Detection and adjustment of abnormal test-day yields

1. Detection and adjustment of abnormal test-day yields (abstract 125) G.R. WIGGANS,* P.M. VANRADEN, and J.C. PHILPOT Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 20705-2350, USA wiggans@aipl.arsusda.gov 301-504-8334

2. Estimation of lactation yields • Inclusion of abnormal yields may reduce accuracy • Cow may be reported as sick on test day • ICAR procedures exclude sick tests below a threshold

3. Calculation of lactation records • Best prediction used since February 1999 for calvings during 1997 or later • All sick tests have been excluded: • Many sick tests with milk yields >45 kg • Sick coding varies by herd and dairy records processing center • No check for abnormally high test-day yield compared with cow's other test days

4. Objectives • Check every test for abnormal yield (not just those coded as sick) • Detect abnormally high or low test yields by comparison with yield from adjacent test days • Define approximately 1% of each tail of the distribution as abnormal • Adjust test-day yield to floor or ceiling

5. Methods • Calculate predicted yield • Compare test-day yield with thresholds • Upper: 150% of predicted yield • Lower: 60% of predicted yield • If test-day yield detected to be abnormal, recalculate predicted yield based on yield from adjacent test days

6. Methods (continued) • If test-day yield still detected to be abnormal, adjust to threshold • Calculate lactation yields using best prediction with adjusted test-day yields

7. Predicted yield calculation • For first test day: predicted yield = following yield – slope (interval) • For second test day and later: predicted yield = previous yield + slope (interval)

8. Predicted yield calculation(continued) • Slope is function of DIM, DIM2, test-day yield, and interaction • Coefficients differ by • Test day (first vs. second and later) • Trait (milk, fat, protein) • Parity (1, 2, 3+) • DIM group (5-49, 50+) to account for peak yield • Previously identified abnormal yields not used • Sick tests with milk yields of <4.5 kg ignored

9. Abnormal yield detected • Predicted yield recalculated by interpolating between adjacent test days • If test-day yield still detected as abnormal, predicted yield recalculated again using second test day after outlier • Herd average used when only 2 test days available

10. Component percentage limits • Detection of abnormal fat and protein yields based on weight (not percentage) • Unlikely component percentages possible • Component percentage limits: • Fat: 2-7% and herd mean – 1% to herd mean + 2% • Protein: 2-5% and herd mean – 1% to herd mean + 1.5%

11. Outlier results • Calvings during 1997 or later • Among >93 million test-day records, abnormal yields found: • Milk: 1.8% • Fat: 3.4% • Protein: 1.9%

12. Improved accuracy? • Correlations of lactations with 1 adjusted outlier with following lactation: • Adjustment of abnormal yields should increase similarity between consecutive lactations

13. August 2002 implementation • Lactation records for calvings during 1997 or later recalculated • Adjusted yields shown on industry web queries of test-day data • Additional data may cause change in status of detected outlier and adjusted yield

14. Summary • All test-day yields evaluated as potential outliers (not just sick tests) • Improved accuracy in estimation of expected yield • Floor or ceiling (instead of deletion) for abnormal yields to reflect actual yield better • Improved correlation between consecutive lactations with outlier adjustment