Water stress and climate change adaptation:

From trait dissection to yield

V Vadez – J Kholova

K Aparna, K Siva Sakhti, M Tharanya, S Medina, Srikanth Malayee, Sudhakarreddy P, S Choudhary, R Baddam, S Dharani, S

Deshpande, R Srivastava, CT Hash

ICRISAT

NGGIBCI meeting – India 20 Feb 2015

Few things on CC / Drought

What we learnt

Trait dissection & mechanisms

Trait assessment for breeding

Linking the pieces with crop simulation

Grain Yield

Grain Number Grain Size & N

Biomass RADN

TE T RUE Rint

vpd

kl LAI SLN Roots

k

T N LNo

A >A

APSIM Generic Crop Template, from Graeme Hammer

Yield and determinants

Yield is not a trait Phenotyping to focus on the “building blocks”

FTSW

0.00.20.40.60.81.0

No

rma

lize

d t

ran

sp

ira

tio

n

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Stage I

Stage II

Stage III

No stress until >60% soil water is depleted

How plant manage water when there is water is critical

Typical response of plant to water deficit

Soil water

Tran

sp

irati

on

0

1

2

3

4

5

6

7

8

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Maxim

um

VP

D (

kPa)

Sahelian Center (Niger)

Patancheru

Vapor pressure deficit (VPD) in the SAT

High VPD – Variable conditions Effect on plant water balance

What is a “drought tolerant” plant?

A plant with: • enough water to fill up grains • no more water after grain filling

Hypotheses: • Tap more water?

• Save/manage water/WUE ?

Focus on “building blocks” of plant water budget / use

Few things on CC / Drought

What we learnt

Trait dissection & mechanisms

Trait assessment for breeding

Linking the pieces with crop simulation

Water extraction at key times

Zaman-Allah et al 2011 Borrell et al 2014 Vadez et al 2013

0

1

2

3

4

5

6

7

8

9

10

21 28 35 42 49 56 63 70 77 84 91 98

Wate

r u

sed

(kg

pl-1

)

Days after sowing

Sensitive

Tolerant

Vegetative Reprod/ Grain fill

Conductance Canopy area

Canopy T°C Staygreen

Less water extraction at vegetative stage, more for grain filling

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 1 2 3 4

WU

(kg

pla

nt-

1 w

eek

-1)

Weeks after booting

ICMH01029

ICMH01040

ICMH01046

PRLT2/89-33

Vadez et al 2013 – Plant Soil

H77/833-2

ICMH02042

Terminal drought sensitive

Terminal drought tolerant

Tolerant: less WU at vegetative stage, more for reproduction & grain filling

Water extraction pattern (WS) in pearl millet

Flowering

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

500 1000 1500 2000 2500 3000 3500

Sta

yg

ree

n s

co

re

Water uptake in week 3 after booting

Stress1 R2 = 0.76**

Stress 2 R2 = 0.79**

Relationship Water extraction vs Staygreen

Staygreen = water available during grain fill

R² = 0.7108

0

4

8

12

16

20

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Gra

in Y

ield

(g

pla

nt-1

)

Early stress

Water uptake in week 3 after booting

More post-anthesis water use, more yield

Relationship Water extraction vs Yield

40 kg grain mm-1

Why different patterns of water use even if no stress ?

Difference in canopy size (tillering, leaf size,

leaf number, LER, etc…

Difference in canopy conductance

Terminal drought sensitive

Terminal drought tolerant

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.50 1.00 1.50 2.00 2.50 3.00 3.50

VPD (kPa)

H77/2 833-2

PRLT-2/89-33

Tran

sp

irati

on

(g

cm

-2 h

-1)

Kholova et al 2010

2 mechanisms of water saving: •Low Tr at low VPD •Further restriction of Tr at high VPD

Transpiration response to VPD in pearl millet

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.62 1.05 1.58 2.01 2.43 3.05 3.45

Tran

spir

atio

n (

g p

l-1 c

m-2

)

VPD (kPa)

VPD-insensitive

VPD-sensitive

Transpiration response to VPD in Sorghum 1 - Germplasm

2.0

3.0

4.0

5.0

6.0

7.0

152 Germplasm

Tran

sp

irati

on

Eff

icie

ncy (

TE)

10 lowest TE are all VPD-Insensitive

10 highest TE are all VPD-sensitive

High TE lines limit transpiration at high VPD

Why are VPD-sensitive lines so interesting?

Staygreen ILs (Stg3 – Stg B) are VPD-sensitive

0.0000

0.0020

0.0040

0.0060

0.0080

0.0100

0.0120

9 11 13 15 17

Tran

sp

irati

on

(g

cm

-2 h

-1)

Time of the day (h)

stg1

stg3

stg4

stgB

R16

B35

Recurrent R16

Stg3

StgB

Transpiration response to VPD in Sorghum 2 - Introgression lines in R16

0.000

0.002

0.004

0.006

0.008

0.010

0.012

10.00 11.30 13.00 14.30

Tran

spir

atio

n r

ate

(g

cm-2

h-1

)

Time of the day

stg1

stg3

stg4

stgB

stgB

S35

B35

Recurrent S35

Stg3

StgB

Transpiration response to VPD in Sorghum 3 - Introgression lines in S35

No effect of Stg QTL in a different background

What mechanisms??

Hydraulic differences in the roots ??

Tran

sp

irati

on

VPD

Apoplast

(Structural)

Symplast

(AQP, …)

Water pathways in the root cylinder

Two pathways have different hydraulic conductance

Hypothesis: Aquaporin control plant water loss ?

Follow-up of transpiration before/after inhibition

VPD - insensitive

0

0.2

0.4

0.6

0.8

1

1.2

No

rm

alized

tran

sp

irati

on

Time(mins)

Apoplast & symplast inhibition

Symplastic inhibition

Apoplastic inhibition

Genetic differences in water transport pathways

VPD-sensitive

VPD-insensitive

VPD-sensitive

Any difference in aquaporin expression In sorghum contrasting for VPD response??

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.62 1.05 1.58 2.01 2.43 3.05 3.45

Tran

sp

irati

on

(g

pl-

1 c

m-2

)

VPD (kPa)

1.9

0.8 0.7

1.1

0.8

1.7

0.8

2.2

2.9

1.9

0.6

1.3

2.9

1.9

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

pip1.3/1.4 pip1.5 pip1.6 pip2.2 pip2.3 pip2.5 pip2.6Rela

tive f

old

expre

ssio

n

of

mR

NA

*

*

*

Aparna K (in prep.)

PIP1 & PIP2 up regulation in VPD – insensitive line ( )

Aquaporin gene expression

Few things on CC / Drought

What we learnt

Trait dissection & mechanisms

Trait assessment for breeding

Linking the pieces with crop simulation

Capacity: 4,800 plots Throughput: 2,400 plots/hour Traits: LA, Height, Leaf angle, …

LeasyScan at ICRISAT

Leaf canopy area and conductance

Canopy Scanning + plant transpiration = live water budget

Leaf canopy conductance

Load Cells

Leaf area

See Chapuis et al 2012

From Welcker et al 2014

Leaf

are

a

Water use

Leaf canopy area

Trait dissection Possible

Field applications

Wind + Light

TºC + RH %

From Deery et al 2014

Lidar scanning

Leaf area response to environmental conditions

Leaf

elo

ng

ati

on

rate

Atmospheric drought

Soil drought

0

1

2

3

4

5

6

7

8

9

10

21 28 35 42 49 56 63 70 77 84 91 98

Wate

r u

sed

(kg

pl-1

)

Days after sowing

Water extraction at key times

Many possible causes Few consequences

From Deery et al 2014 See Prashar et al 2013

Sensitive

Tolerant

Possible Field applications Early vigor (RGB / NDVI)

Infra Red imaging

Staygreen Canopy T°C

Vegetative Reprod/ Grain fill

Conductance Canopy area Early vigor Tillering, ….

Few things on CC / Drought

What we learnt

Trait dissection & mechanisms

Trait assessment for breeding

Linking the pieces with crop simulation

North

Central

South

Far South

Maharasthra

Karnataka

Andhra Pradesh

Southern

Northern

Central

Far South

Stress characterization in sorghum growing area

Characterizing drought based on S/D ratio

Type 3 intermittent stress

Type 2 pre-flowering stress

Type 1 flowering stress

Type 4 post-flowering stress

major stress patterns

0

0.2

0.4

0.6

0.8

1

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600

thermal time (oD)

S/D

vegetative

pre-flowering

post-flowering

post-flowering relieved

mild

3. What yield in which most frequent

scenario?

2. Environmental patterns

Sorghum growing area

1. Well-defined area of interest

average yield

0

200

400

600

800

1000

1200

vegetative pre-flowering post-flowering post-flowering

relieved

mild stress

we

igh

ed

yie

ld (

kg

/ha

)

vegetative

pre-flowering

post-flowering

post-flowering relieved

mild stress4. Which traits

confer advantage

in the most frequent

scenario?

3. Effect of environment on production Kholova et al 2013

major stress patterns

0

0.2

0.4

0.6

0.8

1

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600

thermal time (oDay)

S/D

vegetative

pre-flowering

post-flowering

post-flowering relieved

mild

2. Environmental patterns

3. Yield in different scenarios

4. Trait effects in different scenarios

S35 (senescent line) 7001- stgB - small leaves,

H2O extraction

6008 – stgA - growth

dynamics, tillering

6040 - stg1 - growth

dynamics

R16 (senescent line) K359w -stgB&3 – high

TE, growth dynamic.

Vadez et al. 2011

Grain Yield

Grain Number Grain Size & N

Biomass RADN

TE T RUE Rint

vpd

kl LAI SLN Root

s

k

T N LNo

A >A

Trait analysis in different introgressions

0

500

1000

1500

2000

2500

200 300 400 500 600 700 800

LA

(cm

2)

thermal time (degree days)

S35700160086026

0

5

10

15

20

25

0 200 400 600 800

TP

LA

TTemerg_to_flag

TPLA varying TPLAmax

16

18

2022

24

0

0.2

0.4

0.6

0.8

1

1.2

100 200 300 400 500 600 700 800 900 100011001200130014001500

S/D

thermal time intervals

High TPLAmax

Low TPLAmax

-1000

-800

-600

-400

-200

0

200

400

600

800

1000

0 500 1000 1500 2000 2500 3000

Gra

in y

ield

gain

original grain yield (kg ha-1)

Smaller canopy

(low TPLAmax)

-1000

-800

-600

-400

-200

0

200

400

600

800

1000

0 2000 4000 6000 8000

Sto

ver

yie

ld g

ain

Original stover yield (kg ha-1)

Smaller canopy

(low TPLAmax)

Pre-flowering

Flowering

Post-flowering

Post-flowering relieved

No stress

0

500

1000

1500

2000

2500

0 200 400 600 800 1000 1200 1400

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6sto

ver

(kg

ha

-1);

gra

in (

kg

ha

-1)

thermal time (degree day)

LA

I (m

2 m

-2);

S/D

High TPLAmax

Low TPLAmax

EXAMPLE: TPLA

variability (Leaf area)

Kholová et al. 2014 (FPB)

% of parameter change &

physiological meanining

stress

scenario grain (kg ha-1) stover (kg ha-1) zone grain (kg ha-1) stover (kg ha-1)

value estimate (Rs ha-

1)

0.05 pre-flowering -86 (-70;0) 160 (39;254) Central -71 (-142;28) 259 (130;387) 230

larger canopy

flowering -190 (-366;0) 328 (120;490) FarSouth -25 (-203;180) 418 (266;576) 1715

post-flowering -127 (-278;0) 410 (294;541) North -97 (-212;21) 338 (184;499) 235

post-flowering-relieved -143 (-214;-78) 373 (257;452) South -67 (-189;8) 385 (240;481) 920

no stress 56 (-46;143) 348 (197;449) -0.05

pre-flowering 37 (0;51) -75 (-127;-15) Central 34 (-10;51) -128 (-160;-61) -130

smaller canopy

flowering 126 (43;159) -189 (-223;-

129) FarSouth -3 (-116;97) -184 (-254;-113) -965

post-flowering 61 (-5;119) -207 (-286;-

129) North 56 (-14;140) -184 (-248;-102) -80

post-flowering-relieved 44 (10;80) -145 (-180;-

101) South 34 (0;81) -146 (-194;-84) -220

no stress -32 (-77;16) -140 (-203;-79)

Simulation of trait effect on yield

See Sinclair et al 2010 See Cooper et al 2014

Grain yield increase (g m-2)

Traits targeted to specific zones Chose test locations

0

10

20

30

393 108Fold

-in

cre

ase

Genotypes

Aquaporin gene expression

PIP2;6

PIP2;7

PIP2;9

PIP1;2

PIP1;3

PIP1;4

Trait variability

Genomics (Genetics)

See Cooper et al 2014

Multi-location testing

Crop Simulation (Validation)

Linking-up the pieces

Trait dissection

Field phenotyping

See Lynch et al 2014 See Granier et al 2014 See Cobb et al 2013

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.50 1.00 1.50 2.00 2.50 3.00 3.50 Evaporative demand (VPD)

Can

op

y c

on

du

cta

nce

Importance of water at critical times

Causal – Consequential traits

Combine trait- & field-based phenotyping

Simulation to guide breeding/agronomic targets

Providing options with stochastic values

Key messages…

Thank you

Collaborators: F. Chaumont (Univ. Louvain) G. Hammer / A. Borrell / G McLean / E van Oosterom (Univ. Queensland) B Sine / N Belko / Ndiaga Cisse (CERAAS) C Messina, Anand Pandravada (Pioneer) Hanna Anderberg (Lund Univ.)

Donors: B&MG Foundation GCP ACIAR DFID CRPs

Technicians / Data analyst: Srikanth Malayee Rekha Badham M Anjaiah N Pentaiah

Students: M Tharanya S Sakthi S Medina M Diancoumba

Colleagues: KK Sharma / T Shah / F Hamidou HD Upadhyaya / Bhasker Raj