Plant adaptation to climate change - Scott Chapman

Plant adaptation to climate change – opportunities in breeding

SC Chapman, MF Dreccer, S Chakraborty,

SM Howden

Climate change inQueensland?

CCRSPI Feb 2011 Plant adaptation to climate change - opportunities in breeding


• Climate effects and challenges

• Plant species, genetic diversity and breeding programs

• What breeders do

• Challenges of climate change for breeding

• Integration of genotype, environment and management to develop new breeding systems


Climate change effects on crops

• Seasonal effects (‘intrinsic’)• Increased CO2, reduce water use*• Increased temp = shorter season*• Lower rainfall and drought (?)• Pests, weeds

• Extreme effects (‘catastrophic’)• poor crop establishment• ‘heat shock’ events affecting

grain/fruit set and quality*• pest epidemics• Russia 2010, Australia 2010, China

2011 ?

Fitzgerald/Tauz – wheat and CO2 (Tue, Thur)Sadras – early maturing grapes (Tue)McCaskill – Pome fruit heat damage (Tue)


Who breeds and who pays?

• Breeding is expensive

• International networks and genetic diversity

• Maintenance vs improvement

• How good is breeding?• 1 to 3% yield increase per year



Tractor Genetics and Breeding

• Genotype - $50 each• Blueprint of ‘genes’

• Phenotype - $60 each?• a ‘trait’ interacting with environment

e.g. How good is tractor in the mud?

• What breeders do:• Genotyping – read the ‘blueprints’

• Phenotyping – evaluate performance

• Crossing - parts of tractors

• Do it again…

Breeding for effects ofclimate change

• 5-25 years to breed a variety

• 2050 is 2 to 8 cycles away

• Genotypes – at least 1050 genotypes(cf. 1025 grains of sand on earth…)

• Environments – 1000sAll ‘possible’ farm paddocks

• Traits – 10syield, quality, disease resistance, biomass and energy


Parental pool

Selection of best

Novel germplasm

IndustryoutputNew

cultivars

Crossing

Climate, Management

NewTraits and methods

Challenges for breeding

• Which environments and diseases?

• How much genetic variation?

• What traits and methods to use?

• How to deploy genetics?


Parental pool

Selection of best

Novel germplasm

IndustryoutputNew

cultivars

Crossing

Climate, Management


Which environments?It’s hotter already in the north

• 2000-2009 vs 1960-1969

• First ‘hot’ day (Tmax > 35ºC)

• 3 weeks earlier in north

• 1 week earlier in south

• No change in west

• What flowering date will we need in future?De Li Liu - wheat phenology (Thur)

Zhang, Chenu and Chapman (unpubl.)


Which environments will be where?Latitudinal shift 2050 vs Present

• Frost and heat index

• Which present day stations (start of arrow) best represent ‘future’ climates?

i.e. screening in north is relevant to ‘future’ southern locations

Zhang, Chenu and Chapman (unpubl.)


Which diseases?

• Increased temperature and CO2 levels

• Change in geographic distribution

• More stubble = more necrotrophs

• Increased risk of new races – • Rust strain UG99 broke through against

30 years of resistance








Parental pool

Selection of best

Novel germplasm

IndustryoutputNew

cultivars

Crossing

Climate, Management


Genetic variationTraits for elevated CO2 and temperature

• Development – avoiding high temperature at flowering• Faster (winter crops) or slower (summer)

• Tolerance to extremes (heat, frost, drought, water-logging)• Seedling vigour, pollen sterility, embryo growth

• Leaf growth and tillering• Trade-off for water use

• Biomass accumulation• Low stomatal conductance, higher photosyn rate, higher TE (C3 plants)

• Partitioning/yield components• Carbohydrate storage in stems• Grain growth rate

Photosynthesis

CO2

H2O


N% green leaf

y = 0.778x + 0.94

R2 = 0.76, n=194

1

2

3

4

5

6

7

1 2 3 4 5 6 7Measured

Pre

dic

ted

N% green leaves: stay green monitoringN% green leaf

y = 0.778x + 0.94

R2 = 0.76, n=194

1

2

3

4

5

6

7

1 2 3 4 5 6 7Measured

Pre

dic

ted

N% green leaves: stay green monitoring

Genetic variationPhenotyping heat and drought adaptation

Dreccer, Chapman (unpubl. data)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

300 400 500 600 700 800 900 1000

Wavelength (um)

Ref

lect

ance

Rainfed

Irrigated

Soil

Crop reflectance WSC% stem

y = 0.84x +27.7

R2 = 0.77, n=96

0

50

100

150

200

250

300

350

400

0 100 200 300 400Measured

Pre

dic

ted

Stem WSC

Pre

dic

ted

Visible & IR cameras on Autonomous UAV LAI & canopy cover Canopy Temperature

& water use

Canopy N% for stay-green monitoring

Measured


Genetic variation - connectingGenotype and phenotype for heat and drought

• Association of high grain number and yield with cool canopies

• Collaboration with CIMMYT, Mexico

Pinto et al 2010 Theoretical and Applied Genetics

Linkage group (red = high value favoured by Babax allele)

-3-2-1

123

1B-a

3B-b

4B-b

2B-a

4A-a

6B-a

CTPMg

CTAMg

CTPMv

CTAMv

GM 2

Yield

D02.94

D05.96

D05.89

D05.83

I06.101

D02.94

D05.90

D05.88

H05.75

H06.67

I02.102

D02.69

H05.19

H06.46

I06.94

I06.58

I06.38

H05.42

H05.34

H06.48

H06.32

D02

H05

I02

D02

H05

I02

D02.93

D05.90

D05.88

H05.72

I06.98

D02.93

D05.89

D05.81

H06.75

H06.63

D02.70

H05.20

H06.48

H06.27

I06.72

I06.56

H05.44

H05.36

H05.27

H06.46

H06.27

D05

H06

I06

D05

H06

I06







Parental pool

Selection of best

Novel germplasm

IndustryoutputNew

cultivars

Crossing

Climate, Management



Deploying geneticsComparing breeding methods

• Breeding methods• Pedigree breeding – keeps ‘good’ genes together• Gene pyramiding – introducing ‘new’ genes• Recurrent selection – inbred development• Hybrid breeding – selection on progeny and testcross performance• Clonal, mass and other propagation methods

• Molecular Marker assisted breeding methods• MAB – Marker assisted backcrossing • MAS – Marker assisted selection • MARS – Marker assisted recurrent selection • GWS – Genome wide selection

• Podlich and Cooper (1998) Bioinformatics• Chapman et al (2003) Agron. J.• Wang et al (2004) Crop Sci.• Hammer et al (2005) AJAR• Cooper et al (2005) AJAR• Wang et al (2007) Crop Sci.• Hammer et al (2006) Trends in PlantSci.

http://www.uq.edu.au/lcafs/qugene/


Chapman et al 2007 EuphyticaHammer, G.L. and Jordan, J. (2007) In, G. van Laar (ed.) Gene-Plant-Crop Relations: Scale and Complexity in Plant Systems Research. Frontis Series. Kluwer, Dordrecht, The Netherlands

Deploying genetics:Exploiting G x E x M

• Basis for the green revolution• Dwarf genes + nitrogen + water +

new management system

• Combining molecular technologies and systems modelling

• In a climate-change context• Water-saving• High temperature adapted

Trait genetics

APSI

M

Manager

BiologicalModules

Surface Residue

EnvironmentalModules

ErosionB

ErosionA

Other N moduleor

SoilN

CropC

CropB

CropA

PastureC

PastureB

PastureA

Swimor

Soilwat

Economics Climate

APSIM

Simulate Crop Improvement Strategies

Trait dissection and functional physiology

Cooper et al. 2002, In Silico Biol.

Software and Database Tools

Deploying genetics: A research framework for physiological and genetic simulation of plant breeding

Genotype (AA, Aa, aa)

Phenotype (PAA,PAa,Paa)

Environment (climate, soil, management)

Experiments –physiology and genetics



Germplasm resources

Non-invasive phenotyping

Design of robuststrategies

AIM: identifying superior combinations of ‘useful’ genetic regions and re-packaging these into new varieties in new cropping systems for climate change environments

Genetic mappingand analysis

Photo-synthesis

Grain filling

WSC

Physiologicalanalysis

GxExM


• Which environments and diseases?• Where can we breed now for the ‘future’?

• Necrotophic diseases

• How much genetic variation?• Accessing international networks and companies

• Phenology and per se tolerance

• What traits and methods to use? • Direct and remote phenotyping• Genetic mapping

• How to deploy genetics?• Accelerated breeding strategies

• Marker and gene-aided selection, GM

• Will it be enough?


Parental pool

Selection of best

Novel germplasm

IndustryoutputNew

cultivars

Crossing

Climate, Management


Opportunities



Breeding takes time, but new technologies accelerate it

SC Chapman1, MF Dreccer1, S Chakraborty1, SM Howden2

Acknowledgements

K Chenu3, D Jordan3, G McLean4, GL Hammer3, M Bourgault1, S Milroy1, JA Palta-Paz1, KB Wockner1, B Zheng1

1CSIRO Plant Industry/Climate Adaptation Flagship, Australia 2CSIRO Ecosystem Sciences/Climate Adaptation Flagship, Australia 3QAAFI, The University of Queensland, Australia4DEEDI, Queensland Primary Industries and Fisheries, Australia


• End…

Crop Science is about creating additional “Breeding Knowledge”

• What information will we have in future breeding programs?• Gene locations resolved to gene level• Gene pedigrees resolved to gene level• Gene effects resolved to component trait level

• e.g. height, tillering, flowering

• Selection environments characterised and/or managed• Traits phenotyped with non-invasive technologies• Improved statistical methods• Effects of component traits on yield (target) trait understood?

• Complementary experiments and simulation help to • Anticipate how to best use new technologies • Create “breeding knowledge” from this information• Accelerate genetic gain across an industry or geographic range

Plant adaptation to climate change - Scott Chapman

Technology

Transcript of Plant adaptation to climate change - Scott Chapman