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Integration of models into plant breeding programs Scott Chapman Senior Principal Research Scientist, CSIRO Adjunct Professor, QAAFI, The University of Queensland
AGRICULTURE FLAGSHIP
Crop
System control
Soil
SWIM
Manager Report Clock
SoilWat
SoilN
SoilPH
SoilP
Residue Economics
Fertiliz
Irrigate
Canopy Met
Erosion
Other Crops
Maize
Sorghum
Legume
Wheat
New Module
Manure
Management
ENGINE
Weather
Hands up
• If you ran a model to propose ‘ideotypes’ for breeding
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Hands up
• If you ran a model to propose ‘ideotypes’ for breeding
• Know a breeder
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Hands up
• If you ran a model to propose ‘ideotypes’ for breeding
• Know a breeder
• Talk to one every day
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Hands up
• If you ran a model to propose ‘ideotypes’ for breeding
• Know a breeder
• Talk to one every day
• If you are a breeder…
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
20 years ago…
• We conducted a sensitivity analysis…..
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Today…
• We conducted a sensitivity analysis…..
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Today...
• We conducted a sensitivity analysis…..
• We conducted a genotypic analysis…..
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Biological and genetic constraints - prediction of trait value in maize
• Chenu et al PC&E 2009
• Chenu et al Genetics 2009
• van Eeuwijk et al COPB 2010
QTL network for leaf elongation rate
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
11 QTL for a, b, c and ASI
QTL effect size are relative to line width
Trait
QTL
positive effect negative effect
Welcker et al. J. Exp. Bot. 2007
Chenu et al. Genetics 2009
Genetically accessible landscape….
The psychology of drought stress | Scott Chapman
5
6
7
8
-1.6-1.4-1.2-1.0-0.8
4.5 5.0 5.5 6.0 6.5
c
b
a
a (mm °Cd-1
) b (mm °Cd-1
kPa-1
) c (mm °Cd-1
MPa-1
)
a (mm °Cd-1
)
4.5 5.0 5.5 6.0 6.5
b (
mm
°C
d-1
kP
a-1
)
-1.6
-1.4
-1.2
-1.0
-0.8
Parent 2
Parent 1
b (mm °Cd-1
kPa-1
)
-1.6 -1.4 -1.2 -1.0 -0.8
c (
mm
°C
d-1
MP
a-1
)
5.0
6.0
7.0
8.0Parent 2
Parent 1
c (mm °Cd-1
MPa-1
)
5 6 7 8
a (
mm
°C
d-1
)
4.5
5.0
5.5
6.0
6.5
Parent 1
Parent 2
High VPD - Vegetative water deficit
Yie
ld (
kg h
a-1
)
0
2000
4000
6000
8000
10000
12000
Highest yields were found for genotypes with: - high a (temperature response) - high b (insensitive to high VPD) - low c (low response to soil water)
< -40 % -40 to -20 % -20 to -10 % -10 to -5 % -5 to 0 % 0 to +5 % +5 to +10 % +10 to +20 % +20 to +40 % > +40 %
Chenu et al. Genetics 2009
11 |
LER = dl/dt = (T-T0)(a + b VPDair-leaf + c Y)
Today…
• We conducted a sensitivity analysis…..
• We conducted a genotypic analysis…..
• What else can we do with models in breeding?
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
What do breeders do?
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Zheng B, Chenu K, Dreccer MF, Chapman SC (2012) Global Change Biology 18, 2899-2914. Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties? Zheng B, Biddulph B, Li D, Kuchel H and Chapman SC (2013) J. Exp. Bot. 64 3747-3761 Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments Zheng B, Chenu K, Chapman SC (2015) Global Change Biology (in press) Velocity of temperature and flowering time in wheat – assisting breeders to keep pace with climate change
What do breeders do?
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Zheng B, Chenu K, Dreccer MF, Chapman SC (2012) Global Change Biology 18, 2899-2914. Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties? Zheng B, Biddulph B, Li D, Kuchel H and Chapman SC (2013) J. Exp. Bot. 64 3747-3761 Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments Zheng B, Chenu K, Chapman SC (2015) Global Change Biology (in press) Velocity of temperature and flowering time in wheat – assisting breeders to keep pace with climate change
Parental pool
Selection of best
Novel germplasm
Industry output New
cultivars
Crossing
Climate, Management
New Traits and methods
Roles for Models in breeding Parental
pool
Selection of best
Novel germplasm
Industry output New
cultivars
Crossing
Climate, Management
New Traits and methods
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Thermal time pre- and post-flowering (oCd)
-1500 -1000 -500 0 500
Wa
ter
su
pp
ly/d
em
an
d r
atio
0.0
0.2
0.4
0.6
0.8
1.0
Env. type 1
Env. type 2
Env. type 3
Env. type 4
Environment characterisation - Drought patterns to interpret GxE
Muchow et al 1996 Chapman et al 2000 AJAR
Chenu et al 2013 New Phyto ‘Envirotyping’ Cooper et al 2014
Roles for Models in breeding Parental
pool
Selection of best
Novel germplasm
Industry output New
cultivars
Crossing
Climate, Management
New Traits and methods
WA
NT
QLD
SA NSW
VIC
Roma Dalby
Moree
Dubbo
Loxton
Ceduna
Emerald
Birchip Cummins
Gunnedah Merredin
Esperance Wandering
Geraldton
Port Pirie Wagga Wagga
Longerenong
Wongan Hills
Cairns
Sydney
Brisbane
Melbourne Canberra
Darwin
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Roles for Models in breeding Parental
pool
Selection of best
Novel germplasm
Industry output New
cultivars
Crossing
Climate, Management
New Traits and methods
WA
NT
QLD
SA NSW
VIC
Roma Dalby
Moree
Dubbo
Loxton
Ceduna
Emerald
Birchip Cummins
Gunnedah Merredin
Esperance Wandering
Geraldton
Port Pirie Wagga Wagga
Longerenong
Wongan Hills
Cairns
Sydney
Brisbane
Melbourne Canberra
Darwin
Prediction of Adaptation
‘Breeding for the future’ Chapman et al 2012 CPS
Zheng et al 2012 GCB, 2013 JXB, 2015 GCB, 2015 JXB
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Thermal time pre- and post-flowering (oCd)
-1500 -1000 -500 0 500
Wa
ter
su
pp
ly/d
em
an
d r
atio
0.0
0.2
0.4
0.6
0.8
1.0
Env. type 1
Env. type 2
Env. type 3
Env. type 4
Environment characterisation - Drought patterns to interpret GxE
Muchow et al 1996 Chapman et al 2000 AJAR
Chenu et al 2013 New Phyto ‘Envirotyping’ Cooper et al 2014
WA
NT
QLD
SA NSW
VIC
Roma Dalby
Moree
Dubbo
Loxton
Ceduna
Emerald
Birchip Cummins
Gunnedah Merredin
Esperance Wandering
Geraldton
Port Pirie Wagga Wagga
Longerenong
Wongan Hills
Cairns
Sydney
Brisbane
Melbourne Canberra
Darwin
Heading time model based on VRN1 and PPD1 alleles, earliness per se and > 5000 validations
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Observed days to heading
Sim
ula
ted
da
ys to
he
ad
ing
60
80
100
120
140
N = 340
RMSE = 2.9 d
y = 21.7 + 0.77x
R2 = 0.91
V1P1
N = 339
RMSE = 3.2 d
y = 15.66 + 0.81x
R2 = 0.74
V1P2
60
80
100
120
140
60 80 100 120 140
N = 340
RMSE = 3.7 d
y = 25.8 + 0.71x
R2 = 0.83
V2P1
60 80 100 120 140
N = 340
RMSE = 2.8 d
y = -5.92 + 1.07x
R2 = 0.73
V2P2
Zheng et al. 2013 J Exp Bot Observed days to heading
Sim
ula
ted
da
ys to
he
ad
ing
60
80
100
120
140
160
60 80 100 120 140 160
N = 4475
RMSE = 4.3 d
y = 0.28 + 0.98x
R2 = 0.96
TOSYIE
NVTNAT
PHIAGT
Zheng et al 2015 J Exp Bot – Value of frost tolerance in Australian wheat
• Benefit of 1°C improvement in tolerance is greater in WA
• Full tolerance and new management needed to benefit the East
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Prediction before planting...
• Association mapping
• QTL-based prediction of average heading date
• With Matthieu Bogard (ARVALIS)
• EU ADAPTAWHEAT
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
80 90 100 110
80
85
90
95
100
105
110
115
Observ
ed h
eadin
g d
ate
Predicted heading date
RMSE = 3.4
R² = 0.78
n = 124
WA
Parental pool
Selection of best
Novel germplasm
Industry output New
cultivars
Crossing
Climate, Management
New Traits and methods
Genetic trait value
Chapman et al 2002, Hammer et al 2006, Chenu et al 2009 Hammer et al 2010, 2015 Cooper et al 2014, 2016,
Messina et al 2015
Roles for Models in breeding Parental
pool
Selection of best
Novel germplasm
Industry output New
cultivars
Crossing
Climate, Management
New Traits and methods
WA
NT
QLD
SA NSW
VIC
Roma Dalby
Moree
Dubbo
Loxton
Ceduna
Emerald
Birchip Cummins
Gunnedah Merredin
Esperance Wandering
Geraldton
Port Pirie Wagga Wagga
Longerenong
Wongan Hills
Cairns
Sydney
Brisbane
Melbourne Canberra
Darwin
Prediction of Adaptation
‘Breeding for the future’ Chapman et al 2012 CPS
Zheng et al 2012 GCB, 2013 JXB, 2015 GCB, 2015 JXB
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Thermal time pre- and post-flowering (oCd)
-1500 -1000 -500 0 500
Wa
ter
su
pp
ly/d
em
an
d r
atio
0.0
0.2
0.4
0.6
0.8
1.0
Env. type 1
Env. type 2
Env. type 3
Env. type 4
Environment characterisation - Drought patterns to interpret GxE
Muchow et al 1996 Chapman et al 2000 AJAR
Chenu et al 2013 New Phyto ‘Envirotyping’ Cooper et al 2014
WA
NT
QLD
SA NSW
VIC
Roma Dalby
Moree
Dubbo
Loxton
Ceduna
Emerald
Birchip Cummins
Gunnedah Merredin
Esperance Wandering
Geraldton
Port Pirie Wagga Wagga
Longerenong
Wongan Hills
Cairns
Sydney
Brisbane
Melbourne Canberra
Darwin
Chapman et al. 2003 Agronomy J.
Cooper et al. 2002 In Silico Biol.
Hammer et al 2006 AJAR Genotype
Trait genetics
A
P
S
I
M
Manager
Biological
Modules
Surface Residue
Environmental
Modules
Erosion
B
Erosion
A
Other N moduleor
SoilN
Crop
C
Crop
B
Crop
A
Pasture
C
Pasture
B
Pasture
A
Swimor
Soilwat
Economics Climate
APSIM
Simulate Crop
Improvement
Strategies
Experiments –
physiology and
genetics
Trait dissection and
functional physiology
Phenotype
Software and
Database Tools
Including the breeding dimension: Capturing physiological responses as part of breeding simulations
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Gene to Phenotype Modelling - value of physiological knowledge (but with fake QTL !)
Cycle of selection
0 2 4 6 8 10 12
Yie
ld in
TP
E (
kg
ha
-1)
3800
4000
4200
4400
4600
4800
5000
5200
Marker selection
Weighted marker selection
Physiologically weighted marker selection
G P
Unexplained Explained
Fully
described
Context
dependent
Chapman et al 2003; Hammer et al 2005 AJAR
23 | Interpreting effects of physiological GxE | Scott Chapman
Genetic trait value
Chapman et al 2002, Hammer et al 2006, Chenu et al 2009 Hammer et al 2010, 2015 Cooper et al 2014, 2016,
Messina et al 2015
Roles for Models in breeding Parental
pool
Selection of best
Novel germplasm
Industry output New
cultivars
Crossing
Climate, Management
New Traits and methods
WA
NT
QLD
SA NSW
VIC
Roma Dalby
Moree
Dubbo
Loxton
Ceduna
Emerald
Birchip Cummins
Gunnedah Merredin
Esperance Wandering
Geraldton
Port Pirie Wagga Wagga
Longerenong
Wongan Hills
Cairns
Sydney
Brisbane
Melbourne Canberra
Darwin
Prediction of Adaptation
‘Breeding for the future’ Chapman et al 2012 CPS
Zheng et al 2012 GCB, 2013 JXB, 2015 GCB, 2015 JXB
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Thermal time pre- and post-flowering (oCd)
-1500 -1000 -500 0 500
Wa
ter
su
pp
ly/d
em
an
d r
atio
0.0
0.2
0.4
0.6
0.8
1.0
Env. type 1
Env. type 2
Env. type 3
Env. type 4
Environment characterisation - Drought patterns to interpret GxE
Muchow et al 1996 Chapman et al 2000 AJAR
Chenu et al 2013 New Phyto ‘Envirotyping’ Cooper et al 2014
WA
NT
QLD
SA NSW
VIC
Roma Dalby
Moree
Dubbo
Loxton
Ceduna
Emerald
Birchip Cummins
Gunnedah Merredin
Esperance Wandering
Geraldton
Port Pirie Wagga Wagga
Longerenong
Wongan Hills
Cairns
Sydney
Brisbane
Melbourne Canberra
Darwin
Roles for Models in breeding Parental
pool
Selection of best
Novel germplasm
Industry output New
cultivars
Crossing
Climate, Management
New Traits and methods
Thermal time pre- and post-flowering (oCd)
-1500 -1000 -500 0 500
Wa
ter
su
pp
ly/d
em
an
d r
atio
0.0
0.2
0.4
0.6
0.8
1.0
Env. type 1
Env. type 2
Env. type 3
Env. type 4
Environment characterisation - Drought patterns to interpret GxE
Bio
mas
s
LA
I
Co
ver
Zad
oks
In-season ‘phenotyping’
WA
NT
QLD
SA NSW
VIC
Roma Dalby
Moree
Dubbo
Loxton
Ceduna
Emerald
Birchip Cummins
Gunnedah Merredin
Esperance Wandering
Geraldton
Port Pirie Wagga Wagga
Longerenong
Wongan Hills
Cairns
Sydney
Brisbane
Melbourne Canberra
Darwin
Genetic trait value
Prediction of Adaptation
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Thermal time pre- and post-flowering (oCd)
-1500 -1000 -500 0 500
Wa
ter
su
pp
ly/d
em
an
d r
atio
0.0
0.2
0.4
0.6
0.8
1.0
Env. type 1
Env. type 2
Env. type 3
Env. type 4
In case of fire…. Or Kropff emergency…
• Modeling adds value to breeding, but has to be relevant to what breeders do (molecular biologists had to learn this too....)
Environment characterisation • Models used as ‘virtual checks’ or to provide ‘environment indices’
to assist in the analysis of phenotypes
Prediction of trait value • Specific experiments for ‘global’ coefficients,
e.g. flowering time responses, transpiration efficiency etc • Evaluation of effects of ‘known phenotypic range’ in simulations
Phenotype assessment • Global coefficients + local ‘grid-search’ coefficients to ‘fit’
the observed data and estimate ‘virtual phenotypes
Genetic trait value • Experiments on GxExM landscapes to allow exploration of
impacts of these factors on breeding efficiency and opportunity
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Thermal time pre- and post-flowering (oCd)
-1500 -1000 -500 0 500
Wa
ter
su
pp
ly/d
em
an
d r
atio
0.0
0.2
0.4
0.6
0.8
1.0
Env. type 1
Env. type 2
Env. type 3
Env. type 4
In case of fire….
• Modeling adds value to breeding, but has to be relevant to what breeders do (molecular biologists had to learn this too....)
Environment characterisation • Models used as ‘virtual checks’ or to provide ‘environment indices’
to assist in the analysis of phenotypes
Prediction of trait value • Specific experiments for ‘global’ coefficients,
e.g. flowering time responses, transpiration efficiency etc • Evaluation of effects of ‘known phenotypic range’ in simulations
Phenotype assessment • Global coefficients + local ‘grid-search’ coefficients to ‘fit’
the observed data and estimate ‘virtual phenotypes
Genetic trait value • Experiments on GxExM landscapes to allow exploration of
impacts of these factors on breeding efficiency and opportunity
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
IR imaging
LiDAR: Distance and Intensity
multi- and hyper-spectral imaging
λ
Sirault et al. (2013) FSPM
Real vs. in-silico
Paproki et al. (2012) BMC Plant Biology 12:63
Integrated pipeline
Pipeline development: CSIRO DP (Brisbane) and Agriculture
PlantScan – leaf area and spectral imaging
Field Phenomics in Breeding, Tokyo 10 Feb 2016 | Scott Chapman
Root geometry and plant transpiration platforms (The University of Queensland)
- L-PAD Lysimetry platform - Estimation of water use per
unit leaf area
- Root angle - Selection for narrow or
wide-angle roots
- Both methods validated in sorghum and wheat e.g. Singh et al 2010, 2012; Manschadi et al 2006
Field Phenomics in Breeding, Tokyo 10 Feb 2016 | Scott Chapman
Field Phenomics in Breeding, Tokyo 10 Feb 2016 | Scott Chapman
Deery et al. 2014 MDPI Agronomy
Aerial Imaging platform Equipment
specification – camera lens/speed, aircraft flight specs
Mission planning
Flights Image collation and
geo-reference
Post-processing to generate mosaics
and 3D
Identification of trial images and plots
Extraction of plot images,
straightening and trimming
Image spectral extraction and
analysis
Experiment analysis of plot-level data
31 Applications of models in breeding | Scott Chapman
Crop cover
87% 65% 61% 68%
0730 0805 0920 1020 1100 1200 1415 1505
Diurnal canopy temperature
GEHEAT1 - 28 Sep 2012
3-D lodging estimates
Chapman et al. 2014 MDPI Agronomy
Agronomy Tactical use of information for management (technical or commercial). RS data are inputs to update predictions based on historical weather data and other info.
Initial soil water and nutrient
Current weather data
0
200
400
600
800
1000
1200
1400
1600
1800
0 50 100 150 200
Bio
mas
s o
r yi
eld
(g/
m2
)
Days from sowing
Total biomassYield
RS info
Crop cultivar and management
Past weather data
Test different scenarios (more, less N, water, etc) Obtain a probabilistic outcome based on past weather data
Different scenarios
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
A. Breeding trial with grid of probe genotype/s with extra measurements (ground cover (live images!), tiller/spike number, soil water sensors, canopy temperature, etc).
B. Breeding trial with some more info per plot, aerial or otherwise (ground cover RGB or NIR, canopy temperature, etc).
C. Glasshouse derived parameters
Breeding inputs
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Canopy cover data + simulation = good prediction of LAI
Tota
l bio
mas
s
LA
I
G
rou
nd
co
ver
Z
ado
ks
Long season
If we can track LAI and biomass, seasonal water use will be quite accurate Estimate ‘virtual’ phenotype like water use – as it is VERY hard to measure this directly
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
0
0.2
0.4
0.6
0.8
1
-1500 -1000 -500 0 500 1000
Wat
er s
up
ply
/de
man
d r
atio
Thermal time pre and post anthesis (°Cd)
Probe G
"Better G"
Leaf area N profiles in wheat isolines
Field Phenomics in Breeding, Tokyo 10 Feb 2016 | Scott Chapman
Hammer et al
Where does Crop Physiology & Modelling fit?
Introducing biological knowledge
“The right answer for the right reason!” Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Thermal time pre- and post-flowering (oCd)
-1500 -1000 -500 0 500
Wa
ter
su
pp
ly/d
em
an
d r
atio
0.0
0.2
0.4
0.6
0.8
1.0
Env. type 1
Env. type 2
Env. type 3
Env. type 4
In case of fire….
• Modeling adds value to breeding, but has to be relevant to what breeders do (molecular biologists had to learn this too....)
Environment characterisation • Models used as ‘virtual checks’ or to provide ‘environment indices’
to assist in the analysis of phenotypes
Prediction of trait value • Specific experiments for ‘global’ coefficients,
e.g. flowering time responses, transpiration efficiency etc • Evaluation of effects of ‘known phenotypic range’ in simulations
Phenotype assessment • Global coefficients + local ‘grid-search’ coefficients to ‘fit’
the observed data and estimate ‘virtual phenotypes
Genetic trait value • Experiments on GxExM landscapes to allow exploration of
impacts of these factors on breeding efficiency and opportunity
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Integration of models into plant breeding programs Scott Chapman, Senior Principal Research Scientist, CSIRO Adjunct Professor, QAAFI, The University of Queensland SORGHUM: Graeme Hammer, Vijaya Singh, Erik van Oosterom, David Jordan, Greg McLean, Al Doherty (UQ/QAAFI/DAF) WHEAT: Bangyou Zheng (CSIRO), Fernanda Dreccer (CSIRO), Karine Chenu (UQ/QAAFI) AGRICULTURE FLAGSHIP
Crop
System control
Soil
SWIM
Manager Report Clock
SoilWat
SoilN
SoilPH
SoilP
Residue Economics
Fertiliz
Irrigate
Canopy Met
Erosion
Other Crops
Maize
Sorghum
Legume
Wheat
New Module
Manure
Management
ENGINE
Weather
What’s special about Breeding Phenomics?
• Selection of varieties based on multiple criteria
• Large numbers (1000s) of plants or field plots- different sizes
• Need for precision is higher in later stages • New selections may have advantage of only 1 to 5%
• Stability is necessary – consistent performance over environments
• ‘Agronomic phenomics’ not as demanding
• Increasing the reliability (heritability) of selection in breeding • Need for selection in small plots, not large fields
• Improved precision of measurement
• Greater sampling/replication
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Hammer et al 2016
Genetic Improvement – process
Genotyping
Base Germplasm
METs - Phenotyping -
Crossing Decisions
Selected Lines
Phenotypic prediction
Double haploids
Improved varieties
Inbred lines - Phenotyping -
High thruput phenotyping
Phenotypic prediction
Cycles of selection and evaluation in breeding
Genetic Gain – the breeder’s equation
Pij = µ + Gi + Ej + (GE)ij.
years per cycle
Genomic prediction
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Correlation, heritability and beating the breeder (or not)… • CRy/DRy = rg * sqrt(Hx/Hy) where H = Vg/Vp = Vg/(Vg + Vr/nr) • Hy = Heritability of yield ~ 0.2 to 0.4
• 0.2 might represent in single row plots, 0.4 in large plots
• Hx = Heritability of flowering date ~ 0.6 to 0.8 • Same in different size plots; can increase by replication
If CR/DR = 1 rg = 1/(sqrt(Hx/Hy)) So, implement traits in EARLY selection stages.... … or get into Genomic Selection…
Integration of models into plant breeding programs | Scott Chapman | iCROPM Berlin March 2016
Stage Hy Hx Rg for CR/DR=1
Rg for CR/DR = 1.2
Early 0.2 0.8 0.50 0.60
Mid 0.4 0.8 0.71 0.85
Late 0.5 0.9 0.75 0.90
Next Era of Field Phenomics
• Increased precision (not just speed….) • Smart analysis • Consideration of experiment design, replication, repeatability • Integration of data from field sensors, ground vehicles, aerial
vehicles • Collaboration across
disciplines and capabilities • Physiology,
breeding, imaging, sensor hardware, processing, selection….
Field Phenomics in Breeding, Tokyo 10 Feb 2016 | Scott Chapman