monitoring of the dairy cow for optimizing health and ... · department of animal science, aarhus...

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25 AUGUST 2015HEAD OF DEPARTMENT

KLAUS LØNNE INGVARTSENAARHUSUNIVERSITYDEPARTMENT OF ANIMAL SCIENCE

AU

Monitoring of the dairy cow for optimizing health and production- energy and protein status

Klaus Lønne Ingvartsen

Department of Animal Science, Aarhus University, Tjele, Denmark

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AU-FOULUM

25 August 2015

Head of Department

Klaus Lønne IngvartsenDEPARTMENT OF ANIMAL SCIENCE

AARHUSUNIVERSITYAU

Outline

Introduction

Physiological imbalance – what is it?

Need for surveillance and automated precision management systems

Should we focus on herd, group or individual cow level?

Biomarkers and sensors for energy status

Biomarkers and sensors for energy protein / AA status

Conclusion

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Average herd milk yield close to 10,000 kg / cowConsequences of a 1.5% increase in milk yield / year?

IMPROVED:

Breeding

Feeding

Management

Environment/

Housing

OUTPUT:

- Health?

Reproduction

Milk yield

Efficiency

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Disease incidence relative to days from calving

(Ingvartsen et al., 2003)

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Production diseases are multifactorial

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Nutrition

Management

Prod. system

Genotype

Risk of diseaseClinical

Subclinical

Immuno-

logical

Physiological

Status

Stress

Rumen/

organ

Better to prevent than to treat!

Production and

reproduction

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Changes around calving GH NEFA

IGF-I ketone bodies

insulin glucose

leptin glutamine

cortisol other AA

progesterone immune

estrogen proteins

Environment

• nutrition

• management

• stress

• infection

pressure

Genotype

Peripheral tissue

milk yield

mobilisation of

body tissue

Liver

fat infiltration

collectin secretion

acute phase proteins

synthesis of other

proteins Risk of infections

Immune system hematopoesis

chemotaxis

migration

immune proteins

opsonisation

phagocytosis

oxidative “kill”

Ig production

Physiological – immunological interactions and risk of infections (mod. from Ingvartsen et al. 2003; Ingvartsen & Moyes, 2012, 2015)

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Day 250 of pregnancy:

Foetus weight = 35; energy req. = 2.3 Mcal NEl/day or 1.2 FE/day (35-40% glucose, 55% amino acids, 5-10% acetate)

+ development of mammary tissue etc.

Early lactation, nutrients for 50 kg milk:

2000 g milk fat

1600 g milk protein

2500 g lactose

65 g Ca, 50 g P, 8 g Mg

From dry to late pregnancy and early lactation

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Is it milk yield or acceleration in milk yield that is a risk factor (Ingvartsen et al., 2003, Hansen et al. 2006

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› Cause of increased disease risk:› Probably not yield per se.› Rate of increase in daily milk yield

(acceleration) → Adaptationalproblems.Physiological imbalance?

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Hypothesis: Immune function and health can be improved by reducing the PI in cows, and at the same time it will improve production and reproduction (Ingvartsen et al., 2003, 2006; Ingvartsen and Moyes, 2012)

Definition of PI: cows whose parameters (e.g. glucose, BHBA, NEFA) deviate from the normal, and who consequently have an increased risk of developing diseases (clinical or subclinical) and reduced reproduction and/or production (Ingvartsen, 2006)

Physiological Imbalance (PI)

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Surveillance is essential for prevention

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Manual surveillance is important – but has its limitations

Surveillance at feeding and milking has changed

Large herds

Subclinical problems

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Early identification is key to reduced disease incidence and secures optimal production

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Biomarkers in relation to state:

Production:

Health:

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Efficient management calls for:

Early identification of “risk cows”

Manage animal status & risk by

changing “input” to “risk cows”

Calls for real-time on-farm solutions based on:

Efficient biomarkers

Automated sampling / analysis (sensors)

Biological and biometric models

Ability to describe animal status

Methods to describe risk (e.g. for a disease)

(Autom.) change of “input” for prevention

Optimization at cow and herd level

Proactive

management

Cost effective

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Need for automated precision management systems

There is a need for cost effective automated precision management systems where equipment combines advanced sensors, technologies and biological knowledge to obtain:

low disease incidence and severity,

animal welfare,

low impact on the environment,

requested product quality,

optimal production and reproduction,

profitability for the producer.

Individual cow monitoringcow as its own control optimization

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Weeks around calving

Herd/group vs. individual Effect of parity and TMR energy density on plasma [BOHB]

mM

1,49

1,22

1,00

0,82

0,67

0,55

13.6 MJ DE

pr. kg DM

Parity: ∆ ∆ = 1.; ౦ ౦ = 2.; □ □ = 3.


12.9 MJ DE

pr. kg DM

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Large between cow differences

Days around calving

0 42 84 128 168 210

Days around calving

Total var.Genetic var.Between cows var.Residual


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Glucose

Periparturient changes in NEFA, BHBA and glucose

- the “text book cow”, high yielding and healthy

NEFA

BHBA

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Glucose

NEFA

BHBA


– the mobilizing healthy but low performance cow

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Glucose

NEFA

BHBA


– the mobilizing high yielding risk cow

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Problem and challenge

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EB, blod/urine variables and PI based on blood are not possible/cost effective at commercial settings!

Our objectives are therefore:

Identify potential biomarkers in milk for degree of PI that allow automation

Automated system (like we did in e.g. the “Herd Navigator” system)

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“Off feed” challenge – Cows, design, sampling

29 healthy Holstein cows:

early lactation (n = 14; 22-86 DIM)

mid-lactation (n = 15; 100-217 DIM)

Daily registrations: Feed intake, milk yield and components

Blood collection for analysis of NEFA, BHBA, and glucose

Milk for detailed analysis

Liver samples collected for:

1. Chemical analysis

2. iTRAQ-based quantitative profiling using LC-MS/MS (proteomics)

Nutrient Restriction:

40% NEL requirements

0 9624 48 72-24-72 -48-96

Hours

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Change in energy density caused marked changes in DMI and milk yield

Bjerre-Hapøth et al., 2012

Time relative to restriction, h Time relative to restriction, h

Milk Yield, kg/dDMI, Kg/d

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EB was reduced by reduced energy density

Time relative to restriction, h

EB, Mcal/d

Bjerre-Hapøth et al., 2012

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Changes in milk parameters during nutrient restriction – early and mid-lactation

Early

lact.

Time relative to nutrient restriction (0-96 h)

Mid

lact.

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Are requirements for metabolizable protein fulfilled in early lactation?

Larsen et al. 2014Bell et al., 2000

Metabolisable protein supply

Days relative to calving

-15 -10 -5 0 5 10 15 20 25 30

g/d

0

500

1000

1500

2000

2500

3000Casein

Control

F677-1; n = 4

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Are control animals experiencing imbalance?

Milk yield


-15 -10 -5 0 5 10 15 20 25 30

Milk y

ield

, kg

/d

0

10

20

30

40

50

60 Casein

Control

Ptrt

= < 0.01; PDIM

< 0.01, Ptrt x DIM

= 0.86

F677-1; n = 4

Arterial essential AA


-14 4 15 29-14 4 15 29

µm

ol/L

400

500

600

700

800

900

1000

1100

1200Control

Casein

Ptrans x trt

< 0.01 Ptrt

= 0.22, PDIM

= 0.20, Ptrt x DIM

= 0.02

F677-1; n = 4

Mogens Larsen, pers. comm.

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Treatments – abomasal infusion of protein or water

Foulum exp., 4 cows Canadian exp., 5 cows

Infusion af frie aminosyrer

Dage efter kælvning

0 5 10 15 20 25 30

Am

ino

syre

r, g

/d

0

100

200

300

400

500

600

700

800

n = 5

Infusion af kasein


0 5 10 15 20 25 30

Kasein

, g

/d

0

100

200

300

400

500

600

700

800

n = 4

Basal ration: 15.9 % crude prot.

Larsen et al. 2014 Larsen et al. 2015

Basal ration: 16.4 % crude prot.

Infusion of casein Infusion of amino acids

Days after calving Days after calving

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Milk yield increased by 7 to 8 kg/d/cow

Mælkeydelse


-15 -10 -5 0 5 10 15 20 25 30

kg

/d

0

10

20

30

40

50

60

AAT

Kontrol

Ptrt

< 0.01; PDIM

< 0.01

Mælkeydelse


-15 -10 -5 0 5 10 15 20 25 30k

g/d

0

10

20

30

40

50

60

AAT

Kontrol

Ptrt

< 0.01, PDIM

< 0.01

46.0 ± 0.8

38.2 ± 0.9


Foulum experiment Canadian experiment

Days around calving Days around calving

Are control animals experiencing imbalance?

Milk yield Milk´yield

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High utilization of extra protein

Udnyttelse af ekstra AAT til mælkeprotein


4 15 294 15 29

g/d

0

200

400

600

1200

1400

1600

1800 Mælkeprotein CTRL

Mælkeprotein AAT

Infunderet protein

n = 4

65% udnyttelse

43% udnyttelse

60% udnyttelse

Udnyttelse af ekstra AAT til mælkeprotein


5 15 295 15 29g

/d

0

200

400

600

1200

1400

1600

1800 Mælkeprotein CTRL

Mælkeprotein AAT

Infunderet protein

58% udnyttelse

50% udnyttelse

57% udnyttelse

n = 5Larsen et al. 2014 Larsen et al. 2015



Utilization of extra AAT for milk protein Utilization of extra AAT for milk protein

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Catabolism of amino acids?

Urea i blodet


-14 4 15 29-14 4 15 29

mm

ol/

L

2.5

3.0

3.5

4.0

4.5

5.0

5.5Kontrol

AAT

Ptrans x beh

= 0.01 Pbeh

= 0.03, Pdag

= 0.37

n = 4

Urea i blodet


-14 5 15 29-14 5 15 29

mm

ol/

L

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

Kontrol

AAT

Pbeh

= 0.01, Pdag

= 0.50

n = 5Larsen et al. 2014 Larsen et al. 2015



Urea in blood Urea in blood

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Dry matter intake did not change significantly

Tørstofoptagelse


-15 -10 -5 0 5 10 15 20 25 30

kg

/d

0

5

10

15

20

25

AAT

Kontrol

Ptrt

= 0.36, PDIM

< 0.01

n = 4

Tørstofoptagelse


-15 -10 -5 0 5 10 15 20 25 30kg

/d

0

5

10

15

20

25

30

AAT

Kontrol

n = 5

Ptrt

= 0.02, PDIM

< 0.01

18.8 ± 0.4

20.4 ± 0.5




Dry matter intake Urea in bloodDry matter intake

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Only increased fat mobilization in week one

Langkædede fedtsyrer i blodet


-14 4 15 29-14 4 15 29

m

ol/

L

0

100

200

300

400

500

600

700

Kontrol

AAT

Ptrans x trt

= 0.97 Ptrt x DIM

= 0.07

n = 4

Langkædede fedtsyrer i blodet


-14 5 15 29-14 5 15 29

m

ol/

L

0

200

400

600

800

1000Kontrol

AAT

Ptrt x DIM

= 0.05

n = 5



Plasma NEFA Plasma NEFA

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Animal behavior as indicators

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Diseases:

E.g. abnormal walking, reduced eating time, increased lying -> indicator of lameness.

Basic needs:

Easy access to food and water, milking, resting.

Effect of

increasing

milk yield10,9 9,8

4,7 5,4

8,3 8,8

0

4

8

12

16

20

24

20kg 40kg

Standing/walking

Eating

Lying

Hours/day

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In conclusion - future challenges

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To better understand the physiology and immunology of the dairy cow, particularly in the periparturient period

To understand the biological basis of individual differences

To improve phenotyping by:

Making better use of existing data

Developing new biomarkers for common use in management and genomic selection (e.g. physiological imbalance)

To further develop sensors and technology for future automatic proactive management strategies (incl. optimization)

To find “the local truth”

To optimization at both individual cow and herd level

production, reproduction, risk of disease,

environmental impact, animal welfare, ……

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monitoring of the dairy cow for optimizing health and ... · department of animal science, aarhus...

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