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©Longin
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How to optimize and compare
breeding schemes ?
Dr. Friedrich Longin
State Plant Breeding Institute
University of Hohenheim, Germany
©Longin
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Determination of the optimum allocation of test resources
• How to determine the optimum allocation?
• Which criteria to use for optimization?
• Which data is required for optimization of breeding schemes?
©Longin
Click to edit Master title styleModeling the optimum allocation
needs hierarchically model framework
1. Basic level
• Target criterion
• Trait
2. Breeding level
• Scheme
• Scenario
3. Optimization level
• Test resources
©Longin
Click to edit Master title styleModeling the optimum allocation
needs hierarchically model framework
1. Basic level
• Target criterion = selection gain, probability
• Trait = maize grain yield
2. Breeding level
• Scheme = DHTC, S1TC-DHTC, multi-stage sel.
• Scenario = variance components, budget, selected
fraction, technical requirements,...
3. Optimization level
• Test resources = number of test locations, testers,
replications, DH lines
©Longin
Click to edit Master title styleDetermining the optimum allocation
within a given model framework
1. Basic level
• Selection gain
• Maize grain yield2. Breeding level
• DHTC (I)
• Given scenario3. Optimization level
specific allocation of
the number of testers,
test locations, DH lines,
replications
Computation of
target criterion
AIM: Find the allocation
maximizing selection
gain for that specific
def. of level 1 and 2
= optimum allocation
©Longin
Click to edit Master title styleOutline
Determination of the optimum allocation of test resources
• How to determine the optimum allocation?
• Which criteria to use for optimization?
• Which data is required for optimization of breeding schemes?
©Longin
Click to edit Master title styleDefinition of the target criteria
yihG
• i = selection intensity,
• h = square root of the heritability,
• σy = standard deviation of the target
variable
hgenPPP
• Pgen = prob. to have interesting geno-
type in population,
• Ph = prob. to identify that genotype,
©Longin
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Source: Becker 1993, Longin et al. 2006
Selection intensity and heritability
For a fixed budget, maximization
of ∆G represents a compromise
between a high number of test
candidates and a high number of
locations and replications
©Longin
Click to edit Master title styleDistribution of genotypes in the
breeding population
©Longin
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μ'
α
Selection gain ∆G
Selection gain (ΔG)
= Genotypic mean of
the selected fraction
–genotypic mean of
the entire population
' G
μ
Fre
quencie
s
©Longin
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μ μ'
α
thres-hold
P(q%) =
Percentage
exceeding the
threshold, e.g., the
1- q % quantile of
N(0,1)
Probability to identify superior genotypes
P(q%)
Fre
quencie
s
©Longin
Click to edit Master title styleOutline
Determination of the optimum allocation of test resources
• How to determine the optimum allocation?
• Which criteria to use for optimization?
• Which data is required for optimization of breeding schemes?
©Longin
Click to edit Master title styleEconomic frame and quantitative-
genetic parameters
• Framework:
– Budget for the whole breeding program
– Costs for genotyping, phenotyping, seed
production,…
– Ratio of variance components like:
• Logistics:
– Availability of winter nurseries
– Availability of marker platforms
– Maximum number sof field locations, lines,
crosses,…
22222 :::: eglyglgyg
©Longin
Click to edit Master title styleExamples for maize
• Logistic assumptions
– 10 DH lines can be produced from a single S1 (250 kern.)
– 1 multiplication of DH lines needed to have sufficient
seed for perse test, isolation with tester and further
multiplication
– Two row trials on testcross performance with 33 plants
per row (sowing of 55 kernels per row)
• Economic assumptions
– Costs for producing one DH line = 8 Euro
– Costs for one testcross plot with two rows = 15 Euro
– Costs for one isolation row with 20 plants = 10 Euro
– Costs per hand selfing / crossing = 0.6 Euro
– Costs for one observation row (not harvested) = 6 Euro
– Equal costs in summer and winter season
