Upload
foodcrops
View
166
Download
0
Embed Size (px)
Citation preview
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
Few things on CC / Drought
What we learnt
Trait dissection & mechanisms
Trait assessment for breeding
Linking the pieces with crop simulation
Grain Yield
Grain Number Grain Size & N
Biomass RADN
TE T RUE Rint
vpd
kl LAI SLN Roots
k
T N 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”
FTSW
0.00.20.40.60.81.0
No
rma
lize
d t
ran
sp
ira
tio
n
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
Tran
sp
irati
on
0
1
2
3
4
5
6
7
8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Maxim
um
VP
D (
kPa)
Sahelian Center (Niger)
Patancheru
Vapor pressure deficit (VPD) in the SAT
High VPD – Variable conditions Effect on plant water balance
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
Few things on CC / Drought
What we learnt
Trait dissection & mechanisms
Trait assessment for breeding
Linking the pieces with crop simulation
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
Wate
r u
sed
(kg
pl-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
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0 1 2 3 4
WU
(kg
pla
nt-
1 w
eek
-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
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
500 1000 1500 2000 2500 3000 3500
Sta
yg
ree
n s
co
re
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
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
Gra
in Y
ield
(g
pla
nt-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
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
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
Tran
sp
irati
on
(g
cm
-2 h
-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
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
Tran
spir
atio
n (
g p
l-1 c
m-2
)
VPD (kPa)
VPD-insensitive
VPD-sensitive
Transpiration response to VPD in Sorghum 1 - Germplasm
2.0
3.0
4.0
5.0
6.0
7.0
152 Germplasm
Tran
sp
irati
on
Eff
icie
ncy (
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?
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
Tran
sp
irati
on
(g
cm
-2 h
-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
0.000
0.002
0.004
0.006
0.008
0.010
0.012
10.00 11.30 13.00 14.30
Tran
spir
atio
n r
ate
(g
cm-2
h-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
What mechanisms??
Hydraulic differences in the roots ??
Tran
sp
irati
on
VPD
Apoplast
(Structural)
Symplast
(AQP, …)
Water pathways in the root cylinder
Two pathways have different hydraulic conductance
Hypothesis: Aquaporin control plant water loss ?
Follow-up of transpiration before/after inhibition
VPD - insensitive
0
0.2
0.4
0.6
0.8
1
1.2
No
rm
alized
tran
sp
irati
on
Time(mins)
Apoplast & symplast inhibition
Symplastic inhibition
Apoplastic inhibition
Genetic differences in water transport pathways
VPD-sensitive
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
Tran
sp
irati
on
(g
pl-
1 c
m-2
)
VPD (kPa)
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 pip2.6Rela
tive f
old
expre
ssio
n
of
mR
NA
*
*
*
Aparna K (in prep.)
PIP1 & PIP2 up regulation in VPD – insensitive line ( )
Aquaporin gene expression
Few things on CC / Drought
What we learnt
Trait dissection & mechanisms
Trait assessment for breeding
Linking the pieces with crop simulation
Capacity: 4,800 plots Throughput: 2,400 plots/hour Traits: LA, Height, Leaf angle, …
LeasyScan at ICRISAT
Leaf canopy area and conductance
Canopy Scanning + plant transpiration = live water budget
Leaf canopy conductance
Load Cells
Leaf area
See Chapuis et al 2012
From Welcker et al 2014
Leaf
are
a
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
Leaf
elo
ng
ati
on
rate
Atmospheric drought
Soil drought
0
1
2
3
4
5
6
7
8
9
10
21 28 35 42 49 56 63 70 77 84 91 98
Wate
r u
sed
(kg
pl-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, ….
Few things on CC / Drought
What we learnt
Trait dissection & mechanisms
Trait assessment for breeding
Linking the pieces with crop simulation
North
Central
South
Far South
Maharasthra
Karnataka
Andhra Pradesh
Southern
Northern
Central
Far South
Stress characterization in sorghum growing area
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
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 (oD)
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
average yield
0
200
400
600
800
1000
1200
vegetative pre-flowering post-flowering post-flowering
relieved
mild stress
we
igh
ed
yie
ld (
kg
/ha
)
vegetative
pre-flowering
post-flowering
post-flowering relieved
mild stress4. 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 (oDay)
S/D
vegetative
pre-flowering
post-flowering
post-flowering relieved
mild
2. Environmental patterns
3. Yield in different scenarios
4. Trait effects in different scenarios
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 LAI SLN Root
s
k
T N LNo
A >A
Trait analysis in different introgressions
0
500
1000
1500
2000
2500
200 300 400 500 600 700 800
LA
(cm
2)
thermal time (degree days)
S35700160086026
0
5
10
15
20
25
0 200 400 600 800
TP
LA
TTemerg_to_flag
TPLA varying TPLAmax
16
18
2022
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
Gra
in y
ield
gain
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
Sto
ver
yie
ld g
ain
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
1.6sto
ver
(kg
ha
-1);
gra
in (
kg
ha
-1)
thermal time (degree day)
LA
I (m
2 m
-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)
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
0
10
20
30
393 108Fold
-in
cre
ase
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)
Can
op
y c
on
du
cta
nce
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…
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