FOODCROPS.VN. 2015. V. Vadez. Water stress and climate change adaptation. From trait dissection to yield

1. 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

2. Few things on CC / Drought
What we learnt
Trait dissection & mechanisms
Trait assessment for breeding
Linking the pieces with crop simulation

3. Grain Yield
Grain Number Grain Size & N
 Biomass RADN
TE T RUE Rint
vpd
kl LAISLNRoots k

TN 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”

4. FTSW
0.00.20.40.60.81.0
Normalizedtranspiration
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
Transpiration

5. 0
1
2
3
4
5
6
7
8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MaximumVPD(kPa) Sahelian
Center (Niger)
Patancheru
Vapor pressure deficit (VPD) in the SAT
High VPD – Variable conditions
Effect on plant water balance

6. 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

7. Few things on CC / Drought
What we learnt
Trait dissection & mechanisms
Trait assessment for breeding
Linking the pieces with crop simulation

8. 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
Waterused(kgpl-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

9. 0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0 1 2 3 4
WU(kgplant-1week-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

10. 0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
500 1000 1500 2000 2500 3000 3500
Staygreenscore
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

11. 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
GrainYield(gplant-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

12. 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

13. 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
Transpiration(gcm-2h-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

14. 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
Transpiration(gpl-1cm-2)
VPD (kPa)
VPD-insensitive
VPD-sensitive
Transpiration response to VPD in Sorghum
1 - Germplasm

15. 2.0
3.0
4.0
5.0
6.0
7.0
152 Germplasm
Transpiration
Efficiency(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?

16. 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
Transpiration(gcm-2h-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

17. 0.000
0.002
0.004
0.006
0.008
0.010
0.012
10.00 11.30 13.00 14.30
Transpirationrate(gcm-2h-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

18. What mechanisms??
Hydraulic differences in the roots ??
Transpiration
VPD

19. Apoplast
(Structural)
Symplast
(AQP, …)
Water pathways in the root cylinder
Two pathways have different hydraulic conductance
Hypothesis: Aquaporin control plant water
loss ?

20. Follow-up of transpiration before/after inhibition

21. VPD - insensitive
0
0.2
0.4
0.6
0.8
1
1.2
Normalizedtranspiration
Time(mins)
Apoplast & symplast inhibition
Symplastic
inhibition
Apoplastic
inhibition
Genetic differences in water transport pathways
VPD-sensitive

22. 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
Transpiration(gpl-1cm-2)
VPD (kPa)

23. 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 pip2.6
RelativefoldexpressionofmRNA
*
*
*
Aparna K (in prep.)
PIP1 & PIP2 up regulation
in VPD – insensitive line ( )
Aquaporin gene expression

24. Few things on CC / Drought
What we learnt
Trait dissection & mechanisms
Trait assessment for breeding
Linking the pieces with crop simulation

25. Capacity: 4,800 plots
Throughput: 2,400 plots/hour
Traits: LA, Height, Leaf angle, …
LeasyScan at ICRISAT
Leaf canopy area and conductance

26. Canopy Scanning
+ plant transpiration
= live water budget
Leaf canopy conductance
Load Cells

27. Leaf area
See Chapuis et al 2012
From Welcker et al 2014
Leafarea
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
Leafelongationrate
Atmospheric drought
Soil drought

28. 0
1
2
3
4
5
6
7
8
9
10
21 28 35 42 49 56 63 70 77 84 91 98
Waterused(kgpl-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,
….

29. Few things on CC / Drought
What we learnt
Trait dissection & mechanisms
Trait assessment for breeding
Linking the pieces with crop simulation

30. North
Central
South
Far South
Maharasthra
Karnataka
Andhra Pradesh
Southern
Northern
Central
Far South
Stress characterization in sorghum growing area

31. 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

32. 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 (o
D)
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

33. average yield
0
200
400
600
800
1000
1200
vegetative pre-flowering post-flowering post-flowering
relieved
mild stress
weighedyield(kg/ha)
vegetative
pre-flowering
post-flowering
post-flowering relieved
mild stress
4. 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 (
o
Day)
S/D
vegetative
pre-flowering
post-flowering
post-flowering relieved
mild
2. Environmental patterns
3. Yield in different scenarios

34. 4. Trait effects in different scenarios

35. 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 LAISLNRoot
s
k

TN LNo
A >A
Trait analysis in different introgressions

36. 0
500
1000
1500
2000
2500
200 300 400 500 600 700 800
LA(cm2)
thermal time (degree days)
S35
7001
6008
6026
0
5
10
15
20
25
0 200 400 600 800
TPLA
TTemerg_to_flag
TPLA varying TPLAmax
16
18
20
22
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
Grainyieldgain
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
Stoveryieldgain
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
1.6
stover(kgha-1);grain(kgha-1)
thermal time (degree day)
LAI(m2m-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)

37. 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

38. 0
10
20
30
393 108
Fold-increase
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)
Canopyconductance

39. 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…

40. 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