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Milk Recording Data

Milk Recording Data

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Milk Recording Data

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  1. Milk Recording Data

  2. Topics • Milk recording data • Receiving • Processing • Validation

  3. AIPL Data Sources • Dairy Records Processing Centers (DHI) • Herd test-day - format 14 • Lactation – format 4 • Calf/heifer – format 4 • Reproductive – format 5 • Health – format 6 • Calving ability – format CES

  4. AIPL Data Sources (cont.) • Breed Associations (PDCA) • Pedigree data – format 1 • Type appraisal score – format 7 • Recessive codes – Holstein USA • NAAB • AI sire cross reference – format 395 • Marketing status • Codes A, G, F

  5. Frequency of Receipt • Breed Associations • Varies – weekly to evaluation cutoff • Dairy Records Processing Centers • Daily • NAAB • Evaluation cutoff

  6. Data Exchange Method • All data exchanged electronically • Standardized file naming convention • Computer optimized format • Files compressed via PKzip (Winzip) • Staging area - AIPL’s FTP server • Authentication required

  7. Records Validation/Processing • Automation • Scan for data • Data type determination • Standard naming convention is key • Job stream built • Job stream handles multiple data types • Job stream(s) processed • Data Validation • Stand alone edits • Limits • Industry standards and definitions • Consistency with stored data • 900 unique error codes • Additional job streams initiated • Seek alternative pedigree sources • Calculate 305-day projection – Best Prediction • Process error records

  8. Error records • Errors and conflicts stored in a record and returned to processing center to assist in data correction • Reject – record rejected • Notify – input record accepted but a problem may exist • Change – input record changed to match master

  9. Error records(cont.) • Stored to assist in answering queries • Sometimes forwarded by processing center to milk-recording supervisor or producer for action • Rejected records also available by query on web site – http://aipl.arsusda.gov

  10. Error records query

  11. Processing of data discrepancies for U.S. dairy cattleand effect on genetic evaluations

  12. How Data impacts Evaluation Accuracy • Accuracy of a recorded trait • Example: milk weight • Emphasis and adjustment • Example:milking frequency, milkings weighed • Other animals influenced • Example: parents, progeny, contemporaries

  13. Pedigree and yield edits • Identification (ID) verified for valid breed, country, and number • Canadian ID verified against Canadian Dairy Network data • Some American ID use last digit as internal check

  14. Pedigree and yield edits(cont.) • Birth date • Parentage checked (not too young and not too old for progeny) • Matched to dam calving date • Differences of <1 month allowed • Omitted if embryo-transfer animal

  15. Pedigree and yield edits(cont.) • Birth date(cont.) • Parents not previously in database added with estimated birth date • 3 years before reported animal’s birth date • Revised as data from older siblings received

  16. Pedigree and yield edits(cont.) • Alias detection • Same birth date and full siblings but not twins • Within-herd ID (cow control number) useful in identifying additional ID • Bulls registered in >1 country common cause

  17. Pedigree and yield edits(cont.) • Alias detection(cont.) • Numbers differing by single digit investigated as possible invalid ID • Yield data must not conflict for the data from the 2 IDs to be combined as the same cow

  18. Pedigree and yield edits(cont.) • Yield • Values outside widest range rejected • Values outside more narrow range stored but changed to a floor or ceiling if used • Cow test date checked against herd test date

  19. Pedigree and yield edits(cont.) • Calving date • Cannot overlap previous lactation • Missing calving date may cause breeding to be associated with previous calving

  20. Editing principles • Data either rejected or modified when errors encountered • Effect of rejection • Loss of possibly valuable information • No genetic evaluation for animals of interest • System designed to retain data whenever possible • Data elimination preferred to retention of conflicting data

  21. Example • Animal’s birth date conflicts with dam’s calving date • Both animals already have data in system • Dam ID removed to resolve conflict and to allow records for both animals to remain in database

  22. Data Quality Assurance • Test herd summarization • Field by field comparison of DRPC data • Tolerances for some fields • Types • Data sent to AIPL – format 4, 5, and 14 • Consistency between formats • DRPC exchanged data – STF formats • WEB access • AIPL does not make judgments • Reporting function only

  23. Conclusions • Evaluation accuracy dependent on accuracy of all contributing data • Invalid records diminish accuracy of evaluations for other animals

  24. Conclusions(cont.) • Highly complex system for checking data used in national U.S. genetic evaluations of dairy cattle • Conflicting data from various sources • Harmonized based on which data are expected to be most accurate • Deleted when necessary