Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
15
datasets available to search
ShareScore release 0.9.0
Dataset results
15 results for “grazing quality”
Criollo and Crossbred Steer Comparison: Weight Gain, Grazing, Carcass Quality, 2015-2017
Rarámuri Criollo cows have behavioral traits that are desirable for rangelands in arid environments, but calves from this biotype are difficult to market through conventional methods. One strategy to improve marketability is to crossbreed these cows with traditional beef breed bulls. However, it is unclear whether crossbred calves will achieve marketable weights and carcass qualities on rangeland and whether they will retain the desirable grazing behaviors of their mothers. We evaluated these traits for two cohorts of Rarámuri Criollo (JRC), Mexican Criollo (MC), and Criollo × beef-breed crossbred (XC) steers. Final live and carcass weights of XC were greater than JRC and MC, but all three groups were market ready at 30-mo after finishing on grass. Carcass quality and average daily gain did not differ among biotypes. Both JRC and XC steers exhibited grazing patterns similar to those previously observed in JRC cows. These results suggest JRC, MC, and XC steers can achieve desirable slaughter weights in 30 months using a rangeland-based grass-fed protocol, and JRC and XC steers retain desirable grazing behaviors of JRC cows.
Data from: Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation
<p>This dataset contains the original data used in the article:</p> <p>Harásek M., Klinkovská K. & Chytrý M. (2023) Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation. <em>Applied Vegetation Science</em>, 26, e12726. https://doi.org/10.1111/avsc.12726</p> <p>The data contain plant species composition data from resurveyed vegetation plots in southwestern and central Moravia (Czech Republic). The plots were first surveyed by Milan Chytrý in 1986–1991 (“old plots”) and resurveyed by Martin Harásek, under the supervision of Milan Chytrý, in 2018–2019 (“new plots”).</p> <p>Of the old plots, 86 were sampled between 26 June and 16 September and 8 in May. Their size ranged from 5 to 49 m<sup>2</sup> (mean 33 m<sup>2</sup>). These plots were subjectively selected at different sites to document maximum variation in species composition and environmental conditions of the grasslands and heathlands studied. In each plot, all vascular plant species were recorded, and their covers were estimated using the nine-grade Braun-Blanquet scale (van der Maarel 1979). Plot locations were recorded in the form of text descriptions. Geographic coordinates of approximate location were added for each plot prior to the resurvey by the original surveyor using georeferenced aerial photographs and various information recorded in the field during the first survey, including slope, aspect and elevation. Location uncertainty (mean = 139 m) was indicated as the possible distance of the actual location from the given coordinates.</p> <p>The resurvey was conducted between 4 June and 16 August. Care was taken to select the most likely location of the original plot based on the coordinates of the approximate location, the original site description, and the occurrence of the species recorded during the first survey. New plots always had the same plot size as in the original sampling. Each old plot was resurveyed using 1–3 new plots depending on the uncertainty of the location of the old plot. A total of 94 old plots were resurveyed at 47 sites with 153 new plots. Of these, 71 old plots at 32 sites were in current protected areas, while 23 old plots at 15 sites were outside protected areas. All new plots were located using GPS with a location uncertainty of approximately 5 m.</p> <p>For each old plot resurveyed with more than one new plot, the most similar new plot (based on Bray-Curtis dissimilarity in species composition) was selected, resulting in a dataset of 94 old and 94 new plots (“best-fit dataset”). To test the robustness of the results, we created another dataset (“validation dataset”) that included the least similar of the corresponding new plots for each old plot. This dataset also included the 94 old and 94 new plots. If the old plot was resurveyed using a single new plot, that new plot was included in both the best-fit and validation datasets.</p> <p>The header data structure follows that of the ReSurveyEurope Database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>). In addition, fields are added to indicate whether the new plot was used in the best-fit dataset (Best_fit) or the validation dataset (Validation). The information about location within or outside the protected area is given in the field Protection.</p> <p>The data on species composition and environmental variables are provided in two formats:</p> <ul> <li>Turboveg 2 database (see <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) – file <strong>TurbovegDbBackup_SW_moravia_acidgrass.zip</strong>. For using this dataset in Turboveg, the database dictionary (TurbovegDdBackup_Default dictionary.zip) and the species list (TurbovegSlBackup_Czechia_slovakia_2015.zip) must be installed.</li> <li>Three TXT files with columns separated by tabs: <ul> <li><strong>SW_moravia_acidgrass_species.txt</strong> contains the percentage covers of plant species in the plots, which are mid-values for cover-abundance categories of the Braun-Blanquet scale. Plant nomenclature was harmonised according to Danihelka et al. (2012).</li> <li><strong>SW_moravia_acidgrass _head.txt</strong> contains information on the number of species in each plot (number_species), the number, proportion and relative cover of threatened species (IUCN categories CR, EN, VU, NT, columns CR_NT_number, CR_NT_perc_number and CR_NT_perc_cover), alien species (alien_number, alien_perc_number, alien_perc_cover), species characteristic of dry grasslands (TH_number, TH_perc_number, TH_perc_cover), sand and rock-outcrop grasslands (TF_number, TF_perc_number, TF_perc_cover), mesotrophic grasslands (TD_number, TD_perc_number, TD_perc_cover) and herbaceous ruderal vegetation (XA_XC_number, XA_XC _perc_number, XA_XC _perc_cover) and unweighted means of Ellenberg-type indicator values for light (light), temperature (temperature), moisture (moisture), soil reaction (reaction) nutrients (nutrients) and salinity (salinity) used to test changes in these variables through time.</li> <li><strong>SW_moravia_life_forms.txt </strong>contains information about the assignment of individual species to the life form, which was used to analyse changes in frequency and cover of the life forms.</li> </ul> </li> </ul> <p>These data are also stored in the Czech National Phytosociological Database (Chytrý & Rafajová 2003; <a href="https://botzool.cz/vegsci/phytosociologicalDb">https://botzool.cz/vegsci/phytosociologicalDb</a>) and the ReSurveyEurope database (Knollová et al. 2023; <a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>).</p>
Mitigating ecosystem service tradeoffs in rangelands by using grazing duration and timing to manage water quality
<p>1. Mitigating ecosystem service (ES) tradeoffs is a key management goal in locations where stakeholders value different and potentially conflicting ecosystem services (ESs). However, studies are not often designed to examine how local management actions address ES tradeoffs, and therefore do not provide options that can alleviate conflict.</p> <p>2. In semi-arid rangelands, we examined the potential for managers to mitigate tradeoffs between livestock production and water quality. To move away from solutions that offer cattle removal as a singular management strategy, we examined how cattle presence, plus two elements of rotational grazing - the length of time cattle spend on rangeland (i.e., duration), and the season grazed (i.e., timing), affected stream Escherichia coli (E. coli concentrations). We also modeled how grazing duration and timing affected the ability to meet regulatory benchmarks for water quality throughout a grazing season.</p> <p>3. Grazing duration controlled the length of time E. coli concentrations were high in streams. In short- and medium-duration systems, E. coli concentrations were high for shorter periods of time than in long-duration systems, resulting in fewer violations of national and state water quality standards.</p> <p>4. Stream E. coli concentrations showed a consistent seasonal pattern, starting low in spring, peaking in summer, and declining towards fall. Thus, grazing during spring or fall, rather than in summer, reduced the number of days that E. coli levels exceeded water quality standards.</p> <p>5. Our results suggest that reducing the grazing duration and shifting its timing are complementary strategies that can mitigate the tradeoffs between livestock grazing and water quality without fencing-off riparian areas or removing cattle from pastures with streams.</p> <p>6. Synthesis and applications. In this study, we found grazing duration and timing can be used as tools to mitigate ES tradeoffs between cattle production and water quality in rangeland streams. Shorter grazing durations reduced the number of days <i>E. coli</i> levels were above regulatory limits, as did grazing that occurred either early or late in the season. These results support the idea that rotational grazing can be an effective strategy to manage water quality in semi-arid rangelands. They also highlight the need for more grazing studies that incorporate gradients of duration and timing into study designs.</p>
Mitigating ecosystem service tradeoffs in rangelands by using grazing duration and timing to manage water quality
Open the record for dataset details and reuse information.
Herbivore phenology can predict response to changes in plant quality by livestock grazing
<p>Livestock grazing can have a strong impact on herbivore abundance, distribution and community. However, not all species of herbivores respond the same way to livestock grazing, and we still have a poor understanding of the underlying mechanisms driving these differential responses. Here, we investigate the effect of light intensity cattle grazing on the abundance of two grasshoppers (<i>Euchorthippus cheui</i> and <i>E. unicolor</i>) that co-occur in the same grasslands and feed on the same food plant (the dominant grass <i>Leymus chinensis</i>). The two grasshopper species differ in phenology so that their peak abundances are separated into early- and late-growing seasons. We used an exclosure experiment to monitor grasshopper abundance and food quality in the field under grazed and ungrazed conditions, and performed feeding trials to examine grasshopper preference for grazed or ungrazed food plants in the laboratory. We found that the nitrogen content of <i>L. chinensis</i> leaves continuously declined in the ungrazed areas, but was significantly enhanced by cattle grazing over the growing season. Cattle grazing facilitated the early-season grasshopper <i>E. cheui, </i>whereas it suppressed<i> </i>the late-season grasshopper <i>E. unicolor</i>. Moreover, feeding trials showed that <i>E. cheui</i> preferred <i>L. chinensis</i> from grazed plots, while <i>E. unicolor</i> preferred the leaves from ungrazed plots. We conclude that livestock grazing has opposite effects on the two grasshopper species, and that these effects may be driven by grazing-induced chagnes in plant nutrient content and the unique nutritional niches of the grasshoppers. These results suggest that insects that belong to the same guild can have opposite nutrient requirements, related to their distinct phenologies, and that this can ultimately affect their response to cattle grazing. Our results show that phenology may link insect physiological needs to local resource availabilities, and should be given more attention in future work on interactions between large herbivores and insects.</p>
Data of "Grazing intensity and horn status influence activity on pasture, physiological pre-slaughter reactions and meat quality in beef heifers"
<p>Raw data of the article of "Grazing intensity and horn status influence activity on pasture, physiological pre-slaughter reactions and meat quality in beef heifers". Dataset of 64 crossbred beef heifers. Both experimental independent variables (animal id, horn status, grazing intensity, replicate) and dependent variables (related to physical activity on pasture, behaviour tests, stress physiology and meat quality) are presented.</p>
Herbivore phenology can predict response to changes in plant quality by livestock grazing
Open the record for dataset details and reuse information.
Supplementary material 1 from: Piry S, Berthier K, Streiff R, Cros-Arteil S, Foucart A, Tatin L, Bröder L, Hochkirch A, Chapuis M-P (2018) Fine-scale interactions between habitat quality and genetic variation suggest an impact of grazing on the critically endangered Crau Plain grasshopper (Pamphagidae: Prionotropis rhodanica). Journal of Orthoptera Research 27(1): 61-73. https://doi.org/10.3897/jor.27.15036
: Explanation note: Supplementary tables and figures.
Figure 4 from: Piry S, Berthier K, Streiff R, Cros-Arteil S, Foucart A, Tatin L, Bröder L, Hochkirch A, Chapuis M-P (2018) Fine-scale interactions between habitat quality and genetic variation suggest an impact of grazing on the critically endangered Crau Plain grasshopper (Pamphagidae: Prionotropis rhodanica). Journal of Orthoptera Research 27(1): 61-73. https://doi.org/10.3897/jor.27.15036
Figure 4 Relationships between intra-circle grasshopper density and A. Loiselle kinship coefficient, B. expected heterozygosity and C. FIS.
Figure 7 from: Piry S, Berthier K, Streiff R, Cros-Arteil S, Foucart A, Tatin L, Bröder L, Hochkirch A, Chapuis M-P (2018) Fine-scale interactions between habitat quality and genetic variation suggest an impact of grazing on the critically endangered Crau Plain grasshopper (Pamphagidae: Prionotropis rhodanica). Journal of Orthoptera Research 27(1): 61-73. https://doi.org/10.3897/jor.27.15036
Figure 7 Spatial cross-correlograms between A. grasshopper density and NDVI, B. grasshopper density and the mean genetic differentiation between individuals (MAPI cell values) and, C. NDVI and the mean genetic differentiation between individuals. The x-intercept of the spline-correlogram is the estimate of the distance at which the correlation between variables is not different than expected by chance alone. Dotted lines represent the 95% confidence envelope based on 500 bootstrap resamples.
Figure 1 from: Piry S, Berthier K, Streiff R, Cros-Arteil S, Foucart A, Tatin L, Bröder L, Hochkirch A, Chapuis M-P (2018) Fine-scale interactions between habitat quality and genetic variation suggest an impact of grazing on the critically endangered Crau Plain grasshopper (Pamphagidae: Prionotropis rhodanica). Journal of Orthoptera Research 27(1): 61-73. https://doi.org/10.3897/jor.27.15036
Figure 1 Map of France and location of A. the plain of Crau (Bouches-du-Rhône, France), B. the sampling site located between the sheepfolds 'La Grosse du Levant' and 'La Grosse du Centre', and C. the circles surveyed to detect and sample P. rhodanica. A00-A65: names of circles of the grid A; B04-B51: names of circles of the grid B; C1: additional circle of 100 m diameter (see Material and methods).
Figure 6 from: Piry S, Berthier K, Streiff R, Cros-Arteil S, Foucart A, Tatin L, Bröder L, Hochkirch A, Chapuis M-P (2018) Fine-scale interactions between habitat quality and genetic variation suggest an impact of grazing on the critically endangered Crau Plain grasshopper (Pamphagidae: Prionotropis rhodanica). Journal of Orthoptera Research 27(1): 61-73. https://doi.org/10.3897/jor.27.15036
Figure 6 Maps of A. density of grasshopper in number of individuals per hectare, B. rescale NDVI values and C. mean genetic differentiation between individuals resulting from the MAPI analysis.
Figure 3 from: Piry S, Berthier K, Streiff R, Cros-Arteil S, Foucart A, Tatin L, Bröder L, Hochkirch A, Chapuis M-P (2018) Fine-scale interactions between habitat quality and genetic variation suggest an impact of grazing on the critically endangered Crau Plain grasshopper (Pamphagidae: Prionotropis rhodanica). Journal of Orthoptera Research 27(1): 61-73. https://doi.org/10.3897/jor.27.15036
Figure 3 Illustrations of the Coussoul habitat of Prionotropis rhodanica with A. sheep accompanied by cattle egrets and B. researchers surveying the species using a circle-based method.
Figure 5 from: Piry S, Berthier K, Streiff R, Cros-Arteil S, Foucart A, Tatin L, Bröder L, Hochkirch A, Chapuis M-P (2018) Fine-scale interactions between habitat quality and genetic variation suggest an impact of grazing on the critically endangered Crau Plain grasshopper (Pamphagidae: Prionotropis rhodanica). Journal of Orthoptera Research 27(1): 61-73. https://doi.org/10.3897/jor.27.15036
Figure 5 Linear regression between genetic distance â and geographical distances computed between pairs of individuals. Variation in point density is represented by colors, from blue (low density) to red (high density).
Figure 2 from: Piry S, Berthier K, Streiff R, Cros-Arteil S, Foucart A, Tatin L, Bröder L, Hochkirch A, Chapuis M-P (2018) Fine-scale interactions between habitat quality and genetic variation suggest an impact of grazing on the critically endangered Crau Plain grasshopper (Pamphagidae: Prionotropis rhodanica). Journal of Orthoptera Research 27(1): 61-73. https://doi.org/10.3897/jor.27.15036
Figure 2 Adult of Prionotropis rhodanica.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.