This document discusses a study assessing the impact of key physiological traits on wheat adaptation to contrasting drought environments. The objectives were to (1) assess the relative impact of traits on different drought environments, (2) develop a field-based phenotyping system to estimate seasonal water use, (3) evaluate elite drought-adapted lines from ICARDA, and (4) build phenotyping capability. The study analyzed traits from 245 lines in 38 environments across countries. Results showed specific adaptation of traits like flowering time and tillering to different drought types. Phenotyping methods were developed and workshops held to build capacity in the region.

1. Fernanda Dreccer, H. OuabbouT. Condon, F. Makdis, L. Barnes,, F. Eticha, M.Reynolds,
M. G. Borgognone, S. Udupa, T. Wuletaw, F. C. Ogbonnaya
PLANT INDUSTRY
Impact of key physiological traits
on wheat adaptation to
contrasting drought mega-environments

2. Background
2 |
• Limited water availability: most important
abiotic constraint to yield globally;
increasing uncertainty regarding rising
temperature and atmospheric demand.
• Yield improvement remains slow, due to
the large GxE interaction.
• Given the changing timing and intensity of
drought, different patterns of water use
(and traits underpinning them) are
required to maximise yield.
Traits for drought types | Dreccer
Objectives
I. Assess the relative impact of putative
key traits to different drought mega-
environments.
II. Develop and validate a field-based
phenotyping system to estimate
patterns of crop water use on a seasonal
basis
III. Evaluate ICARDA elite drought adapted
lines and the traits underpinning them.
IV. Build capability in non-invasive
phenotyping, particularly within the
Central and West Asia and North Africa.

3. Traits for increased water productivity.....
Tillering
Early vigour
Water soluble
carbohydrates
Phenology
C13 disc.
Stay green
.....have the potential to
increase transpiration,
shift WU to critical crop
periods, increase water
use efficiency and/or
influence biomass
partitioning to the grains
Traits for drought types | Dreccer
Passioura and Angus

4. Environments, germplasm & methods
CIMMYT,
Mexico
EIAR,
Ethiopia
CSIRO,
AU
INRA/CRRA
Morocco
ICARDA,
Syria and
Lebanon
Summer dominant rainfall
Winter dominant rainfall
Uniform rainfall
 Environments: 38 (YxLxM)
 Up to 245 lines:
• ICARDA elite nurseries
• Trait comparison lines
• Global checks
 Measurements:
• Ground cover
• Canopy temperature
• Phenology
• Biomass, yield and components
• Environment
 Analysis:
• MET analysis; linear mixed
models
Traits for drought types | Dreccer

5. Genetic correlations show specific adaptation for Yield
Traits for drought types | Dreccer5 |
10MX_Cia_RF
10ET_Kul_RF
10ET_De_RF
10ET_Mel_RF
09ET_Mel_RF
09ET_De_RF
10SY_Mal
12MO_SEA_Late
12MO_ZEM_Late
11MO_SEA_RF
11MO_SEA_Irr
10MO_SEA_RF
10MO_SEA_IRR
10SY_TTa_RF
11AU_Tem_RF
10LE_Ter
11SY_Mal
11SY_THa
11SY_Bre_RF
11LE_KDa
10SY_THa
11LE_Ter
10AU_Lee_RF
11AU_Yan_RF
10AU_Lee_IRR
10SY_THa_Late
11MO_Mar_RF
10AU_Gat_IRR_Late
10AU_Gat_RF
10AU_Gat_IRR
10AU_Tem_RF
09AU_Gat_RF
09AU_Gat_IRR
09AU_Gat_IRR
09AU_Gat_RF
10AU_Tem_RF
10AU_Gat_IRR
10AU_Gat_RF
10AU_Gat_IRR_Late
11MO_Mar_RF
10SY_THa_Late
10AU_Lee_IRR
11AU_Yan_RF
10AU_Lee_RF
11LE_Ter
10SY_THa
11LE_KDa
11SY_Bre_RF
11SY_THa
11SY_Mal
10LE_Ter
11AU_Tem_RF
10SY_TTa_RF
10MO_SEA_IRR
10MO_SEA_RF
11MO_SEA_Irr
11MO_SEA_RF
12MO_ZEM_Late
12MO_SEA_Late
10SY_Mal
09ET_De_RF
09ET_Mel_RF
10ET_Mel_RF
10ET_De_RF
10ET_Kul_RF
10MX_Cia_RF
-1.0
-0.5
0.0
0.5
1.0
Et+Mx
S Oz
+ME
N Oz
Morocco In-season rainfall
vs.
Stored soil
moisture

6. Later maturity
advantageous in longer
seasons with intermittent
rainfall
6 |
-2.5
-1.5
-0.5
0.5
1.5
2.5
-15 -10 -5 0 5 10 15
YieldBLUPs
Days to Flowering BLUPs
10MO_SEA
11MO_SEA
spring
winter
• Yields from 2 (2010) to 5 (2011) t/ha.
• Yields were correlated to GN/m2.
• Resources such as radiation and N,
better utilised for grain production
Traits for drought types | Dreccer
In- season rainfall - Morocco

7. Traits for drought types| Dreccer7 |
-2.5
-1.5
-0.5
0.5
1.5
2.5
-200 -100 0 100 200
YieldBLUPs
Spikes per m2 BLUPs
10MO_SEA
11MO_SEA
-2.5
-1.5
-0.5
0.5
1.5
2.5
-200 -100 0 100 200
YieldBLUPs
Spikes per m2 BLUPs
10MO_SEA
11MO_SEA
SVS tin CSIRO
ZAFIR-ICARDA
NEJMAH-ICARDA
In seasons with intermittent
rainfall, more spikes per m2
lead to more yield
independently of flowering
date
• Very low +/no correlation between
flowering date and spike number
SVS tin lines ca. 7 days earlier and
ICARDA lines less than a day later
compared to site average.
In- season rainfall - Morocco

8. 8 |
Stored soil moisture- Ethiopia
-2.5
-1.5
-0.5
0.5
1.5
2.5
-15 -10 -5 0 5 10 15
YieldBLUPs
Days to Flowering BLUPs
10ET_Melkassa
10ET_Dhera
• Yield associated to GN/m2 via grains
per spike, not spike number m2.
• Short spikes with few grains.
• High TE lines had 25% more yield
across sites.
Earlier maturing lines did
better under terminal
drought
Traits for drought types| Dreccer
winter
spring

9. Real-time simulation of LAI, water use:
derived phenotypes
9 |
TotalbiomassLAIGroundcoverZadoks
Long season
Phenotyping- Dreccer
1. Photograph canopy
2. Fit phenology, look for parameter
combination (kl, SLA) to fit GC
with minimum error
3. Estimate LAI, biomass, water use
4. Compare ‘derived phenotypes’,
e.g. total transpiration, season TE
Dreccer, Zheng, Chapman, Chan

10. Conclusions/Next steps
• Initial analysis highlighted the possibilities for E specific adaptation
• A particular flowering pattern can have opposite effects in different drought types.
• Developmental and morphological traits are important drivers of water productivity.
• Flowering and tillering patterns can be combined for yield advantage.
• For crops grown under stored soil moisture, traits saving water for the critical period
(higher TE), tackling spike sterility or sustaining grain growth (high WSC) are likely
candidates for combination.
• Analysis of traits in the context of weather/water availability conditions per pheno-stage
• Phenotyping:
• Streamlined methods to characterise main target traits
• Attempt at integration via phenotyping + simulation with initial encouraging results on the
‘derived phenotype’ for seasonal trends (T, TE). Effort needs to continue.
• Development of cheaper technologies to be deployed more widely
10 | Traits for drought types | Dreccer

11. Outputs/Products
Presentation title | Presenter name11 |

12. Germplasm characterisation
• 38 (YxLxM) across drought mega-environments
• Data: yield, components, height, development and height genes
Dreccer12 |
Germplasm Trait Number Availability
ICARDA lines Drought nurseries up to 200 Free
Seri x Babax RILS WSC, tillering Ca. 20 M. Reynolds
CSIRO lines Tillering (tin), TE (13C), EV Ca. 25 MTA
• Syria: 10 elite germplasm lines were to be planted in on-farm trials involving 30 farmers
in Nov./Dec. 2011. This was thwarted by the unrest.
• Morocco: breeders have selected 23 lines, now in crossing blocks.
• Ethiopia: NARS breeders identified 15-20 lines for performance under drought , stem
and yellow rust resistance to the Ethiopian pathotypes. Crossing started in 2010.
Selections also tested at Preliminary National Variety trials.
Germplasm take-up

13. • 2 workshops on phenotyping for water productivity: Aleppo 2010 and Rabat
2013.
• Trait value in different environments
• Phenotyping methods
• Association mapping
• QEI & QTI including crop simulation for functional responses of WU
Dreccer13 |
Capability building
Papers

14. PLANT INDUSTRY
Thank you
Francis Ogbonnaya
Tony G. Condon
Farid Makdis and Sripada Udupa
Laura Barnes
Hassan Ouabbou
Firdissa Eticha and Solomon Gelalcha
Matthew Reynolds
Gaby Borgognone
Greg Rebetzke, Osman Abdallah
Tadesse Wuletaw