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292 results for “Livestock”
Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model
<p>Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.</p>
CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000-2021) Using Stacking-based Interpretable Machine Learning
<p>Advanced computational methods integrating ensemble learning with interpretable machine learning are essential for precision livestock management under increasing environmental constraints and food security pressures. This study develops a novel stacking-based interpretable machine learning (IML) framework that combines multiple algorithms with SHAP analysis techniques to generate the China's Long-term Ruminant Livestock Distribution in Grazing Livestock Production Systems (CLRD-GLPS) dataset. Our computational approach addresses critical challenges in livestock distribution modelling: livestock segmentation and spatial prediction accuracy. The framework integrates Random Forest, XGBoost, CatBoost, LightGBM, and Extra Trees through a two-layer stacking architecture, enhanced with SHAP (Shapley Additive Explanations) analysis for model interpretability. We also implemented interpretable machine learning for livestock production system segmentation to distinguish grazing from total livestock populations. The stacking ensemble demonstrated superior performance over individual algorithms, achieving R² values of 0.954-0.961 for cattle and 0.896-0.901 for sheep and goats, with improvements of up to 8.3% compared to best performance single-model approaches. Multi-scale validation confirmed computational robustness: livestock segmentation achieved R² = 0.80 at county level, while independent city-level validation of CLRD-GLPS datasets yielded R² = 0.76-0.80. SHAP interpretability analysis revealed distinct environmental drivers, with vegetation indices and topography primarily influencing cattle distribution, while snow conditions and elevation dominated sheep and goat patterns. This computational framework advances livestock distribution modelling through enhanced prediction accuracy, model stability, and interpretability, while the CLRD-GLPS dataset provides essential spatial-temporal information for rangeland sustainability assessments and evidence-based livestock management policies. This dataset is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, grant no. 2019QZKK0906).</p>
EnrichKit: a multi-omics tool for livestock research
<p>This is the backend database for the web application EnrichKit.</p> <p>This <a href="../api/records/10257552/draft/files/EnrichKitDB.sqlite/content">EnrichKitDB.sqlite </a>object is created following this repo - https://github.com/liulihe954/EnrichKitDB</p> <p>The main EnrichKit repo can be found there - https://github.com/liulihe954/EnrichKitWeb</p>
KMA Mapping and alignment statistics : livestock fecal metagenomes against ResFinder and genomes
<p>Three zip archives are included used in the analysis of the European livestock resistome.</p> <p>Two of them contain 'mapstat' files produced by the KMA software using the 'extended features' flag.<br> Each mapstat file thus summarize the mapping and alignment statistics when using KMA on a metagenome against a database.</p> <p>The last archive contains the 'refdata' file used to annotate the genomic mapstat hits. It encodes the taxonomic affilication of sequences hit by one or more samples.<br> </p>
Drone orthomosaics for 'Livestock impacts on an iconic Namib Desert plant are mediated by abiotic conditions'
<p>Drone orthomosaics used in the analyses and figures of: Livestock impacts on an iconic Namib Desert plant are mediated by abiotic conditions, accepted in Oecologia.</p> <p>These drone orthomosaics are a data supplement to the code and data repository here: https://github.com/jtkerb/Nara_Paper_Repo</p>
COMPREHENSIVE LIVESTOCK HEALTH PROGRAM: TARGETED TREATMENT AND HOLISTIC INTERVENTIONS FOR MAJOR PREVALENT DISEASES IN THE LIVESTOCK FARMING COMMUNITY OF DAYNILE DISTRICT, MOGADISHU, SOMALIA.
<p>The general objective of this project was to intervene with the most common livestock diseases in Dayniile district by carrying out a comprehensive campaign for treatment and control. The specific objectives consisted of a treatment campaign, improving infrastructure for establishing disinfectant foot dips and hand washing points, providing disinfectant tools, and finalising community engagement and education by doing training at the farm level.<br>The team visited different donors and added their contribution. After collecting sufficient funds from various sources, the team began the procurement of the necessary materials. This included purchasing veterinary drugs and supplies from local pharmacies and other essentials like stationery. The first activity was treatment campaigns, which were a central aspect of the project. Over 290 animals were treated for various diseases and conditions. The farm manager was informed of the diagnoses, and upon receiving their permission, the appropriate treatments were administered. The second intervention action was a vaccination campaign. The team vaccinated a total of 70 animals against clostridial bacteria, which is one of the most common camel diseases encountered in the area. The third intervention was the establishment of biosecurity facilities at select livestock farms. Among all the farms involved in the project, five were chosen for the provision of enhanced biosecurity measures. These measures included the installation of foot dips and teat dips. The fourth activity was educating livestock farmers on strategies for controlling and preventing livestock diseases. The training was held at Beder Camel Dairy Farm and attended by approximately 10 individuals, comprising 3 females and 7 males. The content of the training was three modules: the first was general farm biosecurity, the second was operational biosecurity, and the third was concern for vaccination. Recommendation: We recommend that each farm hire livestock health specialists to easily implement disease prevention steps and promptly solve each new case.<br> We recommend the livestock association, veterinary clinics, and other institutions working on livestock do routine campaigns that facilitate the determination of prevalent diseases and the treatment of those cases</p>
Data from: The spatial distribution and temporal trends of livestock damages caused by wolves in Europe
<p>The preprint of the corresponding manuscript can be found here: doi: https://doi.org/10.1101/2022.07.12.499715</p> <p>Wolf populations are recovering and expanding across Europe, causing conflicts with livestock owners. We here compiled incident-based livestock damage data caused by wolves across 21 European countries for the years 2018, 2019 and 2020.</p> <p>The file "<strong>wolf_damages_2018_2019_2020_complete_data_to_publish.csv</strong>" contains the following information per incident: country, target species, cause, number of animals killed/injured/missing, assessment level probability, reported date, number of days until inspection, location, incidentID, uniqueID, NUS1_ID, NUTS2_ID, NUTS3_ID, damage prevention measure, number of wolves attacking, latitude, longitude, comments, metadata constraints.</p> <p>The file "<strong>nuts3_regions_and_LC_where_wolves_are_present.csv</strong>" contains information of the percentage of area occupied by wolves per NUTS3 region for selected land cover variables.</p> <p>The file "<strong>prevention_measures.csv</strong>" contains information about the financial support of livestock damage prevention measure per country or NUTS region</p> <p>The file "<strong>wolf_presence_now_vs_50_years_ago_nuts3.csv</strong>" contains information on NUTS3 regions that had a documented wolf presence 50 years ago.</p> <p>The "<strong>scripts_to_publish.zip</strong>" folder contains the scripts that we used to conduct the analyses.</p>
AgrImOnIA: Open Access dataset correlating livestock and air quality in the Lombardy region, Italy
<p>The AgrImOnIA dataset is a comprehensive dataset relating air quality and livestock (expressed as the density of bovines and swine bred) along with weather and other variables. The AgrImOnIA Dataset represents the first step of the <a href="http://www.agrimonia.net">AgrImOnIA project</a>. The purpose of this dataset is to give the opportunity to assess the impact of agriculture on air quality in Lombardy through statistical techniques capable of highlighting the relationship between the livestock sector and air pollutants concentrations.</p> <p>The building process of the dataset is detailed in the <strong>companion paper:</strong></p> <p>A. Fassò, J. Rodeschini, A. Fusta Moro, Q. Shaboviq, P. Maranzano, M. Cameletti, F. Finazzi, N. Golini, R. Ignaccolo, and P. Otto (2023). Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. <em>SCIENTIFIC DATA</em>, 1-19.</p> <p>available <a href="https://rdcu.be/c7T9H">here</a>.</p> <p>This dataset is a collection of estimated daily values for a range of measurements of different dimensions as: air quality, meteorology, emissions, livestock animals and land use. Data are related to Lombardy and the surrounding area for 2016-2021, inclusive. The surrounding area is obtained by applying a 0.3° buffer on Lombardy borders.</p> <p>The data uses several aggregation and interpolation methods to estimate the measurement for all days.</p> <p>The files in the record, renamed according to their version (es. .._v_3_0_0), are:</p> <ul> <li> <p>Agrimonia_Dataset.csv(.mat and .Rdata) which is built by joining the daily time series related to the AQ, WE, EM, LI and LA variables. In order to simplify access to variables in the Agrimonia dataset, the variable name starts with the dimension of the variable, i.e., the name of the variables related to the AQ dimension start with 'AQ_'. This file is archived also in the format for MATLAB and R software. </p> </li> <li> <p>Metadata_Agrimonia.csv which provides further information about the Agrimonia variables: e.g. sources used, original names of the variables imported, transformations applied.</p> </li> <li> <p>Metadata_AQ_imputation_uncertainty.csv which contains the daily uncertainty estimate of the imputed observation for the AQ to mitigate missing data in the hourly time series. </p> </li> <li> <p>Metadata_LA_CORINE_labels.csv which contains the label and the description associated with the CLC class. </p> </li> <li> <p>Metadata_monitoring_network_registry.csv which contains all details about the AQ monitoring station used to build the dataset. Information about air quality monitoring stations include: station type, municipality code, environment type, altitude, pollutants sampled and other. Each row represents a single sensor.</p> </li> <li> <p>Metadata_LA_SIARL_labels.csv which contains the label and the description associated with the SIARL class.</p> </li> <li> <p>AGC_Dataset.csv(.mat and .Rdata) that includes daily data of almost all variables available in the Agrimonia Dataset (excluding AQ variables) on an equidistant grid covering the Lombardy region and its surrounding area. </p> </li> </ul> <p>The Agrimonia dataset can be reproduced using the code available at the GitHub page: <a href="https://github.com/AgrImOnIA-project/AgrImOnIA_Data">https://github.com/AgrImOnIA-project/AgrImOnIA_Data</a></p> <p><strong>UPDATE 31/05/2023</strong> <strong>- NEW RELEASE - V 3.0.0</strong></p> <p>A new version of the dataset is released: Agrimonia_Dataset_v_3_0_0.csv (.Rdata and .mat), where variable <em>WE_rh_min, WE_rh_mean and WE_rh_max </em>have been recomputed due to some bugs<em>.</em></p> <p>In addition, two new columns are added, they are <em>LI_pigs_v2 and LI_bovine_v2 </em>and represents the density of the pigs and bovine (expressed as animals per kilometer squared) of a square of size ~ 10 x 10 km centered at the station localisation.</p> <p>A new dataset is released: the Agrimonia Grid Covariates (AGC) that includes daily information for the period from 2016 to 2020 of almost all variables within the Agrimonia Dataset on a equidistant grid containing the Lombardy region and its surrounding area. The AGC does not include AQ variables as they come from the monitoring stations that are irregularly spread over the area considered.</p> <p><strong>UPDATE 11/03/2023</strong> <strong>- NEW RELEASE - V 2.0.2</strong></p> <p>A new version of the dataset is released: Agrimonia_Dataset_v_2_0_2.csv (.Rdata), where variable <em>WE_tot_precipitation </em>have been recomputed due to some bugs<em>.</em></p> <p>A new version of the metadata is available: Metadata_Agrimonia_v_2_0_2.csv where the spatial resolution of the variable <em>WE_precipitation_t </em>is corrected.</p> <ul> </ul> <p><strong>UPDATE 24/01/2023</strong> <strong>- NEW RELEASE - V 2.0.1</strong></p> <p>minor bug fixed</p> <p><strong>UPDATE 16/01/2023</strong> <strong>- NEW RELEASE - V 2.0.0</strong></p> <p>A new version of the dataset is released, Agrimonia_Dataset_v_2_0_0.csv (.Rdata) and Metadata_monitoring_network_registry_v_2_0_0.csv. Some minor points have been addressed:</p> <ul> <li>Added values for <em>LA_land_use</em> variable for Switzerland stations (in Agrimonia Dataset_v_2_0_0.csv)</li> <li>Deleted incorrect values for <em>LA_soil_use</em> variable for stations outside Lombardy region during 2018 (in Agrimonia Dataset_v_2_0_0.csv)</li> <li>Fixed duplicate sensors corresponding to the same pollutant within the same station<em> </em>(in Metadata_monitoring_network_registry_v_2_0_0.csv)</li> </ul>
Perennial grass recovery following livestock overgazing and shrub removal: an experiment at the Jornada Experimental Range (Jornada Basin LTER), 1996-2016
The objective of this ongoing study is to determine the effect of cattle grazing and shrub removal on the decline and recovery of perennial grasses in a mesquite-invaded black grama grassland on sandy soils in the northern Chihuahuan Desert. The experiment was implemented as a randomized complete block with 3 levels of grazing (summer, winter, and control) and 2 levels of shrub treatment (shrub removal and control) in each of 3 replicate blocks. The 18 experimental units are 0.5 ha (70 x 70 m) exclosures constructed in a mesquite-invaded black grama grassland in the southwest portion of the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Vegetation sampling was conducted with the line-point intercept method. Initial pre-treatment sampling occurred in 1996. Grazing treatments removed 65-80% of aboveground perennial grass biomass over 24-36 hour periods in each of four years from summer 1996 to winter 2000; shrub removal occurred during this time as well. No livestock grazing or shrub removal have occurred since 2000. Post-treatment sampling occurred in 2002, 2009, and 2016.
Figure 3 in Temporal dynamics of invertebrate and aquatic plant communities at three intermittent ponds in livestock grazed Patagonian wetlands
Figure 3. Seasonal variation of total taxa richness (A), mean density (A), and relative contribution of biomass (B) of most abundant groups of aquatic invertebrates at three ponds in a Patagonian wetland (Mallín Crespo) during the study period (May 2008 to April 2009). Livestock stocking period is indicated by the black bar.
Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004
<p>Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)<sup>-1</sup> yr<sup>-1</sup>] or [g N (kg product)<sup>-1</sup> yr<sup>-1</sup>] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. </p> <p>Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector</p>
Livestock activity shifts large herbivore temporal distributions to their crepuscular edges
<p>Wildlife species are transitioning to greater crepuscular and nocturnal activity in response to high human densities. This plasticity in temporal niches may partially mitigate the impacts of human activity but may also result in underestimating human effects on species foraging, predator-prey relationships, and community level interactions. We deployed remote cameras to characterize shifts in herbivore diel activity in protected habitat vs pastoralist landscapes. We then compared species traits including body mass, dietary preferences, and behavioral characteristics as potential predictors of species sensitivity to livestock. Our data capture a significant temporal shift away from core cattle activity for nearly every herbivore species in our study, leading to more crepuscular activity patterns. As livestock were primarily diurnal and predators primarily nocturnal in pastoralist habitat, species that decreased their overlap with livestock were more likely to increase their overlap with potential predators. Other than species' typical daytime activity levels, we found no evidence that any particular trait significantly predicted temporal shifts in response to livestock. Instead, species generally trended toward greater activity levels at dawn, suggesting that cattle have a homogenizing effect on community-wide activity patterns. Our findings highlight how cohabitation with livestock can profoundly alter the temporal niches of wild herbivores. Shifts in diel activity patterns may reduce herbivore foraging time or efficiency and potentially have cascading shifts on predator-prey dynamics. Given that species traits could not predict responses to livestock, our analysis suggests that conservation strategies should consider each species separately when designing interventions for wildlife management.</p>
The key role of production efficiency changes in livestock methane emission mitigation
<p>This dataset contains the R code, the input data, the parameters used, and the updated livestock methane emission for the period 1961-2023 using methods from Chang, J., Peng, S., Yin, Y., Ciais, P., Havlik, P., Herrero, M. (2021). The key role of production efficiency changes in livestock methane emission mitigation. AGU Advances, 2, e2021AV000391. DOI: https://doi. org/10.1029/2021AV000391 </p> <p>1. R code: Chang_et_al_Global_Livestock_CH4_Assessment_1961_2023.R<br>2. Input data and parameters: Data.zip (statistics on historical livestock numbers and production need to be downloaded from FAOSTAT (http://www.fao.org/faostat/en/)<br>3. Results on global livestock methane emissions during 1961-2023 were presented in the Global_Results.xlsx<br>4. Results on livestock methane emissions from enteric fermentation and manure management during the period 1961-2023 in each country/area were shown in the folder named Country_Results: Files are organized as "Country_[XX]CH4_[YY]_[ZZ].csv" where XX indicate emission from enteric fermentation (EF) or manure management (MM); YY indicates method used for the estimates; and ZZ indicates livestock categories.<br>5. Results on gridded livestock methane emissions at a resolution of 5 arc-min using the IPCC Mixed Tier 1 and Tier 2 (2019MT) and Tier 1 (2019T1) methods following the 2019 refinement to the 2006 IPCC guidelines for National Greenhouse Gas Inventories (Vol. 4) (IPCC, 2019): Livestock_CH4_map_5arcmin_1961_2023_2019MT_2019T1.nc4</p> <p>Please contact: Dr. Jinfeng Chang (changjf@zju.edu.cn) for any question on the dataset.</p>
ArMoR Cluster: 5 research projects fight Antimicrobial Resistance in livestock farming
<p>Within Horizon Results Booster programme (HRB), 4 Horizon 2020 projects (AVANT, DISARM, HealthyLivestock and ROADMAP) and 1 BBSRC funded project (AMRILS) have formed the "ArMoR Cluster" to develop a conceptual framework to improve understanding of AMR in livestock systems.</p> <p>Supported by the European Commission, Horizon Dissemination Booster (HRB) contributes to an effective transfer of research and innovation project results to policy makers, industry and society by offering various services as dissemination, exploitation strategy and business plan development to projects.</p> <p>The video is available on YouTube: <strong><a href="https://www.youtube.com/watch?v=rnU35ytdEuM">https://www.youtube.com/watch?v=rnU35ytdEuM</a></strong></p> <p>For any further questions please contact us at:</p> <ul> <li><strong><a href="https://zenodo.org/record/avant@rtds-group.com">avant@rtds-group.com</a></strong> (project AVANT),</li> <li><strong><a href="https://zenodo.org/record/info@disarmproject.eu">info@disarmproject.eu</a></strong> (project DISARM),</li> <li><strong><a href="https://zenodo.org/record/healthylivestockproject@yahoo.com">healthylivestockproject@yahoo.com</a></strong> (project Healthy Livestock) or</li> <li><strong><a href="mailto:roadmap.communication@gmail.com">roadmap.communication@gmail.com</a></strong> (project ROADMAP). </li> </ul>
Livestock management promotes bush encroachment in savanna systems by altering plant-herbivore feedback
<p>This repository contains all code to reproduce the analysis in Koch et al. 2022 "Livestock management promotes bush encroachment in savanna systems by altering plant-herbivore feedback".</p> <p>We use a set of coupled differential equations to describe competition between shrubs and grasses, as well as plant biomass consumption via grazing and browsing. Grazers were assumed to receive a certain level of care from farmers, so that grazer densities emerge dynamically from the combined effect of vegetation abundance and farmer<br>support. Our main goal was to understand how critical transitions from grass-dominated to shrub-dominated system states were affected by the dynamic role of grazing.</p> <p>Our results show that bistability emerges for intermediate levels of farmer support due to positive feedback that arises from competition between shrubs and grasses and from herbivory. We furthermore demonstrate that disturbances, such as drought events, trigger abrupt transitions from the grass dominated to the shrub dominated state and that the system becomes more susceptible to disturbances with increasing farmer support.</p>
2010_FAO_Gridded_Livestock_World
<p><strong>Abstract:</strong></p> <div> <div> <div> <p>A series of Livestock Density Data Layers providing density distribution layers for Bovine, Small Ruminants, Pigs and Poultry. layers derived by multivariate regression statistical modelling of subnational resolution survey and census data between 1995 and 2015. These data are 'totals corrected versions' - so that the densities within each survey or census data polygon are corrected to ensure the derived totals match those from which the statistical models were calculated.</p> <p>Produced for the Agriculture Division at the Food And Agriculture Organisation in Rome</p> <p> </p> </div> </div> </div> <div> </div> <div><strong>File names: </strong></div> <table> <tbody> <tr> <td>FAO Gridded Livestock of the World Cattle</td> </tr> <tr> <td>FAO Gridded Livestock of the World sheep</td> </tr> <tr> <td>FAO Gridded Livestock of the World goats</td> </tr> <tr> <td>FAO Gridded Livestock of the World pigs</td> </tr> <tr> <td> <p>FAO Gridded Livestock of the World Chickens</p> </td> </tr> </tbody> </table> <p> </p>
Collapse and recovery of livestock systems shape fire regimes on the Eurasian steppe: a review of ecosystem and biodiversity implications
<p>This file contains bibliographic data from the literature research; livestock and fire data as well as Google Earth Engine and R-scripts to reproduce all analyses and figures.</p>
Supplement code and data for "A global multi-indicator assessment of the environmental impact of livestock products"
<p>This data and code supplements the publication "A global multi-indicator assessment of the environmental impact of livestock products" by Giorgio A. Bidoglio, Florian Schwarzmueller, Thomas Kastner available here: https://doi.org/10.1016/j.gloenvcha.2024.102853<br><br></p> <p>Version 1.1 add the code to the visualization tool available online at https://livestockimpactassessment.shinyapps.io/multi-indicator_impact_assessment_of_livestock_products/</p>
Fig. 4 in Spread of moniesiosis pathogens in livestock in the Ganja-Gazakh Region of the Republic of Azerbaijan: Bio-ecological features
Fig. 4. Correlation between season and spread of moniesiosis pathogens in goats Рис. 4. Зависимость распространения возбуÃитеΛей мониезиоза от сезонов гоÃа
Fig. 3 in Spread of moniesiosis pathogens in livestock in the Ganja-Gazakh Region of the Republic of Azerbaijan: Bio-ecological features
Fig. 3. Age-dependent dynamics of moniesiosis pathogens Рис. 3. Возрастная Ãинамика заражения мониезиями овец и коз
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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.