Agriculture Reference
In-Depth Information
(a)
(b)
1.2
29
3P
MPG
MPI
1.0
26
0.8
23
0.6
20
0.4
17
Period (weeks)
Period (weeks)
0.2
14
12345678
9 0 1 2
123456789
10
11
12
Fig. 12.4.
Average weekly SID Lys concentration in diets (a) and average weekly SID Lys intake (b) in pigs
fed according to three-phase (3P), group daily-phase (MPG) and individual daily-phase (MPI) feeding
systems in trial AIPF I (see text for treatment details).
Table 12.3
Nutrient intake and nitrogen balance of pigs fed according to a three-phase feeding programme
(3P) obtained by blending fixed proportions of diets A and B, a commercial three-phase feeding programme
(COM), or two daily-phase feeding programmes in which the blending proportions of diets A and B were
estimated daily to match the lysine requirements of the group (MPG) or of each individual pig (AIPF I)
1
.
Treatments
Item
3P
COM
MPG
MPI
SEM
P
values
2
Crude protein intake, g/day
480
a
433
b
433
b
405
b
5.2023
<0.0001
SID Lys intake, g/day
23.8
a
23.9
a
19.7
b
17.4
c
0.4160
<0.0001
Protein intake/ADG,
3
g/kg
433
a
409
b
395
b
368
c
5.3600
<0.0001
SID Lys intake/ADG, g/kg
21.4
b
22.6
a
17.9
c
15.8
d
0.4349
<0.0001
Nitrogen retention, kg/pig
2.17
2.08
2.08
2.06
0.0147
0.6385
Nitrogen excretion, kg/pig
4.04
a
3.52
b
3.54
b
3.17
b
0.0729
0.0002
1
LS means obtained from a repeated measures analysis.
2
Effects of treatment, period and interaction were considered in the statistical analysis. Period was significant (
P
< 0.01) for
all variables. The interaction period × treatment was significant (
P
< 0.05) for crude protein intake, SID Lys intake, protein
intake/ADG and nitrogen excretion.
3
Means within lines followed by different letters are significantly different (
P
< 0.05).
a,b
and
c
indicate significant statistical differences among means within rows.
Conclusions and Perspectives
pig's patterns of feed intake and growth rep-
resents a fundamental paradigm shift in pig
nutrition since nutrient requirements are no
longer a population attribute estimated from
past data as used in actual models (e.g. van
Milgen
et al
., 2008; NRC, 2012) but a dy-
namic process that evolves for each animal
independently following its own feed intake
and growth trajectories. These trajectories
result from each animal's intrinsic (i.e. appe-
tite, genetic growth potential, physiological
state, etc.) and extrinsic (i.e. ambient tem-
perature, humidity, space allowance, group
size, space feeder allowance, etc.) driving
forces. In the proposed feeding approach,
these forces are not explicitly represented in
the model for the real-time estimation of
Feeding growing pigs individually with daily
tailored diets, whose formulation is based on
each animal's real-time patterns of feed in-
take and growth, is a key element of the sus-
tainable precision pig farming system ap-
proach proposed in this chapter and described
elsewhere (Pomar and Pomar, 2012). To feed
individual pigs with daily tailored diets, nu-
trient requirements have to be estimated in
real time using the available information from
the farm. In the context of farms equipped
with precision feeding systems, as those used
in the described experiments, such informa-
tion will be daily feed intake and body
weight. The real-time estimation of individ-
