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.

PROTAC for agriculture: learning from human medicine to generate new biotechnological weed control solutions.

Weed control has relied on the use of organic and inorganic molecules that interfere with druggable targets, especially enzymes, for almost a century. This approach, although effective, has resulted in multiple cases of herbicide resistance. Furthermore, the rate of discovery of new druggable targets that are selective and with favorable environmental profiles has slowed down, highlighting the need for innovative control tools. The arrival of the biotechnology and genomics era gave hope to many that all sorts of new control tools would be developed. However, the reality is that most efforts have been limited to the development of transgenic crops with resistance to a few existing herbicides, which in fact is just another form of selectivity. Proteolysis-targeting chimera (PROTAC) is a new technology developed to treat human diseases but that has potential for multiple applications in agriculture. This technology uses a small bait molecule linked to an E3 ligand. The 3-dimensional structure of the bait favors physical interaction with a binding site in the target protein in a manner that allows E3 recruitment, ubiquitination and then proteasome-mediated degradation. This system makes it possible to circumvent the need to find druggable targets because it can degrade structural proteins, transporters, transcription factors, and enzymes without the need to interact with the active site. PROTAC can help control herbicide-resistant weeds as well as expand the number of biochemical targets that can be used for weed control. In the present article, we provide an overview of how PROTAC works and describe the possible applications for weed control as well as the challenges that this technology might face during development and implementation for field uses. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

The impact of burial depth on Centaurea diluta emergence and modelling of its growth using a nonlinear regression and artificial neural network.

BACKGROUND: Centaurea diluta Aiton (North African knapweed) is a major weed concern in Spain as a result of the limited herbicides capable of controlling it, and the limited knowledge of its biology hinders the development of integrated weed management strategies. RESULTS: The current study presents results from two experiments that aimed to: (i) determine the effect of seed burial on seedling emergence; and (ii) model its phenology progression using sigmoidal (SRM) and artificial neural network models (ANN) based on different cohort emergence times. In the first experiment, burial at 2 cm and 5 cm decreased C. diluta emergence by 54% and 90%, respectively, compared to the emergence at 0 cm. In the second experiment, without crop-weed competition conditions, the emergence delay led to reductions in leaf number, rosette diameter, plant height and dry biomass by 63%, 50%, 59% and 93%, respectively. Seed production per plant exceeded 21 469. According to the growth model, leaf number was the most consistent morphological trait and critical for timing weed control actions, so it was used to compare SRMs and ANNs. On average, ANNs increased the precision in 5.72% (± 2.4 leaves) compared to SRMs. This slight performance of ANNs may be valuable for controlling C. diluta because control methods must be applied at the 4-leaf stage to achieve good efficacy. CONCLUSION: Seed burial at 5 cm depth is an effective method reducing C. diluta emergence. ANNs accurately predicted the leaf number employing environmental variables can help increase the efficiency of C. diluta control actions and reduce the risk of escapes. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Changes in the herbicide sensitivity and competitive ability of Abutilon theophrasti over 28 years: Implications for hormesis and weed evolution.

BACKGROUND: The potential of weed species to respond to selection forces affecting the evolution of weedy traits such as competitive ability is poorly understood. This research characterized evolutionary growth changes in a single Abutilon theophrasti Medik. population comparing multiple generations collected from 1988 to 2016. A competition study was performed to understand changes in competitive ability, and a herbicide dose-response study was carried out to assess changes in sensitivity to acetolactate synthase-inhibiting herbicides and glyphosate over time. RESULTS: When grown in monoculture, A. theophrasti biomass production per plant increased steadily across year-lines while leaf number decreased. In replacement experiments, A. theophrasti plants from newer year-lines were more competitive and produced more biomass and leaf area than the oldest year-line. No clear differences in sensitivity to imazamox were observed among year-lines. However, starting in 1995, this A. theophrasti population exhibited a progressive increase in growth in response to a sublethal dose of glyphosate (52 g a.e. ha-1 ), with the 2009 and 2016 year-lines having more than 50% higher biomass than the nontreated control. CONCLUSION: This study demonstrates that weeds can rapidly evolve increased competitive ability. Furthermore, the results indicate the possibility of changes in glyphosate hormesis over time. These results highlight the importance of the role that rapid (i.e., subdecadal) evolution of growth traits might have on the sustainability of weed management strategies. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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.

Genome-Wide Evolutionary Analysis of Putative Non-Specific Herbicide Resistance Genes and Compilation of Core Promoters between Monocots and Dicots.

Herbicides are key weed-control tools, but their repeated use across large areas has favored the evolution of herbicide resistance. Although target-site has been the most prevalent and studied type of resistance, non-target-site resistance (NTSR) is increasing. However, the genetic factors involved in NTSR are widely unknown. In this study, four gene groups encoding putative NTSR enzymes, namely, cytochrome-P450, glutathione-S-transferase (GST), uridine 5'-diphospho-glucuronosyltransferase (UDPGT), and nitronate monooxygenase (NMO) were analyzed. The monocot and dicot gene sequences were downloaded from publicly available databases. Phylogenetic trees revealed that most of the CYP450 resistance-related sequences belong to CYP81 (5), and in GST, most of the resistance sequences belonged to GSTU18 (9) and GSTF6 (8) groups. In addition, the study of upstream promoter sequences of these NTSR genes revealed stress-related cis-regulatory motifs, as well as eight transcription factor binding sites (TFBS) were identified. The discovered TFBS were commonly present in both monocots and dicots, and the identified motifs are known to play key roles in countering abiotic stress. Further, we predicted the 3D structure for the resistant CYP450 and GST protein and identified the substrate recognition site through the homology approach. Our description of putative NTSR enzymes may be used to develop innovative weed control techniques to delay the evolution of NTSR.

Nitrogen leaching and Tifway bermudagrass response to simultaneous nutrient and pre-emergence herbicide applications.

Pre-emergence (PRE) herbicides are commonly applied simultaneously with fertilizers to turfgrass; however, the influence of PRE herbicides on nitrogen (N) uptake and leaching from turfgrass remains unclear. The hypothesis of this study was that PRE herbicides applied simultaneously with N fertilizers increase N leaching from Tifway 419 bermudagrass [Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt-Davy, 'Tifway'] above that from fallow soil. A nutrient leaching study was conducted from June 2017 through June 2019 in Fort Lauderdale, FL. Treatments consisted of indaziflam (25 g a.i. ha-1 ), prodiamine (540 g a.i. ha-1 ), and oxadiazon (4,480 g a.i. ha-1 ); a nontreated turfgrass control (turfgrass fertilized but not treated with PRE herbicides); and a fallow soil. Fertilizer (15-2-12) was applied every 60 d at 49 kg N ha-1 , and PRE herbicides were applied every 120 d. Pre-emergence herbicides resulted in a 3.6- and 5.5-fold increases in NO3 -N concentration compared with fallow soil during June 2017 and January 2018, respectively, whereas fallow soil resulted in increased NO3 -N concentration during 10 mo and ranged from 3.8- to 15-fold greater than that from turfgrass plots. Turfgrass plots resulted in reduced N leaching of ∼7% during 5 mo compared with fallow soil and did not result in increased N leaching during any month. Cumulative N leached from turfgrass plots ranged from 75 to 120 kg ha-1 and did not differ from fallow soil. Turfgrass growth rate and N uptake were not influenced by PRE herbicide. The results indicated that fertilizers applied with PRE herbicides does not result in increased N leaching or reduced N uptake.

The role of population and quantitative genetics and modern sequencing technologies to understand evolved herbicide resistance and weed fitness.

Evolution of resistance to multiple herbicides with different sites of action and of nontarget site resistance (NTSR) often involves multiple genes. Thus, single-gene analyses, typical in studies of target site resistance, are not sufficient for understanding the genetic architecture and dynamics of NTSR and multiple resistance. The genetics of weed adaptation to varied agricultural environments is also generally expected to be polygenic. Recent advances in whole-genome sequencing as well as bioinformatic and statistical tools have made it possible to use population and quantitative genetics methods to expand our understanding of how resistance and other traits important for weed adaptation are genetically controlled at the individual and population levels, and to predict responses to selection pressure by herbicides and other environmental factors. The use of tools such as quantitative trait loci mapping, genome-wide association studies, and genomic prediction will allow pest management scientists to better explain how pests adapt to control tools and how specific genotypes thrive and spread across agroecosystems and other human-disturbed systems. The challenge will be to use this knowledge in developing integrated weed management systems that inhibit broad resistance to current and future weed-control methods. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Creating Predictive Weed Emergence Models Using Repeat Photography and Image Analysis.

Weed emergence models have the potential to be important tools for automating weed control actions; however, producing the necessary data (e.g., seedling counts) is time consuming and tedious. If similar weed emergence models could be created by deriving emergence data from images rather than physical counts, the amount of generated data could be increased to create more robust models. In this research, repeat RGB images taken throughout the emergence period of Raphanus raphanistrum L. and Senna obtusifolia (L.) Irwin and Barneby underwent pixel-based spectral classification. Relative cumulative pixels generated by the weed of interest over time were used to model emergence patterns. The models that were derived from cumulative pixel data were validated with the relative emergence of true seedling counts. The cumulative pixel model for R. raphanistrum and S. obtusifolia accounted for 92% of the variation in relative emergence of true counts. The results demonstrate that a simple image analysis approach based on time-dependent changes in weed cover can be used to generate weed emergence predictive models equivalent to those produced based on seedling counts. This process will help researchers working on weed emergence models, providing a new low-cost and technologically simple tool for data collection.

Integration of remote-weed mapping and an autonomous spraying unmanned aerial vehicle for site-specific weed management.

BACKGROUND: Unmanned aerial vehicles (UAVs) have been used in agriculture to collect imagery for crop and pest monitoring, and for decision-making purposes. Spraying-capable UAVs are now commercially available worldwide for agricultural applications. Combining UAV weed mapping and UAV sprayers into an UAV integrated system (UAV-IS) can offer a new alternative to implement site-specific pest management. RESULTS: The UAV-IS was 0.3- to 3-fold more efficient at identifying and treating target weedy areas, while minimizing treatment on non-weedy areas, than ground-based broadcast applications. The UAV-IS treated 20-60% less area than ground-based broadcast applications, but also missed up to 26% of the target weedy area, while broadcast applications covered almost the entire experimental area and only missed 2-3% of the target weeds. The efficiency of UAV-IS management practices increased as weed spatial aggregation increased (patchiness). CONCLUSION: Integrating UAV imagery for pest mapping and UAV sprayers can provide a new strategy for integrated pest management programs to improve efficiency and efficacy while reducing the amount of pesticide being applied. The UAV-IS has the potential to improve the detection and control of weed escapes to reduce/delay herbicide resistance evolution. © 2019 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Using choice experiments to understand household tradeoffs regarding pineapple production and environmental management in Costa Rica.

Choices among environmental management alternatives involve tradeoffs where, for example, the benefits of environmental protection may be offset by economic costs or welfare losses to individual agents. Understanding individual or household-level preferences regarding these tradeoffs is not always straightforward, and it often requires an analysis of choices under alternative scenarios. A household survey was used to gather data for a choice experiment, where respondents were asked to choose among pairs of alternative management scenarios about pineapple production in Costa Rica. The experimental design consisted of six attributes that varied on between two and five attribute levels, and the experiment and accompanying survey were administered orally in Spanish. The results show that respondents are willing to make tradeoffs with respect to the management attributes in order to see an overall improvement in environmental quality. Respondents were willing to accept a moderate level of pesticide application, presumably in exchange for paying a lower cost or seeing a gain in another area, such as monitoring or soil conservation. Buffer zones were significant only in the case of large farms. The results have implications for policy decisions that aim to reflect public attitudes, particularly the aspects of pineapple production that matter most to people living near pineapple plantations. The study also highlights the effectiveness of the choice experiment approach in examining household preferences about environmental management in a rural development context.