Genetics of feed efficiency in dairy and beef cattle Donagh Berry 1 & John Crowley 2 1 Teagasc,...

Genetics of feed efficiency in dairy and

beef cattleDonagh Berry1 & John

Crowley2

1Teagasc, Moorepark, Ireland 2University of Alberta, Canada

donagh.berry@teagasc.ie

American Society of Animal Science, Cell Biology Symposium, Phoenix July 2012

Motivation

• World food demand is increasing ….

• Land-base is decreasing …..• More from less!!!

• Genetics is cumulative and permanent• Good• ….and bad!!!

Objective of talk

To challenge the current dogma

(Daily) feed efficiency is the most important trait ever!!

Feed is the largest variable cost

Agree that feed is the largest variable cost but is addressing

daily feed efficiency the best use of resources?

Objective of talk

To challenge the current dogma

We need to collect lots of feed intake

data (for breeding)

Really? (for breeding!!)

(Feed) efficiency – growing animals

• Feed conversion ratio

• Kleiber ratio

• Relative growth rate

• Residual feed intake

• Residual average daily gain

FCR - traditional measure but:•Ratio trait (breeding)

•can be linearised•anyway would you recommend selecting on it?

•Correlated with growth – mature size•Breeding goal can restrict cow size

•Most variation explained by growth•More or less the same for other traits

•….

(Feed) efficiency – growing animals

• Feed conversion ratio

• Kleiber ratio

• Relative growth rate

• Residual feed intake

• Residual average daily gain

FCR - traditional measure because:•Easy to calculate

•The dog on the street knows what it is

•Correlated with growth•Poor animals will unlikely have good FCR

•Never going to recommend single trait selection anyway

• Feed conversion ratio

• Kleiber ratio

• Relative growth rate

• Residual feed intake (RFI)

• Residual average daily gain (RG)

(Feed) efficiency – growing animals

A few points – RFI & RG• Byerly (1941) actually first suggested • RFI & RG are (restricted) selection

indexes• Never more efficient than an optimal selection

index• Is this why it is difficult to explain variation in

RFI??• Is all the heritability we see true heritability in

feed efficiency?• Re-ranking on index versus component traits

• Koch et al. (1963) actually favoured RG• Issues with how RFI/RG is modelled

National breeding objective

Goal = Growth rate + fertility

ADG

Fert.

ADG

Fert.

Goal Goal

ADG

Fert.

Goal

Would you go for the goal or the individual traits?

Residual Feed Intake (RFI)

6

7

8

9

10

11

12

13

6 8 10 12 14 16

Predicted Feed I ntake (kg DM/d)

Act

ual

Fee

d I

ntak

e (k

g D

M/d

)

DMI = ADG + LWT + … + e

Residual Feed Intake (RFI)

6

7

8

9

10

11

12

13

6 8 10 12 14 16

Predicted Feed I ntake (kg DM/d)

Act

ual

Fee

d I

ntak

e (k

g D

M/d

)

More efficient animals “under the

line”

DMI = ADG + LWT + … + RFI

Residual Feed Intake (RFI)

6

7

8

9

10

11

12

13

6 8 10 12 14 16

Predicted Feed I ntake (kg DM/d)

Act

ual

Fee

d I

ntak

e (k

g D

M/d

)

High ADG

Low ADGWhat the producer

wants

6

7

8

9

10

11

12

13

6 8 10 12 14 16

Predicted Feed I ntake (kg DM/d)

Act

ual

Fee

d I

ntak

e (k

g D

M/d

)Residual Daily Gain (RDG)

Daily Gain (kg/d)

Daily G

ain

(kg

/d)

More efficient animals “over the

line”

ADG = DMI + LWT + … + RDG

So…..• RFI is independent of live-weight & growth• RG is independent of live-weight & feed

intake

• -1*RFI + RG must still be independent of live-weight (apparently a favourable characteristic but I’m not sure why given we recommend using selection indexes)• But negative correlation with feed intake

and a positive correlation with gain

An alternative• 2,605 performance test bulls from

Ireland

• Calculated RFI and RG

• Residual intake & gain (RIG) = -

1*RFI+RG

Trait DMI ADG LWT RFI RG RIG

DMI 0.55 0.73 0.59 - 0.03 - 0.35

ADG 0.38 0.37 0.01 0.82 0.47

LWT 0.59 0.34 - 0.17 0.06 0.11

RFI 0.58 0.00 0.00 - 0.46 - 0.87

RG 0.00 0.70 0.00 - 0.40 0.83

RIG - 0.37 0.41 0.00 - 0.85 0.85

Berry and Crowley, (2012)

Genetic

above diag.

Back of the envelope calculations

John Crowley PhD Thesis Top 10% of animals ranked on RFI, RG and RIG

DMI ADGRFI 9.2 1.71RG 10.7 2.18RIG 9.9 2.06

300 kg weight to

gain

Age to slaughter Total DMIRFI 176 1619RG 137 1474RIG 146 1446

Assumed constant ADG and DMI throughout … ridiculous I know!

(Feed) efficiency –lactating animals

• Milk solids per kg live-weight

• Milk solids per kg intake (FCE)

• Intake per kg live-weight

• Residual feed intake

• Residual solids production

RatiosSimple

Principle from beefNot common

Same “(dis)advantages” as FCR

Is RFI/RSP really useful?RFIt = DMIt – ([Milk]t + BWt

0.75 + ΔBWt + BCSt)

RSPt = MSt – (DMIt + BWt0.75 + ΔBWt + BCSt)

DMI: 15.6 kg/dLWT: 452 kgMilk Yld: 24.83 kg/dSimilar elsewhere

DMI: 20.6 kg/dLWT: 602 kgMilk Yld: 24.89 kg/dSimilar elsewhere

RFI: -1.386 kg/dRSP: 0.174 kg

RFI: -1.386 kg/dRSP: 0.194 kg

However ….

• Systems efficiency is key (nationally!)

Where can we make the most gains??

BeefBeefReplaceReplaceCowCowDAIRY DMInDMInDMIn

valuebeefvalueMilkFCEHerd

OffReplaceReplaceCowCow

OffBEEF DMIweanDMInDMIn

loss)(weanVALUEFCEHerd

BeefBeefReplaceReplaceCowCowDAIRY DMInDMInDMIn

valuebeefvalueMilkFCEHerd

However ….

• Systems efficiency is key (nationally!)

OffReplaceReplaceCowCow

OffBEEF DMIweanDMInDMIn

loss)(weanVALUEFCEHerd

Fertility?

Genetics of feed

efficiency

Heritability (h2)• One of the most mis-interpreted

concepts in quantitative genetics

• Proportion of the differences in performance among contemporaries that is due to additive (i.e. transmitted) genetic differences• Growth rate, milk yield ~35%

• Fertility, health <0.05%

• Remaining variation is not all management!!

Heritability – growing animals

Meta-analysis of 45 studies/ populations

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

ADG WT DMI FCR RFI RG KR RGR RI G

Trait

Her

itab

ility

Most performance traits are around 35% heritable

Of course variation is (arguably) more important

σriΔG Genetic gain

IntensityAccuracy

Variation

CVgRFI = 1-3%CVgDMI = 3-6%

h2Information

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

WT DMI FCR RFI

Trait

Her

itab

ility

Heritability – lactating animals

Meta-analysis of 11 studies/ populations

Coefficient of genetic variation 4-7%

Genetic correlations among measuresTrai

t FCR RFI RG

DMI0.39

[-0.57 to 0.90]0.72

[-0.34 to 0.85]-0.03

[-0.03 to 0.00]

ADG-0.62

[-0.89 to 0.75]0.02

[-0.15 to 0.53] 0.82

WT-0.03

[-0.62 to 0.88]-0.01

[-0.40 to 0.33] 0.07

RG-0.89 -0.46

RFI0.75

[-0.21 to 0.93]

Genetic correlations with performanceTrait FCR RFI RG

Lean-0.47

[-0.72 to 0.54]-0.18

[-0.52 to 0.52]

0.03

Fat0.08

[-0.29 to 0.49]0.20

[-0.79 to 0.48] -0.44

Carcass conf

-0.47[-0.6 to -0.02]

-0.30 [-0.56 to 0.29]

0.35

Carcass fat-0.23

[-0.61 to 0.11]0.06

[-0.37 to 0.33] -0.10

Carcass wt-0.44

[-0.69 to -0.26]-0.11

[-0.60 to 0.26]

0.32

Mature weight

-0.62 [-0.62 to -0.54]

-0.23[-0.23 to -0.22]

0.67

Milk 0.03 0.57

Feed intake / efficiency

in a breeding program

Feed efficiency or not feed efficiency….that is the question

• RFI is uncorrelated with weight and ADG• …or is it!!!!

• RFI is derived at the phenotypic level• Does not imply genetic independence

• Simulated feed intake with a phenotypic correlation structure with weight and ADG• h2 RFI = 0.06 ± 0.03• “Picking up” genetic correlations with

weight and ADG

So would you put it in a breeding goal• No! It is a breeding goal in itself!

• Why not?1.Confusing term2.Feed intake economic weight placed

on individual performance traits – transparency, customized indexes

3.Selection bias is genetic evaluations – “uncorrelated” with selection traits

4.Not optimal adjustment for fixed effects

Put feed in

take in

the

breeding goal

Put feed intake in the breeding goal

We need to collect lots of feed intake data (for breeding)

Really? (for breeding!!)Selection index theory

Selection index theory

• Using information on genetic merit of animals for individual traits to predict genetic merit of a composite• Analogous to multiple-regression; PROC

GLM, PROC MIXED, PROC REG• Confounding factors already removed• Used in all breeding objectives• Especially useful for low heritability traits• Also useful in difficult to measure traits

Goal = feed intake (Growing animals)

Traits DMI ADG

ADG 0.78

LWT 0.75 0.68

C’G-1C = 69.8%

Meta-analysis of up to 20 studies

Goal = feed intake (Growing animals)

Traits DMI ADG LWT

ADG 0.78

LWT 0.75 0.68

Fat 0.28 0.09 0.21

C’G-1C = 71.1%

Meta-analysis of up to 20 studies

Goal = feed intake (Growing animals)

Traits DMI ADG LWT Fat

ADG 0.78

LWT 0.75 0.68

Fat 0.28 0.09 0.21

Muscle 0.01 0.19 0.23 0.72

C’G-1C = 89.6%

Meta-analysis of up to 20 studies

Goal = feed intake (Lactating animals)

C’G-1C = 89.4%

Veerkamp & Brotherstone, 1994

Traits DMI Milk LWTStatur

e

Milk 0.59

LWT 0.27 -0.09

Stature 0.13 0.42 0.52

Chest width 0.28 0.24 0.79 0.37

Is it worth going after the remaining

10%

Gaps in knowledge• Is researching daily feed efficiency

the best use of resources to improve system efficiency • We have the parameters to investigate• Personally I would focus on feed intake

• Prediction of feed intake• Phenotypic ≠ genetic• Do not forget selection index theory• KISS

• Water efficiency, methane efficiency

Straying a bit…..• Methane researchers ≈ Feed efficiency

researchers• Feed efficiency• Ratio rates are bad

• Environment• Ratio traits are no longer bad• Phenotype = CH4/kg DMI

• Random simulation of CH4 (h2=0); h2

DMI = 0.49 • h2 CH4/kg DMI = 0.19 ± 0.05

What I want to know…residual methane production (RMP)

CH4= milk + maintenance + intake + body tissue change + e

Any genetic

variation??

Conclusions

• We now know a lot about the feed intake complex

• Time to take stock, evaluate, and prioritise

Acknowledgements

• Financial support:

• ASAS

• EAAP