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Health care as a tool for modern animal husbandry –Example from milk production sector in Denmark

StrateKo

Erling Esben Finn

Veterinarians –with a strategic focus

Erling L. Kristensen – Esben B. Jakobsen – Finn A. Petersen

• Advisory service (herd- and farm specific)

• Education, national/international

• Broad experience, practical/theoretical

− Cows

− Farms and herds

− Advisors around the farm

− Science and development

− Politics

My self …

• 1986: Graduation KVL

• 1986-2002: Mixed private practice (2-8 vets)

• 2002: Certificate in Dairy Herd Health Management (DHHM)− Partnership: KoNet-Praksis -> Dyrlæger & Ko (2006)

• 2002-present: Specialized private cattle practice

• 2004/2008/2013: Educating DHHM (2-year course)

• 2006-2012: Danish Cattle Vet Organization – chairman

• 2007-present: Partnership: StrateKo− External censor at KU Sund (2007-present)

• 2010-2014: Simherd – member of the board

• 2011: Specialist in Dairy Herd Health Management

• 2012-present: Partnership: CMS

Agenda

• Dairy industry in Denmark

• Structural changes

• Herd Health Management – ‘The Dane Concept’

− Case from real life

• Veterinary consultancy

− 3 important steps

Qualitative analysis (motivating farmers)

Quantitative analysis (epidemiology – working the numbers)

Economical analysis (Simherd simulation model – prognosis)

− Case from real life

• Conclusions and recommendations

• …

• Plenum - discussion

Dairy industry in Denmark

• Ideal climate and landscape for milk production

Denmark: 43.000 km2

Arable farming: 26.500 km2 (62 %)

8...|

Dairy farm

Statistics – cattle (2013)

• Cattle stock: 1.583.000 heads

− Milking cows: 567.000

− Suckling cows: 97.000

• National quota = 4.846 mio. kg (organic farming: 10 %)

• Dairy farms, no.: 3.618 (organic farms: 10 %)

− Herd size, av.: 148 (74 % > 200 cows) … max: 2.200 cows

− Breed: 71 % DH – 13 % JER – 7 % DR – 9 % others

− Quota, av. = 1.339 kg (59% of farmers > 1.000 kg)

35 % of quota owned by farmers +50 years old

• Milk quality:

− Bacterial counts per ml, av.: 6.870

− SCC per ml, av.: 211.900

Structural changes –milk production

Milk production per cow, x 1000 kg

2013: 9.138 kg in average

Structural changes –herd dynamics 2013:

- Herd no.: 3.887- Herd size, av.: 148 cows

No. of dairy farmsQuota in average

Structural changes –price of milk

Structural changes –net income

-8 000

-6 000

-4 000

-2 000

-

2 000

4 000

6 000810

540

270

-

-270

-540

-810

2009:Total disaster !

Euro per cow/year

Structural changes –more pressure from the banks

’With the momentary prices on agricultural products- even the best farmers will not make money, and the worst will experience large deficits’

Torben Wiborg, Economist, Jyske Bank

’If we judge the farmer to have a high degree of management we will be very supportive in helping him through the momentary crises’

Søren Porsbjerg, Head of the Agricultural Departement, Nordea

Structural changes –type of farms built

Tied up stall

Free stall – cubicle

No. of new farms

Structural changes –cow mortality

De

adco

ws,

%

Year

Structural changes –dead cows (DIM)

De

adco

ws,

%

Parity 1Parity 2+

Days In Milk (DIM)

Structural changes –from a cows perspective

• Danish agriculture – total debt

− 2009: - 60 bio. $

• Full time farms – deficit

− 2009: - 150 mio. $

− 70 % of farmers with debt > 60 %

• Prices on land under severe pressure

− 2010-present: - 50 %

• Milk quota system in settlement (april 2015)

• Farms going bankrupt?

• Farmers living from borrowed money – having a very slim equity

Structural changes –from a farmers perspective

Farmer

Economist

NutritionistBank

The Herd 2014

Outside or inside?

Where is the vet ?Where is the vet?

Structural changes -conclusions

• Decreasing

− Number of herds

− Number of cows

− Number of vets

− Loyalty from the farmer

’Shopping’

− Price of bulk milk

• Increasing

− Herd size

− Milk yield

− Industrialization

− Demands from society

Documentation

Quality

’The good story’

− Financial challenges

− Competition

What kind of job –cattle vet of yesterday ?

• 07-09.00: Answering the phone

• 09-12.00: Fire brigade (e.g. clinical work)

• 12-13.00: Taking a nap ( … Zzzzz …)

• 13-17.00:

» - Fire brigade, to be continued

» - Dehorning calves

- Blood sampling (IBR, BVD)

» … and maybe even …

- Consultations in pet animal clinic !

• Evening: Invoicing (the last day in the month !)

What kind of job –cattle vet of today ?

• Selling drugs

• Single cow treatment

• Reproduction – pregtest

• ’Gut feeling’-consultancy

• Giving advice (’Universal truth’)

What kind of job –cattle vet of tomorrow ?

• Production diseases

• Advisory service (Herd Health Contracts)

• Hightech on-farm management systems (calibration)

• Collecting/processing valid data

• Epidemiology (’Local truth’) – benchmarking

• Qualitative-/Quantitative analysis

• Economical analysis

• Preventive medicine (intervention/action plans)

• Animal health strategies

• Biosecurity

• Animal welfare

• Emerging diseases and epizootics

• Food safety

Organization – example from DK

’Dyrlæger & Ko’ – Healthy cow/healthy economy

24 vet clinics (net working):- > 100 cattle vets- 1.400 herds (41 %)

The last 25 years …

LeBlanc et al, 2006: ’Major Advances in Disease Prevention in Dairy Cattle’

… the job based on …

Measurering – monitoring – documentation

Conceptual framework

… collecting/processing data …

Milk registration Herd Book Health- and reproduction

Yield Fat/Protein SCC Health reports Working schemes

Surveillance programs National control/eradication programs

Central Cattle Database

Clinical registrationsGenetics

Including 93 % of dairy farmers

The Dane Concept –clinical registrations

ASK THE COW !

Body Condition

Variation within herd …

Ketone Bodies

Uterine Discharge

Pyometra

Vaginal injuries

Hocks

Lameness

’The Concept’

• Standardized

− Clinical registrations (scoring 1-9)

• Systematically

− Cows at risk

Dry cows

Fresh cows

Peak lactating

• Consequently

− All cows in group

− Short intervals – every-/every second week

− Data – handling

Epidemiology … simplified

Diagnosis: Diagnosis:

Patient: Patient:

Cow Herd

Tools: Tools:

Thermometer

Stethoscope Numbers

Bloodsampling Statistics

Therapy: Therapy:

Medical treatment Herd health programs

Epidemiology in practice

• Involves :

− Cost benefit analysis:

Disease –patterns ?

Risk –factors ?

7 must be answered

− Who ?

− What ?

− Where ? Descriptive epi

− When ?

− How many/much ?

− Why ? Analytic epi

− How ? Intervention/prophylaxis

Case from real life –Metritis

Metritis – at cow level

• Easy to diagnose

• Easy to treat

Metritis – at herd level

• Possible reasons?

• Possibleimportance ?

The Farm

• Freestall with beddings

• 220 cows (HF)

• Yield: 8000 kg ECM/cow/year

• 1 group of lactating cows

• 2 groups of dry cows

• Total mixed ration (TMR)

Advisory service

• Weekly farmvisits - clinical examamination of high risk cows

− 1. Fresh cows (5-12 DIM) Scoring

– Body Condition, Ketosis, Metritis, Vaginitis– Hock lesions, Lameness– Feces consistency– CMT

− 2. Peak lactating cows (50 – 70 DIM) Scoring:

– BCS Reproduction (prostaglandin/preg-test)

− 3. Late lactating cows (200 days after insemination/pregnancy) Scoring:

– BCS and CMT Preg-test

Epidemiological studies

• Based on data from

− Clinical registrations

’behind the cow – hands in the dirt’

standardized

high validity

− The Central Cattle Database

HerdView

╔═══════════════════════ Weekly frequency of cows calving════════════════════2═╗

║ ▓ ▓ - 10 # ║

║ ▓ ▓ ║

║ ▓ ▓ ▓ - 8 ║

║ ▓ ▓▓ ▓▓▓ ▓ ▓▓ ║

║ ▓ ▓ ▓▓ ▓ ▓▓▓ ▓ ▓▓ - 6 Parity ║

║ ▓▓ ▓▓▓▓ ▓▓▓ ▓▓▓ ▓ ▓▓ ▓ ▓ ▓ ║

║ ▓▓ ▓▓▓▓▓ ▓▓▓ ▓▓▓▓ ▓▓ ▓▓ ▓▓ ▓ ▓ ▓ ▓ ▓▓ ▓ ▓ - 4 1,2,3,4,,8 ║

║ ▓▓▓ ▓▓▓▓▓▓ ▓▓▓ ▓▓▓▓ ▓▓▓ ▓▓ ▓▓▓ ▓ ▓▓ ▓ ▓ ▓ ▓▓▓▓ ▓▓ ▓ ▓ ║

║▓▓▓▓ ▓▓▓▓▓▓ ▓▓▓▓▓▓▓▓▓▓▓▓ ▓▓ ▓▓▓ ▓ ▓▓▓▓ ▓ ▓▓ ▓▓▓▓ ▓▓▓▓ ▓ - 2 206 calv. ║

║▓▓▓▓_▓▓▓▓▓▓_▓▓▓▓▓▓▓▓▓▓▓▓▓_▓▓▓▓▓▓▓▓_▓_▓▓▓▓_▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓__ ║

╚---Oct-Nov-Dec--Jan-Feb-Mar--Apr-May-Jun--Jul-Aug-Sep--Oct-N 2003─────────────╝

┌─────────────────────── Weekly frequency of cows calving ───────────────────1─┐

│ - 10 # │

│ │

│ - 8 │

│ │

│ - 6 Parity │

│ ▓ ▓ ▓▓ ▓ ▓ │

│ ▓ ▓ ▓▓ ▓ ▓▓ - 4 3,4,5,6,7,8 │

│ ▓▓▓ ▓▓▓ ▓ ▓▓ ▓ ▓▓ │

│ ▓▓▓▓▓ ▓▓▓ ▓ ▓▓ ▓▓▓ ▓ ▓▓▓ ▓ ▓ ▓ ▓ - 2 86 calvings │

│_ ▓_▓▓▓▓▓▓_▓▓▓▓▓▓▓▓▓_▓▓▓_▓▓▓▓▓▓▓▓▓▓__▓▓▓_▓_▓▓__▓▓▓▓ ▓▓▓_▓__ │

└---Oct-Nov-Dec--Jan-Feb-Mar--Apr-May-Jun--Jul-Aug-Sep--Oct-N 2003─────────────┘

HerdView

┌──────────────────── Weekly frequency of metritis--------───────────────────1─┐

│ - 10 # │

│ │

│ - 8 │

│ │

│ - 6 Parity │

│ │

│ - 4 3,4,5,6,7,8 │

│ │

│ ▓ ▓ ▓ ▓ - 2 28 cows │

│ ______▓▓▓▓▓▓▓ ▓▓▓▓▓▓▓_▓▓▓▓▓__▓▓▓▓▓___▓▓___________▓___▓____ │

└---Oct-Nov-Dec--Jan-Feb-Mar--Apr-May-Jun--Jul-Aug-Sep--Oct-N 2003─────────────┘

Metritis – plot (scoring 1-9)

Metritis – parity 3+

Date of calving and vet-examination pp

Ketosis plot (scoring 1-5)

Ketosis – parity 3+

Date of calving and vet-examination pp

BCS – plot (scoring 1-5)

Parity 3+

Date of vet-examination

Conclusion

• 28 cows (parity 3+) treated for metritis

• High frequency of cows with high ketosis scores

• High incidence of cows with considerable loss of BCS

• Statistical significance (Odd Ratio = 2,05) between loss of BCS in the dry periodand metritis postpartum

• Relatively many freshened cows =>

− Overcrowding/high density in the dry- and fresh cow penn =>

− Negative impact on

Feed intake – hygiene – calving management

• Low levels of vitamin E (bloodsamples)

Action program

• Reduce the densitity in the dry– and fresh cow penn

• Optimize ration formulation (energy intake) through the dry period and transition period

• Improve level of hygiene in fresh cow penn

− bedding – more straw

• Improve health of claws

− trimming – cleaniness

• Extra supplements of vitamin E

Veterinary consultancy

• 3 important steps (documentation)

1. Qualitative analysis

2. Quantitative analysis

3. Economical prognosis

• Define key areas -> Action plans

• Evaluation – put up new goals (cyclic in nature)


• Perspective of the bank – typically

− Historical knowledge of the farmer/family

Adding more value will be …

− Defining the real potential

The farmer – the staff

The cow

The herd

The farm

− Strategically considerations/-

calculations

− Action plans <-> reality


• Qualitative methods

− Farm visit (production facilities, animals …)

− Asking the farmer

Expectations to/value of advisory service ?

– Q-sorting test

Motivation ?

– Technique of questioning (open, circular, non-manipulating)

Farmers ’orientation’ (5 different ’families’ )*

– Stability, Orthodox, Self confident, Sceptical, Money-fixated

− Important personal qualities by adviser

Integrity, interest, inspiring confidence, enthusiasm, dedication

− Tools

SWOT-, DiSC-, Risc- and partneranalysis, Mind Map …

* Nanna F. Jensen: Which important attitudes towards advisory service do excist among Danish milk producers? (2014)

Q-sorting test – statements

• 46 different statements (= 4 ’families of perspectives’) – examples

− It changes the business aspect of my farm

− The vet helps to educate my staff

− My knowledge on cows and herd increases

− The vet helps to put up relevant performance indicators

− The vet gets deep insight into the herd – better advice

− Team spirit increases in the dairy setting

− Higher yield

− It makes antibiotics more available

− The vet bill decreases in the long run

− My financial lenders requested it …

Erling L. Kristensen: Valuation of Dairy Herd Health Management (2008)

I BUY A HERD HEALTH MANAGEMENT PROGRAM BECAUSE?

Q-sorting test – sorting



AgreeDisagree

DAIRY FARMERS:

• Teamwork

• Animal welfare

• Knowledge dissemination

• Production

VETERINARIANS:

• Production

• Animal welfare

• Knowledge dissemination

• Teamwork


Q-factor analysis – results(4 ’families of perspectives)

Need for matching up ?


The ’excellent’ adviser


2. Quantitative analysis


− Average key figures

Kg milk per cow

Kg milk delivered

Gross margin?

• Adding more value will be …

− Farm specific analyses

Veterinary Production Analysis (multifactorial – http://vpr.kvl.dk)

HerdView (http://herdview.thysen.dk)

SimHerd (health economy – http://www.simherd.com)

3. Economical prognosis


− Simple forecasting

− ‘Trust in the farmer’

• Adding more value will be …

− Identification of key focus areas

− Prioritize interventions

− Realistic goals

− Action plans

− Economical prognosis ‘Analysis of sensitivity’

SimHerd

• From state-of-the-art scientific model to …

… on-farm decision support tool

… bridging science and practice …

SimHerd –simulation of a dairy herd

• The model is dynamic, stochastic and mechanistic

• Input from the user− The herd

Description of animals and herd characteristics

– 50 state variables

Simulate effects of management and/or production

– 2000 decision variables

• Output from the model− Technical and economic results over time (0 - 10 yrs)

• Analyses of simulated results− Compare alternative scenarios – e.g.

Use of sexed semen

Use of postponed AI

Reduced mastitis risk

Case from real life …

The Bank:’What’s the potentiale of this farmer/herd, Doc?’

The farm

• Young farmer, wife and 3 kids

• 2 unskilled employes from Ukraine

• 258 cows (HF) with youngstock

• Organic farming

• New built freestall (cubicles) and milking parlour in 2007

… the bank demands a major improvement in the gross margin

towards the end of 2010 …

Case - simplified …

• 1. Qualitative analysis

− Good farm facilities

− Good silage (quality/storage)

− Observant/cooperative farmer

… but …

− Poor management

− Poor feeding strategy

Good chances of improvement, though …


• 2. Quantitative analysis

− Low yield 7.195 kg/cow/year (9.500)

− Poor reproduction Insemination rate, cows: 32 % (50)

− High (unvoluntary) culling rate 58 % (30-45)

− Poor udder health SCC: 324.000 (230.000) – Staph. Aureus

− High calf mortality Still births: 15 % (9)

Dead calves (1-180 days): 13 % (7)

− Poor heifer growth Age at 1. calving: 28 months (26)


• 3. Economical analysis – ’What if-scenarios’

1. + 1.000 kg pr. cow pr. year?

2. + 10 % insemination rate (heifers) and + 20 % (cows)?

3. - 50 % still birth?

4. - 50 % calf mortality?

5. - 50 % claw diseases?

6. - 50 % mastitis treatments?

7. - 50 % unvoluntary culling rate?

8. All above simultanously?

Case – simplified …

1. Do your homework

− Qualitative analysis

− Quantitative analysis

− Economical analysis

− … based on the ‘local truth’ (herd specific) …

2. Priorities – what will be the best investment (dollars/hours)?

3. The dialogue is essential in order to explain the results to the farmer,

the user needs profound knowledge on model behavior

It is very valuable and motivating when biology and economy can be linked with credibility …

Motivation –Herd Health Management ? – conclusions

Motivation –Herd Health Management ?

• LegislationMandatory (> 100 cows) – 354 contracts

− 2 welfare visits (= self regulation program) + 1 report per year

Interdisciplinary (Consultant + Vet) or Farmer groups

No drugs (antibiotics) – mostly organic farms

Voluntary (self regulation program on animal welfare mandatory)

− Module 2 – 1701 contracts

Farmer intiating antibiotic treatment (vet defines relevant diagnosis)

18/26 visits (clinical examinations/- registrations) + 4 reports per year

− Module 1 – 1477 contracts

Farmer can treat animals (5 days) after vet treatment

9 visits + 4 reports per year

• Financial sector− Enforced demand on net income – business plan … ?

Yourself

The farmer

The cow

My recommendation – ask ?

The ‘10.000-hours rule’

• It takes 10.000 hours (10 years) to become an ‘expert’

• Eksperts has done a lot of excersing

• You have to burn for the course

Malcolm Gladwell: ‘Outliers: The Story of Success’, 2008

… and …

• Learn what herd dynamics means

• Provide the necessary and sufficient information needed for valuation

− Find out how the suggested management changes affect keyperformance indicators

− Reveal the true goal of the farmer

’There is no universal truth’

Motivation

(your own/ farmer)

Follow up (proces/teambuilding)

Expertise

(overall view)

Results (and joy) are created by

http://strateko.info

Thank you for listening

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