Water stress and climate change adaptation: From trait dissection to yield NGGIBCI meeting, India- 20 Feb 2015

1. Water stress and climate change
adaptation:
From trait dissection to yield
Vincent Vadez – Jana Kholova
Aparna Kakkera, K Siva Sakhti, M Tharanya, Susan Medina,
Srikanth Malayee, Sudhakarreddy Palakolanu, Sunita Choudhary,
Rekha Baddam, Suresh Dharani, Santosh Deshpande,
Rakesh Srivastava, Tom Hash
ICRISAT
NGGIBCI meeting – India 18-20 Feb 2015

2. Today’s presentation
Basic considerations on CC / Drought
Transpiration response to VPD
Possible mechanisms and role of aquaporin
Breeding application
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 its 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
Plants don’t suffer stress until >60% soil water is depl
How plant manage water when there is water is critical
Basic response of plant expose to water deficit
Control of leaf water losses

5. What is a “drought tolerant” plant?
A plant with:
• enough water to fill up grains
• no more water after grain filling
Hypotheses:
• Tap water?
• Save/manage water?
Focus on traits affecting plant water budget

6. Maximum temperature in the SAT
Hypothetic
Temperature
threshold
0
5
10
15
20
25
30
35
40
45
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MaximumT°C
1983-HQ 1992-HQ
2001-HQ 2012-HQ
1983-ISC 1990-ISC
1998-ISC
Headquarter
Sahelian Center
T°C rarely crosses critical limits
for SAT crops

7. 0
1
2
3
4
5
6
7
8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MaximumVPD Sahelian
Center
Headquarter
Vapor pressure deficit (VPD) in the SAT
Prevalent high VPD
Effect on plant water balance
VPD
threshold

8. Region Season Temp. Response (°C) Rainfall Response (%)
Africa Min 25 50 75 Max Min 25 50 75 Max.
West Africa Annual 1.8 2.7 3.3 3.6 4.7 -9 -2 2 7 13
East Africa Annual 1.8 2.5 3.2 3.4 4.3 -3 2 7 11 25
Southern
Africa
Annual 1.9 2.9 3.4 3.7 4.8 -12 -9 -4 2 6
Asia Min 25 50 75 Max Min 25 50 75 Max.
East Asia Annual 2.3 2.8 3.3 4.1 4.9 2 4 9 14 20
Southern Asia Annual 2.0 2.7 3.3 3.6 4.7 -15 4 11 15 20
S.E. Asia Annual 1.5 2.2 2.5 3.0 3.7 -2 3 7 8 15
Introduction
IPCC report 2007

9. Introduction
A changing climate: What are we sure about?
•A steady increase in temperature (1.5-2°C to 4-5 °C)
•CO2 increase
What are we less sure about?
•Rainfall quantity and variability
•Extreme temperature events

10. 0
200
400
600
800
1000
1200
Time (days)
Degredays
Flowering with CC (+ 2°C) Flowering with
Current climate
About 8 days
differences
Crop cycle dynamics vs water use
A loss in light capture
Degre-day accumulation in chickpea (base = 8°C)

11. Climate
scenario
Mean
seasonal
temperature
(OC)
Time to
maturity (d)
%
reduction
Crop yield
(kg/ha)
%
reduction
from
Current
Current 19.6 133 - 1736 -
Current +
1OC
20.6 124 6.5 1612 7.1
Current +
2OC
21.6 117 12.0 1503 13.4
Current +
3OC
22.6 111 15.9 1406 19.0
Current +
4OC
23.6 108 18.7 1322 23.8
Current +
5OC
24.6 105 20.5 1238 28.7
From John Dimes - ICRISAT
Effect on yield in pigeonpea
Crop cycle dynamics vs water use
Shorter cycle lower yield

12. 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 panicle emergence
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

13. 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)
R² = 0.552
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)
Late stress
Early stress
Water uptake in week 3 after booting
Higher yield from higher post-anthesis water use

14. 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
Less water extraction
at vegetative stage,
more for grain filling
Zaman-Allah et al 2011
See Borrell et al 2014
See Vadez et al 2013
Sensitive
Tolerant
Trait dissection
Vegetative Reprod/ Grain fill
Conductance
Canopy area

15. Lysimetric facility at ICRISAT
Morphology Functionality
Shift in how we look at roots
Kinetics of water uptake
2800 “small” PVC / 1600
“large” PVC
Limitations / Challenges:
• Capacity/automation (load cells)
• 3-D in-situ
Strengths:
• Water use efficiency
• Water extraction at key times

16. Variation for water use efficiency
• Huge genetic variation
• Variants used in breeding
FunctionalitySorghum
Pearl millet

17. Today’s presentation
Basic considerations on CC / Drought
Transpiration response to VPD
Possible mechanisms and role of aquaporin
Breeding application
Linking the pieces with crop simulation

18. 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)
From Kholova et al 2010b
2 mechanisms of water saving:
•Low Tr at low VPD
•Further restriction of Tr at high VPD
Transpiration response to high VPD –
Pearl millet

19. Transpiration response to high VPD -
Peanut

20. Mouride
IfVPD<2.09,TR=0.0083(VPD)–0.002
IfVPD≥ 2.09,TR=0.0013(VPD)+0.015
R²=0.97
B UC-CB46
TR=0.0119(VPD)-0.0016
R²=0.97
D
Transpiration response to VPD
- cowpea
Tolerant lines have a breakpoint
(water saving)
Tolerant Sensitive
Belko et al – 2012 (Plant Biology)

21. 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
1 - Introgression lines

22. S35 background
Transpiration response to high VPD
In staygreen introgression lines
ILs do not differ from recurrent S35
for the Tr sensitivity to VPD
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

23. 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 at three weeks after panicle
emergence
Unfilled Profile R2 = 0.76**
Filled Profile R2 = 0.79**

24. Vapor Pressure Deficit (VPD, in kPa)
Transpirationrate(gcm-2h-1)
0.0 2.0 4.0
0.0
1.0
A – Insensitive to VPD – High rate at low VPD
B – Sensitive to VPD – High rate at low VPD
C – Sensitive to VPD – Low rate at low VPD
D – Insensitive to VPD – Low rate at low/high VPD
Main types of Tr response to VPD
Water use
difference
Leaf conductance differences = water
Vadez et al 2013 – FPB in press

25. 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
2 - Germplasm

26. 2.0
3.0
4.0
5.0
6.0
7.0
152 Germplasm tested
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 sorghum so interesting?

27. 4 replications
RH & T hourly recording
Weighing:
7-11am = low VPD
11am-15pm = high VPD
8” pots re-saturated every day
soil evaporation minimized with plastic beads
How to phenotype at large scale?

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

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

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

31. 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

32. Canopy Scanning
+ plant transpiration
= live water budget
Leaf canopy conductance
Load Cells
Limitations / challenges:
• Load cells capacity
• Data management / analysis
Strengths:
• Throughput
• Meta-data

33. Today’s presentation
Basic considerations on CC / Drought
Transpiration response to VPD
Possible mechanisms and role of aquaporin
Breeding application
Linking the pieces with crop simulation

34. Possible mechanisms??
???
Hydraulic
Possibly located in the roots

35. Apoplastic
Pathway
(Structural)
Symplastic
Pathway
(AQP)
Water pathways in the root cylinder
Two pathways have different hydraulic conductance
Hypothesis: Aquaporin control plant water
loss ?
????

36. Apoplastic path inhibition: H-Ferrocyanide +CuSO4
Symplast path inhibition: AgNO3,

37. Follow-up of transpiration before/after inhibition

38. 0
0.2
0.4
0.6
0.8
1
1.2
Normalizedtranspiration
Time
Apoplast & symplast inhibition at low VPD
Apoplastic &
Symplastic
inhibition
Symplastic
inhibition
Apoplastic
inhibition
Apoplastic transport predominant
Low VPD small differences/effects
VPD-sensitive
VPD - insensitive

39. VPD - insensitive
0
0.2
0.4
0.6
0.8
1
1.2
Normalizedtranspiration
Time(mins)
Apoplast & symplast inhibition at high VPD
Symplastic
inhibition
Apoplastic
inhibition
Apoplastic transport less predominant
High VPD larger differences/effects
VPD-sensitive

40. 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)

41. 0
2
4
6
8
10
12
14
16
18
Low TE High TE
HighVPD/LowVPD PIP1;1
PIP1;2
PIP1;3
PIP1;4
PIP2;1
PIP2;2
PIP2;4
PIP2;5
PIP2;6
PIP2;7
PIP2;8
PIP2;9
PIP2;10
PIP relative expression (High VPD/Low VPD)
VPD – insensitive line
increases expression of PIP2
PIP2;6
PIP2;9
PIP2;7
VPD-Insensitive VPD-Sensitive

42. Today’s presentation
Basic considerations on CC / Drought
Transpiration response to VPD
Possible mechanisms and role of aquaporin
Breeding application
Linking the pieces with crop simulation

43. Crop at ICRISAT - H
Abiotic constraints

44. SOL
BIJ
HYD
TAN
BAD
Maharashtra
Karnat
aka
Andhra
Pradesh
Network of location for post-rainy sor

45. Field variability at the ICRISAT-Niger s

46. Effects of human settlement activities on millet growth in the Sahel (micro-variability)
Aerial photograph showing residual effects of changes in soil productivity due to farmers' settlement activities. Numbers indicate the
years during which the settlement of the farmers remained at a particular site. The picture was taken 75 days after sowing from an
altitude of about 300 m above ground. Hardpans (indicated by lacking plant growth) within the boundaries of former settlement areas
are the result of clay applications to the foundations of the five houses belonging to the one extended family. Note that the increases in
millet growth in former settlement areas lasted four to five years. Buerkert et al. 1996. Plant and Soil 180, 29-38.

47. 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
Less water extraction
at vegetative stage,
more for grain filling
Zaman-Allah et al 2011
See Borrell et al 2014
See Vadez et al 2013
From Deery et al 2014
See Prashar et al 2013
Sensitive
Tolerant
Trait dissection Possible
Field applications
Early vigor (RGB / NDVI)
Infra Red imaging
Canopy T°C
Staygreen
Vegetative Reprod/ Grain fill
Conductance
Canopy area

48. 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
Evaporative demand (VPD)
H77/2 833-2
PRLT-2/89-33
Canopyconductance
Modulate conductance
Decrease TR at high VPD
Leaf canopy response to VPD
Water saving
Canopy TºC
Link to root anatomical
differences
Trait dissection
Possible
Field applications
Infra Red imaging
From Araus and Cairns 2014
From Burton et al 2012
Root anatomy

49. Leafarea
Thermal time
A – Fast early LA
B – Slow early LA
C – Fast early LA / small max LA
D – Slow early LA / small max LA
Traits:
Leaf area development dynamics
Speed of
development /
size of canopy
= water
So far no in-vivo way to measure

50. Today’s presentation
Basic considerations on CC / Drought
Transpiration response to VPD
Possible mechanisms and role of aquaporin
Breeding application
Linking the pieces with crop simulation

51. 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. Adaptive
traits enhancing
crop production
& resilience in
given
environmental characterization
1. Well-defined area of interest
Kholová et al. 2013
3. Impact on
the
crop
production
7%
18%
18%17%40%
PHASE I
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

52. Adaptive traits???
Grain Yield
Grain Number Grain Size & N
 Biomass RADN
TE T RUE Rint
vpd
kl LAISLNRoot
s
k

TN LNo
A >A
Crop production
Component traits
contributing to
drought adaptation
Kholová et al. 2014 (FPB
Traits related to water utilization

53. “EFFICIENT WATER MANAGEMENT”
• enough water to fill up grains
• no more water after grain filling
• water is turned to biomass with max. efficiency
•Save water
•Tap water
• Increase WUE
Crop
production
Traits related to water utilization
MODELLING:
predicting traits’ value in given agr

54. S35 (senescent
background)
 7001- stgB - small leaves,
H2O extr.
 6008 – stgA - gr.
dynamics, tillering
 6026 – stg2 - large
leaves
Material: senescent parental lines &stay-green ILs
Grain Yield
Grain
Number
Grain Size
& N
 Biomass RADN
TE T RU
E
Rint
vpd
kl LA
I
SL
N
Ro
o
t
s
k

TN LNo
A >A R16 (senescent
background)
 K359w -stgB&3 – high
TE, gr. dyn.
 K648 - stg4 – short
phyllochron
ct of QTL depends on genetic backgroun
(stg B!) Vadez et al. 2011

55. 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(kgha-1)
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(kgha-1)
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
EXAMPL
E:
TPLA
variabilit
y
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)

56. 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
environment?
Way forward1. Well-defined area of interest
2. Environmental patterns
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

58. Figure 1
North
Central
South
Far South
Maharasthra
Karnataka
Andhra Pradesh
Southern
Northern
Central
Far South
Sorghum growing area in India

59. Characterizing drought
dynamics - based on S/D
ratio simulation and
clustering
Type 3 intermittent stress
Type 2 pre-flowering stress
Type 1 flowering stress
Type 4 post-flowering stress
2 How to characterize

60. 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. Which traits
confer advantage
in the most frequent
environment?
2. Environmental pattern
Sorghum growing area

61. grain yield gain (low TR)
-300
-200
-100
0
100
200
300
400
0 500 1000 1500 2000 2500 3000 3500
original yield (kg/ha)
yieldgain(kg/ha)
1 postflowering
2 flowering
3 postflowering-relieved
4 no stress
5 preflowering
Original yield (kg ha-1)
0
Yield increase (kg/ha) with transpiration
sensitivity to high VPD: Rabi sorghum
Yieldincrease

62. -1 0 +33
Crop modelling used to predict trait effects
15-30% yield increase at high latitudes
% yield increase with transpiration
sensitivity to high VPD: Peanut

63. The VPD response lead to higher TE
It is itself related to differences in AQP gene
expression
Major yield increase possible across crops
Breeding (donors identified)
Harness genetics – Phenotyping (new platform)
In Summary…

64. 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 (Pioneer)
Donors:
B&MG Foundation
GCP
ACIAR
DFID
ICRISAT
Technicians / Data analyst:
Srikanth Malayee
Rekha Badham
M Anjaiah
N Pentaiah
Students:
M Tharanya
S Sakthi
T Rajini
Colleagues:
KK Sharma / T Shah / F Hamidou
HD Upadhyaya / R Srivastava / Bhasker Raj
SP Deshpande / PM Gaur

65. More, Better, Faster, Cheaper: practical needs for
improving the rate of genetic gain
Advances in below and above-ground
phenotyping
Vincent Vadez & Team
ICRISAT
Global Goods – Bill & Melinda Gates Foundation
29 Oct 2014

66. Crop simulation of trait effect on yield
See Sinclair et al 2010
See Cooper et al 2014
Grain yield (g m-2)
Traits targeted
to specific zones
Chose test
locations

67. 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
Thank you

68. RESEARCH APPLICATION
Development of BCNAM pop
(best stay-green donors X best rabi ge
Predicted value of adaptive traits
TRAIT = $ per ha
HT- PHENOTYPING
Environmental
characterization
& trait identification
Ideotypes suiting the targe
with viable management opt

69. Lysimetric evaluation
Transpiration in pots
0.000
0.004
0.008
0.012
0.016
0.020
0.62 1.05 1.58 2.01 2.43 3.05 3.45
Transpiration
(gcm-2h-1)
VPD
Low TE
High TE
0
1
2
3
4
5
6
7
Low TE High TE
TE
grain yield gain (low TR)
-300
-200
-100
0
100
200
300
400
0 500 1000 1500 2000 2500 3000 3500
original yield (kg/ha)
yieldgain(kg/ha)
1 postflowering
2 flowering
3 postflowering-relieved
4 no stress
5 preflowering
Original yield (kg ha-1)
0
AQP gene expression
Modeling of Tr restriction
effect on yield

70. Expansion of modelling
(Best generated grid-data&Future
climatic projections&whole India
modelling (with various crops))
Expansion of the concept to WCA
(Madina)
Sorghum physiology researc
@ICRISAT(AusSoRGM 2014)
Vincent Vade
Jana Kholová
& TEAM

71. • ICRISAT is a non-profit, non-political, International Agricultural
Research Institute
• Established in 1972, operating with an annual budget of US$ 83
million (2013)
• Member of the Consultative Group on International Agricultural
Research (CGIAR)
• Our mandate crops: Sorghum, Pearl millet, Pigeon pea, Chick
pea & Groundnut
• A prosperous, food-secure and resilient
dryland tropics
Our Vision
• To reduce poverty, hunger, malnutrition and
environmental degradation in dryland tropics
Our Mission

72. V. Vadez – C.Tom Hash – Rattan Yadav – T. Nepolean – J. Kholová –
HS Talwar-G. Hammer – E. vanOosterom - A. Borrell – G. McLean –
A. Doherty - T.R. Sinclair – I.M. Rao – S. Beebe – J. Ehlers –
Mainassara Zaman A. – F. Hamidou – P.M. Gaur – E. Monyo – B.
Ntare – J. Devi-Mura – S. Choudhary ……
Our approach brings together
physiologists, breeders & modelers
Thank you
Mission
To reduce poverty, hunger,
malnutrition and environmental
degradation in the dryland tropics