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Introduction to Dairy Records SCAABP Dry-lab

Introduction to Dairy Records SCAABP Dry-lab. Andrew Fidler Oct. 12, 2009. Introduction. National Dairy Herd Information Association (DHIA) 4.4 million cows (47% of national herd) from 23,000 herds (2007) Dairy Records Managements Systems (DRMS) in Raleigh handles 69% of herds, 49% of cows.

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Introduction to Dairy Records SCAABP Dry-lab

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  1. Introduction to Dairy RecordsSCAABP Dry-lab Andrew Fidler Oct. 12, 2009

  2. Introduction • National Dairy Herd Information Association (DHIA) • 4.4 million cows (47% of national herd) from 23,000 herds (2007) • Dairy Records Managements Systems (DRMS) in Raleigh handles 69% of herds, 49% of cows

  3. Introduction • Why? • Determine baseline performance levels • Detect potential problems • Monitor change • Motivate change • Set goals

  4. Introduction • Records analysis will NEVER replace herd visits • Records can highlight areas of concern before a herd visit • Problems detected on-farm can be quantified by records • When you evaluate records, you should end up with more questions than you started with. • Keep it simple

  5. Benchmarking vs. Monitoring • BENCHMARKING • Using a “report card” to show past performance • Provides historical perspective (baseline performance levels) • Doesn’t accurately reflect current performance or predict future performance • Number-based • MONITORING • Tracking parameters (‘monitors’) to detect change or lack of progress • Measure impacts of management changes • Detect undesirable results • Motivate change • Question-based

  6. Monitors The Good The Bad Variation Momentum Lag Bias Proactive Measurable Impact Profit Result in Action

  7. Potential Problems • Variation • One number has a large impact on the result • Problem in small herds or small groups • Ex. Preg rate in small herds – palpating 10 cows, 1 cow will change the result by 10%. *Solution: Add more time to the calculation Ex. Calculate pregnancy rate for the last three 21-day periods instead of the last one.

  8. Potential Problems • Momentum • Too much time goes into the calculation • Makes changes difficult to detect (change is dampened) • Ex. Rolling Herd Averages, “annual” calculations *Solution: Use less time in the calculation Ex. Test Day Avg. instead of Rolling Herd Avg.; 21-day Preg Rate instead of Annual Preg Rate

  9. Potential Problems • Lag • Time between when an event occurs and when it is measured • Ex. Age at first calving – the actual event is the conception, but it isn’t measured until calving 9 months later *Solution: Monitor the earliest event Ex. Age at conception, or PROJECTED age at first calving

  10. Potential Problems • Bias • When data (or a population) is ignored or not included in the calculation • Ex. Conception Rate – measures conceptions per breeding, but doesn’t account for animals that weren’t bred • Out of 100 heifers, if 50 are bred and 40 conceive, CR is 80% (but 50 heifers not accounted for) • If all 100 are bred and 60 conceive, CR is 60%, but 20 more pregnancies have been created!

  11. Areas of Interest • Milk Production • Reproduction • Health • Herd Management (culling) • Heifers • Financial

  12. Records Analysis • Browsing the Herd Summary • Production-based Analysis • Question-based Analysis

  13. Milk Production • Rolling Herd Average • Average milk production per cow per year • Significant momentum; too many contributing factors • Test Day Average Milk • Most current average recorded daily milk production per cow • Many contributing factors (DIM, Lact. #, season, etc.) • Std. 150 Day Milk • Adjusts TD Avg. Milk as if each cow were at 150 DIM • Removes DIM as a contributing variable • Projected Mature Equivalent 305 Day Milk (ME 305) • Adjusts TD Avg. Milk as if each cow were a mature cow that had a complete standard lactation • Can compare groups or individuals regardless of DIM or Lact. #

  14. Reproduction • Days to 1st Service • Days from calving to first breeding • Affected by VWP, heat detection, and reproductive health • Service or Heat Intervals • Days between detected heats or breedings • Indicator of heat detection • May be affected by early embryonic death

  15. Reproduction • Conception Rate • Proportion of breedings that result in conception • “% Successful” on Yearly Repro Summary on DHI-202 • Biased – excludes cows not bred (missed heats  increased CR) • Services per pregnancy • Inverse of CR

  16. Reproduction • Calving Interval • Time between calvings • Biased – excludes 1st lact. Cows and culled cows • Lag – problem getting cows pregnant today doesn’t show up until 9 months later • Momentum – Calculated on an annual basis • Days Open • Time from calving to conception • Biased – excludes open cows, or has to make assumptions for ‘Projected Days Open’ • Momentum - Calculated on an annual basis

  17. Reproduction • Pregnancy Rate [# pregnancies created] / [# eligible] per unit time • “eligible” = open, beyond the VWP, not a “DNB” • Time • 21 d (or multiple 21 d periods) • Test period • Palpation day

  18. Health • Disease • Cows left herd • Often poorly recorded; inaccurate • Udder Health • Somatic Cell Counts • Categorized by Lact. #

  19. Herd Management (Culling) • Cows Entered and Left the Herd • Reasons often not reported • Appropriate culling % variable

  20. Heifers • Avg. Age at First Calving • Lag – event (conception) occurred 9 months ago • Biased – excludes heifers not yet calved • Avg. Projected Age at First Calving / Age at Conception • Minimizes lag • Biased – excludes open heifers • Avg. Age at First Breeding • Minimizes lag, momentum

  21. Production-based Records Analysis • Evaluate “Key Production Parameters” to identify problems • Investigate source of problems by evaluating “Diagnostic Indicators” • Based on benchmarks or industry standards

  22. Key Production Parameters • Herd Performance • Milk/Cow/Day • Lactation Status • Days in Milk (DIM) • Reproductive Performance • Pregnancy Rate (PR) • Udder Health • Somatic Cell Count (SCC) • Cow Management • Cull Rate (CR)

  23. Herd Performance • Milk/Cow/Day • The cheapest milk a producer can make is the next 5-10 pounds each cow produces • Fixed costs already covered; only additional associated costs are marginal costs – mostly feed • Goal: 70 – 90 lbs/cow/day

  24. Lactation Status • Days in Milk (DIM) • Production decreases .15-.20 pounds for every day past 150 DIM • Goal: 170-185 DIM • If higher, look for reproductive problems • If ok, but production is too low, consider fresh cow performance, peak milk, and persistency

  25. Reproductive Performance • Pregnancy Rate • Percent of eligible estrous cycles that resulted in a pregnancy over a given period of time • Goal: 22-25%

  26. Udder Health • Somatic Cell Count • Mastitis  lost income, higher cull rates, increased veterinary expenses • Goal: <200,000 cells

  27. Cow Management • Cull Rate • (Sold + Died) / (Avg. herd size) • High cull rates  Higher cost of replacements • Goal: <35%

  28. Scenario #1 • Low milk production • Check DIM. . . • Avg. DIM = 170 • Check ‘Production Diagnostic Indicators’. . . • Peak milk, Summit milk, Fresh cow performance, Persistency • Contributing Factors. . . • Dry cow management, Transition cow management, Cow comfort, Ration formulation and Bunk management

  29. Scenario #2 • Low milk production • Check DIM. . . • Avg. DIM = 250 • Check pregnancy rate (PR). . . • Most recent PR = 8%; Annual PR = 9% • Check “Reproduction Diagnostic Indicators” • Heat detection, Conception rate, % of animals not serviced by 70 DIM, services per conception, etc.

  30. Scenario #3 • High Somatic Cell Count (SCC) • Stratify somatic cell scores by parity and stage of lactation • Check udder health management practices, mastitis treatment protocols, milking procedures, environment.

  31. Question-based Records Analysis • Production: • How are the “good” cows doing? • How many “bad” cows are in the herd? • Are the fresh cows getting off to a good start? • Reproduction: • Are cows getting pregnant? • Will herd size be maintained? • Health: • How are fresh cows doing? When are cows getting sick? • How is udder health? When is mastitis occurring? • Herd Management: • Is culling appropriate? • Heifers: • Are youngstock healthy and performing?

  32. Production • Good cows: • How high are the highest milking cows in peak lactation? • DIM vs. Milk graph • Peak Milk • Bad cows: • <50 lbs • DIM vs. Milk graph • “Failures”: >100 DIM, <30 lbs, and OPEN • Should be <2% • Are the fresh cows getting off to a good start? • DIM vs. Milk graph

  33. Reproduction • Getting pregnant: • Pregnancy rate • Pregnancy rate by DIM • First Service – • Days to First Service (VWP + 18) • First Service Conception Rate (>50%) • Repeat Breeders – • Heat Detection (>70%) • Conception Rate (>40%); Services per Conception (<2.25) • JMR – • “Average Days Late”

  34. JMR (Average Days Late) • A current measure of reproductive efficiency of small herds • Based on how long it takes a cow to get pregnant after the VWP • Can adjust VWP for individual cows • Only counts open and unknown cows • A penalty is assigned to cows beyond the VWP that have not been diagnosed pregnant: • Diagnosed open: days since VWP • Not bred: days since VWP • Bred by not yet checked: days from VWP to last breeding • Assuming they are pregnant to avoid over-penalizing • Sum of penalty days is then divided by the number of breeding cows in the herd

  35. Reproduction • Will herd size be maintained? (“Pregnancy Hard-Count”) • Need 10% of milking herd in calvings each month • 65% of those from cows (+ 15% abortions) • 35% from heifers (+ 2% abortions) • Convert to a 21 d period by dividing by 30.4 and multiplying by 21 • Convert to a 2 week period to find out how many new pregnancies are required at preg check

  36. Health • Fresh Cows • Disease Rates • Fresh Cow Survival • Herd Health • Disease Rates • Why are cows leaving the herd?

  37. Health • Udder Health • Current SCC vs. Previous SCC graph • <20% SCC >4 • <10% chronics, <10% new infections • Stratify by Lact. # and DIM

  38. Health • Youngstock • Height and Weight tracking • Holsteins: 52 in. hip height, 75 lbs. at breeding (400 d) • 85% mature weight at calving • Disease Rates

  39. “Not everything that counts can be counted, and not everything that can be counted counts.” • Albert Einstein

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