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opennotspecifiedDec 2016View details →

FIGURE 5. Yuanamia producta. A in Review of the grass-feeding leafhopper genera Miradeltaphus Dash & Viraktamath and Yua namia Zhang & Duan (Hemiptera: Cicadellidae: Deltocephalinae: Deltocephalini)

FIGURE 5. Yuanamia producta. A: habitus, dorsal view; B: habitus, lateral view; C: face.

opennotspecifiedDec 2016View details →

FIGURE 1 in Review of the grass-feeding leafhopper genera Miradeltaphus Dash & Viraktamath and Yua namia Zhang & Duan (Hemiptera: Cicadellidae: Deltocephalinae: Deltocephalini)

FIGURE 1. Miradeltaphus sanogae sp. n. A: habitus, dorsal view; B: habitus, lateral view; C: face.

opennotspecifiedDec 2017View details →

FIGURE 8 in Trachelus stipa (Hymenoptera: Cephidae), a new stem sawfly from Central Anatolia associated with feather grass (Stipa holosericea, Poaceae)

FIGURE 8. Trachelus stipa ovipositing into the stalk of Stipa holosericea.

opencc-by-4.0Feb 2024View details →

Figure 3 from: Andri Deswati D, Anggadiredja K, Nuryanti Garmana A (2024) Potent antioxidant activity of black grass jelly (Mesona palustris BL) leaf extract and fractions. Pharmacia 71: 1-5. https://doi.org/10.3897/pharmacia.71.e117435

Figure 3 Results of antioxidant activity test using FRAP. The activity is represented by reducing power.

opencc-by-4.0Feb 2024View details →

Figure 2 from: Andri Deswati D, Anggadiredja K, Nuryanti Garmana A (2024) Potent antioxidant activity of black grass jelly (Mesona palustris BL) leaf extract and fractions. Pharmacia 71: 1-5. https://doi.org/10.3897/pharmacia.71.e117435

Figure 2 Results of antioxidant activity test using ABTS (A) and DPPH (B). The activity is represented by IC50.

Figure 1 from: Phookamsak R, Hongsanan S, Bhat DJ, Wanasinghe DN, Promputtha I, Suwannarach N, Kumla J, Xie N, Dawoud TM, Mortimer PE, Xu J, Lumyong S (2024) Exploring ascomycete diversity in Yunnan II: Introducing three novel species in the suborder Massarineae (Dothideomycetes, Pleosporales) from fern and grasses. In: Wijayawardene N, Karunarathna S, Fan X-L, Li Q-R (Eds) Taxonomy and secondary metabolites of wood-associated fungi. MycoKeys 104: 9-50. https://doi.org/10.3897/mycokeys.104.112149

Figure 1 Phylogram of the best-scoring ML consensus tree of taxa in Bambusicolaceae and Occultibambusaceae. The new isolate is indicated in blue. Isolates from type materials are in bold. The ML ultrafast bootstrap and Bayesian PP values greater than 60% and 0.90 are shown at the nodes.

Figure 2 from: Phookamsak R, Hongsanan S, Bhat DJ, Wanasinghe DN, Promputtha I, Suwannarach N, Kumla J, Xie N, Dawoud TM, Mortimer PE, Xu J, Lumyong S (2024) Exploring ascomycete diversity in Yunnan II: Introducing three novel species in the suborder Massarineae (Dothideomycetes, Pleosporales) from fern and grasses. In: Wijayawardene N, Karunarathna S, Fan X-L, Li Q-R (Eds) Taxonomy and secondary metabolites of wood-associated fungi. MycoKeys 104: 9-50. https://doi.org/10.3897/mycokeys.104.112149

Figure 2 Phylogram of the best-scoring ML consensus tree of Trichobotrys species in Dictyosporiaceae and closely-related families viz. Didymosphaeriaceae, Lentitheciaceae, Morosphaeriaceae, Sulcatisporaceae and Trematosphaeriaceae. The new isolate is indicated in blue. Isolates from type materials are in bold. The ML ultrafast bootstrap and Bayesian PP values greater than 70% and 0.95 are shown at the nodes.

Figure 5 from: Phookamsak R, Hongsanan S, Bhat DJ, Wanasinghe DN, Promputtha I, Suwannarach N, Kumla J, Xie N, Dawoud TM, Mortimer PE, Xu J, Lumyong S (2024) Exploring ascomycete diversity in Yunnan II: Introducing three novel species in the suborder Massarineae (Dothideomycetes, Pleosporales) from fern and grasses. In: Wijayawardene N, Karunarathna S, Fan X-L, Li Q-R (Eds) Taxonomy and secondary metabolites of wood-associated fungi. MycoKeys 104: 9-50. https://doi.org/10.3897/mycokeys.104.112149

Figure 5 Trichobotrys sinensis (KUN-HKAS 129041, holotype) A, B the appearance of colonies on the host surface C mycelium D–H conidiophores bearing conidiogenous cells and conidia I conidia in a short acropetal chain J–N conidia O culture characteristics on PDAP conidioma forming on PDA after eight weeks Q pycnidial wall R–T conidiogenous cells (note: T = stained in Congo red) U conidia. Scale bars: 100 μm (P); 50 μm (C); 10 μm (D–H, Q–U); 5 μm (J–N).

Figure 3 from: Phookamsak R, Hongsanan S, Bhat DJ, Wanasinghe DN, Promputtha I, Suwannarach N, Kumla J, Xie N, Dawoud TM, Mortimer PE, Xu J, Lumyong S (2024) Exploring ascomycete diversity in Yunnan II: Introducing three novel species in the suborder Massarineae (Dothideomycetes, Pleosporales) from fern and grasses. In: Wijayawardene N, Karunarathna S, Fan X-L, Li Q-R (Eds) Taxonomy and secondary metabolites of wood-associated fungi. MycoKeys 104: 9-50. https://doi.org/10.3897/mycokeys.104.112149

Figure 3 Phylogram of the best-scoring ML consensus tree of taxa in Periconiaceae and the closely-related families Lentitheciaceae and Massarinaceae. The new isolate is indicated in blue. Isolates from type materials are in bold. The ML ultrafast bootstrap and Bayesian PP values greater than 50% and 0.95 are shown at the nodes.

Figure 4 from: Phookamsak R, Hongsanan S, Bhat DJ, Wanasinghe DN, Promputtha I, Suwannarach N, Kumla J, Xie N, Dawoud TM, Mortimer PE, Xu J, Lumyong S (2024) Exploring ascomycete diversity in Yunnan II: Introducing three novel species in the suborder Massarineae (Dothideomycetes, Pleosporales) from fern and grasses. In: Wijayawardene N, Karunarathna S, Fan X-L, Li Q-R (Eds) Taxonomy and secondary metabolites of wood-associated fungi. MycoKeys 104: 9-50. https://doi.org/10.3897/mycokeys.104.112149

Figure 4 Bambusicola hongheensis (KUN-HKAS 129042, holotype) A the appearance of ascomata on the host surface B vertical section of an ascoma C, D peridia E pseudoparaphyses F, G asci embedded in pseudoparaphyses H–K ascospores L, M ascospores stained in India Ink show a thin mucilaginous sheath surrounding ascospores. Scale bars: 100 μm (B); 20 μm (C–G); 10 μm (H–M).

Figure 6 from: Phookamsak R, Hongsanan S, Bhat DJ, Wanasinghe DN, Promputtha I, Suwannarach N, Kumla J, Xie N, Dawoud TM, Mortimer PE, Xu J, Lumyong S (2024) Exploring ascomycete diversity in Yunnan II: Introducing three novel species in the suborder Massarineae (Dothideomycetes, Pleosporales) from fern and grasses. In: Wijayawardene N, Karunarathna S, Fan X-L, Li Q-R (Eds) Taxonomy and secondary metabolites of wood-associated fungi. MycoKeys 104: 9-50. https://doi.org/10.3897/mycokeys.104.112149

Figure 6 Periconia kunmingensis (KUN-HKAS102239, holotype) A, B the appearance of fungal colonies on host substrate C–E conidiophores F, G closed-up conidiophores with spherical heads H, I conidiogenous cells bearing conidia J conidia catenate in acropetal short chain K–P conidia. Scale bars: 500 µm (A, B); 50 µm (C–E); 20 µm (F, G); 10 µm (J); 5 µm (H, I, K–P).

opencc-by-4.0Nov 2024View details →

Irish Grass Clover Dataset (VistaMilk)

Irish grass clover dataset Overview The Irish Grass Clover Dataset, collected in Ireland by a group of researchers working at the  VistaMilk Science Foundation Ireland Research Centre , comprises high-resolution images of herbage biomass. The dataset includes ground-truth annotations for herbage mass (kg DM/ha) and post-cutting herbage height (cm), capturing the composition percentages of grass, weed, and clover. This document provides details about the dataset, including collection methods, image specifications, and associated ground-truth data. Dataset description The dataset contains multiple subsets of data collected using a tripod mounted camera, a handheld phone or a drone. The dataset can be divided into two main subsets: the Camera & Phone Images subset and the Drone Images subset.  Camera & Phone Images: This dataset was gathered in 2020 in Ireland using both a high-resolution Canon camera (Canon EOS 90D Camera - Canon Europe) and a smartphone camera (iPhone 6). All the images were collected at Moorepark Farm managed by Teagasc, aiming to capture the biomass composition comprising grass, weeds and clover. Each image corresponded to a 0.5×0.5 m quadrat with 5-6 images taken for each of the 26 plots. Ground truth (GT) is provided for a subset of these images while other images are collected without GT. The herbage within quadrats for which GT was collected was harvested at 2-4 cm above ground level using Gardena hand shears (Accu 60, Gardena International GmbH, Ulm, Germany) immediately after image capture. Fresh weight was recorded and the harvested herbage was separated, oven-dried for 16 hours and weighed to give dry matter yield. For each of the  GT images the following labels are provided: total dry herbage mass (kg DM/ha), dry grass biomass percentage (%), dry clover biomass percentage (%), dry weed biomass percentage (%), fresh grass biomass percentage (%), fresh clover biomass percentage (%), fresh weed biomass percentage (%), and sward height post-cutting (cm). Additional views were taken using the smartphone for validation and generalisation purposes. All labelled phone images were collected at the exact same quadrats/locations where some of the camera images were taken, therefore they share the same GT values. A larger number of unlabeled camera & phone images were also collected at random locations across the same plots where the labelled images were collected. The contents of this subset can be summarised as: * 525 GT camera images divided into 418 train set labelled images and 107  validation set labelled images. * 124 GT phone  images divided into 17 train set labelled images and 107  validation set labelled images. * 1072 unlabelled camera images  * 1112 unlabelled phone images    Drone Images An extension of the Camera & Phone Images subset was created in late Autumn of 2021 where drone images were collected in the same 23 herbage paddocks originally studied with camera & phone images. At each paddock between 7 to 36 drone images at an altitude between 6 and 12 metres were captured. The drone used is the DJI Mavic 2 Pro 1 with its default camera, taking pictures at a resolution of 5472 × 3648. A total of 331 drone images with their associated altitude were obtained. Because of the huge areas covered by drone images, the ground-truth we collect is limited to the dry herbage mass at the paddock level and we omit the grass height and biomass percentage information. Two ground-truth estimation methods were utilised for the drone images: the first is a visual estimation performed on site at the time of the image collection by two human experts, the second is following the protocol of Egan et al. [1], where two 1.2 × 8 metres strips in the paddocks are cut at 4 cm above ground level (typical cow grazing height) using an Etesia lawn mower (Etesia UK. Ltd., Warwick, UK). A 100 grams sample is collected from the cut material and dried at 95°C for 16 hours to obtain the dry herbage mass. Structure The file structure for this repository is as follows: irish_dataset_all/         |_README.md         |         |_camera         |        |_train.csv // training images (filenames and GT annotations)         |        |_train_red.csv // a smaller training subset of 52 images     names and annotations         |        |_val.csv // validation images names and annotations         |        |_images/              // The training and test camera images         |_phone         |        |_phone_gt_train.csv // training images (filenames and GT annotations)         |        |_phone_gt_train.csv // validation images (filenames and annotations)         |        |_images/              // training and test phone images         |                 |_camera_unlab                     // unlabeled Camera images         |         |_phone_unlab                     // unlabeled Phone images         |         |_drone                                      |        |_images             // Drone images, organized by paddock         |        |_labels.csv             // image filenames and associated paddock level herbage mass ground truth (including visually estimated)         |        |_paddock_gt.csv     // paddock level herbage mass ground truth (including visually estimated) License: This dataset is provided under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).  This means you are free to: Share: Copy and redistribute the material in any medium or format. Adapt: Remix, transform, and build upon the material. However, these permissions are subject to the following terms: Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. Non-Commercial: You may not use the material for commercial purposes. ShareAlike: If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. By using this dataset, you agree to abide by these terms. For more details about the license, visit https://creativecommons.org/share-your-work/cclicenses/. Acknowledgements and fair use: Please cite us if our work and data helps your research ! @inproceedings{albert2021semi,   title={Semi-supervised dry herbage mass estimation using automatic data and synthetic images},   author={Albert, Paul and Saadeldin, Mohamed and Narayanan, Badri and Mac Namee, Brian and Hennessy, Deirdre and O'Connor, Aisling and O'Connor, Noel and McGuinness, Kevin},   booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},   pages={1284--1293},   year={2021} } @inproceedings{albert2022unsupervised,   title={Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation},   author={Albert, Paul and Saadeldin, Mohamed and Narayanan, Badri and Mac Namee, Brian and Hennessy, Deirdre and O'Connor, Noel E and McGuinness, Kevin},   booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},   pages={1636--1646},   year={2022} } @article{albert2022utilizing,   title={Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation},   author={Albert, Paul and Saadeldin, Mohamed and Narayanan, Badri and Mac Namee, Brian and Hennessy, Deirdre and O'Connor, Aisling H and O'Connor, Noel E and McGuinness, Kevin},   journal={arXiv preprint arXiv:2204.09343},   year={2022} } References [1] Egan, Michael, Norann Galvin, and Deirdre Hennessy. "Incorporating white clover (Trifolium repens L.) into perennial ryegrass (Lolium perenne L.) swards receiving varying levels of nitrogen fertilizer: Effects on milk and herbage production." Journal of Dairy Science 101, no. 4 (2018): 3412-3427 [a]Get license added here.

opencc-zeroNov 2021View details →

Can prescribed fires restore C4 grasslands invaded by a C3 woody species and a co-dominant C3 grass species?

Prescribed fire is used to reduce woody plant invasion and restore herbaceous production and diversity in grasslands and savannas worldwide. Here we determined if a concentrated series of repeated-winter, repeated-summer, or alternate-season (winter and summer) fires in a short timeframe ("transition fires") could catalyze the restoration of C4 perennial grasses in Southern Great Plains, USA grasslands that had become dominated by a fire-tolerant C3 woody N2-fixer (honey mesquite, Prosopis glandulosa) and a C3 perennial bunchgrass (Texas wintergrass, Nassella leucotricha). We applied transition fires over a 5-year span, and maintenance fires on a portion of each plot 7 or 8 years later. We measured herbaceous standing biomass and cover and soil variables (soil organic C, N, δ13C and δ15N) in unburned, transition-burned and maintenance-burned treatments. Greater δ13C at 10-20 (-17 ‰) than 0-10 (-20 ‰) cm depth increment confirmed that vegetation was historically mostly C4 grassland that shifted towards C3 dominance. Transition treatments with summer fire were most effective at top-killing mesquite, but no treatments root-killed >3%. Regrowth of top-killed mesquite was similar in all treatments and reached pre-fire height by 9 to 10 years post-fire. Herbaceous production and cover responses showed that: (1) alternate-season transition fires increased C4 mid-grass, but did not change Texas wintergrass, (2) repeated-summer fires reduced Texas wintergrass, but did not change C4 mid-grass, and (3) repeated-winter fires did not change C4 mid-grass or Texas wintergrass compared to the unburned control. All maintenance fires stimulated Texas wintergrass biomass and cover, thus eliminating the reduction of Texas wintergrass caused by repeated-summer transition fires. There were no long-term effects of transition fires on soil C, N, δ13C or δ15N. Results advance our understanding of the expectations and limitations of prescribed fire in shifting a woodland alternate state toward what was historically a fire supported C4 grassland/savanna.

opennotspecifiedMar 2021View details →

FIGURE 3 in Agrostis barikii (Poaceae: Agrostidinae), a new grass species from Western Himalaya, India

FIGURE 3. Geographic distribution of Agrostis barikii. (HP: Himachal Pradesh; UK: Uttarakhand).

opencc-zeroDec 2021View details →

Root traits reveal safety and efficiency differences in grasses and shrubs exposed to different fire regimes

Roots are key components of terrestrial ecosystems, yet little is known about how root structure and function vary across a broad range of species, functional groups, and ecological gradients in situ. We assessed how woody and grass root anatomical traits vary among soil depths and different fire frequencies to better understand the water-use strategies exhibited by these two functional groups in tallgrass prairie experiencing woody encroachment. Specifically, we asked: (1) Do root anatomical traits differ with fire frequency or soil depth? (2) Do relationships between anatomical traits that confer hydraulic safety versus efficiency vary by fire frequency or soil depth? (3) Is root anatomy associated with integrative root traits (e.g., root diameter, specific root length (SRL), and root biomass)? (4) When scaled by root biomass, do root water-use traits impact the capacity for water uptake? We collected grass and woody roots from 10, 30, and 50 cm deep soil in areas burned every 1, 4, and 20-years. We then measured xylem conduit diameter, conduit cell wall thickness, conduit number, conduit mechanical safety (t/b), stele area, endoderm thickness, hydraulic diameter, theoretical hydraulic conductivity, and root-system theoretical hydraulic conductance. We observed: (1) Woody roots had high hydraulic conductance in shallow soils and greater mechanical strength in deeper soils, which may provide a competitive advantage in less frequently burned, more diverse plant communities; (2) Shallow grass roots had unique trait combinations at the anatomical and root-system levels (thinner, more numerous conduits and higher root-system hydraulic conductance compared to deeper roots) that likely allow these plants to rapidly use water but tolerate dry soils under multiple fire regimes; and (3) hydraulic safety versus efficiency tradeoffs translate between different hierarchical scales (i.e., from anatomical to integrative root traits). These results provide anatomical evidence to explain water-use dynamics in tallgrass prairie and also provide novel insight regarding functional strategies that may facilitate the conversion from grassland to shrubland in less frequently burned tallgrass prairie. Future work should investigate these dynamics in situ, as they may explain current and future patterns of woody-grass coexistence in tallgrass prairies.

opennotspecifiedJan 2022View details →

FIGURE 3 in Rediscovery and IUCN threat assessment of Themeda saxicola (Poaceae: Andropogoneae), an endemic grass from the Eastern Ghats, India

FIGURE 3. Distribution map of Themeda saxicola.

opennotspecifiedJan 2022View details →

FIGURE 1. A in Rediscovery and IUCN threat assessment of Themeda saxicola (Poaceae: Andropogoneae), an endemic grass from the Eastern Ghats, India

FIGURE 1. A. Habitat of Themeda saxicola granite rock Hill

opencc-zeroMar 2022View details →

Field survey quadrat data - Exotic perennial grass invasion profiles differ between temperate threatened grassy communities

Aim: Exotic perennial grasses are significant invaders of native grassy communities and frequently multiple species invade communities, some from nearby agricultural areas. There is little understanding of the landscape distribution of many species, making prioritisation for control a difficult decision. Location: New South Wales, Eastern Australia Methods: We undertook field surveys of exotic perennial grasses at 139 sites from nine grassy threatened ecological communities across four regions and assessed whether the profiles of exotic species varied amongst regions and communities. We used a ranking of invasion risk based on plant characteristics to identify exotic perennial grasses that were likely to be the most invasive and then tested whether this ranking predicted the level of invasion measured in the survey. Results: Using multivariate analysis we found that the threatened grassy communities surveyed were significantly invaded by exotic perennial grasses and that these assemblages were regionally distinct and distinct for most plant communities. Five widespread invaders were particularly established in all regions and communities, but regions also had distinct sets of invaders contributing significantly to degradation. Invasion by trade-off species was the most significant threat to grassy communities in all regions. We showed that species with higher risk rankings based on plant characteristics were recorded in more sites but there were a few grasses that were more invasive than their ranking predicted. Main conclusions: Our findings indicate that management of grassy plant communities for exotic perennial grasses should be undertaken at the community level although there are a suite of species that are important invaders in the whole landscape where improved understanding of pathways of invasion are needed for management across regions. We identified a set of species which are important invaders but are not a focus in management currently, largely because many of these are species used in pastures. Our study illustrates that higher levels of invasion were associated with species that were ranked more invasive on plant characteristics and this ranking could be used to initially allocate priorities for management of threatened plant communities. Trade-off species remain the major cause of degradation and must be included in discussions of regional conservation.

opencc-zeroDec 2021View details →

Validation of the best bet Urochloa (Brachiaria) grass cultivars for increasing feed availability and improve livestock productivity in selected sites in Kenya

The trials were carried out at the Kenya Agricultural and Livestock Research Organization's Centre Mwea and Kamweti Agricultural Training Centre (ATC), both in Kirinyaga County.