DHI Records for SCC Investigations. Jeffrey Bewley and Michele Jones University of Kentucky

Transcription

1 DHI Records for SCC Investigations Jeffrey Bewley and Michele Jones University of Kentucky

2 Why do We Need Data? If you can t measure it, you can t manage it Accurate and efficient decisions Plan for the future Analyze the past Control the production system

3 Decision Levels Operational Management by exception (i.e. low milk yield, activity) Risk management (i.e. alerts on treated animals) Record keeping (i.e. breeding details, quality assurance) Tactical Proactive management strategies Intra-herd comparison (i.e. breaking herd into virtual groups) Strategic Long-term decision making and benchmarking (i.e. response to grain, achievement of cow performance targets, labor efficiency)

4 Competitive Advantage Business Intelligence and Analytics Optimization Predictive Modeling Forecasting/Extrapolation Statistical Analysis Alerts Query/Drill Down Ad hoc Reports Standard Reports What s the best that can happen? What will happen next? What if these trends continue? Why is this happening? What actions are needed? What exactly is the problem? How many, how often, where? What happened? Competing on Analytics, Davenport, Harris Degree of Intelligence

5 Benchmarking Comparing our business to those that are the best to learn how they achieve success Minimum performance is above average Benchmarks can come from Past performance Projected performance Performance of similar farms

6 Why Benchmark? To set realistic short and long-term goals To predict trends in relevant business sectors To facilitate out of the box thinking Compare with average or best businesses To assist in setting goals relative to state-ofthe-art practices

7 Limitations of Benchmarking Quality of data Protecting of proprietary information Number of observations too small to draw conclusions Who are your peers (geography, size, production level.??) Numbers may be outdated or not fit situation today Not considering differences in strategy Only comparing to similar businesses Groupthink-Bad Becoming Normal (i.e. reproduction, lameness, SCC)

8 Why DHIA? Individual milk weights (culling/breeding decisions) Individual somatic cell counts (culling/treatment decisions) Reproduction records Trend analysis Comparing with similar herds

9 Case Study

10

11 Ask the right questions Generally, producers will lead you to the answer Understand whether issue is high SCC, clinical mastitis, or both Ask for bulk tank SCC Ask for additional data Treatment records, cultures, etc.

12 SLOW DOWN: Data without knowing on farm/cow situation can lead to incorrect and dangerous conclusions

13 Limitations Variation Particularly in small herds Momentum Takes time for change Lag Time between event and when it is measured Bias Data ignored or excluded

14 Farm follow-up Cow hygiene Cow comfort Milking procedures Dry cow management Teat end health Milking equipment

15 DHI Information Lots Ahead

16 Hot Sheet

17 DHIA Hot Sheet Breakdown Cow Number Test Day Milk (Lbs) Test Day Protein % Test Day Milk Urea Nitrogen % Somatic Cell Count in Thousands (Ex: 7,352,000 Condition Affecting Record (example: in heat, abnormal Cow Barn Name Days in Milk Test Day Fat % Test Day Solids Non-Fat % Somatic Cell Count Score Lactation # Predicted Bulk Tank Average SCC without this cow in the tank % of Cells in Tank Contributed by This Cow

18 Strategic Quarter Milker Use

19 Change of Career: Trailermycin

20 Culture and Treat????

21 Don t stop there! Those are all band-aid approaches Dig deeper Culture Shift focus to prevention

22

23

24 Goal >85% Goal <5% Look for Differences by Lactation Number This represents how much milk was and milk income was lost because of SCC problems

25 Stage of Lactation Look for Trends by Stage of Lactation. For example, is SCC higher in early lactation? What percent of cows have SCCS > 3.9 (SCC> 240,000). Goal <15%

26 Look for trends by season. Check when an increase started.

27 % New Infections (Goal <5%) This Herd:29 Cows (13.5%) % Chronic Infections (Goal <15%) This Herd:32 Cows (14.9%) Sample Herd Consistently Clean Cows (Goal >85%) This Herd:130 Cows (60.5%) Current Versus Last Test SCC Score % Cures (Goal-more cures than new infections) This Herd:24 Cows (11.2%)

28 % New Infections (Goal <10%) This Herd:19 Cows (15.0%) % Chronic Infections (Goal <10%) This Herd:5 Cows (3.9%) Sample Herd Consistently Clean Cows (Goal >75%) This Herd:77 Cows (60.6%) % Cures (Goal-more cures than chronic infections) This Herd:26 Cows (20.5%) First Test SCC versus Last Test in Previous Lactation SCC

29 DHI 427 Udder Health Monitor

30 DHI 427 Udder Health Monitor

31 DHI 427 Udder Health Monitor

32 DHI 427 Udder Health Monitor

33 DHI 427 Udder Health Monitor

34 DHI 427 Udder Health Monitor

35 DHI 427 Udder Health Monitor

36 DHI 427 Udder Health Monitor

37 DHI 421-Test Day Bulk Tank

38 DHI 340 SCC Management Report

39 DHI 340 SCC Management Report

40

41 DairyMetrics Compare your herd to others using DRMS services!

42

43

44

45

46 Compare Your Herd to Others in Your Region General Production Udder Health Reproduction Genetics Features Percentile ranking Number of Herds in Comparison Averages Standard Deviation Minimum & Maximum

47 Udder Health

48 If you don t measure it you can t manage it.

49 Health Conditions Default Health Conditions User defined

50 Protocols & Chores Add your own Chores Follow the label to add milk and meat withholds Create your own protocols Can be triggered by specific inputs or health events.

51 Record Health Data & Analyze it with Trackers Activity Tracker Conception Tracker Heifer Tracker Inventory Tracker Maternity Tracker

52 Activity Tracker

53 Activity Tracker

54 Activity Tracker Graphs

55 Acknowledgements The Southeast Quality Milk Initiative project is supported by Agriculture and Food Research Initiative Competitive Grant no from the USDA National Institute of Food and Agriculture.

56 Any Questions? Jeffrey Bewley Michele Jones