The most successful method used for improving the growth rate of broilers is genetic
selection. Improvements in nutrition, housing and disease resistance have been
impressive, yet genetic selection is purported to have contributed the majority of the
tremendous increase in growth rate that has taken place over the past 50 years (McKay,
2008). Many selection strategies are available, but not all are suitable, as the choice is
dependent on the objective of the breeder. Selection strategies are bound to change over
time as different traits become more important, and this has been the case in the broiler
industry: focus was initially placed predominantly on growth rate, but the negative genetic
correlation that exists between growth rate and reproductive and liveability traits has
forced breeders to change their position, especially as growth rate has almost reached its
upper limit and reproductive traits lag behind. This has resulted in a change from single
trait to multiple trait selection.
In the exercise reported here, four selection strategies commonly used for single trait
selection, namely individual, between family, within family and family-index selection, were
applied to a simulated broiler population using the Monte Carlo method of simulation, and
constructed with the use of genetic parameters obtained from the literature. Theoretical
and simulated methods of the four selection strategies were compared. A fifth selection
strategy, index selection, was applied to represent multiple trait selection. The relative
merit of each selection procedure was then compared, as well as the results obtained
from the theoretical and simulated methods. Construction of the selection index was
complex in comparison to single trait selection, as each trait included in the index had to
be assigned an economic value. This value is representative of the relative importance of
that trait to the overall profitability, or ability to save costs in the operation. Therefore traits
favourable to profitability, or having the ability to reduce production costs, are given a
heavier weighting and will consequently achieve a relatively larger improvement when
applied to the selection index. A model was constructed using production rates, income
and costs to represent the current overall economic situation in the industry. This was
then used to determine cost economic values, which represent the saving in cost per unit
improvement in each of the economically important traits, and revenue economic values,
calculated as the value of each unit improvement attained in each of the economically
important traits. Body weight remains the most profitable trait in a broiler enterprise; however breeder egg
production is equally important as the industry would fail without sufficient day-old broilers.
Therefore, it would be beneficial to determine whether current egg production levels could
be maintained, or even improved, whilst improvement is made to the growth rate of the
progeny.
The above statement was found to be possible with the use of index selection. This
multiple trait selection strategy proved capable of defying the negative genetic correlation
that exists between body weight and egg production by improving egg production to 60
weeks by eight eggs, and body weight at 35 days by 259 grams. Furthermore, in some
cases index selection was able to achieve improvements in some traits greater than those
attained with single trait selection, whilst simultaneously improving certain negatively
correlated traits. Index selection has illustrated its superiority over single trait selection
strategies and its relative value to the poultry industry. / Thesis (M.Sc.Agric.)-University of KwaZulu-Natal, Pietermaritzburg, 2009.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/10885 |
| Date | January 2009 |
| Creators | Tempest, Justine Claire. |
| Contributors | Gous, Rob Mervyn. |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0023 seconds