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1,072 results for “Pigs”
Resilience of pig producers in Europe
<p>This dataset collected for the organic core POWER project to assess resilience capacities of organic pig prodcuers in Austria, Danemark, Italy, Sweden and Switzerland. These datasets have been anonymized. </p> <p> </p> <p>The resilience farm data are all data that where observed at farm level, and contain farm characterisitcs, namely</p> <table> <tbody> <tr> <td>variable</td> <td>description</td> <td>values</td> </tr> <tr> <td>farm id</td> <td>unique identifier of the farm</td> <td>characters, including country code based on ISO2</td> </tr> <tr> <td>breeding type</td> <td>type of pig entreprise on the found on the farm</td> <td>breeding, finishing or both</td> </tr> <tr> <td>entrerprise_x</td> <td>description of other entreprises found on the farm</td> <td>feed production, cash crop, chicken, sheep, dairy, beef, direct marketing, tourism, on-farm processing, horse housing.</td> </tr> <tr> <td>number non-pig entreprise</td> <td>number of entreprise describes</td> <td>integer</td> </tr> <tr> <td>structure</td> <td>type of pig housing structure</td> <td>permanent, temporary, both</td> </tr> <tr> <td>outdoor area</td> <td>type of oudoor access for pig</td> <td>concrete, shifting arable land, permanent pasture</td> </tr> <tr> <td>LSU</td> <td>livstock standard units computed following Eurostat standards</td> <td>numeric</td> </tr> <tr> <td>pig/ha</td> <td>intensity of production as LSU/UAApig</td> <td>numeric</td> </tr> <tr> <td>self-sufficiency</td> <td>percentage of pig feed produced on farm</td> <td>numeric</td> </tr> <tr> <td>UAA pig</td> <td>utilized agricultural area for the pig production</td> <td>numeric</td> </tr> <tr> <td>UAA total</td> <td>utilized agricultural area of the farm</td> <td>numeric</td> </tr> </tbody> </table> <p> </p> <p>The resilience data is the result of the interpretation of farmers' resilience narratives, which were interpreted been interpreted using the Meuwissen et al, 2019 farming systems framework. The data is in long fromat and represents a particular resilience capacity related to a specific shock. More particularly, the data contains the follwing information</p> <p> </p> <p> </p> <table> <tbody> <tr> <td>variables name</td> <td>description</td> <td>values</td> </tr> <tr> <td>farm id</td> <td>unique identifier of the farm</td> <td>characters, including country code based on ISO2</td> </tr> <tr> <td>country</td> <td>country code</td> <td>based on ISO2</td> </tr> <tr> <td>question related to shocks</td> <td>shocks to which the resilience narrative related to</td> <td>input cost, price, outbreak, climate, legislation, labour, general</td> </tr> <tr> <td>narratives (a= first, b=second)</td> <td>identifier of the narrative within a question</td> <td>a, b</td> </tr> <tr> <td>capacity</td> <td>resilience capacity following the Meuwissen et al (2019) framework</td> <td>robustness, adaptability, transformability, non-resilience</td> </tr> <tr> <td>resilience attribute type</td> <td>resilience attribute based on an expanded interpretation the Meuwissen et al (2019) framework (see paper)</td> <td> <p>functional diversity, response diversity, modularity, tighness of feedback, social capital, attitude, system reserve (physical captial -inherent), system reserve (physical capital -use), system reserve (natural capital -inherent) system reserve (human capital - use)</p> </td> </tr> <tr> <td>resilience attribute</td> <td>description of the attribute that led to the resilience attribute type classification</td> <td> <p>ability to convert to cash crop, ability to offer good working conditions, ability to switch brand, access to financial services, access to technical solutions, adapted crops, adding finishing section, adjust feed production, adjust volume of pig production, adjusting paddock size to enable double fencing, advisory and veterinary services, believe in organic, brand building with social media, build temporary shelter, build up savings, by-product through partnership, capacity to access more land, change external feed, change feed ratio, conservable end product, create microclimates, create new brand, created a young farmer network, customer relation, decrease pig, decrease pig production, direct marketing, diverse farm, diverse sale channels, do something else, double fencing, efficiency, entrepreneurship, excess cereal production, exploring governance model as no successor, family labour, farmer owned value chain, fencing, financial lock-in, flexible infrastructure (enabling), flexible pig keeping system, forest system, good indoor infrastructure, good infrastructure, good relation to customers, governmental support, habit, has margin, home feed production, inadequate salary, increase cash crop, increase own work, increase own working time, independent feed ratio, indoor keeping, indoor production, innovator, innovator (one welfare) , insurance, margins, mechanisation, mobile mode of production, neighbor network, neighborhood early warning, neighborhood network, new cooling infrastructure, niche production, no competition, no fencing option, no own farm, land or infrastructure, no qualified staff required, offering jobs to young people, other livestock, part time worker, partnership with other farmers, producing more home grown feed, profit, reduced pig production, rely on sectoral organization, resistant breed, robust animals, robust breed, sectoral power, sectoral response, short term feed contracts, social media, soil health, split production on other farms, staffing agency (through advisory services), sufficient outdoor space, sufficient pasture, sufficient space, sufficient space (enabling), switch to indoor production, switch to other livestock, tiredness in the sector, Too big to fail, training, unique pig keeping system, up-to-date infrastructure, volunteer networks, wallow, work with nature</p> </td> </tr> </tbody> </table> <p> </p> <p>To compute the resilience capacity score (<em>Cscore)</em></p> <p>assign 0 to lack of resilience, 1 to robustness, 2 to adpababilty and 3 to transformability. If there is more than one narrative with a different capacity, the average score between mentionned capacities was taken.</p> <p>Use following R code in dplyr</p> <p><em>mydata<-</em> ResilienceDataPreProcessed<em> %>% </em></p> <p><em> mutate(code = ifelse(capacity=="robustness", 1,ifelse(capacity=="adaptability",10,ifelse(capacity=="transformability",100,ifelse(capacity =="no resilience capacity",1000,ifelse(capacity=="no long term resilience capacity",10000,ifelse(capacity=="no short term resilience",1000,NA)))))))%>% </em></p> <p><em> group_by(farm, question)%>% </em></p> <p><em> summarise(Ccode=sum(code))%>% </em></p> <p><em> mutate(Cscore=ifelse(Ccode==1|Ccode==2| Ccode==3, 1,ifelse(Ccode==20|Ccode==10|Ccode==111,2, ifelse(Ccode==100|Ccode==200,3, ifelse(Ccode==11|Ccode==21, 1.5,ifelse(Ccode==110|Ccode==120, 2.5,ifelse(Ccode==101,3,ifelse(Ccode==1000,0,ifelse(Ccode==10001|Ccode==1001,0.5,NA)) ))) ))))</em></p> <p> </p> <p><strong>Resilience questionnaire</strong></p> <p><strong>Farm number:</strong></p> <p><strong>Farm name or ID:</strong></p> <p><strong>Country:</strong></p> <p> </p> <p><strong>System descriptors</strong></p> <table> <tbody> <tr> <td> <p>Breeding or finishing (or both)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Indoor or outdoor (or a mix)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Organic or conventional</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Number of years organic</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>1) Has your farm experienced significant challenges in the last 5 years?</strong></p> <table> <tbody> <tr> <td> <p>Yes or no?</p> </td> <td> <p>Yes / No</p> </td> </tr> <tr> <td> <p>If "no", what factars (farm/external) created this resilience?</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>If "yes", please describe the 1st challenge</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)?</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>Did this change your management or farm structure subsequently (and how)?</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>If "yes", please describe a 2nd challenge</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)?</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>Did this change your management or farm structure subsequently (and how)?</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>2) In the future, how do you feel your pig system would cope with these challenges:</strong></p> <p>a) Decreasing or negative margins due to increased feed or other input costs?</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p>b). Decreasing or negative margins due to reduced pig prices?</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p>c) Wide spread disease outbreak such as African Swine Fever</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p>d) Climate change impact, e.g. severe storms, flooding, drought, hot seasons</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p>e) Changing legislation impact, e.g. increased floor space allowance indoors, mandatory access to pasture, more land required (lower stocking densities to reduce nutrient loads from pasture systems or in general for the whole farm)</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p>f) Shortage in qualified ‘work-mass’ (difficulties in recruiting qualified employees)</p> <table> <tbody> <tr> <td> <p>Very severely (e.g. bankruptcy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Severely (e.g. closure of pig enterprise)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Strong impact (e.g. large reduction in production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Short term impact (e.g. reduced production)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Little impact (e.g. change ration)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Why?</p> <p> </p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge)</p> </td> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p><strong>3) Any other comments on resilience of their system?</strong></p> <p><strong>4) General comments/system description?</strong></p>
Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig
Open the record for dataset details and reuse information.
Whole-genome genotype data for French Large White pigs from two distinct sampling times
<p>Genotype data at plink binary format for 36 pigs from the french Large White breed: 13 animals from the female line born in 2014 and 2015, 13 animals from the male line born between 2012 and 2016, and 10 animals from a common ancestral line, born in 1977. These genotypes were obtained from individual whole genome sequencing (WGS) data, whiwh are available at https://www.ebi.ac.uk/ena under the accession number PRJEB51909.</p> <p>Two different genotype datasets were obtained from the raw WGS:</p> <p>1) snp20_auto_cr (.bed/bim/fam): High quality autosomal SNPs, called by 3 different software, with a call rate of at least 90%</p> <p>2) all10_auto (.bed/bim/fam): All SNPs or indels called by at least one of 3 different software.</p> <p>More details about these datasets and their use can be found in the following study:</p> <p>Boitard et al (under revision): Whole-genome sequencing of cryo-preserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines.</p>
Regional legumes in pig feeding
<p>Andreas Müller, farm manager of Müller Stiftsgrundhof GbR in Backnang, Germany, feeds his 800 fattening pigs, as well as 170 sows including piglets, completely with regional feed. The feed for his animals is largely produced on his own farm. In agriculture, Mr. Müller follows the approaches of regenerative agriculture. The farm has been using a variety of catch crops for 40 years. On 6.5 hectares, he grows alfalfa, which he processes into silage. Lucerne silage keeps the digestive system of our domestic pig healthy by meeting its raw fiber requirements. In addition, this legume contributes to a certain extent to the protein supply of the animals and has a positive effect on the arable soils. The webinar video is in German, with subtitles available in German and English.</p>
Faba Beans in Pig Feeding Trials
<p>Dr. Peadar Lawlor, Teagasc and John Ryan of Adesco talk to Tim O'Donovan of Seedtech about their research into feeding faba beans instead of imported protein to pigs. The video is an output of the Legumes Translated Horizon 2020 project and is available in English with subtitled in English and German. </p>
Cellular and Humoral Immune Responses after Immunisation with Low Virulent African Swine Fever Virus in the Large White Inbred Babraham Line and Outbred Domestic Pigs
<p>Raw data for manuscript. Contains temperature, clinical scores, qPCR, blood cell numbers and immune responses over time for two groups of pigs immunised with low virulent African swine fever virus and challenged with highly virulent virus. Data for each panel or figure is displayed on a separate worksheet in the file. The readme worksheet contains a brief description of each figure. The majority of data is displayed in an XY table format, with the number of days post immunisation with low virulent virus indicated.</p>
Small-angle X-ray scattering datasets for imaging crossing fibers in mouse, pig, monkey, and human brain
<p>Small-angle X-ray scattering datasets for resolving crossing fibers (myelinated neuronal axon bundles), as described in</p> <p>"<strong><em>Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering</em></strong>"</p> <p>deposited in bioRxiv:</p> <p>https://doi.org/10.1101/2022.09.30.510198</p>
Behaviour, Welfare and Performance Records from Duroc Pigs
<p>This is a database generated within Feed-a-Gene (H2020) and GENEF (Spanish INIA) projects. It comprise feeding, growth and body performance of Duroc pigs. It also includes feeding behavoiur data as recorded by automatic feeding stations and also a number of behaviour and welfare traits assessed in a reduced subset of animals.</p> <p>This database comprise Deliverable D2.1 from Feed-a-Gene, a document describing the data structura can be get in (link to EU publication of the Report).</p> <p> </p>
Amine metabolites in pigs fed a diet with spray dried plasma protein as functional protein source
<p><span>We evaluated the effects of diets formulated with either soybean meal (SBM) as a reference protein source or SDPP in pigs. Blood amine profiles were analysed to evaluate the effects of the diets at a systemic level. <span>Blood samples were collected via the ear-vein for plasma preparation at at dissection days (d28-29) after the morning meal ingestion. </span></span></p> <p><span>For plasma, blood samples were collected in sterile Vacuette tubes containing lithium-heparin and immediately centrifuged at 3,000x g for 10 min at 4°C and plasma was extracted. Plasma were stored at -80°C for further analysis on levels ofsystemic amine metabolite profiles. </span></p> <p>The protocol outlined in the following publication was used for detecting plasma amine levels:</p> <ul> <li>Noga MJ, Dane A, Shi S, Attali A, van Aken H, Suidgeest E, et al. Metabolomics of cerebrospinal fluid reveals changes in the central nervous system metabolism in a rat model of multiple sclerosis. <span><span><span>Metabolomics. 2012;8(2):253-63.</span></span></span></li> <li><span><span><span>van der Kloet FM, Bobeldijk I, Verheij ER, Jellema RH. </span></span></span>Analytical Error Reduction Using Single Point Calibration for Accurate and Precise Metabolomic Phenotyping. Journal of Proteome Research. 2009;8(11):5132-41.</li> </ul>
Cows, Pigs and People: Example data of cubic insulin from three different species recorded on Diamond Light Source I24
<p>Data collected at 100K on 10th May 2024 at I24 (Diamond Light Source) to investigate automatic grouping of datasets containing very subtle differences. Crystals grown by Cicely Tam following standard techniques with coordination from Felicity Bertram. For each of bovine, porcine, and human insulin, 10 degree wedges are included. Insulin from these three sources differ by 1-3 amino acids, but are otherwise structurally isomorphous. </p> <p>The purpose of the data upload is to make data available for tutorials using the DIALS toolchain (see e.g. examples at https://github.com/graeme-winter/dials_tutorials) however data are available for all purposes without limitation. </p> <p>Key:</p> <p>CIX - bovine insulin</p> <p>PIX - porcine insulin</p> <p>X - human insulin</p>
Dataset of fungal communities observed on decomposing pig carcasses in New Jersey
<p>This dataset contains estimated count data for fungal taxa identified using ITS metabarcoding collected from decomposing fetal pig carcasses placed in grasslands of New Jersey, USA.</p> <p>FungiPigDecomp_Data.csv is a file that contains the estimated count data at the level of taxonomic resolution possible for each replicate, at each stage of decomposition, across three body districts.</p> <p>FungiPigDecomp_Methods.docx is a summarized version of the sampling method relevant to interpreting the data.</p> <p>FungiPigDecomp_Descriptive.txt is a file describing the column headers in "FungiPigDecomp_Data.csv".</p>
Study of pig manure digestate pretreatment - Results and Figures
<p>This work evaluates the release of phosphorus contained in the digestate from the anaerobic digestion of pig manure, through an acidification process. The objective of this acidification is to increase the amount of phosphorus available in the digestate liquid fraction and, subsequently, recover this element by chemical precipitation in the form of struvite or calcium phosphate. Two digestate samples (one fresh and one old) were studied and treated by adding various amounts of sulphuric acid to the different digestate fractions (raw digestate, solid fraction and liquid fraction). For the raw digestate, phosphorus releases higher than 95% were obtained for pH 4.0. In the last part of the experiment, the influence of acid pre-treatment on the reaction yield of phosphorus precipitation, in the form of struvite or calcium phosphate, was determined. Improvements in reaction yield were obtained up to 15% for struvite and 80% for calcium phosphate, increasing also in 7.5 times the amount of phosphorus available in the digestate liquid fraction, for both cases.</p>
Dataset of hair cortisol concentration in 950 finishing pigs on 20 commercial farms
<p>This dataset contains the hair cortisol concentrations of 950 finishing pigs. Pig hair was sampled as part of a study funded by the European project HealthyLivestock. Pigs were sampled in two separate batches on 20 farms (24 pigs/batch, two batches/farm. NB: 10 samples could not be analyzed at the laboratory). Farms were located in western France. A reference to the article relating to this dataset will be added when the article will be published.</p> <p>-The first sheet includes the 950 hair cortisol concentrations, distinguishing the batches and farms where pigs were sampled.</p> <p>-The second sheet includes the estimation of the average size of a pig batch on the 20 farms where hair was sampled + the estimation of the percentage of pigs sampled per batch.</p>
Data from: Genome wide assessment of genetic variation and population distinctiveness of the pig family in South Africa
<p>Genetic diversity is of great importance and a prerequisite for genetic improvement and conservation programs in pigs and other livestock populations. The present study provides a genome wide analysis of the genetic variability and population structure of pig populations from different production systems in South Africa relative to global populations. A total of 234 pigs sampled in South Africa and consisting of village (n = 91), commercial (n = 60), indigenous (n = 40), Asian (n = 5) and wild (n = 38) populations were genotyped using Porcine SNP60K BeadChip. In addition, 389 genotypes representing village and commercial pigs from America, Europe and Asia were accessed from a previous study and used to compare population clustering and relationships of South African pigs with global populations. Moderate heterozygosity levels, ranging from 0.204 for Warthogs to 0.371 for village pigs sampled from Capricorn municipality in Eastern Cape province of South Africa were observed. Principal Component Analysis of the South African pigs resulted in four distinct clusters of (i) Duroc; (ii) Vietnamese; (iii) Bush pig and Warthog and (iv) a cluster with the rest of the commercial (SA Large White and Landrace), village, Wild Boar and indigenous breeds of Koelbroek and Windsnyer. The clustering demonstrated alignment with genetic similarities, geographic location and production systems. The PCA with the global populations also resulted in four clusters that where populated with (i) all the village populations, wild boars, SA indigenous and the large white and landraces; (ii) Durocs (iii) Chinese and Vietnamese pigs and (iv) Warthog and Bush pig. <i>K</i>= 10 (The number of population units) was the most probable ADMIXTURE based clustering, which grouped animals according to their populations with the exception of the village pigs that showed presence of admixture. AMOVA reported 19.92% – 98.62% of the genetic variation to be within populations. Sub structuring was observed between South African commercial populations as well as between Indigenous and commercial breeds. Population pairwise <i>F<sub>ST</sub></i>analysis showed genetic differentiation <i>(P < 0.05)</i>between the village, commercial and wild populations. A per marker per population pairwise <i>F<sub>ST</sub></i>analysis revealed SNPs associated with QTLs for traits such as meat quality, cytoskeletal and muscle development, glucose metabolism processes and growth factors between both domestic populations as well as between wild and domestic breeds. Overall, the study provided a baseline understanding of porcine diversity and an important foundation for porcine genomics of South African populations.</p>
Figure 2 in Swimming and bipedal bottom-running in the pig-nosed turtle Carettochelys insculpta Ramsay, 1886
Figure 2. Detail of hindlimb action during bipedal bottom running in large Carettochelys insculpta. Numerals indicate field sequence. Tail is stippled. Short horizontal lines indicate substratum beneath hindlimbs. Note that, at field 6, neither rear limb is in contact with the substratum.
Fecal and Blood Metabolites of Pigs
<p>The experimental design of the animal study and the sanitary challenge model used have been described by Van der Meer et al. (2020). Pigs were divided into high sanitary condition (HSC) or to low sanitary condition (LSC), for details please see the original publication by Van der Meer et al. (2020). At the dissection day, three pigs per room were euthanized to collect blood and digesta samples for further analysis. We used colon digesta and blood samples from pigs in this study that received a diet with a basal amino acid (AA) ratios (indicated as “diet AA-B” in the paper of van der Meer et al. 2020) and a protein content of CP 168 g/kg; LSC (n=18) and HSC (n=18). </p> <p>These samples were analyzed by Nuclear Magnetic Resonance (NMR) and by Triple Quad Mass Spectrometry (TQMS). The details of these laboratory analysis are described in the journal article entitled "Sanitary conditions affect the colonic microbiome and the colonic and systemic metabolome of female pigs" (doi: will update accordingly). The data uploaded here are by the format of the Joint Committee on Atomic and Molecular Physical Data (JCAMP). Moreover the metadata file shows the link between the samples and their corresponding group, i.e. HSC or LSC, and other characteristics.</p>
Raw landmarks related to the paper, "Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages "
<p>PLEASE NOTE: This dataset has been superseeded by an updated version which has the correct number of specimens as referred to in the below article. It can be accesssed at: https://doi.org/10.5281/zenodo.14262754</p> <p> </p> <p> </p> <p>Raw coordinates (p x k = 82 x 3) of domestic and wild pig skulls that form the dataset for the paper, "­Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages "</p>
Feral pig (Sus scrofa) disturbance facilitates establishment of resource-acquisitive species in Hawaiian forest understories
<p>In this study, we quantify the effects of leaf traits and dispersal attributes on species responses to pig soil disturbance at two spatial scales – 0.5 m<sup>2</sup> patches embedded along 20 m transects within sites – across a gradient of pig density in a Hawaiian montane wet forest using Bayesian mixed models. </p> <p>Native and non-native species demonstrated divergent responses, with increasing presence and abundance of non-native species in the understory as soil disturbance within patches and sites increased. Dominant patterns in measured traits tracked the leaf economic spectrum (LES), with non-native species tending toward resource-acquisitive traits. Species with resource-acquisitive traits, regardless of identity, were favored with disturbance and responded positively to light availability in disturbed sites. Models showed species primarily dispersed by wind were more prevalent in disturbed patches and sites than those dispersed by endozoochory, while seed mass had no effect.</p>
Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)
<p>The rapid expansion of wild pigs (<em>Sus scrofa</em>) throughout the United States (US) has been fueled by unlawful introductions, with invasive populations causing extensive crop losses, damaging native ecosystems, and serving as a reservoir for disease. Multiple states have passed laws prohibiting the possession or transport of wild pigs. However, genetic and phenotypic similarities between domestic pigs and invasive wild pigs – which overwhelmingly represent domestic pig-wild boar hybrids – pose a challenge for the enforcement of such regulations. We sought to exploit wild boar ancestry as a common attribute among the vast majority of invasive wild pigs as a means of genetically differentiating wild pigs from breeds of domestic pigs found within the US. We organized reference high-density single nucleotide polymorphism genotypes (1,039 samples from 33 domestic breeds and 382 samples from 16 wild boar populations) into five genetically cohesive reference groups: mixed-commercial breeds, Durocs, heritage breeds, primitive breeds, and wild boar. Building upon well-established genetic clustering approaches, we structured the test statistic to describe the difference in the likelihood of a given genotype's ancestry vectors (<em>sensu</em> genetic clustering analysis) if derived strictly from the four described domestic pig reference groups versus allowing for admixture from the wild boar group. By fitting statistical distributions to test statistics of reference domestic pigs, we characterized the distribution of the null hypothesis – that a given genotype descends strictly from domestic pig reference groups. We tested the approach with simulated genotypes and empirical data from an additional 29 breeds of domestic pig represented by 435 unique genotypes; all associated test statistics for simulated and empirical domestic pig challenge sets fell within the distribution of reference domestic pigs. We then evaluated 6,566 invasive wild pigs sampled across the contiguous United States, of which 63% exceeded the maximum threshold for domestic pigs and could be statistically classified as possessing wild boar ancestry. This approach provides a scientific foundation to enforce regulations prohibiting the possession of this destructive invasive species. Further, this computationally efficient and generalizable approach could be readily adapted to quantify gene flow among ecological systems of conservation or management concern.</p>
Detailed information on cost and sale prices on Polish pig market in the period 2017-mid2022
<p>The database contains detailed information on costs and sale prices of piglets and finishers on Polish martket in the period January 2017 - July 2022. The prices of cereals used for feeding are taken from the average monthly data of the Ministry of Agriculture and the daily stock exchange quotations of Agrolok. The remaining operational costs (veterinary costs, utilities, labor, and transport) were assumed at a constant average level established on the basis of the reference methodological publication of the Danish research and development organization called “Seges Innovation” (2022) and a manual on pig farming (Pawłowski, 2020). The same sources were used to determine the optimal feeding model, which is important for calculating feed costs. Assumptions for calculating feed cost, the cost of falls, labor costs in piglet and finisher production, as well as piglet transportation costs are presented in the excel sheet.</p>
ScienceDex guides
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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.