grm 2013: drought phenotyping and modeling across crops -- v vadez
DESCRIPTION
TRANSCRIPT
GCP-ARM – Lisbon 27-30 Sept 2013
Objective 5: Cross-crop issues
Drought phenotyping and modeling across crops
ICRISAT – CIAT – ISRA – Univ North Carolina
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
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
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
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
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
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
Cowpea
Similar results in cowpea and chickpea
Grain yield and post-anthesis water use
Water use
PhD Thesis Omar Halilou
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
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
Water use / WUE
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
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
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
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
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)
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
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)
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
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
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
Modeling of critical traits
Marksim weather can be used to test trait effects
Can we use data from weather generator??
-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
-33 0 +1
15-30% yield decrease, especially at high latitudes
% yield decrease for not having transpiration sensitive to high VPD:
-26 0
20% yield decrease almost everywhere
% yield decrease for having shorter crop duration genotype
(a)
(b)
Yield increase with VPD response in soybean
From Sinclair et al (in review)
Probability of success
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
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