After selection the animals are mated for the first time and offspring will be born. In this example animals on average will have two batches of offspring (single or litters). The length of the generation interval is equal to the age in between the births of both batches. Assumption in this figure is that the number of offspring born in each batch is the same. If not, then the (length of the) generation interval needs to be weighted according to the number of offspring in each batch.
For example, in a sheep breed the ewes will have their first batch of offspring (a single) at 1 year of age, and the second offspring (a single ) at 2 years of age. The generation interval in that case is (1*1 + 1*2)/(1+1) = 1.5 years . However, if those same ewes would generally have single in the second batch, but some would have twins, so that the average number of offspring in the second batch would be 1.3 lambs, then the generation interval would become (1*1 + 1.3*2)/(1+1.3) = 1.56 years.
For animals that are selected on the performance of their first progeny the ‘counting’ only starts from the second batch of offspring onwards. This is presented in the lower part of figure 4. Otherwise the principle is exactly the same as with selection based on own performance or sibs. It is clear that the generation interval will become longer if selection is based on progeny testing.
If we continue the example with the sheep, selection is based on the first offspring. Now the ewes all get the opportunity to produce an extra batch of offspring, so that each ewe will produce 2 batches after being selected as parents. The average age of the third batch of offspring is at 3 years of age, and the ewe on average will have 1.5 lambs. The generation interval will become: (1.3*2 + 1.5*3)/(1.3+1.5) = 2.54 years.
The genetic gain thus far was expressed per generation. Now that we have calculated how many years are in a generation, we can express the genetic gain per year:
Note that there is a relation between accuracy of selection and generation interval. The accuracy can be increased by improving the information sources to base the EBV on. Information on performance of (large numbers of) progeny gives the highest accuracy. However, it also takes a long time to collect this information. In other words: the generation interval increases. So the improvement in genetic gain per year because of the increased accuracy may be outbalanced by the increase in generation interval. Also, producing lots of offspring of parents that have not been approved for breeding will cost a lot of money.
If we briefly go back to the example with the jumping rabbits: The breeder was happy when selection was based on performance of 12 offspring. However, he may want to look into the matter in more detail because it will depend on the litter size whether this number can be achieved with a single batch of offspring, or whether multiple batches are required. Multiple batches mean more time and the generation interval in rabbits is low. In such situations it may be a consideration to accept a slightly lower accuracy of selection, but manage more generations of selection in the same time frame. It may result in more genetic gain per time unit in the longer run.
Thus:
Optimising genetic gain will require a balance between increase of the accuracy and increase of the generation interval |