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in an average reduction in nitrogen excre-
tion of 15% over the four parities (Table 4.5 ).
Further improvements can be achieved
by the use of multiphase feeding during ges-
tation. This can be realized in practice by
using computerized automated feeding sys-
tems. Two gestation diets were formulated
differing in amino acids and CP contents.
The diets contained 3.0 g digestible lysine
and 99.7 g CP, and 5.5 g digestible lysine
and 145 g CP per kilogramme of feed, re-
spectively. The two diets were mixed in ad-
equate proportions to meet, on a daily basis,
the amino acid (and digestible phosphorus)
requirement ( Fig. 4.5c) . Compared to the
single diet feeding strategy, the multiphase
strategy reduced intake of CP and SID lysine
by 14% and 17%, respectively, and nitrogen
excretion by 20% (Table 4.5 ). With this
strategy, over the four parities, gestation
diets 1 and 2 contributed to 35% and 65%
of total gestation feed intake, respectively.
Compared to the one-phase feeding strat-
egy, the two-phase and the multiphase feed-
ing strategies (Fig. 4.5d) reduced consump-
tion and excretion of phosphorus by 5% and
9%, and by 7% and 12%, respectively ( Table 4.5 ) .
When the change in feeding strategy was
combined with phytase supplementation,
phosphorus excretion was reduced by 20%
between the extreme strategies.
The effect of the feeding strategy on feed
cost is not easy to assess because of its  high
sensitivity to the relative price of the different
feed ingredients. Moreover changing the feed-
ing strategy may result in extra costs for stor-
age or distribution of feed when the number
of feeds used on the same farm increases. The
cost of feed is about 6% lower with the two-
phase strategy compared with the one-phase
feeding strategy (Table 4.5 ), and 8% lower
with multiphase feeding. This indicates that
improving the feeding strategy of sows during
gestation appears a promising approach to re-
ducing nitrogen and phosphorus excretion
while simultaneously reducing feeding cost.
evaluate different feeding strategies for
sows, from both a nutritional and environ-
mental perspective. These tools address
nutrient utilization in a dynamic way and
allow identification of the limiting factors
in the diets and/or excessive supplies.
Knowledge of how nitrogen and phos-
phorus deposition evolve over time in re-
lation to feed intake is essential if nitrogen
and phosphorus excretion are to be re-
duced.
Adapting the feeding strategy during
gestation to better account for the evolu-
tion of nutrient requirement appears a
promising approach to reducing nitrogen
and phosphorus excretion without increas-
ing feed cost. However, from a practical
point of view, this may be difficult to
achieve, especially in smaller herds. The
two-phase feeding strategy during gesta-
tion requires differentiating the type of
diets according to parity and stage of gesta-
tion. The multiphase feeding strategy
could be easier to adopt by using auto-
mated sow feeding stations. Moreover, this
strategy allows one to better account for
the variability in nutrient requirements be-
tween sows, by considering individual
body condition at mating.
In the future, different objectives could
be identified for the evolution of such
models. The first objective would be to com-
bine a sow nutrition model, such as In-
raPorc, and a sow farm model, such as the
stochastic dynamic model developed by
Martel et al . (2008). This would allow con-
sideration of the effect of nutrition in the
farm model, for example, on sows delay of
return into oestrus after weaning, as well as
to predict the variability in nutritional re-
quirements resulting from variability of per-
formance between sows, or according to
time. The second objective would be use
the set of equations from the InraPorc model
in order to develop algorithms for real-time
calculation of nutrient requirements ac-
cording to housing conditions based on
actual (and previous) performance of each
individual sow, and implement these algo-
rithms in automated sow feeding stations,
as proposed by Pomar et al . (2010) for grow-
ing pigs.
Conclusion
Simulation models and decision support
tools, such as InraPorc, can be used to
 
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