Breeding by integrative design for making more productive, resilient and consumer preferred crop varieties

www.iita.orgA member of CGIAR consortium
Breeding by integrative design for
making more productive, resilient and
consumer preferred crop varieties
Asrat A. Amele

Outline
• Introduction: why integrative design?
• The model
• User profiling and requirement analysis
• Target traits and mechanisms: what works
where?
• Recombining traits and alleles
• Conclusion

Why integrative design?
• Secure access to sufficient, safe and nutritious food for a
growing world population is an enormous challenge
• World food production needs to be increased by 70% until
2050 (FAO, 2009)
• Limits of agricultural land available and issues of climate
change
• Modified/new environments and stress
factors
• Meet current food demand and future needs
• Rely on yield increase by more efficient farming

Why integrative breeding?
• Breeding is the first step in a long chain of necessary
actions to increase crop yield
• But, crop breeding is getting complicated by number of
requirements for the new varieties
• Productive
• Resilient
• Nutritious
• Safe
• Consumer/market preferred
• Added values
• Dynamics of social organizations and complexity of
contextual factors encircle the crop
Breeders are dealing with
•Changing context
•Moving target
• To meet the
present and future
needs

Why integrative design?
In order to address these
•Methodologically plural approach/model
• that makes use of tools/practices from social
and biological science arena to develop
robust varieties
• More productive
• Resilient
• Customer/market appreciated

2. The model: working principle
 Productive variety may not be successful if it is not
tasteful or has no market
User needs cannot only be considered as an
articulated existing demand. The demand is also partly
an extrapolated demand for which it is essential to
understand environment and market trends and to know
characteristics that the genepool can provide
 can be created through exposure
Phenotype = combined effect of G + E + S

Traits and
mechanisms
Integrative design

1. User profiling and requirements
• User groups/market share
• What materials under current use?
• How users value their materials (attributes used to
assess)?
• The extent to which their current varieties provide these
traits?
 Such analysis provides opportunities for
strategic development and introduction of
new materials
• Desk study
• Expert and focus group
discussion
• Individual interview
• Exposure to garmplasm

Germplasm appraisal
• Farmer qualitative assay
• Molecular markers
• Common beans
• Potatoes
Helps to understand nature of materials
and level of diversity to define breeding
strategy that meet end-user need
What materials under current use?

Farmer qualitative assay:- the case of
common beans

Relative importance of the varieties
• Grown/preferred by
majority within and
across sites
• Grown/preferred by few
within and across site
• Grown/preferred by
specific groups/sites

Molecular assay

Ethiopia Both Kenya

-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2
-0.2-0.10.00.10.20.3
PCA with Lynch distance
pca2[, 1]
pca2[,2]
Red: Eritrea, Green= Rwanda, Blue= Uganda and Black= Kenya
Potato landraces
from East Africa

How users value the materials?
 Inventory of the preference criteria used to
describe and discuss the varieties
 Importance of these traits in describing
and discussing varieties

Ranking of criteria considered important by evaluators for
assessing potato varieties at harvest
Score
Orderof
importance Score
Orderof
importance Score
Orderof
importance
Tuberskincolor 19 IV 16 V 35 V
Tubersize 21 III 31 III 52 III
Tubernumber 37 II 34 II 71 II
Tubershape 18 V 19 IV 37 IV
Freenessfrompestanddisease 40 I 50 I 90 I
Men Women Total
Criteria

Farmer assessment and selection of new materials
Farmer criteria vs local varieties
Farmer criterial vs new genotypes

Fig. Comparison of genotypes based of logistic regression analysis of farmers perceptions on
different traits. Log odds ratio in y-axis indicates the chance of being in a high (very good for
in performance for that particular trait) or low (poor for the trait) response category. The
higher log odds ratio mean the genotype is rated good the trait than poor in many of the rating
exercises and vice versa.

Farmers impression with Andean potatoes in Ethiopia
Ranking of clones at harvest Ranking of clones after cooking

Logistic regress analysis of farmers preference for potato clones
e9= CIP704205

• User preferences are variable between gender, locations and individuals
• Rural small-scale producers preferences and markets are not static
• Recording farmers rating or ranking for traits in breeding may not be
effective or predictive of adoption for new variety and resulting variation
among farmers.
• Impossibility of capturing farmers preference at a single-point snapshot
rather use of combination of methods for capturing and using farmers
preferences and concerns in plant breeding when this relates to common
beans and drought breeding for southern Ethiopia
• Conventional idea of developing varieties that have the same colour, size
and shape of grains/tubers as what farmers traditionally grow may
actually restrict the introduction and exposure of farmers to novel,
attractive and adapted germplasm.

User profiling and requirement analysis
Traits users value most/trait
benchmarks for improvement
Rational use of adapted germplasm
to harness the power of heterosis
 Increase diversity by introducing
new alleles from exotic germplasm
into adapted background
To produce novel locally-adapted
varieties

2. Traits and mechanisms: which works where
General targets in case of potatoes
A. Traits “missing” from advanced
populations and adapted varieties
B. Higher levels and more highly
heritable sources of needed traits
C. Complementary sources to build
durable resistance to variable
pathogens.
D. Horizon scanning: emerging
constraints; climate change.
E. Opportunities for new/added-value
traits not previously attended by
breeding programs
 Pests, PVY, PVX, BW
resistance
 PLRV resistance
 Late blight resistance
 Heat, salinity and
drought tolerance
 Nutritional value
(micronutrient density)

SN
Accession
Number
Vareity Name
Country of
first
selection/rel
ease
Year of
release
Countries
where
released
Tigoni Kenya 1998 Kenya
Lulimile 2011 Mozambique
Tengeru 2012 Tanzania
Thandizo Malawi
Victoria Uganda 1991 Uganda
Victoria 1998 Burundi
Asante 1998 Kenya
Asante 2012 Tanzania
Kinji DRC
Victoria Rwanda
Zikomo Malawi
Madagascar
Belete Ethiopia 2009 Ethiopia
Kenya mpya 2010 Kenya
Meru 2012 Tanzania
Uganda 11 Burundi? 1985 Burundi
Kenya sifa 2002 Kenya
Gahinga DRC
3
2
1 CIP381381.13
CIP381381.20
CIP393371.58
CIP7200974
Traits defining variety release in multiple
countries
LB
(R)
PVX(ER)
Earl
y

Fe and Zn profiling local and breeding lines and breeding gains in potato
5
10
15
20
25
30
Cycle 0 Cycle I (NCD I) Cycle II (NCDII)
mg/kgZn(DW)
9
21
21
25
24
28
> 20%
10
20
30
40
50
Cycle 0 Cycle I (NCD I) Cycle II (NCDII)
mg/kgFe(DW)
15
25
21
33
27
42
> 40%
Ranges for breeding lines
Fe (31-42ppm)
Zn (21-52 ppm)

Trait correlations when selections for high tuber yield
Fe (21.1ppm) Zn (27.7ppm)
TTY (19.4tha-1) -0.18 -0.21
ATW (67.6gm) -0.21 -0.21
DM (26.3%) 0.02 -0.24
TNTPL (20) -0.26 -0.19
TTWPL (0.7kg) -0.34 -0.29
Selection for high Fe content
Fe (34.2ppm) Zn (36.8ppm)
TTY (5.5 tha-1) -0.37 -0.20
ATW (55.7 gm) -0.25 -0.02
DM (26.1%) -0.38 -0.58
TNTPL(10) -0.26 -0.31
TTWPL(0.2kg) -0.37 -0.37

Quality traits in potato

Improved grain filling
and seed yield
Greater biomass
partitioning
Increased leaf area
development
Acquisition of nutrients and water
Consumer preference
Traits and Mechanisms: which works where
General targets in case of
common beans

Phenotyping for root traits

Non-stress

Combine stress for drought and BSM infestation

Any molecular base for farmer preference?

3. Combining traits and alleles

X
♂♀
Clonal
Selection
Progeny of heterozygous
parents

In process adding new traits to a
variety
Impossible to recover
variety when adding a
single trait
Impossible to recover
variety when adding a
single trait
Everything shuffles!

Support
Populations
New Cycles
Evaluation
Genetic studies
• Parental value
• Heritability
• Variability
• Heterosis
Advanced
populations
Elite lines
3. Combining traits and alleles
Incorporation
Nobilisation and
Introgression of
new diversity
ProgenitorsProgenitors

Formalize parent choice base on relatedness, complementarity and
breeding value
Reduce time
Use of more growing season
Two season
Off season
End-user participation
Female
Parent
Male
Parent
Shangi Early Early
bulking,
Tigoni 381381.13 MR S Medium High
yield
Tuber
greening
Asante 381381.2 MR wide
adaptati
on, good
big &
fewer
tubers
Desiree S S S S Drought
tolerant,
chipping
susceptib
le to
virus
Kenya mpya 393371.58 387170.16 389746.2 white-
cream
cream oblong shallow s ER s R high
yield, LB
392797.22
Unica
387521.3 APHRODITE red cream oblong shallow ER R R MR MS medium Heat
tolerant
Yield,
PLRV
CIP 388515.22
B-71-240.2
386614.16=(XY.
16)
white-
cream
white round shallow ER ER R Heat
tolerant
Yield,
PLRV
CIP 388615.22
Sherehiya 393385.39 387231.7 387170.9 red cream oblong slightly
deep
HS R S R HS High
tuber
number
no big
tuber
size
Meru
Mugaruro
397073.7 392823.4=(LR93
.120)
392820.1=(C93.
154)
cream white oblong shallow ER ER R Heat
tolerant
398208.505 393371.58 392633.64 White-
cream
pale
yellow
oblong shallow ER ER R Heat
tolerant
398190.615 393077.54 392639.2 White-
cream
cream Long-
oblong
shallow s ER R Heat
tolerant
CIP 398201.510 393242.5 392633.64 pink cream oblong shallow R HT
very dark
for
chipping
CIP 399002.52 395262.2 395292.3 red cream oblong shallow R R R R
flavour
(v.good)
dark
chipping
color
CIP 399079.28 395274.1 395257.6 red cream round
slightly
deep R R R R
Dark
chipping
$frying
color
CIP 399074.23 395262.2 395247.1 pink cream
Long-
oblong shallow R R R R
Long
dormancy
CIP 399072.21 395259.2 395271.6 pink cream oblong shallow R R R R
CIP 399001.53 395259.2 395255.3 red cream oblong shallow R R R R
CIP 399054.16 395230.1 395299.3 pink
pale
yellow
Long-
oblong shallow R R R R
Dutchrobjin
Good
attribut
es
Bad
attribut
es
GCAPVY PVX PLRV LB BW Crop
duratio
n
Name/Code CIP code
Pedigree Tub
skin
color
Tub
flesh
color
Tub
shape
eye
depth
Combine traits and alleles

Optimizing selection/evaluation process
Increase selection accuracy
Data management tools
Automation of field phenotyping techniques to
reduce exp’t error

Optimizing selection and evaluation
Testing networks (TON and CoP)
Systematic characterization of germplams and
sites
Monitor heritability and correlations between screening sites
and target population of environments
Monitory breeding progress by head-to-head comparison
with materials from other program
Traits and direction as basis for comparison
Establish standards for advancement
 Benchmarking
Variety Countries Resistance score1
Kinigi Rwanda,
Uganda
4.45
Key Abeba Ethiopia 3.53
Cruza-148 Rwanda 2.56
Jalene Ethiopia 1.04
Asante/Vict
oria
Kenya,
Uganda
7.00

 Conclusion

Use of integrative design
Accelerate the pace and precision of crop breeding research to develop
and deliver robust varieties for smallholder farmers
How?
By intuitively knowing the roads to achieve the targets
By careful plan to change the genetics of materials from their present
level of performance and productivity to reach a defined target
By use state-of-the-art selection, field testing and data collection
techniques/tools to reduce error and speed selection turn
By Just-in-time delivery of improved varieties down the line to
farmers/growers


Conclusion
Breeding by integrative design
Smart team capable of what to aim for and when
Clever tools that transform breeder’s way of doing
Smart germplasm for recombining alleles
Smart money

Thank you

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