9.9.1 An example: beef cattle breeding

Beef cattle breeding is rather small in the Netherlands. In countries like Australia or the USA, or within European countries like France or the UK, beef cattle breeding is a much larger business. The cows graze on large areas of land and are not handled at regular basis. Therefore, AI is not a very useful tool in the reproduction. Most farmers buy bulls for natural mating and let them graze with the cows. The very large farms also breed their own bulls.

Consider a population of beef cattle that is selected for increased growth. The heritability is 0.35 and the phenotypic standard deviation (σ_p) is 0.2 kg / day. The females are selected on their own performance. As the population size is supposed to remain constant and females can produce about three calves in their lives, 2/3 of the females need to be selected to produce sufficient animals for replacement (remember that both male and female calves are born!). A selected proportion of 0.67 results in a selection intensity (i_f) of 0.54. The accuracy of selection for selection on own performance is equal to  √h²  so r_IH,f = 0.59.

The males are selected based on the performance of 100 progeny, resulting in a r_IH of 0.95. Each male is mated to 10 females, resulting in a selected proportion of 0.10 * 0.67 = 0.067. The selection intensity thus is 1.95 (check in the table in 9.5.1).

Finally, the genetic standard deviation is equal to the square root of h² *  = 0.35 * 0.2² = 0.118. What is the genetic gain in this population?

Filling all that information into the formula results in a genetic gain per generation of:

 = 0.257 (kg/day)

Genetic gain per generation does not provide the insight in the genetic improvement that was intended. To achieve that, the genetic gain per generation needs to be scaled to genetic gain per year. The average age of the females when they produce their average offspring is 4.5, so L_f = 4.5. Males are selected after progeny information has become available, resulting in a generation interval of 5 yrs. Predict the genetic gain per year in this population.

Scaling the genetic gain per generation to the generation interval results in a genetic gain per year of:

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