GRM 2013: Drought phenotyping and modeling across crops -- V Vadez
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Transcript of GRM 2013: Drought phenotyping and modeling across crops -- V Vadez
![Page 1: GRM 2013: Drought phenotyping and modeling across crops -- V Vadez](https://reader034.fdocuments.net/reader034/viewer/2022051609/547a2a83b4af9fb9158b4a61/html5/thumbnails/1.jpg)
GCP-ARM – Lisbon 27-30 Sept 2013
Objective 5: Cross-crop issues
Drought phenotyping and modeling across crops
ICRISAT – CIAT – ISRA – Univ North Carolina
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Water uptake / Root Water use / WUE Reproduction and partitioning Modeling
Sub-Activity 5: Training
Trait value predicted
Refined protocols More tools
Better pheno- typing data
Phenotyping of cell-based processes – toward gene discovery
Purpose: Looking at similar traits across species
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Lysimetric system: in CIAT and ICRISAT-Niger
Total water extracted Kinetics of water extraction Root length density at different depth Relationships RLD vs Water extraction
To measure:
Lysimetric assessments
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Root length density and water extraction
Drought root length density (cm cm-3)0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75D
roug
ht w
ater
ext
ract
ion
(kg
plan
t-1)
5.5
6.0
6.5
7.0
7.5
8.0
8.5
BRB 191
PAN 127
SUG 131
VAX 1
BAT 477
DOR 364
CAL 143
VAX 3
RCW
SEA 5
SEA 15
SER 16
SEQ 1003SEQ 11CAL 96
SAB 259
RAA 21
ICA Quimbaya
SER 8
Mean: 0.56LSD0.05: 0.13
SEC 16
Mean: 6.84LSD0.05: 1.53
r = 0.08
No relation between water extraction (WS) and root length / RLD
Beans Chickpea
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Post-rainy season Rainy season
0
2
4
6
8
10
12
14
16
0 1000 2000 3000 4000 5000 6000 7000
Pod
yiel
d (g
pla
nt-1
)
Total water extracted (g plant-1)
0123456789
10
0 1000 2000 3000 4000 5000 6000 7000
Pod
yiel
d (g
kg-
1)
Total water extracted (g plant-1)
No relationship between total water extracted and grain yield
0
2
4
6
8
10
12
14
0 1000 2000 3000 4000 5000 6000 7000
Pod
yiel
d (g
pla
nt-1
)
Total water extracted (g plant-1)
Cowpea
Peanut
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
0 1000 2000 3000 4000 5000 6000 7000
Pod
yiel
d (g
pla
nt-1
)
Total water extracted (g plant-1)
Bean
Peanut
Rainy season Rainy season
Pod yield and water extraction
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Water extraction pattern (WS)
Zaman-Allah, Jenkinson, Vadez 2011 JXB
0123456789
10
21 28 35 42 49 56 63 70 77 84 91 98
Cum
ulat
ed W
ater
Use
d (k
g pl
-1)
Days after sowing
Flowering
8 Sensitive lines
12 Tolerant lines
Tolerant: less WU at vegetative stage, more for reproduction & grain filling
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Zaman-Allah, Jenkinson, Vadez 2011 JXB
0123456789
10
21 28 35 42 49 56 63 70 77 84 91 98
Wat
er u
sed
(kg
pl-1
)
Days after sowing
Sensitive
Tolerant
Tolerant: EUW = 27 kg grain mm-1
Grain yield and post-anthesis water use
Chickpea
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Cowpea
Similar results in cowpea and chickpea
Grain yield and post-anthesis water use
Water use
PhD Thesis Omar Halilou
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Seed yield relates to higher pre-flowering water use Nitrogen issue?? (Sinclair & Vadez 2013 Crop&Pasture Science)
Pre-anthesis
Beans
Grain yield and pre- / post-anthesis water use
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0.0
2.0
4.0
6.0
8.0
WW-HN WW-LN WS-HN WS-LN
Yiel
d (g
/pla
nt) W
S
0.0
2.0
4.0
6.0
8.0
WW-HN WW-LN WS-HN WS-LN
Yiel
d (g
/pla
nt) W
S
02468
10121416
HN-WW LN-WW HN-WS LN-WS
Yiel
d (g
pla
nt-1
) WS
Cowpea
Bean
Effect of high N (HN) or low N (LN) treatments under water stress (WS) and irrigation (WW)
Peanut
Among the three legumes, peanut is least sensitive to low N Low N is more a problem than drought for bean
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Water use / WUE
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Leaf
are
a
Thermal time
A – Fast early LA B – Slow early LA
C – Fast early LA / small max LA
D – Slow early LA / small max LA
Canopy development dynamics
Water use difference
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Field trial
0 5 10 15 20 250
1000
2000
3000
4000
5000
6000
A = 2,91Fleur 11WW condition
R² = 0,999
Nodes number
Leaf
are
a (c
m²)
Field trial
0 5 10 15 20 250
1000
2000
3000
4000
5000
6000
A = 2,63ICG 1834WW condition
R²= 0.91
Nodes number
Leaf
are
a (c
m²)
PhD training of Oumaru Halilou - Niger
Large variation available
Peanut
Coefficients relating leaf area to node number
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y = 23.302e0.2562x R² = 0.9367
0
2000
4000
6000
8000
10000
12000
0 5 10 15 20 25
Leaf
are
a of
five
pla
nts
(cm
2)
Node number on main stem
y = 11.995e0.31x R² = 0.9607
0
2000
4000
6000
8000
10000
12000
0 5 10 15 20 25
Node number on main stem
Coefficients relating leaf area to node number
MSc training of Ruth Wangari - Kenya
Chickpea
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Rainy season (VPD<2kPa) R² = 0.03
0123456789
10
0.0 1.0 2.0 3.0
R² = 0.65
0
4
8
12
16
0.0 1.0 2.0 3.0
Post Rainy Season (VPD>2kPa)
TE variation and link to yield depends on season
Transpiration efficiency – Peanut and relationship to yield
Pod
yiel
d (g
pla
nt-1
) 250% range
60% range
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Mouride
If VPD < 2.09, TR = 0.0083 (VPD) – 0.002 If VPD ≥ 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 in cowpea
Tolerant lines have a breakpoint (water saving)
Tolerant Sensitive
Belko et al – 2012 (Plant Biology)
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Phenotypic variation in cowpea RIL CB46 x IT93K-503-1 (sensitive/Tolerant)
0
10
140 220120 180
5
100 20080 160
25
20
15
Plant transpiration (g plt-1 h-1) Total canopy conductivity (g cm-2 h-1)
0.0200
5
0.0300 0.03750.02750.01750
0.0325
25
0.02500.0225 0.0350
20
15
10
IT93K-503-1
CB46
IT93K-503-1
CB46
PhD training of Nouhoun Belko – Burkina Faso
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R² = 0.64
-40
-30
-20
-10
0
10
20
30
40
50
0.000 0.010 0.020 0.030 0.040 0.050 0.060
Resi
dual
tran
spira
tion
Transpiration rate under high VPD
What drives transpiration in that population??
Leaf area (69%)
Conductance at high VPD (64% of residual)
Get QTL for both these traits PhD training of Nouhoun Belko – Burkina Faso
R² = 0.69
0
50
100
150
200
250
0 200 400 600 800 1000 1200
Tota
l tra
nspi
ratio
n (g
pla
nt-1
)
Leaf area (cm2 plant-1)
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QTLs from ICI Mapping – Drought tolerance traits
VuLG1 VuLG2 VuLG3 VuLG4 VuLG5 VuLG6 VuLG7 VuLG8 VuLG9 VuLG10 VuLG11
Plant transp., leaf area, stem DW, leaf DW 12-18% phenotypic variance (High allele from CB46)
Canopy conductance 12-16% phenotypic variance (High allele from IT93K-503-1)
SLA, 20% phenotypic variance (High allele from CB46)
SLA, 14% phenotypic variance (High allele from IT93K-503-1)
From Phil Roberts/Tim Close and team
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QTLs from ICI Mapping – Drought tolerance traits
From Phil Roberts/Tim Close and team
Select RILs having different “dosage” of these QTLs and test them across contrasting drought scenarios
TraitNameChromo
somePosition
(cM)Flanking markers LOD PVE(%)
Additive effect
Positive allele
Plt DW 2 4 1_0113 - 1_0021 3.1 15.5 0.3 CB46SLA 2 31 1_1139 - 1_1061 3.6 14.4 -11.5 IT93K-503-1LA 2 85 1_0834 - 1_0297 4.0 18.5 57.0 CB46Leaf DW 2 85 1_0834 - 1_0297 2.8 13.4 0.2 CB46Plant transp Total 6h 2 85 1_0834 - 1_0297 2.9 13.1 8.9 CB46Conductance High VPD 5 19 1_0806 - 1_0557 3.2 16.3 0.0 IT93K-503-1Conductance Low VPD 5 20 1_0806 - 1_0557 2.8 13.3 0.0 IT93K-503-1Conductance Low VPD 5 23 1_0806 - 1_0557 3.3 14.0 0.0 IT93K-503-1Conductance Low VPD 7 13 1_0279 - 1_1482 3.6 15.0 0.0 IT93K-503-1SLA 9 25 1_0051 - 1_0048 4.9 19.7 13.5 CB46Conductance high VPD 9 52 1_0425 - 1_1337 2.6 11.5 0.0 IT93K-503-1
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Vapor Pressure Deficit (VPD, in kPa)
Tran
spira
tion
rate
(g c
m-2
h-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
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Modeling of critical traits
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Marksim weather can be used to test trait effects
Can we use data from weather generator??
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-77 0 +9
Pod yield differences between rainfed and irrigated conditions
• Drought affected countries for peanut: Senegal, Mali, Niger, Burkina + Few spots in Ivory Coast
• Genotypes developed for WCA region can’t be the same for the entire region
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-33 0 +1
15-30% yield decrease, especially at high latitudes
% yield decrease for not having transpiration sensitive to high VPD:
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-26 0
20% yield decrease almost everywhere
% yield decrease for having shorter crop duration genotype
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(a)
(b)
Yield increase with VPD response in soybean
From Sinclair et al (in review)
Probability of success
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Training on drought phenotyping Long term training Few of the trainees: Ruth Wangari (Chickpea RIL) Abalo Hodo TOSSIM (Groundnut CSSL) Omar Halilou (Groundnut) – Crop modeling Nouhoun Belko (Cowpea) – Trait mapping – Crop modeling Jaumer Ricaurte (Bean) – Trait mapping – Crop modeling
Training
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In Summary / “products”:
An approach to drought QTL for several water use traits in different crops Generation of scenarios / probability maps in the “production stage” for peanut, chickpea, soybean. Trainees (Oumaru, Belko, Ruth, Jaumer, …) on both eco-physiology of drought adaptation and modeling