Agriculture Reference
In-Depth Information
nutrient requirements for a population based
on the single deterministic response will
introduce a bias against individuals with a
higher nutrient requirement. These errors
can be magnified during the optimization
process, which is dependent on the herd nu-
trient responses. Not only is the introduc-
tion of animal genetic variation essential for
more accurate nutritional optimization but,
according to Knap (1995), it also influences
financial outcomes through the subsequent
variation in production characteristics (feed
intake, growth rate, backfat, hot carcass
weight, lean yield and gross profit). Further
reasons for considering between-animal
variation in pig modelling are: (i) to predict
more accurately the optimum strategy for
shipping pigs to market to increase the pro-
portion of 'full-value' pigs per close-out;
and (ii) to enhance production through bet-
ter utilization of space and minimizing per-
formance failures. There are other sources of
variation (feed and physical environment)
that influence the individual's response but
these will not be addressed in this chapter.
In general, variation is introduced into the
model by randomly generating a value
around the mean and standard deviation of:
(i) the genetic potential parameters; (ii) ini-
tial size; (iii) ability to cope with social
stress; and (iv) health score.
Initial size (body weight for a given age)
Individuals within a population are likely
to have different starting body weights for a
given age, and therefore different amounts
of protein, lipid, water and ash. Assuming a
fixed starting age, initial body weight will
vary according to the population mean
weight and standard deviation. This vari-
ation at the start of the growing period will
be a significant factor affecting the variation
in body weights at slaughter. Wellock
et al
.
(2004) modelled the effect of varying the
standard deviation of initial body weight
from 0 kg to 12 kg and concluded that this
variation would substantially affect the sub-
sequent population mean growth response.
Based on previous grower-finisher trials at
Nutreco Canada, the coefficient of variation
of feeder pigs (initial weights close to 25 kg)
varied from 0.06-0.17 with an average of
0.11. Part of the variation in starting weight
will be derived from the individual's poten-
tial growth rate, and therefore start weight
will be correlated with the genetic param-
eters (Wellock
et al
., 2004). Individuals with
the highest growth potential will tend to have
the highest initial weight.
Ability to cope with social stress
Earlier studies have clearly demonstrated
that individual pigs within a pen interact dif-
ferently with each other, and these inter-
actions can affect individual performances
(Tindsley and Lean, 1984). Data from Giroux
et al
. (2000) indicate that social interactions
can account for 9% of the variation in average
daily gain (ADG) in growing pigs. Socially
dominant individuals, often larger individ-
uals, are less affected by social stresses and
tend to perform better than their subordinates
when exposed to suboptimal production con-
ditions (e.g. high stocking density, inadequate
feeder space) (Botermans, 1999). Wellock
et al
.
(2003c) proposed a modelling approach to
incorporate social interactions between indi-
viduals and the effects these have on sub-
sequent performance. The authors introduced
a genetic parameter (A2C) to describe 'the
Genetic potential
As described in a previous paper (Ferguson,
2006) and other similar models (Knap 2000;
Pomar
et al
., 2003; Wellock
et al
., 2003a),
the genetic potential growth of an individ-
ual pig can be defined in principle by three
components: (i) potential rate of maturing
(
B
) or its uncorrelated with
Pm
equivalent
(
B
* =
B
×
Pm
0.27
); (ii) mature protein weight
(
Pm
); and (iii) inherent fatness or desired fat
level relative to protein weight (
LPm
). Data
for these parameters and their variability
are limited and confined to a generic esti-
mate for the genotype irrespective of gender.
Typical coefficient of variation (CV) values
are 0.01-0.03 for
B
*, 0.05-0.07 for
Pm
and
0.10-0.15 for
LPm
.
