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Modeling nutrient utilization in pigs

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(1)

Modeling nutrient utilization in pigs

Symposium on Inheritance, History, and Innovation in Animal Nutrition and Feed Science Sichuan Agricultural University, June 26-28, 2016

(2)

Outline

Variation in energy utilization and energy values

Mechanistic (nutritional) modeling of growth

Concepts

Efficiency of nutrient utilization

Model-derived amino acid requirements

Dealing with variation among animals

Conclusions

(3)

Sauvant et al., 2004

0 5 10 15 20 25 3035 40

GE (kJ/g DM) 45

GE values of feed ingredients

(4)

0 5 10 15 20 25 30 35

Energy value (kJ/g) 40

GE values of nutrients

(5)

Lys Met Cys Thr Trp Ile Leu Val Phe Tyr His Arg Ser Gly Ala Glu Gln Pro Asp Asn

0 5 10 15 20 25 30 35

Energy value (kJ/g)

GE values of amino acids

(6)

10 15 20 25 30 35 40 60

70 80 90 100

NDF (%)

Energy digestibility (%)

growing pigs: 0.90

Le Goff and Noblet, 2001

Effect of fiber on energy digestibility

(7)

10 15 20 25 30 35 40 60

70 80 90 100

NDF (%)

Energy digestibility (%)

growing pigs: 0.90

sows: 0.64

Effect of fiber on energy digestibility

Le Goff and Noblet, 2001

(8)

0 10 20 30 40 50 60 70 80 90 100 0.80

0.82 0.84 0.86 0.88 0.90 0.92 0.94 0.96 0.98 1.00

crude protein (% in DM)

ME/DE

Sauvant et al., 2004

The ME value of protein depends on its utilization

(9)

deposited protein digestible

protein

excess protein

carbon chain urea

2 NH3 + CO2 + 4 ATP → urea (22.6 kJ/g N)

The ME value of protein depends on its utilization

(10)

Lys Met Cys Thr Trp Ile Leu Val Phe Tyr His Arg Ser Gly Ala Glu Gln Pro Asp Asn

0 5 10 15 20 25 30

35 urea carbon chain

Energy value (kJ/g)

The ME value of protein depends on its utilization

(11)

nutrient intake nutient deposition

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

protein lipid carbohydrate fiber ash

Rate, kg DM/d

The transformation of dry matter into a pig

(12)

Nutritional modeling of growth

(13)

protein deposition

water deposition ash

deposition lipid

deposition

body weight gain

nutrient intake

Concepts used in nutritional growth models

(14)

0 50 100 150 200 250 300 350 400 450 500 0

20 40 60 80 100 120

Protein intake, g/d

Protein deposition, g/d

Whittemore and Fawcett, 1974

upper limit to protein deposition

(PDmax)

gross efficiency of protein utilization

Protein deposition depends on protein intake

(15)

0 5 10 15 20 25 30 35 40 45 50 0

20 40 60 80 100 120

DE intake, MJ/d

Protein deposition, g/d

Whittemore and Fawcett, 1974

Protein deposition also depends on energy intake

upper limit to protein deposition

(PDmax)

~ minimum LD/PD ratio

(16)

Key concepts

Growth is mainly determined by PD and LD

Upper limit to PD

Energy partitioning rule (between PD and LD)

Protein quality affects PD

These (can) change during growth

(17)

lipid

protein starch sugars fiber

intermediary metabolism

deposited

lipid ATP

deposited protein

heat

The transformation of organic matter into a pig

(18)

theoretical experimental

starch  lipid 0.84 0.84

lipid  lipid 0.97 0.88

protein  lipid 0.67* 0.52

protein  protein 0.87* 0.60

*no protein turnover

The energy efficiency of nutrient transformation

van Milgen, 2002

(19)

van Milgen, 2002

60 70 80 90 100 110 120

kJ ME / ATP

Energy cost of ATP synthesis

(20)

glucose

intermediary metabolism

ATP

lipid glycogen

non-essential amino acids

~74.2 kJ/ATP

Energy cost of ATP synthesis

(21)

direct 74.2 kJ/ATP = 100%

via glycogen (muscle) 97%

via glycogen (liver) 95%

via glutamate (amino acid) 95%

via glutamate (protein) 82%

via lipid 80%

Energy cost of ATP synthesis

(22)

(essential) amino acids

protein synthesis

 5 ATP degradation

 0 ATP glucose or

acetylCoA

energy cost

amino acid “loss”

Energy cost of protein deposition

(23)

protein turnover

cycles* energy efficiency

0 0.87

1 0.78

2 0.70

3 0.64

*5 ATP/cycle

glucose as energy source (74 kJ/ATP)

Energy cost of protein deposition

(24)

Halas et al., 2004

Towards a more mechanistic approach

(25)

Creating simplicity in complexity

www.rennes.inra.fr/inraporc/

(26)

diet ileal indigestible

specific endogenous losses standardized ileal digestible

minimum catabolism

(=100% - maximum efficiency)

excess deposition

basal endogenous losses maintenance

available

Factorial calculation of amino acid requirements

for growing pigs

(27)

Item Value

Body weight, kg 50

DM intake, kg/d 2

Protein deposition, g/d 150 Lys content in body protein, % 6.96 Minimum catabolism of Lys, % 28 Maintenance Lys requirement,

mg/(kg BW0.75)/d 28.4

Basal endogenous losses,

mg/kg DM intake 313

0 2 4 6 8 10 12 14 16 18

deposition

minimum oxidation maintenance

basal endogenous losses

SID Lys requirement, g/d

Factorial calculation of amino acid requirements

for growing pigs

(28)

Protein deposition and feed intake vary differently during growth

0 20 40 60 80 100 120 140 160

0 500 1000 1500 2000 2500 3000 3500

0 20 40 60 80 100 120 140 160

Body weight, kg

Feed intake, g/d Protein deposition, g/d

Amino acid requirement ~ protein deposition feed intake

The bottom line:

We have to construct these curves!

(29)

Lysine utilization according to InraPorc

(30)
(31)

The InraPorc and NRC models for growing pigs

Conceptually very similar, but different approaches towards:

basal endogenous losses

efficiency of amino acid use

variation among animals

Does it matter?

(32)

Model-derived Lys requirements for growing pigs

20 40 60 80 100 120 140

0 1 2 3 4 5 6 7 8 9 10

InraPorc NRC

Body weight, kg

SID Lys requirement, g/kg diet

(33)

Model-derived SID Thr:Lys requirements for growing pigs

20 40 60 80 100 120 140

56 58 60 62 64 66 68 70

InraPorc NRC

Body weight, kg

SID Thr:Lys requirement, %

(34)

Amino acid InraPorc NRC

Met 30 29

Met + Cys 60 58

Thr 65 (64-65) 64 (61-68)

Trp 18 18

Val 70 66 (65-68)

Ile 55 53

Leu 100 101

Phe 50 61

Phe + Tyr 95 95

His 32 34

Arg 42 46

Average ideal amino acid profile for growing pigs

(35)

70 80 90 100 110 120 130 140 0.3

0.5 0.7 0.9 1.1 1.3

Age, d

Lys requirement, %

Dealing with variation among pigs:

which pig in the population do you want to feed?

(36)

1850 1980

real-time monitoring

precision livestock farming?

2010

nutrition as “art”

modeling &

computing characterization

nutrient discoveries

1950

The future?

(37)

Acknowledgements:

Ludovic BROSSARD1 Jean-Yves DOURMAD1

Kees DE LANGE2 Serge DUBOIS1 Michel ÉTIENNE1

Jean NOBLET1 Bernard SÈVE1 Alain VALANCOGNE1

1INRA-Agrocampus Ouest

2University of Guelph

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