Leading the way in Agriculture and Rural Research, Education and Consulting

Towards Identifying Novel Livestock Phenotypes

in Different Feed and Production Systems

Mizeck Chagunda

SRUC

Acknowledgements

• SRUC Dairy Research Centre

Team

• Peter Lovendahl : University of Aarhus

• Liveness Banda: Lilongwe, Malawi

• Nic Friggens: Agri Paris Tech/INRA

• Stephen Ross: SUDT: Singapore

• Dave Ross, Eileen Wall &

Malcolm Mitchell: SRUC

• Laura Randall: University of Nottingham

• Reuben Newsome: University of

Nottingham

• Dave Roberts: SRUC

• Jan Philipsson & SLU for invitation

Outline

• Feed and Production Systems

• Novel Phenotypes

• Some examples: – Reproduction

– Feed utilisation efficiency and environmental impact

– Cow Health

• Implications and Application

The Langhill Herd

2 Feeding Systems

Home-grown grazing & ration grown on farm (7000 kg milk/cow/year,

36 % of DM)

By-products ration based on co-products (11000 kg milk/cow/year,

50% DM)

2 Genetic Lines

Control

Select

SRUC Dairy Centre

Long running G x E experiment

200 cows

BPS, BPC, HGS, HGC

Recording and Goal

• Both traditional and advanced technologies for large scale measurements of phenotypes

• Measure both normal and difficult to measure traits

• Goal: improved resource efficiency; reduced environmental impact; improved animal health while producing high quality product.

Consistency across groups

• Staff

• Housing

• 3 x daily milking

• Health and fertility

• Young stock rearing

• S and C managed together

• Replacement policy - 3 lactations

• Same conserved forages offered within group

• Data as a commodity

Smallholder Dairy Systems

Why Phenotypes?

• Observable characteristics

• Phenotype is King (Mike Coffey)

Source: Council for Responsible Genetics

Why Phenotypes?

Why Novel Phenotypes?

• Traditional and conventional phenotypes have served us well, however…

• The Times They are Changin’ (Bob Dylan, 1964)

• Global challenges of Food Security and Climate Change

• Require animals that can adapt to… and practices that can mitigate climate change

Traits

Reproduction

Feed Utilisation Efficiency

and Environmental Impact

Health

Cow Activity

• Pedometers

• Neck collars

Reproduction cycles

• Gold standard for reproductive activity is progesterone

• The normal cycles are usually easier to identify

• The start of cycling or lack of it is the problem

• Impossible in heifers (using milk)

Friggens & Chagunda, 2006

0

2

4

6

8

10

12

14

16

18

1 9 17 25 33 41 49 57 65 73 81 89 97

P4

Days after calving

Need for automation

• Difficult with manual sampling

• Need for specialised equipment/techniques (e.g. ELISA, RIA, Herd Navigator, etc)

Friggens & Chagunda, 2006

Profiles from Activity Meters

Lovendahl & Chagunda, 2011

Activity

Days to first high activity episode

Breed Parity N Median SD

Red Dane 1 86 35 18

2 69 26 27

3 42 25 22

Holstein 1 90 36 30

2 53 40 30

3 31 39 32

Jersey 1 50 37 32

2 43 30 22

3 22 41 33

Milk recording data obtained using automatic milking units (Robots) 267

lactations; 111298 milkings

Lovendahl, Chagunda, et al 2010

Average daily activity in different genetic and

feeding systems

Variable Production system

BPC BPS HGC HGS

Milk yield (litres/day) 30.5 36.8 24.5 26.1

BEC (MJ/day) 4902 4356 4284 3938

No of steps/day 1336 1324 1644 1612

Motion index/day 50.3 49.0 62.1 61.3

Standing duration (hrs) 12.90 12.61 13.54 13.63

Feeding duration (h/d) 4.39 4.83 5.05 5.13

Banda, et al. 2016

Overall motion index two feed types

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Mo

tio

n in

dex

Time (days)

Dairy mash

Maize bran

Banda, et al. 2016

Water Intake vs Cow Activity

0

10

20

30

40

50

60

70

80

90

100

-15 -10 -5 0 5 10 15

Wat

er In

take

an

d A

ctiv

ity

Days to Oestrus

Activity

Water Intake

Any Genetic Component to it?

Genetic and Phenotypic variance, ACTIVITY

0

0,5

1

1,5

2

2,5

3

3,5

0 100 200 300

DIM

Variance

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

S_Total

Animal=P+G

S_Perm

S_Gene

Residual

T

H2

Løvendahl and Chagunda, 2006

Implications

• The cow indicates when ready to be served

• Continuous data flow and hence applicable in

both large scale and smallholder systems

• Potential to be used as a bench mark to

Artificial Insemination (AI) and fertility

Enteric Methane

Respiration calorimetric chambers

Breath Measurement in Livestock

• Breath analysis is steadily gaining ground in

livestock production.

• Examples – Lassen et al. 2012

– Ross et al 2012

– Garnsworthy, 2012

– Laser Methane Detector (Chagunda et al, 2009; 2011; 2013; 2015)

• Providing a platform for cow-side methods

Laser Methane Detector

• Based on infrared absorption

spectroscopy

• Using a semiconductor laser as

a collimated excitation source

• Employs second harmonic

detection of wavelength

modulation spectroscopy to

establish methane concentration

Non-contact and non-invasive

• Amenable to animal

welfare

• Safe for operator and

animal

• Able to take measurements

without disturbing the

animals

Recording Duration

0

200

400

600

800

1000

1200

1400

1600

0 100 200 300 400 500 600

cow1814

Breath Eructation

0

200

400

600

800

1000

1200

1400

1600

0 100 200 300 400 500 600 700

cow1953

ppm

ppm

Time, s

Relationship between measurements

y = 0.6983x + 114.95

0

100

200

300

400

500

600

700

0 100 200 300 400 500 600 700 800

Laser Methane Detector (ppm)

Meta

bolic

Cham

ber

(ppm

)

Chagunda, M.G.G and Yan T. 2011..

Any Genetic Component?

• (Predicted) methane

heritable and varies

– Chr 2 (chr signif only)

• International

development of

genomic predictions for

novel traits from

resource populations

Laser methane

h2 = 0.06-0.13

rg ~ 0.6 (highest)

Pickering, Chagunda et al, 2015

Implications

• Methane contributes to climate change

• Enteric methane reflects feed energy loss

• Implications on Feed Conversation efficiency

• Feed energy loss is money lost and GHG

increased

Body Energy Content

1500

2000

2500

3000

3500

4000

1 6 11 16 21 26 31 36 41

Bo

dy e

nerg

y c

on

ten

t (M

J)

Time (weeks)

BPC BPS

HGC HGS

Body Condition Score and Lameness

• BCS was found to be significantly associated with lameness

• BCS minimum target threshold of ≥2 for control of severe lameness Randal, Green, Chagunda et al. 2015

Health Indicators in Milk

• Disease are traditionally categorical traits

• But disease is a creeping phenomenon

• Continuous indicators in milk

Chagunda et al. 2006

0

20

40

60

80

100

120

130 150 170 190 210 230 250 270 290

Time from calving

mic

ro m

oles

/min

; and

L

0

2

4

6

8

10

12

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

130 150 170 190 210 230 250 270 290

Time from Calving

Out

Ris

k

Mastitis risk profile

Chagunda et al. 2006

What have we learnt so far

• Importance of setting up robust recording

systems

• New Phenotypes will help us improve efficiency

• Partnerships are very important (e.g. across

country genetic valuations)

• Cows are always communicating

…thank you for your attention

If Phenotype is King…

…then Recording System is the Prince