Dag Lindgren Uppsala May 14

1. POPSIM etcNote that this is discussion, not firm statements, and written very late yesterday evening without checking. But as we have one hour to fill... Dag Lindgren Uppsala May 14

2. Possible reasons why Breeding Cycler favors a 2-step strategy more than POPSIM • Cycling is penalized by 3 years budget and a drop in gain for increase in coancestry. (Here Breeding cycler may be to harsh to cycling) • Optimization for timing gives higher weight and longer time in field to the more efficient progeny-test. • Even other optimisations may be more efficient in a 2 stage strategy. • POPSIM does not credit extra gain for a two years longer phenotypic test in two step.

3. POPSIM vs. BREEDING CYCLER • Two steps, first phenotype when progeny testing of pre-selected has been run by BREEEDING CYCLER and when POPSIM. POPSIM runs indicate they are similar in annual gain, while BREEDING CYCLER found it 20% better in main scenario. What could the reasons be?? • BREEDING CYCLER considers cost for crosses and generation turn over and optimizes more, including duration for field testing and cycling, POPSIM does not. This is a possible reason for the different results.

4. Timing considerations

5. Two stage is an efficient use of the breeding cycle • Progeny testing and phenotypic testing uses the breeding cycle much more effective for testing if used 2 stage wise than if used as two independent subsequent cycles. Thus it is surprising for me that 2 stage does not appear still better than phenotypic in POPSIM.

6. Timing considerations

7. Efficient use of breeding cycle • Note that progeny-testing utilizes the breeding cycle for field testing very inefficient • Two stage and combined utilizes most of the breeding cycle for field testing • Phenotypic is rather efficient but not in top

8. POPSIM • Does not adapt well to testing time. Longer field testing does not increase gain. Most strategies use different testing time and can thus not be fairly compared. This is not trivial to mend and need some thinking. • Does not consider costs for e.g. grafts, but cost is easily adjusted for that, and needs no change in the code. The difficulty is actually that not much effort has been done to estimate costs. • Calculates gene diversity loss and can make gene diversity loss equal in different scenarios, but has no mechanism for ranking scenarios with different gene diversity loss. But a cost and genetic penalty of gene diversity loss can easily be made and needs no change in the code. • Invests rather much energy in creating the actual base line situation. That is history and just set to base line. It is a bit unfortunate that the creation of the founder population depends on a variable among scenarios, so this trivial factor may cause false differences among scenarios. The latest will probably be remedied but at the cost of an additional input variable. • It is bloody difficult to set up mating schemes with desires about a high degree of PAM and restrict against half-sib mating. To restrict for half-sibs is easier for a real breeder, but a real breeder would also find it hard to make perfect PAM except for SPM.

9. Strategy 5, combined • Development of this part seems to be very worthwhile and for me looks as the most important and promising development. However we have differences in opinion about concepts and terminology, and I suggest that we discuss that more and try to reach a consensus before writing the actual code. The manus Dag, Darius and Ola is one of the sources for this discussion. As it also tries to exploit basically this strategy, it would be better if the concepts converged. E.g. I feel more comfortable with “breeding population” rather than “pre-selections”, and now call Olas pre-selections for candidate parents.