Precision agriculture for SAT; Near future or unrealistic effort?
Precision agriculture for SAT;
Near future or unrealistic effort?
Jana Kholová and col.
ICRISAT
AuSoRGM- 22nd July - 2015
Overview
• Characterizing target environment
• Relevant phenotype for SAT
• Genetic determination of relevant phenotype
• HT-phenotyping
• Phenotype value
• System complexity & link to socio-economy
Grain Yield
Grain Number Grain Size & N
Biomass RADN
TE T RUE Rint
vpd
kl LAISLNRoots k
TN LNo
A >A
APSIM Generic Crop Template, from Graeme Hammer
Which ”phenotype” is linked to yield
improvement in target agro-ecology
(SAT – terminal drought)?
Yield is consequence of
GxExM
Research concepts – relevant phenotyping
Focus on the
“causal phenotype”
Relevant phenotype for SAT?
Grain Yield
Grain Number Grain Size & N
Biomass
RADN
TE T RUE Rint
vpd
kl LAISLNRoots k
TN LNo
A >A
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
WU 3 weeks after stress imposition (L plant-1)
GrainYield(gplant-1) Grain yield and water use
R² = 0.7436
0
2000
4000
6000
8000
10000
12000
0 2000 4000 6000 8000 10000 12000 14000 16000
Post-anthesiswateruse
Pre-anthesis water use (g plant-1)
constitutive WU
defines
grain-filling under
terminal drought
Pre-/post-anthesis water use
Vadez et al. 2012
Relevant phenotype for SAT?
Constitutive WU :
Grain Yield
Grain Number Grain Size & N
Biomass
RADN
TE T RUE Rint
vpd
kl LAISLNRoots k
TN LNo
A >A
Vapor Pressure Deficit (VPD; kPa)
Transpirationrate(gcm-2h-
1)
0 2 4
0
1
LA conductivity
LA Thermal time
& LA
Basic research on
WU componentsVadez et al. 2010-2015
Kholová et al. 2010-2014
Grain Yield
Grain Number Grain Size & N
Biomass
RADN
TE T RUE Rint
vpd
kl LAISLNRoots k
TN LNo
A >A
Example: WU components – genetic determination
Effect of QTL depends on genetic background
(stg 3A&B!)
R16 (senescent parent)
+ stg3A&3B QTL
VPD response -> high TE
S35 (senescent parent)
+ stg3A&3B QTL
small leaves
Vadez et al. 2011
Stay-green ILs
Grain Yield
Grain Number Grain Size & N
Biomass
RADN
TE T RUE Rint
vpd
kl LAISLNRoot
s
k
TN LNo
A >A
“causal phenotype”
(HT-phenotyping)
“consequential phenotype”
(High precision field trials)
No.oflinesphenotyped
%oflinesholdingdesired
phenotype
Phenotyping principle
Platforms linkage! LeasyScan
Lysimetry
Field
Example: System complexity
Crop value = f(quantity + quality; socio-economic context)
Stay-green sorghum; grain quality
0
2
4
6
8
10
12
14
16
S35 7001 6008 6026 6040 6008 S35 7001 6026 6040
proteins(%)
stay-green isolines
~ 20% QTL effect
Control Drought
?Price per unit of protein?
Link to socioeconomics
4
5
6
7
8
9
10
11
12
protein(%)
management
Drought
~ 15% management effect
Control
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
R4D requires multidisciplinarity!
• Bioinformatists
• Technology developers
• Physiologists
• Breeders
• Modelers
• Socioeconomists
• Nutritionists….
Thank you
Mission
To reduce poverty, hunger,
malnutrition and environmental
degradation in the dryland tropics
Editor's Notes
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…
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!!!!
Pre-anthesis WU linked to post-anthesis WU; post-anthesis WU linked to grain yiled
WU in time is defined by LA and LA conductivity during the growth; there is substantial variability in populations
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)
What is the value of the variability in building blocks for breeding programmes?? Traditional multilocation trials can be approximated in silico with modell