Graph and download economic data for Employment for Agriculture, Forestry, Fishing and Hunting: Hog and Pig Farming (NAICS 1122) in the United States (IPUAN1122W010000000) from 1987 to 2024 about hogs, livestock, hunting, forestry, fishing, agriculture, NAICS, IP, employment, and USA.

United States PPI: Farm Products: Slaughter Livestock: Hogs: Sows data was reported at 82.200 1982=100 in Jun 2018. This records a decrease from the previous number of 91.500 1982=100 for May 2018. United States PPI: Farm Products: Slaughter Livestock: Hogs: Sows data is updated monthly, averaging 80.700 1982=100 from Jan 1971 (Median) to Jun 2018, with 569 observations. The data reached an all-time high of 215.100 1982=100 in Apr 2014 and a record low of 15.200 1982=100 in Dec 1998. United States PPI: Farm Products: Slaughter Livestock: Hogs: Sows data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I017: Producer Price Index: By Commodities.

U.S. wild pig species distribution model dataThis is the final dataset used to derive the wild pig distribution model for the contiguous United States presented in the accompanying publication, along with predictive model output values. Please see the ReadMe file for further details.wildpig_sdm_data.csv

Livestock distribution in the United States (U.S.) can only be mapped at a county-level or worse resolution. We developed a spatial microsimulation model called the Farm Location and Agricultural Production Simulator (FLAPS) that simulated the distribution and populations of individual livestock farms throughout the conterminous U.S. Using domestic pigs (Sus scrofa domesticus) as an example species, we customized iterative proportional-fitting algorithms for the hierarchical structure of the U.S. Census of Agriculture and imputed unpublished state- or county-level livestock population totals that were redacted to ensure confidentiality. We used a weighted sampling design to collect data on the presence and absence of farms and used them to develop a national-scale distribution model that predicted the distribution of individual farms at a 100 m resolution. We implemented microsimulation algorithms that simulated the populations and locations of individual farms using output from our imputed Census of Agriculture dataset and distribution model. Approximately 19% of county-level pig population totals were unpublished in the 2012 Census of Agriculture and needed to be imputed. Using aerial photography, we confirmed the presence or absence of livestock farms at 10,238 locations and found livestock farms were correlated with open areas, cropland, and roads, and also areas with cooler temperatures and gentler topography. The distribution of swine farms was highly variable, but cross-validation of our distribution model produced an area under the receiver-operating characteristics curve value of 0.78, which indicated good predictive performance. Verification analyses showed FLAPS accurately imputed and simulated Census of Agriculture data based on absolute percent difference values of < 0.01% at the state-to-national scale, 3.26% for the county-to-state scale, and 0.03% for the individual farm-to-county scale. Our output data have many applications for risk management of agricultural systems including epidemiological studies, food safety, biosecurity issues, emergency-response planning, and conflicts between livestock and other natural resources.

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.

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.

2014 Swine CAFO Study SE for Agricultural Antibiotic Resistance in Mississippi State, Mississippi The environmental influence of farm management in concentrated animal feeding operations (CAFO) can yield vast changes to the microbial biota and ecological structure of both the pig and waste manure lagoon wastewater. While some of these changes may not be negative, it is possible that CAFOs can enrich antibiotic resistant bacteria or pathogens based on farm type, thereby influencing the impact imparted by the land application of its respective wastewater. The purpose of this study was to measure the microbial constituents of swine-sow, -nursery, and -finisher farm manure lagoon wastewater and determine the changes induced by farm management. A total of 37 farms were visited in the Mid-South USA and analyzed for the genes 16S rRNA, spaQ (Salmonella spp.), Camp-16S (Campylobacter spp.), tetA, tetB, ermF, ermA, mecA, and intI using quantitative PCR. Additionally, 16S rRNA sequence libraries were created. Overall, it appeared that finisher farms were significantly different from nursery and sow farms in nearly all genes measured and in 16S rRNA clone libraries. Nearly all antibiotic resistance genes were detected in all farms. Interestingly, the mecA resistance gene (e.g. methicillin resistant Staphylococcus aureus) was below detection limits on most farms, and decreased as the pigs aged. Finisher farms generally had fewer antibiotic resistance genes, which corroborated previous phenotypic data; additionally, finisher farms produced a less diverse 16S rRNA sequence library. Comparisons of Camp-16S and spaQ GU (genomic unit) values to previous culture data demonstrated ratios from 10 to 10,000:1 depending on farm type, indicating viable but not cultivatable bacteria were dominant. The current study indicated that swine farm management schemes positively and negatively affect microbial and antibiotic resistant populations in CAFO wastewater which has future “downstream” implications from both an environmental and public health perspective. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/651220ea-a65e-43a1-9c28-433c27464cae

Porcine reproductive and respiratory syndrome (PRRS) is an endemic disease causing important economic losses to the US swine industry. The complex epidemiology of the disease, along with the diverse clinical outputs observed in different types of infected farms, have hampered efforts to quantify PRRS’ impact on production over time. We measured the impact of PRRS on the production of weaned pigs using a log-linear fixed effects model to evaluate longitudinal data collected from 16 sow farms belonging to a specific firm. We measured seven additional indicators of farm performance to gain insight into disease dynamics. We used pre-outbreak longitudinal data to establish a baseline that was then used to estimate the decrease in production. A significant rise of abortions in the week before the outbreak was reported was the strongest signal of PRRSV activity. In addition, production declined slightly one week before the outbreak and then fell markedly until weeks 5 and 6 post-outbreak. Recovery was not monotonic, cycling gently around a rising trend. At the end of the study period (35 weeks post-outbreak), neither the production of weaned pigs nor any of the performance indicators had fully recovered to baseline levels. This result suggests PRSS outbreaks may last longer than has been found in most other studies. We assessed PRRS’ effect on farm efficiency as measured by changes in sow production of weaned pigs per year. We translated production losses into revenue losses assuming an average market price of $45.2/weaned pig. We estimate that the average PRSS outbreak reduced production by approximately 7.4%, relative to annual output in the absence of an outbreak. PRRS reduced production by 1.92 weaned pigs per sow when adjusted to an annual basis. This decrease is substantially larger than the 1.44 decrease of weaned pigs per sow/year reported elsewhere.

The rapid expansion of wild pigs (Sus scrofa) 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, Duroc..., We assembled the Sus scrofa reference set from previously published high-resolution SNP genotypes, restricting analysis to genotypes produced with Illumina BeadArray technology (San Diego, California) across multiple commercially available arrays (Illumina PorcineSNP60, Illumina PorcineSNP60 v2, Genomic Profiler for Porcine HD, licensed exclusively to GeneSeek, a Neogen Corporation, Lansing, Michigan; Ramos et al., 2009). We augmented previously published genotypes (detailed in Smyser et al., 2020) with a subset of novel genotypes produced for this study (Appendix S1: Table S1). We restricted our analyses to loci that were available across all datasets (influenced by loci shared across arrays and the extent to which publicly available datasets were filtered by authors prior to publication) and mapped to autosomes (Sscrofa11.1 genome assembly; Warr et al., 2020). In sum, we included 33 breeds and 16 populations of European wild boar, representing a total of 1,421 reference samples (Table..., Files are presented as a PLINK binary file family (.bed/.bim/*.fam). Files can be opened with PLINK (freely available at https://www.cog-genomics.org/plink/) or with many other software platforms., We present data files and computer code necessary to recreate the analyses described in:

"Probabilistic genetic identification of wild boar hybridization to support control of invasive wild pigs (Sus scrofa)" by Timothy J. Smyser, Peter Pfaffelhuber, Rachael M. Giglio, Matthew G. DeSaix, Amy J. Davis, Courtney F. Bowden, Michael A. Tabak, Arianna Manunza, Valentin Adrian Balteanu, Marcel Amills, Laura Iacolina, Pamela Walker, Carl Lessard, and Antoinette J. Piaggio and published in Ecosphere

The files include genotypes for 8,422 Sus scrofa, representing 1,421 reference samples organized into K5 reference groups (mixed-commercial breeds, Duroc, heritage breeds, primitive breeds, and European wild boar), 6,566 wild pigs sampled across the invaded range within the contiguous United States, and 435 domestic pigs sampled from 29 Western breeds that were excluded from the reference set to serve as a test set, with genotypes compiled in a * .bed/.bim/.fam (binary PED file) file format. All...

WartyPig Dataset

  Description

This dataset was prepared to aid in the creation of a machine learning algorithm that would classify the white blood cells in thin blood smears of juvenile Visayan warty pigs. The creation of this dataset was deemed imperative because of the limited availability of blood smear images collected from the critically endangered species on the internet. The dataset contains 3,457 images of various types of white blood cells (JPEG).… See the full description on the dataset page: https://huggingface.co/datasets/1aurent/WartyPig.

This data-set shows the most recent global model of the pigs distribution. It is the first update (version 2.01) of the recently published Gridded Livestock of the World (GLW) 2.0 (May 2014). More information and access to the data of the GLW version 2.0 are in the dedicated web-site: http://livestock.geo-wiki.org/ The GLW 2007 remains available for download in FAO Geonetwork. However, a quantitative assessment of change is not possible between the GLW 2007 and the GLW 2.0 (and its updates) due to different modeling techniques, spatial resolution, predicting variables and training data. The bibliographic reference to the GLW 2.0 and its updates is: Robinson TP, Wint GRW, Conchedda G, Van Boeckel TP, Ercoli V, Palamara E, Cinardi G, D’Aietti L, Hay SI, and Gilbert M. (2014) Mapping the Global Distribution of Livestock. PLoS ONE 9(5): e96084. doi:10.1371/journal.pone.0096084 The supplementary information includes a list of the observed data used to train this version of the pigs model.

The timeline shows the per capita consumption of pork in the United States from 2015 to 2023, and provides a forecast until 2034. The U.S. per capita consumption of pork amounted to 50.2 pounds in 2023.

Effective management of widespread invasive species such as wild pigs (Sus scrofa) is limited by resources available to devote to the effort. Better insight of the effectiveness of different management strategies on population dynamics is important for guiding decisions of resource allocation over space and time. Using a dynamic population model, we quantified effects of culling intensities and time between culling events on population dynamics of wild pigs in the USA using empirical culling patterns and data-based demographic parameters. In simulated populations closed to immigration, substantial population declines (50–100%) occurred within 4 years when 20–60% of the population was culled annually, but when immigration from surrounding areas occurred, there was a maximum of 50% reduction, even with the maximum culling intensity of 60%. Incorporating hypothetical levels of fertility control with realistic culling intensities was most effective in reducing populations when they were closed to immigration and when intrinsic population growth rate was too high (> = 1.78) to be controlled by culling alone. However, substantial benefits from fertility control used in conjunction with culling may only occur over a narrow range of net population growth rates (i.e., where net is the result of intrinsic growth rates and culling) that varies depending on intrinsic population growth rate. The management implications are that the decision to use fertility control in conjunction with culling should rely on concurrent consideration of achievable culling intensity, underlying demographic parameters, and costs of culling and fertility control. The addition of fertility control reduced abundance substantially more than culling alone, however the effects of fertility control were weaker than in populations without immigration. Because these populations were not being reduced substantially by culling alone, fertility control could be an especially helpful enhancement to culling for reducing abundance to target levels in areas where immigration can’t be prevented.

Background: The H1N1 pandemic (H1N1pdm09) lineage of influenza A viruses (IAV) emerged in North America in 2009 and caused a human influenza pandemic. It spread rapidly due to its efficient transmission and limited human immunity, replacing the previous human seasonal H1. Human-to-swine transmission of H1N1pdm09 IAV has since contributed to genetic diversity in pigs. While most were not sustained, approximately 160 spillovers persisted in pigs for at least one year and reassorted with other endemic swine IAVs in most cases.Methods: We sought to identify how transmission and reassortment with endemic IAV viruses in swine impact virus traits and zoonotic risk in this study. We conducted a swine pathogenesis and transmission study using four swine H1N1pdm09 viruses derived from different human influenza seasons that had acquired different gene segment combinations after spillovers into swine. Nasal swabs, serum, bronchoalveolar lavage fluid, and formalin-fixed lower respiratory tract tissues were collected to assess viral infection, replication, and shedding.Results: Ongoing circulation and reassortment resulted in viruses with variable virulence, shedding, and transmission kinetics. The H1N1pdm09 viruses retained antigenic similarities with the human vaccine strain of the same season of incursion but showed increasing antigenic distances with human seasonal H1N1 vaccine strains from other seasons.Conclusions: Human seasonal H1N1 viruses are capable of replicating and transmitting in swine, and there is potential for these human-to-swine spillovers to reassort with endemic swine IAV. Controlling IAV at the human-swine interface has the benefit of reducing IAV burden in swine and subsequent zoonotic risk of swine IAV.

Get statistical data on Ontario exports of live pigs by number of head to the United States.

United States Exports: 3-Digit: Pig Iron & Spiegeleisen, Sponge Iron, etc data was reported at 103.040 USD mn in May 2018. This records an increase from the previous number of 72.819 USD mn for Apr 2018. United States Exports: 3-Digit: Pig Iron & Spiegeleisen, Sponge Iron, etc data is updated monthly, averaging 31.362 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 103.040 USD mn in May 2018 and a record low of 10.338 USD mn in Dec 2002. United States Exports: 3-Digit: Pig Iron & Spiegeleisen, Sponge Iron, etc data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA014: Trade Statistics: SITC: Exports: FAS.

Lean Hogs fell to 99.64 USd/Lbs on October 1, 2025, down 0.21% from the previous day. Over the past month, Lean Hogs's price has risen 4.29%, and is up 17.68% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lean Hogs - values, historical data, forecasts and news - updated on October of 2025.