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Low Input Tree Breeding Strategies

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Input close to operative Swedish conifer breeding. 12. 18. 24. 30. 4. 6. 8 ... Multigenerational comparison of testing strategies in Swedish conifer breeding ... – PowerPoint PPT presentation

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Title: Low Input Tree Breeding Strategies


1
Low Input Tree Breeding Strategies
  • Dag Lindgren1 and Run-Peng Wei2,3
  • 1Department of Forest Genetics and Plant
    Physiology
  • Swedish University of Agricultural Sciences
  • SE-901 83,Umeå, Sweden
  • 2 South China Agricultural University, Wushan,
    Guangzhou 510642, China
  • 3 Sino-Forest Corporation, Sun Hung Kai Centre,
    Wanchai, Hong Kong
  • October 9, 2006, Turkey

2
No strict limit between high input and low!
  • Thinking low input helps making high input more
    efficient
  • Input level can vary between tiers (elite vs
    main)
  • Other factors than budget important

3
High-input techniques
  • Breeding values estimated from offspring or
    relatives
  • Test plantations
  • Clone archives
  • Controlled crosses
  • Known pedigrees
  • Orchards intensively managed exclusively for
    seed production
  • Grafts for seed production.

4
Low input situations
  • Poor
  • Unstable organisation
  • Uncertain continuity
  • No specialists
  • Minor program.

5
Low-input techniques
  • Selection on phenotypes instead of testing of
    genotypes
  • No records of tree ID or pedigree
  • Wind pollination
  • Seeds derived from stands used for other
    purposes
  • Cheap plantations created for future seed
    production and long term improvement.

6
Low-input techniques
  • Thin stands rather intense to get better pollen
  • Harvest seeds from best trees for production and
    long term improvement.

Buttry to make predictions of inbreeding,
coancestry and diversity.
7
Plantations combining objectives
  • Plantations which look and are managed similar to
    "normal" plantations
  • Limited need of specialised competence and
    organisational stability
  • Multiple use (options for seeds, improvement,
    wood, conservation...)
  • Can function as seed collection area cheap
    trees may be cut for seed collection (climbing
    often too expensive)
  • Not too long rotation time (to keep cones
    harvestable and to speed up improvement
  • Close to local organisation, enterprise and
    people better and cheaper management.

8
Phenotypic selection
  • No tree identities required
  • No computer required
  • No strict objective measures required
  • Transparent (not black box)
  • Can be executed immediately in field
  • A type of selection forwards
  • Also called mass-selection
  • Similar to Nature, sustainable and environmental.

9
Phenotypic selection
  • No separate test populations needed!

10
Testing doubtful low-input
  • more complicated
  • more demanding on temporal and organisational
    stability
  • requires trust in future
  • Not needed if relaying on phenotypic selection.

11
Phenotypic selection
  • Phenotypic selection may be as powerful or more
    powerful than BLUP (selection for best estimate
    of breeding values), as I will show.
  • For following slides Combined index selection is
    a breeding value estimate based on performance of
    an individual as its sibs.
  • There are procedures for finding the most
    efficient selection at a certain diversity in a
    population of a large number of large families.
    Ill show

12
Maximising gain at a given diversity by selection
in infinite normal distributions. h20.25 and
P0.1
Gain
0
1
Modified From Lindgren and Wei 1993
13
  • This was theoretical mathematical. To make it
    more realistic a simulator (POPSIM, Mullin) was
    used. Input close to operative Swedish conifer
    breeding.

14

Restricted selection for Phenotypic and Combined
index, conciders both individual and family) in a
population created by 20 parents with family size
20, h20.5. Points correspond to restriction
intensity. Simulation (POPSIM). Balanced
selection means 2 selections per parent
Phenotypic
Combined index
Balanced
Andersson 1999 and others
15
Note
  • Phenotypic selection as good as restricted
    combined index compared at same gene diversity!
  • Now lets consider without restrictions

16

Phenotypic
Combined index
Balanced
Andersson 1999 and others
17
Comparing Three Selection strategies
30
Phenotypic
24
Combined index
Gain
Balanced
18
12
4
6
8
10
12
14
Effective number (Ns)
18
  • In the following diversity is measured as loss of
    gene diversity since tree improvement started.
    This equivalent to status number as used in
    earlier figures, but scale and direction on the
    diversity axis changes
  • Phenotypic selection works with multigenerational
    programs also

19
Restricted selection for Phenotypic and Combined
index during multiple generations A population
with a family structure, h20.5, family size 20
Gain
0
0.1
0.2
0.3
0.4
0.5
Loss of gene diversity
Andersson 1999 and others
20
  • Phenotypic selection is compatible also in a
    multi-generation program
  • For unrestricted selection genetic variation get
    exhausted. In the long run phenotypic selection
    give more gain
  • However, if breeding population large and
    heritability small, this exhaustion takes long
    time (next figure).

21
One and five generations of restricted selection
in a population with a family structure, h20.05,
family size 500. Low heritability and large
families favor combined index
Phenotype
Combined index
40
5 generations
30
Gain
20
10
1 generation
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Loss of gene diversity
22
Development of Gain and Gene Diversity over five
generations of selection in a population with a
family structure, h20.05, family size 500, for
three selection strategies.
40
Combined index
After five generations
30
Phenotypic
Gain
20
Balanced
10
After first generation
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Loss of gene diversity
23
  • Combined index selection does not seem to give a
    superior gain and erodes diversity. However,
    comparisons can be made at variable breeding
    population size!

24
Comparison under variable breeding population size
  • The size of the breeding population is under the
    breeders control and could be different
    (optimized) for different strategies
  • Comparisons were done under a fixed plant number
    (1280) as fixed resource
  • Simulations by POPSIM similar to earlier.

Li and Lindgren 2006
25
Gain for phenotypic selection compared to
combined index selection after first generation
under fixed test plant number
Combined index selection seems not inferior, and
thus gets rehabilitated. Combined index is much
better only when heritability is low
Li and Lindgren 2006
26
Gain for phenotypic selection compared to
combined index selection after five generations
Phenotypic selection is better at high
heritability The alternatives become similar
efficient when the gene diversity is high Low
heritability favours combined index selection. At
moderate or high heritabilities phenotypic
selection seems equal or slightly superior after
some breeding generations
27
  • These comparisons assume the size of the breeding
    population is a free resource, and that is
    certainly not the case.

28
Multigenerational comparison of testing
strategies in Swedish conifer breeding
  • Clonal testing is much superior to
    progeny-testing
  • Phenotypic testing better than progeny-testing at
    low budget

Danusevicius and Lindgren 2002
29
How may clonal testing look like in practice in
low budget?
  • Clone trial of Eucalyptus camaldulensis converted
    to seed orchard based on clonal performance in
    the trial

Verghese et al 2004
30
Fertility variation matters for accumulation of
coancestry over generations
  • It can be predicted
  • Female contributions can be controlled

31
Fertility variation matters for accumulation of
relatedness over generations
Control over female is powerful and easy (count
seeds)
The development over generations in a closed
population of 154 teak trees based on their
observed fertility variations (Bila et al. 1999)
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