Much information above farm level has been aggregated, often because it is essential to organize payments and to monitor for quality and ensure traceability of products. For example, slaughterhouses, or beef processing plants, accept animals from feedlots for slaughtering and manufacturing of meat and meat-derived products. In the United States, three large beef processors dominate this part of the meat industry: IBP, a subsidiary of Tyson Foods; ConAgra, and Excel Corp., a subsidiary of Cargill. Of these, only ConAgra and Excel own feedlots; company-owned feedlots account for a minority of the food animals shipped to slaughterhouses. Individual processing plants collect large amounts of information on animals that they slaughter; however, the different companies record different amounts and type of data.
The dairy industry supports an industry-level system to allow efficient use of artificial insemination with semen from elite sires. These systems allow real-time identification of cows and matching with sires that are suitable for mating based on a measure of inbreeding between the two individuals. Organizations, such as the National Dairy Herd Improvement Association (http://www.dhia.org/), promote development of data standards and systems for integration of data across states.
There is also significant aggregation of data beyond the farm level by milk processors. Milk payment schemes use quantity and quality of milk provided to determine individual farm payments, which requires sophisticated systems to capture and aggregate production data at the processor level. Systems that monitor production in farm animals can be sensitive detectors of disease.
Similar processor-level data systems exist for commercial egg, poultry, and pig production systems.
There is little uniformity of standards and formats between species, production systems, and even processors that compete within the same industry. Systems that monitor production in farm animals can be sensitive detectors of disease. Again, variation in computer software has resulted in differences in data standards and formats between and within organizations. This will inhibit the development of systems to centralize this data for the purpose of surveillance for disease.
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