1. Precision agriculture for SAT; Near future or unrealistic effort?
Jana Kholová and col.
ICRISAT
AuSoRGM- 22nd July

2. Overview Characterizing target environment Relevant phenotype for SAT
Genetic determination of relevant phenotype
HT-phenotyping
Phenotype value
System complexity & link to socio-economy

3. 4. Relevant phenotype??? 1. Target environments
2. Environmental patterns
18%
17%
40%
In phase I we characterized environment - in the main production region every 3-4 year the grain yield fails due to drought. We are trying to identify component traits which could bring yield advantage in the most frequent environment…
4. Relevant phenotype???
18% 7%
3. Impact on production
Kholová et al. 2013

4. (SAT – terminal drought)?
Research concepts – relevant phenotyping
Yield is consequence of
GxExM
Grain Yield
Grain Number
Grain Size & N
 Biomass
RADN TE T
RUE
Rint
vpd kl
LAI
SLN
Roots k  N
LNo A
>A
Focus on the
“causal phenotype”
Yield is the result of many plant mechanisms – in each environment mechanisms (building blocks) contributing to yield advantage are different – therefore it is better to focus on components of yield rather than yield itself.
Phenotyping causes, rather than consequences!!!!
Which ”phenotype” is linked to yield improvement in target agro-ecology
(SAT – terminal drought)?
APSIM Generic Crop Template, from Graeme Hammer

5. Relevant phenotype for SAT?
WU 3 weeks after stress imposition (L plant-1)
Grain Yield (g plant-1)
Grain yield and water use
Grain Yield
Grain Number
Grain Size & N
 Biomass
RADN TE T
RUE
Rint
vpd kl
LAI
SLN
Roots k  N
LNo A
>A
constitutive WU defines
grain-filling under
terminal drought
Pre-/post-anthesis water use
Pre-anthesis WU linked to post-anthesis WU; post-anthesis WU linked to grain yiled
Vadez et al. 2012

6. Relevant phenotype for SAT?
Grain Yield
Grain Number
Grain Size & N
 Biomass
RADN TE T
RUE
Rint
vpd kl
LAI
SLN
Roots k  N
LNo A
>A
Relevant phenotype for SAT?
Constitutive WU :
Vapor Pressure Deficit (VPD; kPa)
Transpiration rate (g cm-2 h-1) 2 4 1
LA conductivity LA
Thermal time
& LA
WU in time is defined by LA and LA conductivity during the growth; there is substantial variability in populations
Basic research on
WU components
Vadez et al
Kholová et al

7. Effect of QTL depends on genetic background
Example: WU components – genetic determination
Grain Yield
Grain Number
Grain Size & N
 Biomass
RADN TE T
RUE
Rint
vpd kl
LAI
SLN
Roots k  N
LNo A
>A
Stay-green ILs
R16 (senescent parent)
+ stg3A&3B QTL
VPD response -> high TE
S35 (senescent parent)
+ stg3A&3B QTL
small leaves
To investigate adaptive traits variability we use stay-green NILs descended from senescent parents (R16&S35). The lines descended from R16 showed variability in TE and LA development; lines related to S35 showed variability in water extraction capacity, LA dynamics – these are various entry-point (components) which can lead to improvement of crop production. However, stg-QTL effect is not universal, stg B appears to work across backgrounds (despite effect is different in both backgrounds)
Effect of QTL depends on genetic background
(stg 3A&B!)
Vadez et al. 2011

8. “consequential phenotype”
Phenotyping principle
LeasyScan
Lysimetry
Field
% of lines holding desired phenotype
“consequential phenotype”
(High precision field trials)
Grain Yield
Grain Number
Grain Size & N
 Biomass
RADN TE T
RUE
Rint
vpd kl
LAI
SLN
Roots k  N
LNo A
>A
No. of lines phenotyped
“causal phenotype”
(HT-phenotyping)
Platforms linkage!

9. = $ ? + Value of phenotype? – in silico predictions Model Grain
Environment
Canopy size +
= $ ?
Model
Grain
Pre-flowering
Flowering
Post-flowering
Post-flowering relieved
No stress
What is the value of the variability in building blocks for breeding programmes?? Traditional multilocation trials can be approximated in silico with modell

10. Example: System complexity
Crop value = f(quantity + quality; socio-economic context)
Stay-green sorghum; grain quality
~ 20% QTL effect
Control
Drought
~ 15% management effect
Control
?Price per unit of protein?
Link to socioeconomics

11. RESEARCH APPLICATION Conclusions; Structure of research
Value of traits
(crop model & GxExM)
Genetic determination of phenotype
Environmental characterization
& relevant traits
ideotypes & management
to regions
(precision Ag for SAT)
Breeding populations
Socio-economics
Feedback!!!
HT-phenotyping

12. Thank you R4D requires multidisciplinarity! Bioinformatists
Technology developers
Physiologists
Breeders
Modelers
Socioeconomists
Nutritionists….
Mission
To reduce poverty, hunger, malnutrition and environmental degradation in the dryland tropics
Thank you