance management, applied to Palmer amaranth control on a representative cotton farm. This positive model compares a proactive resistance management strategy to a reactive strategy with lower short-run costs, but greater risk of herbicide resistance developing. RESULTS The proactive strategy can pay for itself within 1-4 years, with a yield advantage of 4% or less if the yield advantage begins within 1-2 years of adoption. Whether the proactive strategy is preferable is sensitive to resistance onset and yield losses, but less sensitive to cotton prices or baseline yields. Industry rebates to encourage residual herbicide use (to delay resistance to post-emergence treatments) may be too small to alter grower behavior or they may be paid to growers who would have used residuals anyway. Rebates change grower behavior over a relatively narrow range of model parameters. The size of rebates needed to induce a grower to adopt the proactive strategy declines significantly if growers extend their planning horizon from 1 year to 3-4 years. CONCLUSIONS Whether proactive resistance management is more profitable than a reactive strategy is more sensitive to biological parameters than economic ones. Simulation results suggest growers with longer time horizons (perhaps younger ones) would be more responsive to rebate programs. More empirical work is needed to determine how much rebates increase residual use above what would occur without them. © 2017 Society of Chemical Industry.

The ability to distinguish between crops and weeds using sensors from a distance will greatly benefit the farming community through improved and efficient scouting for weeds, reduced herbicide input costs and improved profitability. In the present study, we examined the utility of femtosecond laser-induced breakdown spectroscopy (LIBS) for plant species differentiation. Greenhouse-grown plants of dallisgrass, wheat, soybean and bell pepper were evaluated using LIBS under an ambient environment. LIBS experiments were performed on the leaf samples of different plant species using a femtosecond laser system with an inexpensive lightweight detector. Temperatures of laser-induced plasma in plants depend on many parameters and were determined for each of the study species by the constituent elements interacting with femtosecond laser pulses. Using elemental calcium transitions in plant tissue samples to measure plasma temperatures, we report consistent differences among the four study species, with aver...

The management of glyphosate-resistant Palmer amaranth has been a challenge in southern United States cropping systems. Registration of dicamba-resistant crops will provide an alternative management option to control herbicide-resistant Palmer amaranth populations, particularly those having resistance to herbicide Groups 2, 3, 5, 9, 14, and 27. However, repeated use of sublethal doses of dicamba may lead to rapid evolution of herbicide resistance, especially in Palmer amaranth—a species with a strong tendency to evolve resistance. Therefore, selection experiments with dicamba were conducted on Palmer amaranth using sublethal doses. In the greenhouse, a known susceptible Palmer amaranth population was subjected to sublethal dicamba doses for three generations (P1–P3). Susceptibility of the individuals to dicamba was evaluated, and its susceptibility to 2,4-D was characterized. Based on the greenhouse study, following three generations of dicamba selection, the dose required to cause 50% mortality increased from 111 g ae ha−1for parental individuals (P0) to 309 g ae ha−1for the P3. Furthermore, reduced susceptibility of the P3to 2,4-D was also evident. This research presents the first evidence that recurrent use of sublethal dicamba doses can lead to reduced susceptibility of Palmer amaranth to dicamba as well as 2,4-D. Here, we show that selection from sublethal dicamba doses has an important role in rapid evolution of Palmer amaranth with reduced susceptibility to auxin-type herbicides.

A yellow nutsedge biotype (Res) from an Arkansas rice field has evolved resistance to acetolactate synthase (ALS)-inhibiting herbicides. TheResbiotype previously exhibited cross-resistance to ALS inhibitors from four chemical families (imidazolinone, pyrimidinyl benzoate, sulfonylurea, and triazolopyrimidine). Experiments were conducted to evaluate alternative herbicides (i.e., glyphosate, bentazon, propanil, quinclorac, and 2,4-D) currently labeled in Arkansas rice–soybean production systems. Based on the percentage of aboveground dry weight reduction, control of the yellow nutsedge biotypes with the labeled rate of bentazon, propanil, quinclorac, and 2,4-D was < 44%. Glyphosate (867 g ae ha−1) resulted in 68 and > 94% control of theResand susceptible yellow nutsedge biotypes, respectively, at 28 d after treatment. Dose-response studies were conducted to estimate the efficacy of glyphosate on theResbiotype, three susceptible yellow nutsedge biotypes, and purple nutsedge. Based on the dry weights, theResbiotype was ≥ 5- and ≥ 1.3-fold less responsive to glyphosate compared to the susceptible biotypes and purple nutsedge, respectively. Differences in absorption and translocation of radiolabeled glyphosate were observed among the yellow nutsedge biotypes and purple nutsedge. The susceptible biotype had less14C-glyphosate radioactivity in the tissues above the treated leaf and greater radioactivity in tissues below the treated leaf compared to theResbiotype and purple nutsedge. Reduced translocation of glyphosate in tissues below the treated leaf of theResbiotype could be a reason for the lower glyphosate efficacy in theResbiotype. No amino acid substitution that would correspond to glyphosate resistance was found in the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene of theResbiotype. However, an amino acid (serine) addition was detected in the EPSPS gene of theResbiotype; albeit, it is not believed that this addition contributes to lower efficacy of glyphosate in this biotype.

Agricultural systems experience numerous management-associated events during the growing season that can significantly influence seasonal and annual carbon balances. Measurements of carbon fluxes from agricultural fields using micrometeorological techniques such as eddy covariance can improve our understanding of management and weather-driven changes in carbon budgets. In-situ carbon flux data are also valuable in developing remote sensing-based models for extrapolation of biome-specific carbon budgets to higher spatial scales. In this study, net ecosystem carbon dioxide exchange (NEE) was continuously measured for two years (2017 and 2018) from a cotton (Gossypium hirsutum) field in College Station, Texas, USA. The measured NEE was partitioned into assimilatory (Gross Primary Production, GPP) and respiratory (Ecosystem respiration, Reco) fluxes. There were substantial differences in carbon fluxes between the two years, which were driven by variations in meteorological conditions and growth of weeds. Due to dry conditions, growing season carbon uptake in 2018 was reduced (883 g C m−2) compared to 2017 (947 g C m−2). While the growing season of 2018 was dry, the post-harvest off-season was remarkably wet with nearly 68% of the annual precipitation occurring after harvest (848 mm). This favored aggressive growth of weeds, resulting in substantial off-season carbon uptake in 2018 (374 g C m−2 in 2018 compared to 100 g C m−2 in 2017). Overall, the site was a net carbon source (175 g C m−2) in 2017, whereas it was a slight carbon sink (-5 g C m−2) in 2018. A significant correlation was found between satellite-derived normalized difference vegetation index (NDVI) and GPP (R2 0.78 in 2017 and 0.72 in 2018). Given that correlation, it would be possible to broaden these results to the wider region by estimating GPP with satellite data.

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