U.S. cereal rye winter cover crop growth database.

Winter cover crop performance metrics (i.e., vegetative biomass quantity and quality) affect ecosystem services provisions, but they vary widely due to differences in agronomic practices, soil properties, and climate. Cereal rye (Secale cereale) is the most common winter cover crop in the United States due to its winter hardiness, low seed cost, and high biomass production. We compiled data on cereal rye winter cover crop performance metrics, agronomic practices, and soil properties across the eastern half of the United States. The dataset includes a total of 5,695 cereal rye biomass observations across 208 site-years between 2001-2022 and encompasses a wide range of agronomic, soils, and climate conditions. Cereal rye biomass values had a mean of 3,428 kg ha-1, a median of 2,458 kg ha-1, and a standard deviation of 3,163 kg ha-1. The data can be used for empirical analyses, to calibrate, validate, and evaluate process-based models, and to develop decision support tools for management and policy decisions.

Adaptive constraints at the range edge of a widespread and expanding invasive plant.

Identifying the factors that facilitate and limit invasive species' range expansion has both practical and theoretical importance, especially at the range edges. Here, we used reciprocal common garden experiments spanning the North/South and East/West range that include the North American core, intermediate and range edges of the globally invasive plant, Johnsongrass (Sorghum halepense) to investigate the interplay of climate, biotic interactions (i.e. competition) and patterns of adaptation. Our results suggest that the rapid range expansion of Johnsongrass into diverse environments across wide geographies occurred largely without local adaptation, but that further range expansion may be restricted by a fitness trade-off that limits population growth at the range edge. Interestingly, plant competition strongly dampened Johnsongrass growth but did not change the rank order performance of populations within a garden, though this varied among gardens (climates). Our findings highlight the importance of including the range edge when studying the range dynamics of invasive species, especially as we try to understand how invasive species will respond to accelerating global changes.

Use of synthetic images for training a deep learning model for weed detection and biomass estimation in cotton.

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 R2 values 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.

Evaluating Cross-Applicability of Weed Detection Models Across Different Crops in Similar Production Environments.

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.

Sorghum bicolor x S. halepense interspecific hybridization is influenced by the frequency of 2n gametes in S. bicolor.

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.

Considering weed management as a social dilemma bridges individual and collective interests.

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.

Target-site mutation accumulation among ALS inhibitor-resistant Palmer amaranth.

BACKGROUND: Palmer amaranth (Amaranthus palmeri S. Wats) is one of the most common and troublesome weeds in the USA. Palmer amaranth resistance to acetolactate synthase (ALS) inhibitors is widespread in the USA, as in Arkansas. The cross-resistance patterns and mechanism of resistance are not known. Experiments were conducted to determine cross-resistance to ALS inhibitors and identify target-site mutations in 20 Palmer amaranth localities from 13 counties in Arkansas. RESULTS: All Palmer amaranth localities tested had plants cross-resistant to imazethapyr, flumetsulam, primisulfuron, pyrithiobac and trifloxysulfuron. The dose of trifloxysulfuron that caused 50% control was 21-56-fold greater for resistant accessions than for susceptible ones. All but three resistant plants analyzed had one or two relative copies of ALS; one plant had seven relative copies. All resistant plants tested (18 localities) carried the Trp574Leu mutation, which is known to confer broad resistance to ALS inhibitors, supporting the cross-resistance pattern observed. Besides the Trp574Leu mutation, 30% of localities had individuals with one additional resistance-conferring mutation including Ala122Thr, Pro197Ala or Ser653Asn. CONCLUSION: The Trp574Leu mutation in ALS is the primary mechanism of resistance to ALS inhibitors in Palmer amaranth from Arkansas, USA. In some localities, multiple mutations have accumulated in one plant. All localities tested contained plants with resistance to five families of ALS inhibitors. Localities with extremely high resistance to ALS inhibitors, and those outside the subset we studied, may harbor non-target site resistance mechanisms. ALS inhibitors are generally no longer effective on Palmer amaranth in these localities from the US mid-south. © 2018 Society of Chemical Industry.

Selective fertilization with phosphite allows unhindered growth of cotton plants expressing the ptxD gene while suppressing weeds.

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 phosphite dehydrogenase (ptxD) gene gain an ability to convert phosphite (Phi) into orthophosphate [Pi, the metabolizable form of phosphorus (P)]. Such plants allow for a selective fertilization scheme, based on Phi as the sole source of P for the crop, while offering an effective alternative for suppressing weed growth. Here, we show that, when P is supplied in the form of Phi, ptxD-expressing cotton (Gossypium hirsutum L.) plants outcompete, in both artificial substrates and natural soils from agricultural fields, three different monocot and dicot weed species intentionally introduced in the experiments, as well as weeds naturally present in the tested soils. Importantly, the ptxD/Phi system proved highly efficacious in inhibiting the growth of glyphosate-resistant Palmer amaranth. With over 250 weed species resistant to currently available herbicides, ptxD-transgenic plants fertilized with Phi could provide an effective alternative to suppressing the growth of these weeds while providing adequate nutrition to the crop.

An ecological perspective on managing weeds during the great selection for herbicide resistance.

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.

Opportunities and challenges for harvest weed seed control in global cropping systems.

The opportunity to target weed seeds during grain harvest was established many decades ago following the introduction of mechanical harvesting and the recognition of high weed-seed retention levels at crop maturity; however, this opportunity remained largely neglected until more recently. The introduction and adoption of harvest weed seed control (HWSC) systems in Australia has been in response to widespread occurrence of herbicide-resistant weed populations. With diminishing herbicide resources and the need to maintain highly productive reduced tillage and stubble-retention practices, growers began to develop systems that targeted weed seeds during crop harvest. Research and development efforts over the past two decades have established the efficacy of HWSC systems in Australian cropping systems, where widespread adoption is now occurring. With similarly dramatic herbicide resistance issues now present across many of the world's cropping regions, it is timely for HWSC systems to be considered for inclusion in weed-management programs in these areas. This review describes HWSC systems and establishing the potential for this approach to weed control in several cropping regions. As observed in Australia, the inclusion of HWSC systems can reduce weed populations substantially reducing the potential for weed adaptation and resistance evolution. © 2017 Society of Chemical Industry.

Impact of Combined Abiotic and Biotic Stresses on Plant Growth and Avenues for Crop Improvement by Exploiting Physio-morphological Traits.

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.

Positive and normative modeling for Palmer amaranth control and herbicide resistance management.

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.

Multiple-Herbicide Resistance Is Widespread in Roadside Palmer Amaranth Populations.

Herbicide-resistant Palmer amaranth is a widespread issue in row-crop production in the Midsouthern US. Palmer amaranth is commonly found on roadside habitats in this region, but little is known on the degree of herbicide resistance in these populations. Herbicide resistance in roadside Palmer amaranth populations can represent the spread of an adaptive trait across a selective landscape. A large-scale survey was carried out in the Mississippi Delta region of eastern Arkansas to document the level of resistance in roadside Palmer amaranth populations to pyrithiobac and glyphosate, two important herbicides with broad history of use in the region. A total of 215 Palmer amaranth populations collected across 500 random survey sites were used in the evaluations. About 89 and 73% of the surveyed populations showed >90% survival to pyrithiobac and glyphosate, respectively. Further, only 3% of the populations were completely susceptible to glyphosate, while none of the populations was completely controlled by pyrithiobac. Among the 215 populations evaluated, 209 populations showed multiple resistance to both pyrithiobac and glyphosate at varying degrees. Dose-response assays confirmed the presence of high levels of herbicide resistance in the five selected populations (≥ 25-fold compared to a susceptible standard). Results demonstrate the prevalence of multiple-herbicide resistance in roadside Palmer amaranth populations in this region. Growers should be vigilant of Palmer amaranth infestation in roadsides adjacent to their fields and implement appropriate control measures to prevent likely spread of herbicide resistance into their fields.

Pollen-mediated transfer of herbicide resistance in Echinochloa crus-galli.

BACKGROUND: Pollen-mediated gene flow (PMGF) can facilitate the dispersal and spread of herbicide resistance from one weed population to another within an agricultural landscape. The aim of this study was to quantify the extent of PMGF in Echinochloa crus-galli (barnyardgrass), an important herbicide-resistant weed species in the United States and across the world. RESULTS: Gene flow declined exponentially with distance, and the double exponential decay model predicted an average gene flow of 5.6% when the pollen donor and recipient plants were at a close distance of 0.25 m from each other (12.5% at 0 m). Gene flow declined by 90% at 0.9 m from the pollen source, yet gene flow was detected as far as 50 m (the farthest distance studied). The farthest gene flow occurred in directions of the fastest wind events, but mean gene flow levels were similar among the directions. CONCLUSION: Results indicate that long-distance, landscape-scale PMGF is unlikely in barnyardgrass, but gene flow is likely to occur between adjacent fields at levels greater than initial frequencies of resistance alleles in natural, unselected populations. Thus, any resistance management strategy should consider the likelihood that PMGF can contribute to the spread of herbicide resistance between production fields.

Modelling the dynamics of feral alfalfa populations and its management implications.

BACKGROUND: Feral populations of cultivated crops can pose challenges to novel trait confinement within agricultural landscapes. Simulation models can be helpful in investigating the underlying dynamics of feral populations and determining suitable management options. METHODOLOGY/PRINCIPAL FINDINGS: We developed a stage-structured matrix population model for roadside feral alfalfa populations occurring in southern Manitoba, Canada. The model accounted for the existence of density-dependence and recruitment subsidy in feral populations. We used the model to investigate the long-term dynamics of feral alfalfa populations, and to evaluate the effectiveness of simulated management strategies such as herbicide application and mowing in controlling feral alfalfa. Results suggest that alfalfa populations occurring in roadside habitats can be persistent and less likely to go extinct under current roadverge management scenarios. Management attempts focused on controlling adult plants alone can be counterproductive due to the presence of density-dependent effects. Targeted herbicide application, which can achieve complete control of seedlings, rosettes and established plants, will be an effective strategy, but the seedbank population may contribute to new recruits. In regions where roadside mowing is regularly practiced, devising a timely mowing strategy (early- to mid-August for southern Manitoba), one that can totally prevent seed production, will be a feasible option for managing feral alfalfa populations. CONCLUSIONS/SIGNIFICANCE: Feral alfalfa populations can be persistent in roadside habitats. Timely mowing or regular targeted herbicide application will be effective in managing feral alfalfa populations and limit feral-population-mediated gene flow in alfalfa. However, in the context of novel trait confinement, the extent to which feral alfalfa populations need to be managed will be dictated by the tolerance levels established by specific production systems for specific traits. The modelling framework outlined in this paper could be applied to other perennial herbaceous plants with similar life-history characteristics.

Occurrence of alfalfa (Medicago sativa L.) populations along roadsides in southern Manitoba, Canada and their potential role in intraspecific gene flow.

Alfalfa is a highly outcrossing perennial species that can be noticed in roadsides as feral populations. There remains little information available on the extent of feral alfalfa populations in western Canadian prairies and their role in gene flow. The main objectives of this study were (a) to document the occurrence of feral alfalfa populations, and (b) to estimate the levels of outcrossing facilitated by feral populations. A roadside survey confirmed widespread occurrence of feral alfalfa populations, particularly in alfalfa growing regions. The feral populations were dynamic and their frequency ranged from 0.2 to 1.7 populations km(-1). In many cases, the nearest feral alfalfa population from alfalfa production field was located within a distance sufficient for outcrossing in alfalfa. The gene flow study confirmed that genes can move back and forth between feral and cultivated alfalfa populations. In this study, the estimated outcrossing levels were 62% (seed fields to feral), 78% (feral to seed fields), 82% (hay fields to feral) and 85% (feral to feral). Overall, the results show that feral alfalfa plants are prevalent in alfalfa producing regions in western Canada and they can serve as bridges for gene flow at landscape level. Management of feral populations should be considered, if gene flow is a concern. Emphasis on preventing seed spill/escapes and intentional roadside planting of alfalfa cultivars will be particularly helpful. Further, realistic and pragmatic threshold levels should be established for markets sensitive to the presence of GE traits.

The demography of feral alfalfa (Medicago sativa L.) populations occurring in roadside habitats in Southern Manitoba, Canada: implications for novel trait confinement.

PURPOSE: Feral populations of cultivated crops can act as reservoirs for novel genetically engineered (GE) traits and aid in trait movement at the landscape level. However, little information is available on the potential of cultivated crops to become feral. In this study, we investigated the ferality of alfalfa populations (non-GE version) occurring in roadside habitats. Knowledge on the nature of roadside alfalfa populations would be useful for designing efficient trait confinement protocols and coexistence strategies in alfalfa. METHODS: We investigated roadside alfalfa populations from 2006 to 2009 in three rural municipalities (Hanover, MacDonald, and Springfield) in Southern Manitoba, Canada. We studied the demography of these populations including seedbank, seedling recruitment, and fecundity and examined the impact of road verge mowing on key life stages of these populations. We also compared the growth and reproductive attributes of roadside and cultivated alfalfa populations. RESULTS: Alfalfa is reproductively successful in roadside habitats and capable of establishing self-perpetuating populations. A substantial portion of the alfalfa seeds we extracted from seedbank samples were viable but not germinable, suggesting some degree of seedbank persistence in roadside habitats. In the roadside habitat, alfalfa seedlings recruited successfully, however, seedling mortality was high when seedlings were in close proximity to well-established alfalfa plants. Mowing dramatically reduced the reproductive success of roadside alfalfa. Generally, the growth and reproduction of roadside alfalfa was comparable to cultivated alfalfa except for total fecundity. CONCLUSIONS: Considering the long lifespan (>10 years) of alfalfa and the levels of fecundity, seedbank, and seedling survival we observed, long-term persistence of roadside alfalfa populations seems reasonable. In the context of novel trait confinement, our results suggest that feral alfalfa populations required to be managed if there is a desire/need to confine novel traits in alfalfa.