Crop E SWIM N G SoilPH I N E Irrigate Fertiliz Manurecommunications.ext.zalf.de › ... ›...

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


• Know a breeder


Hands up


• Know a breeder

• Talk to one every day


Hands up


• Know a breeder

• Talk to one every day

• If you are a breeder…


20 years ago…

• We conducted a sensitivity analysis…..


Today…



Today...


• We conducted a genotypic analysis…..



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


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 genotypic analysis…..

• What else can we do with models in breeding?


What do breeders do?


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?


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



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


pool

Selection of best

Novel germplasm

Industry output New

cultivars

Crossing

Climate, Management


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



pool

Selection of best

Novel germplasm

Industry output New

cultivars

Crossing

Climate, Management


WA

NT

QLD

SA NSW

VIC

Roma Dalby

Moree

Dubbo

Loxton

Ceduna

Emerald

Birchip Cummins

Gunnedah Merredin

Esperance Wandering

Geraldton


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



-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




WA

NT

QLD

SA NSW

VIC

Roma Dalby

Moree

Dubbo

Loxton

Ceduna

Emerald

Birchip Cummins

Gunnedah Merredin

Esperance Wandering

Geraldton


Longerenong

Wongan Hills

Cairns

Sydney

Brisbane

Melbourne Canberra

Darwin

Heading time model based on VRN1 and PPD1 alleles, earliness per se and > 5000 validations


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


Prediction before planting...

• Association mapping

• QTL-based prediction of average heading date

• With Matthieu Bogard (ARVALIS)

• EU ADAPTAWHEAT


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


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


pool

Selection of best

Novel germplasm

Industry output New

cultivars

Crossing

Climate, Management


WA

NT

QLD

SA NSW

VIC

Roma Dalby

Moree

Dubbo

Loxton

Ceduna

Emerald

Birchip Cummins

Gunnedah Merredin

Esperance Wandering

Geraldton


Longerenong

Wongan Hills

Cairns

Sydney

Brisbane

Melbourne Canberra

Darwin






-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




WA

NT

QLD

SA NSW

VIC

Roma Dalby

Moree

Dubbo

Loxton

Ceduna

Emerald

Birchip Cummins

Gunnedah Merredin

Esperance Wandering

Geraldton


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


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


pool

Selection of best

Novel germplasm

Industry output New

cultivars

Crossing

Climate, Management


WA

NT

QLD

SA NSW

VIC

Roma Dalby

Moree

Dubbo

Loxton

Ceduna

Emerald

Birchip Cummins

Gunnedah Merredin

Esperance Wandering

Geraldton


Longerenong

Wongan Hills

Cairns

Sydney

Brisbane

Melbourne Canberra

Darwin






-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




WA

NT

QLD

SA NSW

VIC

Roma Dalby

Moree

Dubbo

Loxton

Ceduna

Emerald

Birchip Cummins

Gunnedah Merredin

Esperance Wandering

Geraldton


Longerenong

Wongan Hills

Cairns

Sydney

Brisbane

Melbourne Canberra

Darwin


pool

Selection of best

Novel germplasm

Industry output New

cultivars

Crossing

Climate, Management



-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


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


Longerenong

Wongan Hills

Cairns

Sydney

Brisbane

Melbourne Canberra

Darwin

Genetic trait value




-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



-1500 -1000 -500 0 500

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Env. type 2

Env. type 3

Env. type 4

In case of fire….











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



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

2014_Gatton_2014_09_02_visual_fly-through.mp4

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


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


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


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


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


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In case of fire….











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


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


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…


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….


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