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