The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: Hog productionGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoProjection: Web Mercator Auxiliary SphereSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. You should account for these values when symbolizing or doing any calculations.Commodities included in this layer:Hogs - Inventory - Inventory of Hogs: (1 to 24 Head)Hogs - Inventory - Inventory of Hogs: (25 to 49 Head)Hogs - Inventory - Inventory of Hogs: (50 to 99 Head)Hogs - Inventory - Inventory of Hogs: (100 to 199 Head)Hogs - Inventory - Inventory of Hogs: (200 to 499 Head)Hogs - Inventory - Inventory of Hogs: (500 to 999 Head)Hogs - Inventory - Inventory of Hogs: (1,000 or More Head)Hogs - InventoryHogs - Operations with Inventory - Inventory of Hogs: (1 to 24 Head)Hogs - Operations with Inventory - Inventory of Hogs: (25 to 49 Head)Hogs - Operations with Inventory - Inventory of Hogs: (50 to 99 Head)Hogs - Operations with Inventory - Inventory of Hogs: (100 to 199 Head)Hogs - Operations with Inventory - Inventory of Hogs: (200 to 499 Head)Hogs - Operations with Inventory - Inventory of Hogs: (500 to 999 Head)Hogs - Operations with Inventory - Inventory of Hogs: (1,000 or More Head)Hogs - Operations with InventoryHogs - Operations with Sales - Sales of Hogs: (1 to 24 Head)Hogs - Operations with Sales - Sales of Hogs: (25 to 49 Head)Hogs - Operations with Sales - Sales of Hogs: (50 to 99 Head)Hogs - Operations with Sales - Sales of Hogs: (100 to 199 Head)Hogs - Operations with Sales - Sales of Hogs: (200 to 499 Head)Hogs - Operations with Sales - Sales of Hogs: (500 to 999 Head)Hogs - Operations with Sales - Sales of Hogs: (1,000 or More Head)Hogs - Operations with SalesHogs - Sales, Measured in US Dollars ($)Hogs - Sales, Measured in Head - Sales of Hogs: (1 to 24 Head)Hogs - Sales, Measured in Head - Sales of Hogs: (25 to 49 Head)Hogs - Sales, Measured in Head - Sales of Hogs: (50 to 99 Head)Hogs - Sales, Measured in Head - Sales of Hogs: (100 to 199 Head)Hogs - Sales, Measured in Head - Sales of Hogs: (200 to 499 Head)Hogs - Sales, Measured in Head - Sales of Hogs: (500 to 999 Head)Hogs - Sales, Measured in Head - Sales of Hogs: (1,000 or More Head)Hogs - Sales, Measured in HeadHogs, Production Contract - Operations with ProductionHogs, Production Contract - Production, Measured in Head Geography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

European Pigs Production by Country, 2023 Discover more data with ReportLinker!

European Pigs Subsidies by Country, 2023 Discover more data with ReportLinker!

This dataset displays the annual pork exports of the United states. The data is displayed by country on a scale of carcass weight by 1000 pounds. The time period covered is 2003 to Jan 2008

European Pigs Price Index by Country, 2022 Discover more data with ReportLinker!

This dataset displays the annual US import of pork. This is measured in carcass weight by 1000 pound scale. The data is available from 2003 - January of 2008.

IntroductionUganda is a pork-producing country in East Africa. The African swine fever virus (ASFV) has had a devastating impact on the country’s pig industry. The movements of pigs and pork are a major pathway of spreading ASFV. This study was done to describe the live pig supply chain within and through districts that are impacted by African swine fever (ASF) in Uganda.MethodsA pig farmer survey in districts known to have ASFV was done using a semi-structured questionnaire available in English and two local languages. In total, 99 farmers were interviewed across five districts. Farmers were conveniently and purposively selected by local government veterinary officials. An online key informant survey was also used to validate farmer responses.ResultsMost farmers interviewed in all districts reported to source and sell most of their pigs from within their district the farm was in, although there was variation by district and pig type. In relation to pig type, 89.7% of farmers sourced sows, 80.0% sourced boars, and 96.4% sourced weaned pigs from the district where the farm was located. As for sales, 91.3% of farmers sold sows, 92.7% sold boars, 91.9% sold weaned pigs, and 92.2% sold market pigs in the district where the farm was located. There was also variation to whom pigs were sold and sourced by pig type.ConclusionThis information is useful when planning the scale and focus of disease control programs based on animal movement. This study revealed that pig disease control programs can be targeted to smaller regions. Furthermore, there is a need for farmers and pig traders to be educated on and adhere to veterinary regulations of animal movement and good biosecurity practices to reduce disease spread when purchasing and selling pigs from known ASFV infected areas.

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.

The resilience farm data are all data that where observed at farm level, and contain farm characterisitcs, namely

    variable
    description
    values


    farm id
    unique identifier of the farm
    characters, including country code based on ISO2


    breeding type
    type of pig entreprise on the found on the farm
    breeding, finishing or both


    entrerprise_x
    description of other entreprises found on the farm
    feed production, cash crop, chicken, sheep, dairy, beef, direct marketing, tourism, on-farm processing, horse housing.


    number non-pig entreprise
    number of entreprise describes
    integer


    structure
    type of pig housing structure
    permanent, temporary, both


    outdoor area
    type of oudoor access for pig
    concrete, shifting arable land, permanent pasture


    LSU
    livstock standard units computed following Eurostat standards
    numeric


    pig/ha
    intensity of production as LSU/UAApig
    numeric


    self-sufficiency
    percentage of pig feed produced on farm
    numeric


    UAA pig
    utilized agricultural area for the pig production
    numeric


    UAA total
    utilized agricultural area of the farm
    numeric

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

    variables name
    description
    values


    farm id
    unique identifier of the farm
    characters, including country code based on ISO2


    country
    country code
    based on ISO2


    question related to shocks
    shocks to which the resilience narrative related to
    input cost, price, outbreak, climate, legislation, labour, general


    narratives (a= first, b=second)
    identifier of the narrative within a question
    a, b


    capacity
    resilience capacity following the Meuwissen et al (2019) framework
    robustness, adaptability, transformability, non-resilience


    resilience attribute type
    resilience attribute based on an expanded interpretation the Meuwissen et al (2019) framework (see paper)

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)

    resilience attribute
    description of the attribute that led to the resilience attribute type classification

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

To compute the resilience capacity score (Cscore)

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.

Use following R code in dplyr

mydata<- ResilienceDataPreProcessed %>%

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)))))))%>%

group_by(farm, question)%>%

summarise(Ccode=sum(code))%>%

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)) ))) ))))

Resilience questionnaire

Farm number:

Farm name or ID:

Country:

System descriptors

Breeding or finishing (or both)

Indoor or outdoor (or a mix)

Organic or conventional

Number of years organic

1) Has your farm experienced significant challenges in the last 5 years?

Yes or no?

Yes / No

If "no", what factars (farm/external) created this resilience?

If "yes", please describe the 1st challenge

What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)?

Did this change your management or farm structure subsequently (and how)?

If "yes", please describe a 2nd challenge

What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)?

Did this change your management or farm structure subsequently (and how)?

2) In the future, how do you feel your pig system would cope with these challenges:

a) Decreasing or negative margins due to increased feed or other input costs?

Very severely (e.g. bankruptcy)

Severely (e.g. closure of pig enterprise)

Strong impact (e.g. large reduction in production)

Short term impact (e.g. reduced production)

Little impact (e.g. change ration)

Why?

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)

b). Decreasing or negative margins due to reduced pig prices?

Very severely (e.g. bankruptcy)

Severely (e.g. closure of pig enterprise)

Strong impact (e.g. large reduction in production)

Short term impact (e.g. reduced production)

Little impact (e.g. change ration)

Why?

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)

c) Wide spread disease outbreak such as African Swine Fever

Very severely (e.g. bankruptcy)

Severely (e.g. closure of pig enterprise)

Strong impact (e.g. large reduction in production)

Short term impact (e.g. reduced production)

Little impact (e.g. change ration)

Why?

How are you prepared for this potential challenge? (what are

European Number of Organic Fattening Pigs Share by Country (Units (Heads)), 2023 Discover more data with ReportLinker!

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: Animal TotalsGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoProjection: Web Mercator Auxiliary SphereSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. You should account for these values when symbolizing or doing any calculations.Commodities included in this layer:Animal Totals - Expense, Measured in US Dollars ($)Animal Totals - Operations with ExpenseAnimal Totals, (Excl Breeding) - Expense, Measured in US Dollars ($)Animal Totals, (Excl Breeding) - Operations with ExpenseAnimal Totals, Breeding - Expense, Measured in US Dollars ($)Animal Totals, Breeding - Operations with ExpenseAnimal Totals, Incl Products - Operations with SalesAnimal Totals, Incl Products - Sales, Measured in US Dollars ($)Animal Totals, Products Only, (Excl Aquaculture Products & Honey) - Operations with Sales: TotalAnimal Totals, Products Only, (Excl Aquaculture Products & Honey) - Sales, Measured in US Dollars ($): TotalGeography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Dividend-Payout-Ratio Time Series for Dongrui Food Group Co Ltd. Dongrui Food Group Co., Ltd. operates pig breeding farms. The company breeds, supplies, and slaughters live pigs; and provides feed and fertilizers that are used for pig breeding. It offers its products under the Dongrui brand name. The company was founded in 2002 and is based in Heyuan, China.

Global Export of Pigs', Hogs' or Boars' Bristles and Hair and Waste Thereof Share by Country (Kilograms), 2023 Discover more data with ReportLinker!

Fecal samples have often been used as a proxy for studying the gut microbiota. However, the fecal microbiota does not fully reflect the gut microbiota composition. To elucidate the biogeographical characteristics and interaction networks of porcine gut microbiota, we systematically determined the compositions and co-abundance networks of gut microbiota from small to large intestine using 2,955 microbial samples from ileum, cecum, and feces of F6 (715) and F7 (687) pigs which were slaughtered at the age of 240 days from an experimentally designed heterogeneous pig population by crossing eight divergent breeds using 16S rRNA gene sequencing. The gut microbial composition showed significant spatial heterogeneity. The diversity of the gut microbiota progressively increased along the intestinal tract. Significantly spatial heterogeneity was also observed in the co-abundance networks. The numbers of OTUs showing co-abundance correlations with other OTUs were increased from ileum to cecum and feces. We found that the stronger the co-abundance correlation, the higher the gut location specificity of the co-abundance relationships. Only 644 (0.2%) co-abundance relationships among OTUs existed in all three gut locations. Prevotella had the highest number of stable co-abundance relationships, followed by Bacteroidales, Bacteroides, S24-7, and Lachnospiraceae. Topological analysis found that the co-abundance network of OTUs in the ileum showed random network characteristics, while the co-abundance networks of OTUs in the cecum and feces showed the scale-free network characteristics in both pig populations. Compared with the co-abundance networks in the cecum and feces, the networks in the ileum had fewer nodes, but more edges, indicating that the ileum microbiota was a microbial ecosystem with a smaller number of microbial species, but closer interactions. However, the pairwise co-abundance correlations between OTUs were more independent in the cecum. The co-abundance network in the ileum had the lowest stability, but the highest vulnerability, while the co-abundance network in the cecum exhibited the highest stability, but low vulnerability. Finally, we characterized the gut location-specific microbial co-abundance relationships. Characterizing the different phylogenetic structures of gut microbiota in different intestinal biogeographic niches would help to explore the spatial heterogeneity of microbial physiological functions and to develop the strategy regulating gut microbiota targeting to specific gut locations.

This layer contains the number of livestock (pigs, sheep, goats, horses, buffalo, cattle, chickens, and ducks) in each country. The default symbology highlights most common livestock in each country, but with a few changes to the symbology, the map can also show the distribution of each livestock individually. The inspiration for this layer came from the FAO (Food and Agriculture Organization of the United Nations) site which is home to eight maps highlighting livestock distribution around the world. The source data, last updated in August 2018, contain the global distribution of each livestock in 2010 expressed in total number of livestock per pixel (5 min of arc) according to the Gridded Livestock of the World database (GLW 3). Click here to download the data from Harvard's Dataverse. This layer is derived from the tif file of the dasymetric product of the absolute number of animals per pixel (4,230 by 2,160 pixels of 0.083333 decimal degrees resolution).The following steps were taken to convert from a tif file to the country dataset:Download tifUse the "Int" tool to convert the pixel values to an integerRun the "Raster to Polygon" tool to convert the tif to a vectorImplement the "Intersect" tool to split vector pixels at the country boundariesFind the sum of each livestock in each country using the "Summary Statistics" toolJoin the "Summary Statistics" table output to the country layer with the "Join" toolPublish the country layer with the sum of each livestock to ArcGIS OnlineMap the most common livestock in each country with the Predominant Category drawing style in Smart MappingClick here to view the map of this layer.

This study investigated how gene expression is affected by dietary fatty acids (FA) by using pigs as a reliable model for studying human diseases that involve lipid metabolism. This includes changes in FA composition in the liver, blood serum parameters and overall metabolic pathways. RNA-Seq data from 32 pigs were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA). Our aim was to identify changes in blood serum parameters and gene expression between diets containing 3% soybean oil (SOY3.0) and a standard pig production diet containing 1.5% soybean oil (SOY1.5). Significantly, both the SOY1.5 and SOY3.0 groups showed significant modules, with a higher number of co-expressed modules identified in the SOY3.0 group. Correlated modules and specific features were identified, including enriched terms and pathways such as the histone acetyltransferase complex, type I diabetes mellitus pathway, cholesterol metabolism, and metabolic pathways in SOY1.5, and pathways related to neurodegeneration and Alzheimer’s disease in SOY3.0. The variation in co-expression observed for HDL in the groups analyzed suggests different regulatory patterns in response to the higher concentration of soybean oil. Key genes co-expressed with metabolic processes indicative of diseases such as Alzheimer’s was also identified, as well as genes related to lipid transport and energy metabolism, including CCL5, PNISR, DEGS1. These findings are important for understanding the genetic and metabolic responses to dietary variation and contribute to the development of more precise nutritional strategies.

This dataset shows the number of S. Typhimurium isolations and incidents in cattle on GB Premises, between 2010-2014. The data are grouped by phage type, the numbers of isolations and incidents are given, and the whole is grouped by year. An 'isolation' is defined as the first report of a salmonella isolate (a cultured instance of Salmonella from a sample) from a known group of animals on a single occasion. An 'incident' is the confirmation of the same Salmonella type on one or more occasion within a set time period (usually thirty days), and within the same group of animals or same location.
The laboratory facilities are UKAS accredited to BS EN ISO 17025:2000 (Lab Nos. 0941, 1769 and 2112) for an extensive range of tests supported by proficiency testing accredited to ISO/IEC Guide 43-1 1997 (Lab No. 0004). APHA is certificated to BS EN ISO 9001:2000 for ‘the provision of a range of specialist veterinary scientific services to the Government and other interested parties worldwide’ (Certificate Nos. LRQ 4000436, 4001071, 0962413 and 4001392). Additionally, APHA holds Good Laboratory Practice and Good Manufacturing Practice approval and complies with the Joint Code of Practice for Research projects and Good Clinical Veterinary Practice quality standards. APHA Weybridge is accredited to BS EN ISO 14001:2004 for environmental management system.

Global Pork Production at Farm Gate Share by Country (Million Euros), 2023 Discover more data with ReportLinker!

Preadipocyte differentiation plays an important role in lipid deposition and affects fattening efficiency in pigs. In the present study, preadipocytes isolated from the subcutaneous adipose tissue of three Landrace piglets were induced into mature adipocytes in vitro. Gene clusters associated with fat deposition were investigated using RNA sequencing data at four time points during preadipocyte differentiation. Twenty-seven co-expression modules were subsequently constructed using weighted gene co-expression network analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed three modules (blue, magenta, and brown) as being the most critical during preadipocyte differentiation. Based on these data and our previous differentially expressed gene analysis, angiopoietin-like 4 (ANGPTL4) was identified as a key regulator of preadipocyte differentiation and lipid metabolism. After inhibition of ANGPTL4, the expression of adipogenesis-related genes was reduced, except for that of lipoprotein lipase (LPL), which was negatively regulated by ANGPTL4 during preadipocyte differentiation. Our findings provide a new perspective to understand the mechanism of fat deposition.

European Road Freight Volume of Raising of Swine and Pigs Sector by Country, 2023 Discover more data with ReportLinker!