1. Objective 5: Cross-crop issues
Activity 1: Drought phenotyping
Across crops
Update on Year 2
ICRISAT – CIAT – ISRA – Univ North Carolina

2. Purpose: Looking at similar traits across species
Hypothesis 1: A “drought tolerant” plant has:
enough water to fill up grains
no more water after grain filling
Hypothesis 2: Crop species share same
adaptation strategies
Options:
• Save water
• Tap water
• Secure reproduction

3. Outputs to TLII
Sub-Activity 5: Training
Water use / productivity
Water uptake
Reproduction and partitioning
Modeling
Trait value Refined protocols Better pheno-
predicted More tools typing data
Phenotyping of cell-based processes – toward gene discovery

4. Water use / productivity
Basic response of plant exposed to water deficit
1.2
Normalized transpiration
1.0
Stage I
0.8 Stage II
0.6
Stage III
0.4
0.2
0.0
1.0 0.8 0.6 0.4 0.2 0.0
FTSW
How plant manage water when there is water is critical
To measure: Soil moisture thresholds for transpiration decline
Canopy conductance (Tr in g cm-2 h-1)
Tr response to VPD
Leaf area development

5. Water use / productivity
Groundnut Cowpea Bean Chickpea
Soil moisture thresholds for
transpiration decline x xxx x xxx
Canopy conductance (g cm-2 h-1) x xxx x xx
Tr response to VPD xx xxx x xx
Leaf area development xx x
Zaman-Allah et al., 2011 JXB
Zaman-Allah et al 2011 FPB
Belko et al 2012 - FPB
Belko et al 2012 – Plant Biology

6. Water use / productivity
Groundnut Cowpea
A LSD ICG 11862 ICG 12235 B LSD Bambey-21 IT82E-18
ICG 13787 ICG 4598 ICGV 12000 IT97K-556-6 KVX-525 UC-CB46
ICGV 02189 ICGV 02266 ICGV 11088 IT84S-2049 IT93K-503-1 IT93K-693-2
ICGV 97182 ICGV 97183 Mouride Suvita 2
0.080 0.08
0.070 0.07 Sensitive
Sensitive
Leaf conductance (gH2 O cm-2 h-1 )
Leaf conductance (gH 2 O cm-2 h-1 )
0.060 0.06
0.050 0.05
0.040 0.04
0.030 0.03
0.020 0.02
Tolerant
0.010 Tolerant 0.01
0.000 0.00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
17:00
18:00
19:00
08:00
16:00
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
Time of the day (H) Time of the day (H)
In cowpea, clear discrimination tolerant/sensitive
In groundnut, Tr differences at high VPD are smaller
From Issa Faye, Nouhoun Belko, Vadez (in prep)

7. Water use / productivity
Cowpea - WW Cowpea - WS
2.50 (C) 2.00 (D)
WW y = -13.32x + 2.33 y = -17.68x + 2.56
WS
R² = 0.401 R² = 0.756
Outdoors Outdoors
P = 0.0113 P = 0.0000
2.00
Transpiration efficiency (g kg-1)
Transpiration efficiency (g kg-1)
1.50
1.50
1.00
1.00
0.50
0.50
0.00 0.00
0.000 0.050 0.100 0.150 0.200 0.000 0.050 0.100 0.150
Transpiration rate (g H20 cm-2 h-1) Transpiration rate (g H20 cm-2 h-1)
High transpiration rates lead to low TE
Work on going to test hypothesis across crops
Belko et al 2012 - FPB

8. Water use / productivity
Relationship between water use efficiency and seed yield
Bean – WS 10
SER 16
CIAT) r = 0.89***
Drought seed yield (g plant-1)
SEA 15
9 SEQ 1003
SER 8
SEQ 11
CAL 96
8 ICA Quimbaya
SEC 16
RAA 21
SEA 5
7 Mean: 7.10 CAL 143 VAX 3
LSD0.05: 2.2 DOR 364
BAT 477
6
VAX 1
5
4
PAN 127
3 SUG 131 BRB 191 Mean: 1.06
LSD0.05: 0.41
2
0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
Drought water use efficiency (g kg-1)
Seed yield differences are closely related to TE
Same results in India, but…..

9. Water use / productivity
Relationship between water use efficiency and seed yield
Bean – WS
ICRISAT)
Nitrogen seems to play a central role in that relationship

10. Water use / productivity
Groundnut – WS
Post-rainy season Rainy season
12
15
Pod yield - WS
Pod Yield - WS
10
10
8
5 6
4
0
2
0.00 0.50 1.00 1.50 2.00 2.50 3.00
-5 0
Transpiration Efficiency 0.00 0.50 1.00 1.50 2.00 2.50 3.00
Transpiration Efficiency
Compare the most contrasting lines for the
transpiration response to high VPD

11. Water use / productivity
Leaf area development in chickpea
Sensitive
Tolerant
Large variations in leaf development in contrasting chickpea
Leaf and root development closely matches
Possible differences in RUE at early stages
Hydraulic differences?

12. Transpiration response to 1 mM H2O2 in chickpea
Sensitive to Insensitive to
AQP inhibitor AQP inhibitor
1.8
1.6
1.4
1.2
NTR
1
0.8
0.6 Control
1 mM H2O2
Before treatment
0.4
10
30
50
70
90
110
130
150
170
190
210
230
250
270
290
310
330
350
370
390
410
430
450
470
Time (mn)

13. Water use / productivity
TPLA varying TPLA_inflection_ratio
25
0.66
20 0.33
0.5
15 0.33
TPLA
0.66
10
5
0
0 200 400 600 800
TPLA max = 20
TTemerg_to_flag TPLA_prod_coef - 0.018
The coefficients are used as input to the crop model
Similar work is taking place in groundnut
Similar work needs to be done in cowpea

14. Lysimetric assessments
Lysimetric system
Total water extracted
To measure: Kinetics of water extraction
Max rooting depth
Root length density
Relationships RLD vs Water extraction

15. Lysimetric assessments
Groundnut Cowpea Bean Chickpea
Total water extraction xxx x x xxx
Kinetics of water extraction xx x x xxx
Root length density (RLD) xxx xx x xxx
Maximum rooting depth xxx xx x xxx
Relationships Roots vs water xxx x xxx
Relationships yield vs water xxx xxx
Zaman-Allah et al., 2011 JXB
Ratnakumar & Vadez 2011 FPB
Belko et al 2012 – In preparation
Belko et al 2012 – Plant Biology

16. Lysimetric assessments
Relationship between maximum root depth or RLD
and seed yield
Beans
10 10
SEA 15 SEA 15
r = 0.48*** r = 0.30*
Drought seed yield (g plant-1)
Drought seed yield (g plant-1)
SER 16
SER 16
9 SEQ 1003 9 SEQ 1003
SER 8 SEQ 11 RCW SER 8
CAL 96 SAB 259 CAL 96 SEQ 11
RCW SAB 259 8
8 ICA Quimbaya ICA Quimbaya
RAA 21 RAA 21
SEA 5 SEA 5
7 DOR 364
7 Mean: 7.10 CAL 143 DOR 364
Mean: 7.10 CAL 143
VAX 3 LSD0.05: 2.2 VAX 3
LSD0.05: 2.2 BAT 477 BAT 477
6 VAX 1
6
VAX 1
5 5
4 4 PAN 127
PAN 127
BRB 191
3 BRB 191
3 SUG 131 Mean: 0.56
SUG 131 Mean: 98.7
LSD0.05: 0.13
LSD0.05: 21
2 2
70 80 90 100 110 120 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75
Drought length of the longest root (cm) Drought root length density (cm cm-3)
Poor relations between yield under WS and root length or RLD
Similar results in chickpea in India

17. Lysimetric assessments
Relationship between maximum root depth or RLD and
water extraction
Beans
Drought water extraction (kg plant-1)
Drought water extraction (kg plant-1)
8.5 8.5
PAN 127 r = 0.25* PAN 127 r = 0.08
CAL 143
CAL 143
8.0 8.0
SEA 15 SEA 15
7.5 7.5
SEC 16 SEC 16
RCW SER 8
SER 8 RCW
SUG 131
7.0 DOR 364 SUG 131 DOR 364
SEQ 11 7.0
Mean: 6.84 SEA 5 SER 16 Mean: 6.84 SER 16 SEQ 11
SEA 5 CAL 96
LSD0.05: 1.53 CAL 96 LSD0.05: 1.53
6.5 RAA 21 SEQ 1003 RAA 21 SEQ 1003
BAT 477
6.5
SAB 259 BAT 477
VAX 3 SAB 259 VAX 3
ICA Quimbaya ICA Quimbaya
6.0 6.0 VAX 1
BRB 191 VAX 1
Mean: 98.7 Mean: 0.56
LSD0.05: 21 BRB 191 LSD0.05: 0.13
5.5 5.5
70 80 90 100 110 120 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75
Drought length of the longest root (cm) Drought root length density (cm cm-3)
No relation b’ween water extraction (WS) and root length / RLD
Similar results in chickpea in India

18. Lysimetric assessments
Relationship between drought seed yield and water
Beans extraction
Pre-Flowering stage Grain-Filling stage
Seed yield differences are related to higher pre-flowering water extraction
“ “ to lower grain filling water extraction
Nitrogen seems to play a central role in these relationships
Trend is different in chickpea

19. Vegetative and pod yield under high / low nitrogen and
under well-watered and water stress conditions
Beans
Nitrogen supply seems to be a more critical factor
than drought for seed yield

20. Lysimetric assessments
Relationship between drought seed yield and water
extraction
Cowpea
Similar results in cowpea and chickpea

21. Correlation coefficients between seed yield and plant attributes of
20 common bean genotypes grown in lysimeters at CIAT-Colombia
Plant trait Irrigated Drought
Day to flowering 0.03 -0.33**
Days to maturity 0.08 -0.62***
Water use efficiency (g kg-1) 0.63*** 0.89***
Stem biomass (g plant-1) 0.43*** -0.30*
Pod harvest index (%) -0.01 0.23
Maximum rooting depth (cm) 0.16 0.48***
Total root length (m plant-1) 0.17 0.30*
Root length density (cm cm-3) 0.17 0.30*
Root length density at the 0-15 cm soil layer (cm cm-3) 0.01 -0.29*
Root length density at the 30-45 cm soil layer (cm cm-3) 0.18 0.30*
Root length density at the 45-60 cm soil layer (cm cm-3) 0.12 0.44***
Root length density at the 60-75 cm soil layer (cm cm-3) 0.08 0.12
Root length density at the 75-90 cm soil layer (cm cm-3) 0.09 0.28*
Total root biomass (g plant-1) 0.26* 0.22

22. Modeling of critical traits

23. Groundnut Cowpea Bean Chickpea
Model availability xxx xxx xxx xxx
Parameterization of key cultivars xx xxx
Modelling water use traits x x
Modeling root traits xxx
Developing maps (India) x NA NA x
Developing maps (ESA – WCA) xx xx
Zaman-Allah et al., 2011 JXB
Ratnakumar & Vadez 2011 FPB
Belko et al 2012 – In preparation
Belko et al 2012 – Plant Biology

24. Faster root growth in Chickpea
5
Percentage yield increase
0
Faster root growth
-5
-10
-15
0 50 100 150 200 250
Baseline Yield at locations
Negative effect of faster root growth (= faster water depletion)

25. Altered depth of water extraction in Chickpea
15
Increased depth
Percentage yield increase
5
of water extraction
-5
-15
-25
Decreased depth
-35 of water extraction
-45
0 50 100 150 200 250
Baseline Yield at locations
Water extraction at depth is what really matters

26. RLD and water extraction seldom correlate

27. Altered depth of water extraction +/- faster rooting
15
5
Percentage yield increase
-5
-15
-25
-35
-45
0 50 100 150 200 250
Baseline Yield at locations
Decreased depth of water extraction
Decreased depth of water extraction
+ Faster root growth
Increased depth of water extraction
Increased depth of water extraction
+ Faster root growth

28. Faster leaf development +/- faster rooting
25
20
15
Percentage yield increase
10
5
0
-5
-10
-15
Increased leaf area
-20 Increased leaf area
-25 + Faster root growth
0 50 100 150 200 250
Baseline Yield at locations
Again, faster rooting brings a negative effect

29. Irrigation at key time during grain filling
50
Percentage yield increase
40
30
20
30 mm irrigation
10
at R5
0
-10
0 50 100 150 200 250
Baseline Yield at locations
The effect is larger than the best genetic effect

30. So far, few locations
Can Marksim-generated weather be used??
Predictions from Marksim weather deviate from
those obtained from observed weather

31. Marksim weather can be used to test trait effects

32. Modeling & mapping the benefits of particular trait in the targeted regions
Region with low
probability of yield increase
Probability of yield
increase after introduction
of trait X into standard
genotype Region with high
probability of yield increase
Capacity to test trait effects acrossWCA and ESA)
Work on-going in chickpea and groundnut
Soon will start with soybean

33. Training
Training on drought phenotyping
Long term training
Year 2 trainees:
Vincent Vadez – Crop modeling
Year 3 plans:
Abalo Hodo TOSSIM (Groundnut CSSL???)
Omar Halilou (Groundnut) – Crop modeling
Nouhoun Belko (Cowpea) – Trait mapping – Crop
modeling
Jose Polania (Bean) – Trait mapping – Crop modeling

34. Thank you