Monogram 29 April 1 - May 2015

Rothamsted Research

Identifying and exploiting traits for transpiration efficiency and drought tolerance in adapted and

ancestral wheat germplasm 1Y. Mahmood, 1I. King, 1J. King, 1P. Carvalho, 1J. De Silva, 1D. Wells, 1S. Mooney, 1M. Bennett, 1S. Keating, 1L. York, 2S. Griffiths, 2L. Wingen and 1J. Foulkes 1Plant and Crop Sciences, University of Nottingham, LE12 5RD, UK. 2Department of Crop Genetics, John Innes Centre, NR4 7UH, UK

Predicted global climate change (temperature and precipitation)

Predicted impacts of climate change on global yield

www.ipcc.ch/report/ar5/wg2/

Impact of drought on UK wheat yield Distribution of available water

to 1.2 m in UK wheat Rainfall contour map

Foulkes et al. 2001, 2002, J Agr Sci; FCR 2007.

Gleadthorpre Notts AWC = 140 mm

Grain Yield = WU x WUE x HI

OPTIMISE WUE • High Δ13C

• Photo. capacity • Specific leaf N

MAXIMIZE WATER CAPTURE • RLD at depth

• β (distribute roots deeper) • Specific root length

EARLINESS • Extend stem elongation

phase • Early onset GS31

MAXIMIZE HARVEST INDEX • Stem WSC reserves

• Functional stay green

Traits associated with yield under drought

Years: 2009-10 & 2010-11

Split plot design (3 reps): plot size 1.6 x 12 m

Main plot: Fully irrigated (trickle irrigation)

Unirrigated

Split plot (variety): 1. Avalon

2. Beaver

3. Cadenza

4. Cordiale

5. Glasgow

6. Hereward

7. Hobbit

8. Istabraq

9. M.Widgeon

10. Oakley

11. Panorama

12. Paragon

13. Rialto

14. Savannah

15. Soissons

16. Xi19

17. Zebedee

17 Cultivar wheat panel selection informed by

LINK 0986 Wheat WUE project, Eric Ober

Wheat genotype panel

Target physiological traits: • Grain 13C (WUE) • Flag leaf δ18O (Water Use) • Stem WSC Reserves (HI) • Canopy water status (NDVI)

WGIN – Grain Yield response to Drought

Sutton Bonington 2009-10 and 2010-11

Month Rainfall mm (% LTM)

2010 2011

January 33.0 (62) 33.2 (62)

February 41.6 (95) 44.6 (101)

March 36 (67) 1.2 (2)

April 24 (55) 23 (53)

May 16.2 (35) 27.8 (61)

June 69.2 (152) 45.4 (100)

July 42.6 (86) 17.8 (36)

Irrigated vs Unirrigated mid grain filling

● Water-use efficiency (WUE) is the ratio of above-ground dry matter production to evapotranspiration.

Figure 1. Definitions of ‘water use efficiency’. The scheme represents the several definitions of

water use efficiency (WUE) used in the text. A, net photosynthetic rate expressed as

μmol CO2 m−1 s−1; E, transpiration rate expressed as mmol H2O m−2 s−1; Δ13C, carbon isotope

discrimination.

Tambussi et al. 2007 Field Crops Res

• High internal leaf CO2 conc. (Ci) = low WUE

• WUE12C/13C isotope ratio of fixed CO2 can be used as an indicator of WUE

• Low discrimination against 13CO2 → high WUE

Grain Δ13C vs Grain yield

Sutton Bonington 2009-10 and 2010-11

• Grain Δ13C positively associated with yield under drought – indicator of ability to access water

• Trade-off between WUE and water use

Grain Δ13C vs Stom. Cond. & Amax

Sutton Bonington 2009-10 and 2010-11 Unirrigated

LI-6400XT (Li-Cor)

Stomatal conductance

Amax (maximum photosynthesis rate)

Quantify trade off between water-use efficiency and water use

Use δ18O as an indicator of transpiration to allow stomatal and Ps effects on 13C to

be teased apart

Barbour et al. (2000) Aust. J. Plant

Physiol., 27, 625-637

Oxygen isotope ratio of leaf and grain

material correlates with stomatal

conductance and grain yield in irrigated

wheat

δ18O of leaf tissues can be used as an indirect

measure of transpiration and water use

• 18O/16O ratio determined by enrichment in the leaf water due to transpiration. • Leaf water enriched due to preferential loss of the lighter H2

16O during evaporation. • An increase in leaf transpiration decreases leaf T oC (hence intercellular vapour pressure) resulting in less H2

18O enrichment at the evaporating site.

Oxygen isotope ratio ~ Leaf transpiration

Δ13C and Δ18O in 2010-2011

High water use/ High productivity

in dry years

Leaf Sp. Wt. correlates Δ13C with Δ18O residual

WU

E re

lati

ve t

o w

ater

use

Low

High

• Flag leaf specific weight at anthesis explains positive and

negative departures from overall negative relationship

between water use and water use efficiency

Multiple linear regression (P<0.001) accounted for 71% of the yield

under drought and showed that:

GY with 13C (WUE) : GY with stem WSC GY with Normalized Water Index - 1

GY = 1.03 Δ13C + 0.08 stem WSC + 12.3 NWI-1 - 41.9

Drought tolerant ideotype

Estimated value of traits to avoid or minimise effects of drought in UK

Traits summary

Variety character How it might work Value

High Δ13C grain Captures extra water High

Flag leaf ‘stay-green’ Extends grain filling during late drought

High

Low canopy T oC/ deep roots Captures extra water High

High stem sugars Buffers effects of post-flowering drought on grain filling

Moderate

Early flowering Advances grain filling before the drought risk period

Neutral

Awns Use less water per unit growth Slight

2010-11 and 2012-13 expts

• Rialto x Savannah DH population phenotyping for yield physiological traits

(94 lines and 2 parents)

• Mapped SNPs (1886)

• 2 sites: Nottingham - irrigated & unirrigated; JIC, Norwich - unirrigated

• Target traits

- Δ13C grain

- Senescence kinetic

- Stem WSC

QTL Detection Savannah x Rialto DH pop

Collaboration Simon Griffths, Luzie Wingen, Simon Orford, Frances Thistlethwaite (JIC)

Ria

Sav y = 0.8014x + 4.2325 R² = 0.44***

8

8.5

9

9.5

10

10.5

11

11.5

12

12.5

5 6 7 8 9 10

Gra

in Y

ield

(t

ha-1

) Ir

riga

ted

Grain Yield (t ha-1) Unirrigated

L2 L39 L47

Sutton Bonington 2010-11

Ria Sav

y = 0.4617x + 6.3044 R² = 0.18***

8.5

9

9.5

10

10.5

11

6 6.5 7 7.5 8 8.5 9

Gra

in y

ield

(t

ha-1

) Ir

riga

ted

Grain yield (t ha-1) Unirrigated

LSD (5%):

Irr 2.97*

Line 0.51 ***

Int 0.95 ^^

LSD (5%)

Irr 0.037*

Line 1.12***

Int 1.59*

Sutton Bonington 2012-13

Sav x Ria DH Yield Response to Drought

Δ13C vs grain yield: Savannah x Rialto DH pop

2010-11

y = 0.3973x - 0.0898 R² = 0.08, P = 0.02

6.5

7.0

7.5

8.0

8.5

9.0

18.8 19 19.2 19.4 19.6 19.8 20 20.2

Gra

in Y

ield

(t

ha

-1)

Grain Δ 13C(‰)

Savannah

Rialto

High WUE Low WUE

2012-13

2012-13

21

Aravinda Kumar. B, Azam-Ai, S, Weightman, R. and Foulkes, M.J.

2011. Relationships between carbon isotope discrimination and grain yield

in winter wheat under well watered and drought conditions

Journal Agricultural Science (Camb) 147, 259-272

Δ13C vs grain yield: Beaver x Soissons DH pop

R2 = 0.15

R2 = 0.34

2013

Savannah x Rialto DH (PhD Yadgar Mahmood)

Savannah x Rialto QTLs for Δ 13C

2A

2011 and 2013

2013

2011

3A

2011

24

6A

Savannah x Rialto QTLs for grain yield

3A

SB13UNIRR SB13IRR

SB11UNIRR SB11IRR JIC12

SB13UNIRR SB13IRR

SB11UNIRR SB11IRR JIC12

SB13UNIRR SB13IRR

SB11UNIRR SB11IRR JIC12

3A

25

Biomass Harvest index

3A

6A

2A

SB13UNIRR SB13IRR

SB11IRR

Savannah x Rialto QTLs for Biomass and HI

SB11UNIRR SB13UNIRR SB13IRR

SB11IRR SB11UNIRR

26

Canopy ToC

5B 6D

Leaf rolling

2B 5A

Sav x Ria QTLs for Canopy ToC and leaf rolling

SB Unirrigated 2012-3

Savannah x Rialto QTL Summary

3A 94 4 13.9 36.651 AGDM SB13UNIRRI R

4D 25.8 2.5 8.2 -31.358 AGDM SB13UNIRRI S

6A 83.1 2.6 8.7 -29.678 AGDM SB13UNIRRI S

6D 106 2.885 13.179 -0.251 CTEMP SB13UNIRRI S

5B 291 2.378 10.999 0.273 CTEMP SB11UNIRRI R

3A 80.2 3.775 16.886 -0.148 GR13C SB11UNIRRI S

2A 161 3.4 16 0.135 GR13C SB11UNIRRI R

3A 116 6.7 26.4 0.572 GRYLD JIC12 S

3A 87 3.066 13.948 0.237 GRYLD SB13IRRI S

3A 103.3 7.346 30.223 0.3 GRYLD SB13UNIRRI R

6A 47.6 2.7 9.7 -0.419 GRYLD JI12 S

2D 19 8 30.5 0.277 LFROL SB11UNIRRI R

5A 121.1 1.5 4.7 0.181 LFROL SB11UNIRRI R

• QTLs for grain Δ 13C accounting for 16-17% of phenotypic variance

• QTL for grain yield and grain Δ13 C on chr 3A under drought – pleiotropic effect?

Three wheat DH populations in well 5 watered and

drought environments in Australia

- Cranbrook/Halberd,

- Sunco/Tasman

- CD87/Katepwain

“The grain Δ13C QTL effects were commonly

small, accounting for a modest 1-10% of the total

additive genetic variance,”

Rebetzke et al. 2008 Theor. Appl Genet.

Quantitative trait loci for carbon isotope discrimination

are repeatable across environments

Comparison with other QTL studies for Δ13 C

Transfer of an entire alien genome to wheat in overlapping segments

Diversity for WUE in ancestral wheat

Triticum urartu

Rye

Aegilops speltoides

Thinopyrum bessarabicum

Ian King, Julie King

Glasshouse experiment 2013 and 2104

PhD Student Yadgar Mahmood

ERC – Future roots: Root phenotyping

Ancestral and elite wheat germplasm

Seedling level

Plant level

Crop level

Jon Atkinson talk (this session)

Larry York talk (this session)

Project lead: Malcolm Bennett

• Identify wheat root traits and regulatory

genes that control Water and N uptake

• Create high-throughput µCT system, hardware & software

• Mathematical modelling & statistical analysis to improve root architecture

The Hounsfield Facility An X-ray Computed Tomography Facility for Rhizosphere Research

Key Features

• 3 CT Scanners working from 0.5 µm to 5 mm resolution

• Accommodating samples up to 25 cm diam. & 1 m length

• Rapid scanning within 10 minutes

• Automated sampling system enabling 4-D visualisation

• Automated root imaging via RooTrak

• New Building opened June 2014

A Large Scale Automated Phenotyping Platform

CRLD ≈ 1 cm cm-3

Root Traits Framework

Carvalho P. & Foulkes, M.J. 2012. Encyclopedia of Sustainability Science and Technology.

35

Glasshouse

Soil extraction

Root washing

Root scanning

Carvalho, P., Azam-Ali, S, Foulkes M.J. 2014.

Quantifying relationships between rooting traits and

water uptake under drought in Mediterranean barley

and wheat. Journal Integrative Biology 56, 455-469

Defining the Critical Root Length Density in Cereals

Summary • Ability to access water appears to be key to productivity under UK drought.

• Grain 13C Δ correlated with grain yield under drought. Physiological basis ~

increased stomatal conductance, deeper roots?

• QTLs for grain 13C Δ for 16-17% of phenotypic variance. QTL on Chr3A pleiotroic effects of QTL for yield?

• High throughput phenotyping platforms for screening rooting traits in development - at seedling, plant, crop scale

• Phenotyping for wider variation in stomatal aperture traits and rooting traits and identification of regulatory genes is ongoing

Acknowledgments

Ian King Jukie King

Jayalath De Silva Pedro Carvalho

Yadgar Mahmood Larry York

Shaunagh Keating Sacha mooney

Malcolm Bennet

Simon Griffiths Luzie Wingen Simon Orford Luzie Wingen

Low WUE High WUE

13C flag leaf vs grain yield

Sutton Bonington Mean 2009-10 & 2010-11

Mid grain fill

Irrigated

Unirigated

7.0

8.0

9.0

10.0

11.0

12.0

13.0

14.0

20.0 20.5 21.0 21.5 22.0 22.5

Gra

in y

ield

t h

a-

1

ΔO o/oo 18 enrichment

Series1

Series2

Unirr

Irr

39

• Conflicting results obtained in various crops under different growing conditions on the association

between 12/13C and yield - range from no relationship between delta and yield to negative or

positive relationships, depending on the crop and the environment.

• Deep or dense root system which would promote soil moisture capture and WU is correlated

across genotypes with low WUE (Pinheiro et al., 2005; Kobata et al., 1996) but high ability to

• Favorable genotypic plant waterstatus under drought stress as reflected in measurements of

canopy temperature is correlated with lowWUE across genotypes (Araus et al., 1993; Frank et al.,

1997; Read et al., 1991; Zong et al., 2008).

• Genotypic variation in WUE under limited water regimes is affected more by variation in the

denominator (WU) rather than by variation in the nominator (biomass) (Blum, 2005).

• The successful and widely cited case for dryland wheat grain yield improvement with selection for

high WUE (low carbon isotope discrimination) in NSW Australia (Condon et al., 2002) can be

explained by the fact that wheat is grown there mainly on stored soil moisture. (Fig.

6 in Condon et al., 2002).

5. Drought resistance was found to be associated with low WUE when analyzed by delta under

limited water supply (e.g.Araus et al., 2003; Morgan et al., 1993; Ngugi et al., 1994;

Solomon and Labuschagne, 2004).

‘‘water uptake (WU), water-use efficiency (WUE), and harvest index (HI) are

drivers of yield.’’ Whereas HI is also largely influenced by WU and plant water status, it

can be concluded that WU alone is the main (not the exclusive)

driver of yield under drought stress.

40

The 13C/12C ratios (R) of leaves grains analysed using an elemental analyser (Carlo

Erba 2100) interfaced to an isotope ratio mass spectrometer.

Results expressed as δ13C values, using a secondary standard calibrated against

Vienna Pee Dee Belemnite calcium carbonate (VPDB).

δ13C = (Rsample - Rstandard) - 1

The C isotope discrimination (Δ 13C) of plant parts is then calculated as:

Δ 13C = (δ13Ca - δ13Cp) /[1 +(δ13Cp/1000)]

where δ13Ca and δ13Cp refer to air and plant C isotope compositions, respectively.

d13C of free atmospheric CO2 taken as -8‰ (Farquhar et al., 1989).