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Models in Broiler Nutrition:
a Quest for Optima
New Zealand Poultry Industry Conference
12-13 October, 2010
Palmerston North, NZ
Background
• Many variables in broiler production are nonlinear
– BW vs. Age
– Allometric growth (composition of gain)
– Carcass composition (maintenance costs)
14 2
2
15
12
10 9 8 8 7
15
17
19
20
22
22
22
22
22
1
7
15
16
17
17
0
10
20
30
40
50
60
70
80
90
100
0 14 28 42 56
% o
f ca
rcas
s
Age (d)
Breast
Legs
Fatpad
Other
Heart
GI tract
Change in Composition
Males
Objective
• To develop robust nonlinear prediction equations to describe growth and development in response to
– nutrient intake• ‘Prestarter’ nutrition
• Dietary ME
• Dietary balanced protein
– Genotype• sex
Experimental Design
– 2 x 2 x 3 x 5 factorial arrangement of treatments
– 2 sexes
– 2 prestarter nutrient densities (0 to 11 d)• Recommendation† for maximized growth rate and feed
efficiency
• Recommendation† for reduced feed cost
†Cobb Broiler Nutrition Supplement, 2004, Cobb-Vantress, Inc., Siloam Springs, AR, 72761
Experimental Design
– 3 ME levels (11 to 56 d)
• 94%
• 97%
• 100% of recommendation†
– 5 Dietary Balanced Protein (DBP) levels
• 85%
• 92.5%
• 100%
• 107.5%
• 115% of recommendation†
E94
E97
E100
P85
P92.5
P100
P107.5
P115
†Recommendation for maximized growth rate and feed efficiencyCobb Broiler Nutrition Supplement, 2004, Cobb-Vantress, Inc., Siloam Springs, AR, 72761
Approach
• Strain: Cobb x Avian 48
• Individual weekly BW data on 1,200 broilers
• Serial dissection of 2,224 broilers
– 8 birds per sex x PS interaction at 11 d
– 2 birds per treatment at 3 and 4 wk of age
– 4 birds per treatment semi-weekly from 4.5 to 8 wk of age
Variables Measured
• BW
• Feed intake
• Processing – weights of
– Eviscerated carcass (feet, head and neck removed)
– Breast (P. major + P. minor)
– Abdominal fatpad (including fat on gizzard)
– Legs (drum + thigh)
– Wings
Nonlinear models
• Feed intake
• BW
• Yield
Feed Intake Model
ME intake = f(BW, ME, Lys, ADG, Sex)
ME intake = 0.14BW0.456(ME/Lys)1.227(1-0.013 x Sex) + 0.67ADG1.44 (1-0.17 x Sex)
150 170 190 210 230 250 270 290 310
Re
lati
ve c
on
trib
uti
on
to
ME
inta
ke
ME/Lys (kcal/g)
P85
P100
P115
Feed Intake – Sex Effect
0
100
200
300
400
500
600
700
0.0 1.0 2.0 3.0 4.0
ME
inta
ke (
kcal
/d)
BW (kg)Females Males Females Pred Males Pred
Feed Intake – ME Effect
0
100
200
300
400
500
600
700
0.0 1.0 2.0 3.0 4.0
ME
inta
ke (
kcal
/d)
BW (kg)
E94 E97 E100 E94 Pred E97 Pred E100 Pred
How Does Nutrient Intake Affect
BW Gain?
),,,( ageCPLysMEfBW
Young birds:BW gain is
Protein driven
BW gain switches toME driven
BW gain switches toME driven
ME drivenBW gain -Improvingresponse
at low CP level
ME drivenBW gain -Improvingresponse
at low CP level
ME drivenBW gain -Improvingresponse
at low CP level
How Does Nutrient Intake Affect
Breast Muscle Growth?
),,,( ageCPLysMEfADGBreast
Young birds:Breast growth is
Protein driven
1.63 g
Breast growth becomes more
ME driven:Follows BW
1.77 g
3.48 g
4.53 g
4.65 g
Predicted BW Curves: Sex
0
1
2
3
4
5
0 7 14 21 28 35 42 49 56
BW
(kg
)
Age (d)
Female
Male
Source of Variation
Sex P<0.0001
• Males were heavier than females
*Mixed Gompertz model
BW Curves: Prestarter
0
1
2
3
4
0 7 14 21 28 35 42 49 56
BW
(kg
)
Age (d)
PSHigh
PSLow
Source of Variation
PS P=0.0002
• Prestarter nutrient levels had a persistent effect on BW
*Mixed Gompertz model
Predicted BW Curves: Energy
0
1
2
3
4
0 7 14 21 28 35 42 49 56
BW
(kg
)
Age (d)
E94
E97
E100
Source of Variation
ME x sex P<0.05
• In females, no ME effect on BW• In males, E94 > E97 > E100
*Mixed Gompertz model
Predicted BW Curves: Protein
0
1
2
3
4
0 7 14 21 28 35 42 49 56
BW
(kg
)
Age (d)
P85
P92.5
P100
P107.5
P115
Source of Variation
DBP ns*Mixed Gompertz model
Yield Analysis (nonlinear)
• Allometric analysis was conducted using Huxley’s equation
– y was the weight of the carcass part (g)
– x was the weight of the eviscerated carcass (g)
– a and b were coefficients, estimated using the NLIN procedure of SAS
baxy
Huxley, Julian S. 1932. Problems of Relative Growth. 36 Essex Street W. C., London: Methuen & Co. Ltd.
y=0.166x1.081
y=0.135x1.112
Bre
ast
(g)
Source of Variation
Sex P<0.0001
Fatp
ad (
g)
y=0.0026x1.269
y=0.0008x1.488
Source of Variation
Sex P<0.0001
y=0.374x0.979
y=0.321x1.002
Legs
(g)
Source of Variation
Sex P<0.0001
y=0.368x0.848
y=0.292x0.882
Win
gs (
g)
Source of Variation
Sex P<0.0001
y=0.155x1.092
y=0.176x1.075
Bre
ast
(g)
Source of Variation
PS P=0.0378
P115 y=0.171x1.081 a
P107.5 y=0.163x1.087 ab
P100 y=0.171x1.080 b
P92.5 y=0.180x1.070 c
P85 y=0.176x1.071 d
P115P107.5P100P92.5P85
Source of Variation
DBP P<0.0001
-50
-40
-30
-20
-10
0
10
20
0 1000 2000 3000 4000Bre
ast
We
igh
t (g
, re
lati
ve t
o c
on
tro
l)
BW (g)
P115 P107.5 P100 P92.5 P85
-40
-30
-20
-10
0
10
20
30
40
50
0 500 1000 1500 2000 2500 3000
Bre
ast
(g, r
ela
tive
to
P1
00
mal
es)
BW (g)
P85 P92.5 P100 P107.5 P115P85 P92.5 P100 P107.5 P115
FemalesMales
P85 y=0.007x1.212 a
P92.5 y=0.009x1.133 b
P100 y=0.013x1.075 c
P107.5 y=0.015x1.044 d
P115 y=0.002x1.339 e
P115P107.5P100P92.5P85
Source of Variation
DBP P<0.0001
0
3
6
9
12
15
0
4
8
12
16
20
E94 E97 E100 P85 P100 P115 Female Male
Fat
Pro
tein
Protein (%) Fat (%)
ba
b ab
ab
abb
bc
a
ab
c
Carcass Composition (52 d)
Source: B. L. Schneider, MSc thesis
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
0
5
10
15
20
25
30
35
40
45
E94 E97 E100 P85 P100 P115 Female Male
Fat
Pro
tein
Protein (%) Fat (%)
aba b ns ns
ns ns ns
P. Major Composition (52 d)
Source: B. L. Schneider, MSc thesis
Conclusions
• Sex, prestarter nutrition, and subsequent dietary ME and DBP levels had significant nonlinear effects on
– Feed intake
– BW
– Yield dynamics
• Practical nonlinear models were robust enough to identify similar trends as ANOVA
• Strain-, environment-, and management-specific data will improve the prediction value of the model in commercial applications
Now go solve your nutritional puzzles!
Who knows what doors predictive modeling will open for you?
Questions