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(Hauschild et al ., 2010). Determinations of
nutrient requirements or optimal nutrient
levels are therefore difficult to obtain, due
to the curvilinear nature of population re-
sponses and to the progressive decrease in
the marginal efficiency of the limiting nu-
trients observed in animals (Bikker et  al .,
1994; O'Connell et al ., 2005) or in simula-
tion studies (Pomar et al ., 2003; Wellock et al .,
2004; Brossard et  al ., 2009; Hauschild
et al ., 2010). Variability among the animals
of a given population significantly contrib-
utes to the decrease in nutrient efficiency
over varying nutrient levels (Curnow, 1973),
independently of animal variation arising
from genetic (Knap, 2000; Knap and Jorgensen,
2000; Pomar et al ., 2003), environmental or
animal management sources (Wellock et al .,
2004). Furthermore, Pomar et  al . (2003)
demonstrated that increasing the time over
which animal responses are measured in-
creases the curvilinearity of the responses,
which also contributes to the  curvilinear na-
ture of marginal nutrient efficiencies. None-
theless, the empirical approach can be used to
determine the optimal amounts of nutrients
that need to be provided to populations to
optimize production efficiencies from animal,
economic or environmental perspectives.
Any attempt to extrapolate these findings to
other production situations calls for caution
(Baker, 1986; Pomar et al ., 2003; Hauschild
et al ., 2010).
In the factorial method, however, daily
requirements are estimated as the sum of
the requirements for maintenance and pro-
duction (Fuller and Chamberlain, 1982).
These requirements are estimated for each
nutrient or its precursor and take into ac-
count the efficiency with which each nutri-
ent is used for each metabolic function (van
Milgen and Noblet, 2003). For a given grow-
ing period, requirements are assumed to be
the amount of the given limiting nutrient
that will allow the animal to perform its
needed functions in a normal manner and,
thus, without limiting growth. For example,
as performed by Cloutier et al . (2013), main-
tenance Lys requirements can be estimated
adding the basal endogenous losses (0.313 g
Lys/kg DM × daily feed intake), losses re-
lated to desquamation in the digestive tract
(0.0045 g Lys/kg 0.75 day × BW 0.75 ) and losses
related to basal renewal of body proteins
(0.0239 g Lys/kg 0.75 day × BW 0.75 ) (van Milgen
et  al ., 2008). The requirements of Lys for
growth can be estimated assuming 16% pro-
tein in daily gain (de Lange et al ., 2003), 7%
Lys in protein gain (Mahan and Shields,
1998) and 72% Lys retention efficiency (Mohn
et  al ., 2000). Lys requirements estimated
with the factorial method as implemented
in this example are driven by BW (Lys basal
endogenous, desquamation, protein renewal
losses), BW gain (Lys retention) and, to a
lesser extent, by feed intake (basal endogen-
ous losses). Because pigs within a popula-
tion differ in terms of BW and growth po-
tential, each pig has its own requirement
and this requirement evolves over time ac-
cording to each pig's own pattern of feed in-
take and growth. When the factorial method
is used to estimate the nutrient requirements
of a population of animals, it is common
practice to use the average pig to represent
the population. However, care has to be
taken with this assumption since using the
average pig to feed the population implies
that half of the population will be overfed while
the other half will be underfed (Brossard
et  al ., 2009; Hauschild et  al ., 2010), thus
leading to undesired population perform-
ance. Furthermore, unlike the empirical
method, the factorial method estimates nu-
tritional requirements using information
from one individual at one specific point in
time. Thus, changes that occur during the
growing interval under study are not evalu-
ated. However, when the objective is to maxi-
mize animal performance, maximum require-
ments normally appear at the beginning of
each feeding phase (Brossard et  al ., 2009).
Variation between animals in estimated re-
quirements for maintenance and growth
and in metabolic nutrient efficiencies can-
not easily be considered given the limited
knowledge available in relation to the fac-
tors that can modulate these requirements
and efficiencies.
Ultimately, both methods of estimating
nutrient requirements are based on experi-
mental results from trials studying the re-
lationship between nutrient intakes and
animal responses. In the empirical method,
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