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422 results for “Weeds”
Harmful algal bloom and aquatic weeds data from the Sacramento-San Joaquin Delta, collected to evaluate the impact of the 2021 Temporary Urgency Change Order and Emergency Drought Barrier
Condition 8 of the June 2021 Temporary Urgency Change Order for the Central Valley Project (CVP) and State Water Project (SWP) requires a special study of harmful algal blooms (HABs) in the Sacramento–San Joaquin Delta (Delta) and the spread of submersed aquatic vegetation (SAV), and floating aquatic vegetation (FAV), also referred to as “aquatic weeds”. A report on the study was submitted to the State Water Resources Control Board on June 1, 2022. This data package contains all publicly available data used in the report, including visual cyanobacteria reports, cyanotoxin data, water quality, nutrients, flow/hydrodynamics, chlorophyll-a concentrations, temperature, coverage of SAV and FAV, use of herbicides, and human populations. Many of these data were derived from other datasets, though some were collected specifically for this study
Swiss public's acceptance and sustainability perceptions of food produced with chemical, digital and mechanical weed control measures and the influence of information source on technology perception in agriculture
<p><span>This data was obtained from an online survey conducted with the Swiss public from the two biggest language regions (German and French) in Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age, gender and language region. The final sample contained </span><span>542 respondents. </span><span>In the first part of the survey, respondents provided basic sociodemographic information. In the second part, their sustainability perceptions regarding four different weed management practices (full-surface spraying, hoeing machine, spot spraying and precise spraying) were investigated. Respondents were then assigned to one of five information source groups, in which information on a hoeing and a milking robot was presented, using 5 different information sources (male/female farmer, male/female scientist, no source). Technology perception was assessed using several questions and aspects. Finally, respondents answered several questions assessing their attitudes towards the perception of farmers, food technology neophobia, chemophobia and the importance of naturalness. The survey can be used and adapted to different contents, aiming to investigate public perception of smart farming technologies and the influence of information sources on technology perception. </span></p>
Database of weeds in cultivation fields of France and UK, with ecological and biogeographical information
<p>The database includes a list of 1577 weed plant taxa found in cultivated fields of France and UK, along with basic ecological and biogeographical information.<br> The database is a CSV file in which the columns are separated with comma, and the decimal sign is ".".<br> It can be imported in R with the command "tax.discoweed <- read.csv("tax.discoweed_18Dec2017_zenodo.csv", header=T, sep=",", dec=".", stringsAsFactors = F)"</p> <p>Taxonomic information is based on TaxRef v10 (Gargominy et al. 2016),<br> - 'taxref10.CD_REF' = code of the accepted name of the taxon in TaxRef,<br> - 'binome.discoweed' = corresponding latine name,<br> - 'family' = family name (following APG III),<br> - 'taxo' = taxonomic rank of the taxon, either 'binome' (species level) or 'infra' (infraspecific level),<br> - 'binome.discoweed.noinfra' = latine name of the superior taxon at species level (different from 'binome.discoweed' for infrataxa),<br> - 'taxref10.CD_REF.noinfra' = code of the accepted name of the superior taxon at species level.</p> <p>The presence of each taxon in one or several of the following data sources is reported:<br> - Species list from a reference flora (observations in cultivated fields over the long term, without sampling protocol),<br> * 'jauzein' = national and comprehensive flora in France (Jauzein 1995),<br> - Species lists from plot-based inventories in cultivated fields,<br> * 'za' = regional survey in 'Zone Atelier Plaine & Val de Sèvre' in SW France (Gaba et al. 2010),<br> * 'biovigilance' = national survey of cultivated fields in France (Biovigilance, Fried et al. 2008),<br> * 'fse' = Farm Scale Evaluations in England and Scotland, UK (Perry, Rothery, Clark et al., 2003),<br> * 'farmbio' = Farm4Bio survey, farms in south east and south west of England, UK (Holland et al., 2013)<br> - Reference list of segetal species (species specialist of arable fields),<br> * 'cambacedes' = reference list in France (Cambacedes et al. 2002)</p> <p>Life form information is extracted from Julve (2014) and provided in the column 'lifeform'.<br> The classification follows a simplified Raunkiaer classification (therophyte, hemicryptophyte, geophyte, phanerophyte-chamaephyte and liana). Regularly biannual plants are included in hemicryptophytes, while plants that can be both annual and biannual are assigned to therophytes.</p> <p>Biogeographic zones are also extracted from Julve (2014) and provided in the column 'biogeo'.<br> The main categories are 'atlantic', 'circumboreal', 'cosmopolitan, 'Eurasian', 'European', 'holarctic', 'introduced', 'Mediterranean', 'orophyte' and 'subtropical'.<br> In some cases, a precision is included within brackets after the category name. For instance, 'introduced(North America)' indicates that the taxon is introduced from North America.<br> In addition, some taxa are local endemics ('Aquitanian', 'Catalan', 'Corsican', 'corso-sard', 'ligure', 'Provencal').<br> A single taxon is classified 'arctic-alpine'.</p> <p>Red list status of weed taxa is derived for France and UK:<br> - 'red.FR' is the status following the assessment of the French National Museum of Natural History (2012),<br> - 'red.UK' is based on the Red List of vascular plants of Cheffings and Farrell (2005), last updated in 2006.<br> The categories are coded following the IUCN nomenclature.</p> <p>A habitat index is provided in column 'module', derived from a network-based analysis of plant communities in open herbaceous vegetation in France (Divgrass database, Violle et al. 2015, Carboni et al. 2016).<br> The main habitat categories of weeds are coded following the Divgrass classification,<br> - 1 = Dry calcareous grasslands<br> - 3 = Mesic grasslands<br> - 5 = Ruderal and trampled grasslands<br> - 9 = Mesophilous and nitrophilous fringes (hedgerows, forest edges...)<br> Taxa belonging to other habitats in Divgrass are coded 99, while the taxa absent from Divgrass have a 'NA' value.</p> <p>Two indexes of ecological specialization are provided based on the frequency of weed taxa in different habitats of the Divgrass database.<br> The indexes are network-based metrics proposed by Guimera and Amaral (2005),<br> - c = coefficient of participation, i.e., the propensity of taxa to be present in diverse habitats, from 0 (specialist, present in a single habitat) to 1 (generalist equally represented in all habitats),<br> - z = within-module degree, i.e., a standardized measure of the frequency of a taxon in its habitat; it is negatve when the taxon is less frequent than average in this habitat, and positive otherwise; the index scales as a number of standard deviations from the mean.</p>
Weed surveys in Oilseed rape fields in the LTSER Zone Atelier Plaine & Val de Sèvre
<p>Dataset used in Berquer, A.; Martin, O.; Gaba, S. Landscape Is the Main Driver of Weed Assemblages in Field Margins but Is Outperformed by Crop Competition in Field Cores. Plants 2021, 10, 2131. https://doi.org/10.3390/plants10102131</p> <p>Because there personnal and sensitive data, agricultural practices data are not given. Please contact Sabrina Gaba or Vincent Bretagnolle if you are interested in a collaborative project.</p>
Data from: Synergistic effects of grass competition and insect herbivory on the weed Rumex obtusifolius in an inundative biocontrol approach
<p>Data are from a field experiment to test for synergistic interactions between grass competition and herbivory on <i>Rumex obtusifolius</i>, a prominent weed in temperate grasslands worldwide.</p><p><i>Rumex obtusifolius</i> was grown in the presence and absence of competition from the grass <i>Lolium perenne</i> and subjected to herbivory through targeted inoculation with root-boring <i>Pyropteron</i> spp.</p><p>To explore whether the interactive effects of competition and herbivory were size-dependent, <i>R. obtusifolius</i> was planted covering a large range of plant sizes found in managed grasslands.</p><p>The experimental layout followed a split-split plot design. Main-level factor was <i>L. perenne</i> competition, split-level factor was herbivory application, split-split-level factor was initial root mass of <i>R. obtusifolius</i>. Main-plots were arranged according to a randomized complete block design on the site (8 blocks, each containing a <i>L. perenne</i> competition and a no competition treatment).</p>
Raw data: Specialized metabolites accumulation pattern in buckwheat is strongly influenced by accession choice and co-existing weeds
<p>Screening suitable allelopathic crops and crop genotypes that are competitive with weeds can be a sustainable weed control strategy to reduce the massive use of herbicides. In this study, three accessions of common buckwheat <em>Fagopyrum esculentum</em> Moench. (Gema, Kora, and Eva) and one of Tartary buckwheat <em>Fagopyrum tataricum</em> Gaertn. (PI481671) were screened against the germination and growth of the herbicide-resistant weeds <em>Lolium rigidum </em>Gaud. and <em>Portulaca oleracea</em> L. The chemical profile of the four buckwheat accessions was characterised in their shoots, roots, and root exudates in order to know more about their ability to sustainably manage weeds and the relation of this ability with the polyphenol accumulation and exudation from buckwheat plants. Our results show that different buckwheat genotypes may have different capacities to produce and exude several types of specialized metabolites, which lead to a wide range of allelopathic and defence functions in the agroecosystem to sustainably manage the growing weeds in their vicinity. The ability of the different buckwheat accessions to suppress weeds was accession-dependent without differences between species, as the common (Eva, Gema, and Kora) and Tartary (PI481671) accessions did not show any species-dependent pattern in their ability to control the germination and growth of the target weeds. Finally, Gema appeared to be the most promising accession to be evaluated in organic farming due to its capacity to sustainably control target weeds while stimulating the root growth of buckwheat plants.</p>
The ACRE Crop-Weed Dataset
<p><strong>For a detailed description of this dataset</strong>, based on the <em>Datasheets for Datasets</em> (Gebru, Timnit, et al. "Datasheets for datasets." <em>Communications of the ACM</em> 64.12 (2021): 86-92.), check the <strong>ACRE_datasheet.md</strong> file.</p> <p><strong>For what purpose was the dataset created?</strong><br> The ACRE dataset was created within the scope of the METRICS project to serve as a benchmark for weed detection models in various tasks, including object detection, semantic segmentation, and instance segmentation. The Agri-Food Competition for Robot Evaluation (ACRE) is a benchmarking competition specifically designed for autonomous robots and smart implements, with a primary focus on agricultural activities like weed removal and field navigation. These capabilities play a vital role in facilitating the transition to Digital Agriculture. The ACRE competition, which can be found at https://metricsproject.eu/agri-food, is part of the METRICS project, an EU-funded initiative dedicated to the metrological evaluation and testing of autonomous robots.</p> <p><strong>What do the instances that comprise the dataset represent?</strong><br> The instances consist of RGB images depicting both crop and weed plants. The crop category encompasses two species: maize (Zea mays) and beans (Phaseolus vulgaris). On the other hand, the weed category encompasses four species: ryegrass (Lolium perenne), mustard (Sinapis arvensis), matricaria (Matricaria chamomilla), and lamb's quarter (Chenopodium album).</p> <p><strong>Is there a label or target associated with each instance?</strong><br> Every image in the dataset is accompanied by an XML file that contains instance segmentation annotations.</p> <p><strong>What mechanisms or procedures were used to collect the data?</strong><br> The data collection process involved the use of a four-wheel skid-steering robot that was equipped with a Basler acA2000-50gc RGB camera. The camera was mounted on the robot in such a way that its principal axis was directed perpendicular to the ground. It had a resolution of 2046 x 1080 pixels. The robot was teleoperated and operated at an average speed of 0.2 m/s. To capture the data, the camera's stream was recorded in rosbag format. For this purpose, the camera was connected to a PC running Ubuntu 18.04 and ROS Melodic via an Ethernet interface.</p>
Machine Demonstration: mechanical weed control in soybeans
<p>This video was provided by the EU funded project Legumes Translated, which supports the production and use of grain legumes in Europe. On the project website <a href="https://www.youtube.com/redirect?v=Bm_5JluTpc0&event=video_description&redir_token=Sf_vP1RmyENuQtEbJbo_0YXcxpN8MTU3ODQxNTk2OUAxNTc4MzI5NTY5&q=https%3A%2F%2Fwww.legumestranslated.eu">https://www.legumestranslated.eu</a> you will find more information and practical guidelines on the production and use of grain legumes. More Info: «Mechanical weed control in organic soy cultivation - how and when to use which machine?» <a href="https://www.youtube.com/redirect?v=Bm_5JluTpc0&event=video_description&redir_token=Sf_vP1RmyENuQtEbJbo_0YXcxpN8MTU3ODQxNTk2OUAxNTc4MzI5NTY5&q=https%3A%2F%2Fwww.bioattualita.ch%2Fcoltura%2Fa">https://www.bioattualita.ch/coltura/a</a>... Weed control is one of the main factors of economic success in organic soybean production. This video presents the following machines for mechanical weed control: 1. Weeding between and in the rows MATER Macc Unica-F Einböck Chopstar Garford Robocrop Schmotzer 2. Machines working row-independent Treffler TS 620/3M Einböck Aerostar-Rotation Carre Rotanet</p>
Figure 29 in A remarkable new species of Himalusa Pace from Thailand (Coleoptera, Staphylinidae, Aleocharinae): phytophagous aleocharine beetle with potential for bio-control of skunkvine-related weeds in the United States
Figure 29. Himalusa thailandensis: a larva that emerged from a swollen petiole of Paederia sp. leaf.
Adaptive introgression from maize has facilitated the establishment of teosinte as a noxious weed in Europe
<p>This is the total genotyoping matrix we used for the analyses.<br> The first line of the file contains the identifiers of the samples and each subsequent line the genotype at each SNP The first column contains the identifier of the SNPs.</p> <p>Genotype data for the 70 French teosintes was combined with published and available data for the following material: 40 accessions of Spanish teosintes (1), 314 accessions of parviglumis (2, 3), 332 accessions of mexicana (2, 3), 94 maize landraces from Meso- and Central-America (4) and 155 maize inbred lines from North-America and Europe (5)</p> <ol> <li> <p>Trtikova M, Lohn A, Binimelis R, Chapela I, Oehen B, Zemp N, Widmer A, Hilbeck A (2017) Teosinte in Europe – searching for the origin of a novel weed. Scientific Reports 7, 1560. DOI: https://doi.org/10.1038/s41598-017-01478-w</p> </li> <li> <p>Aguirre-Liguori JA, Tenaillon MI, Vásquez-Lobo A, Gaut BS, Jaramillo-Correa JP, Montes-Hernandez S, Souza V, Eguiarte LE (2017) Connecting genomic patterns of local adaptation and niche suitability in teosintes. Molecular Ecology 26, 4226-4240. DOI: https://doi.org/10.1111/mec.14203</p> </li> <li> <p>Pyhäjärvi T, Hufford MB, Mezmouk S, Ross-Ibarra J (2013) Complex patterns of local adaptation in teosinte. Genome Biology and Evolution 5, 1594–1609. DOI: https://doi.org/10.1093/gbe/evt109</p> </li> <li> <p>Takuno S, Ralph P, Swarts K, Elshire RJ, Glaubitz JC, Buckler ES, Hufford MB, Ross-Ibarra J (2015) Independent molecular basis of convergent highland adaptation in maize. Genetics 200, 1297–1312. DOI: https://doi.org/10.1534/genetics.115.17832</p> </li> <li> <p>Unterseer S, Pophaly SD, Peis R, Westermeier P, Mayer M, Seidel MA, Haberer G, Mayer KFX, Ordas B, Pausch H, Tellier A, Bauer , Schön CC (2016) A comprehensive study of the genomic differentiation between temperate Dent and Flint maize. Genome Biology 17, 137. DOI: https://doi.org/10.1186/s13059-016-1009-x</p> </li> </ol>
RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment
<p>This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level.. <br><br>The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops.</p> <p>The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number><br><strong>_1</strong>: Blue<br><strong>_2</strong>: Green<br><strong>_3</strong>: Red<br><strong>_4</strong>: Near Infrared<br><strong>_5</strong>: RedEdge<br><br>Example: An image name <strong>IMG_0200_3 </strong>represents the scene number<strong> 200</strong> in <strong>Red channel</strong></p> <p>This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format.</p> <p>The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article. <br><br>Field experimentation roles:</p> <p>The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.</p> <p>Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. </p> <p>Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.</p>
Data for: Functional redundancy of weed seed predation is reduced by intensified agriculture
<p>Intensive agriculture, a driver of biodiversity loss, can diminish ecosystem functions and their stability. Biodiversity can increase functional redundancy and is expected to stabilize ecosystem functions. Few studies however have explored how agricultural intensity affects functional redundancy and its link with ecosystem function stability. Here, within a continent-wide study, we assess how the functional redundancy of seed predation is affected by agricultural intensity and landscape simplification. By combining carabid abundances with molecular gut content data, functional redundancy of seed predation was quantified for 65 weed genera across 60 fields in four European countries. Across weed genera, functional redundancy was reduced with high field management intensity and simplified crop rotations. Moreover, functional redundancy increased the spatial stability of weed seed predation within fields. We found that ecosystem functions are vulnerable to disturbance in intensively managed agroecosystems, providing empirical evidence of the importance of biodiversity for stable ecosystem functions across space.</p>
FIGURE 1 in Biological control of weeds in Australia: the last 120 years
FIGURE 1 Number of weed biological control agent releases per decade (known deliberate releases only).
FIGURE 2 in Biological control of weeds in Australia: the last 120 years
FIGURE 2 Releases of plant pathogens per decade for weed biological control (known deliberate releases only).
CoFly-WeedDB: A UAV image dataset for weed detection and species identification
<p>The CoFly-WeedDB contains 201 RGB images (~436MB) from the attached camera of DJI Phantom Pro 4 from a cotton field in Larissa, Greece during the first stages of plant growth. The RGB images were collected while the Unmanned Aerial Vehicle (UAV) was performing a coverage mission over the field's area. During the designed mission, the camera angle was adjusted to -87°, vertically with the field. The flight altitude and speed of the UAV were equal to 5m and 3m/s, respectively, aiming to provide a close and clear view of the weed instances. All images have been annotated by expert agronomists using the LabelMe annotation tool, providing the exact boundaries of 3 types of common weeds in this type of crop, namely (i) Johnson grass, (ii) Field bindweed, and (iii) Purslane. The dataset can be used alone and in combination with other datasets to develop AI-based methodologies for automatic weed segmentation and classification purposes.</p>
G-matrix stability of clinally diverging populations of an annual weed
<p>How phenotypic and genetic divergence among populations is influenced by the genetic architecture of those traits, and how microevolutionary changes in turn affect the within-population patterns of genetic variation, are of major interest to evolutionary biology. Work on <em>Ipomoea hederacea</em>, an annual vine, has found genetic clines in the means of a suite of ecologically important traits, including flowering time, growth rate, seed mass, and corolla width. Here we investigate the genetic (co)variances of these clinally varying traits in two northern range-edge and two central populations of <em>Ipomoea hederacea </em>to evaluate the influence of the genetic architecture on divergence across the range. We find 1) limited evidence for clear differentiation between Northern and Southern populations in the structure of <strong>G</strong>, suggesting overall stability of <strong>G</strong> across the range despite mean trait divergence and 2) that the axes of greatest variation (g<sub>max</sub>) were unaligned with the axis of greatest multivariate divergence. Together these results indicate the role of the quantitative genetic architecture in constraining evolutionary response and divergence among populations across the geographic range.</p>
Supplementary material 1 from: Gildenhuys E, Ellis A, Carroll S, Le Roux J (2013) The ecology, biogeography, history and future of two globally important weeds: Cardiospermum halicacabum Linn. and C. grandiflorum Sw. NeoBiota 19: 45-65. https://doi.org/10.3897/neobiota.19.5279
Supporting information for species distribution modelling of Cardiospermum species using native range presences and global pseudo absences. (doi: 10.3897/neobiota.19.5279.app) File format: Micrisoft Word Document (doc).:
Supplementary material 2 from: Bonnett G, Kushner J, Saltonstall K (2014) The reproductive biology of Saccharum spontaneum L.: implications for management of this invasive weed in Panama. NeoBiota 20: 61-79. https://doi.org/10.3897/neobiota.20.6163
Proportion of seeds that germinated each week between September and December from samples taken at 12 sites. 100 seeds were germinated from each of three replicate samples and tested for germination in laboratory conditions. Results are presented as the mean and the error bar represents the standard error of the mean.
Supplementary material 1 from: Bonnett G, Kushner J, Saltonstall K (2014) The reproductive biology of Saccharum spontaneum L.: implications for management of this invasive weed in Panama. NeoBiota 20: 61-79. https://doi.org/10.3897/neobiota.20.6163
The table gives the latitude and longitude, description and number of genotypes found among the 3 plants of Saccharum spontaneum tested from each of 22 Sites. Sites 1–12 were used to assess seed germinability through time.
Figure 7 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 7. Annual, perennial, dicot, and monocot weed biomass in each weed management treatment pooled across fields. Similar letters above bars indicate no significant difference using separate Fisher's LSD tests (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.