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Comparison of long-term breeding strategies using phenotype, clonal, progeny testing for Eucalyptus Darius Danusevičius 1,2 and Dag Lindgren 1 1 - Department.

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Presentation on theme: "Comparison of long-term breeding strategies using phenotype, clonal, progeny testing for Eucalyptus Darius Danusevičius 1,2 and Dag Lindgren 1 1 - Department."— Presentation transcript:

1 Comparison of long-term breeding strategies using phenotype, clonal, progeny testing for Eucalyptus Darius Danusevičius 1,2 and Dag Lindgren 1 1 - Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, S-901 83, Umeå, Sweden. 2 - Lithuanian Forest Research Institute, Girionys, LT-4312, Kaunas reg., Lithuania.

2 Objective: comparing long term cycling strategies based on phenotype, clone or progeny testing by considering gain, diversity, cost and time Phenotype testing (…n) N=50 (…n), (…m) and selection age were optimized under a budget constraint Clone or progeny testing N=50 (…n) (…m)

3 Long-term benefit 1. Depth of the pocket Other things, e.g. to well see the road 2. Gain per time (engine) 3. Diversity potential

4 Long-term breeding benefit Group Merit per time = (GAIN – C * DIVERSITY LOSS) / TIME Diversity Gain C small breeding pop large breeding pop

5 The long-term program Testing Recurrent cycles of mating, testing and balanced selection N S =50 Within family selection Mating

6 Cycle time and cost Recombination cost Cost per tested genotype (CL & PR) Cost per test plant (= 1’$’) Cycle cost Under a budget constraint Recombination time Time for production of test plants Testing time Cycle time Time is money

7 Scenarios LowMainHigh While testing an alternative parameter value, the other parameters were at main scenario values lower reasonable bound Genetic parameters Time components Cost components assumed typical for Ecalyptus higher reasonable bound

8 And then we did the thing … Results Parameters Breeding cycler at www.genfys.slu.se/staff/dagl.

9 For all tested scenarios, clone strategy was superior Results GMG/Y, % PhenotypeClone Progeny If resemblance between genotype and phenotype is high, there is less need to test it. Phenotype ~ Clone; Phenotype ~ Progeny

10 Short rotation (=high J-M correlation at normal rotation), favored PH as it is cheap and the budget constraint allows fast testing (= higher gain per time). GMG/Y, % PhenotypeClone Progeny

11 Dominance variance had a minor effect and was less favourable for Clone strategy. Diversity loss had a minor effect (~ 25 or 80 individuals). BP can be sublined according to BV of the members. For PH, cycles are shorter = faster loss of diversity; and if BP is small, PH ~ PR.

12 Effect of genotype cost was small. Increasing the genotype cost is an option only if other benefits can be achieved. PhenotypeClone Progeny GMG/Y, % Cost per test plant was important, but less important for phenotype strategy.

13 Phenotype strategy is better the lower the budget is, but at high budget it is not superior to Progeny strategy PhenotypeClone Progeny GMG/Y, % At short Tbefore, PR ~ PH, thus, for PR the first flowering could be speeded up and at a high cost as increase of genotype-dependent cost was not so important.

14 Conclusions Clonal testing is suggested to be the best testing strategy. Phenotype testing is most to its advantage at high h 2. If clone testing is not an option, it seems preferable to progeny testing at short rotations and low budget. Progeny testing can be better than phenotype testing when h 2 is very low, flowering early, budget high and rotation long.

15 Breeding Cycle Analyser If your breeding plan is based on cycling and within family selection, then which is the best testing strategy for selection of the new parents? Find the answer by the aid of this simulator which allows you to consider gain, diversity, cost and time simultaneously. It is easy to use and is just a few mouse clicks away from you at www.genfys.slu.se/staff/dagl 1.Set the parameters common for all the testing alternatives 2. Set specific parameters for each testing alternative and find optimum test size and time 3.Check the final result


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