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

Introduction

• Why?

– Determine baseline performance levels – – Detect potential problems Monitor change – Motivate change – Set goals

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

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

The Good

Proactive Measurable Impact Profit Result in Action

Monitors

The Bad

Variation Momentum Lag Bias

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.

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

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

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!

Areas of Interest

• • • • • • Milk Production Reproduction Health Herd Management (culling) Heifers Financial

Records Analysis

• Browsing the Herd Summary • Production-based Analysis • Question-based Analysis

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. #

Reproduction

• Days to 1 st – 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

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

Reproduction

• Calving Interval – Time between calvings – Biased – excludes 1 st 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

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

Health

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

Herd Management (Culling)

• Cows Entered and Left the Herd – Reasons often not reported – Appropriate culling % variable

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

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

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)

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

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

Reproductive Performance

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

Udder Health

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

Cow Management

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

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

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.

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.

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?

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

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”

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

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

• Fresh Cows – Disease Rates – Fresh Cow Survival

Health

• Herd Health – Disease Rates – Why are cows leaving the herd?

Health

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

Health

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

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