vs. ideotyping: Opportunities and of model assisted crop design …agritrop.cirad.fr/561006/1/document_561006.pdf · 2015-05-29 · Phenotyping vs. ideotyping: Opportunities and Limitations

Phenotyping vs. ideotyping: Opportunities and Limitations of model‐assisted crop design

drawing from genetic diversity drawing from genetic diversity

Delphine Luquet Michael DingkuhnDelphine Luquet, Michael Dingkuhn

CIRAD, AGAP research unit

Montpellier FranceMontpellier, France

9th of February 2011

International Conference on Crop Improvement, Ideotyping and Modeling for African Cropping Systems under Climate Change (CIMAC), 2011/02/07-09, Stuttgart-Hohenheim, Germany

CC translates into increasing staple food commodity prices

!

!

!!

!

400 450

Ton

2000 2050 No climate change 2050 CSIRO NoCF 2050 NCAR NoCF

CC will contribute to higher Food prices

2050:According to models, a less favorable climate for agriculture 100

150 200 250 300 350

ars

Per M

etric

T

g(Tropics & subtropics)

Andrew Jarvis, CIAT/CCAFS

-50

100

Rice Wheat Maize Soybeans

Dol

la


CC translates into increasing staple food commodity prices

!

!

!!

!

400 450

Ton

2000 2050 No climate change 2050 CSIRO NoCF 2050 NCAR NoCF

CC will contribute to higher Food prices

2050:According to models, a less favorable climate for agriculture 100

150 200 250 300 350

ars

Per M

etric

T

g(Tropics & subtropics)

Andrew Jarvis, CIAT/CCAFS

-50

100

Rice Wheat Maize Soybeans

Dol

la


Talk Structure

Crop improvement & CCV

Place of phenotypic plasticity

Pl t d li t t h t i d id t iPlant modeling to support phenotyping and ideotyping

Plant modeling vs. molecular breeding:

Ongoing research

Understanding genetic & physiological architecture of complex traits

Outlook


Crop improvement vs. CCV:Place for phenotypic plasticityp yp p y

• What is PP?Adapti e changes in plant organi ation d ring ontogenesis– Adaptive changes in plant organization during ontogenesis

– Broad adaptation through adjustment to variable conditions

• Why needed under CC?• Why needed under CC?– CC will increase variability

– Water will be scarcer (water is a great stabilizator!)Water will be scarcer (water is a great stabilizator!)• Heat, cold, drought, salinity, soil fertility, weed competition

• Problem of trade‐offs with yield pot.– Plastic plants = variable plant types; at what cost?

• Problem of trait complexity– How to measure?

– Complex genetics?


Inherent capacity to dynamically regulate

Phenotypic plasticity+NInherent capacity to dynamically regulate

morphogenesis (Nicotra et al. 2010)• Based on compensatory source‐sink processes • Maintains functioning reproduction & production

+N

Maintains functioning, reproduction & production when conditions fluctuate

• Includes more than morphology: Phenology, physiological defenses…

‐P

p y g

Examples of traits needed under greater climatic variability

Phenology‐ Adaptive phase duration (temporal compensation and stress escape)‐ Rapid development for vigour and high yield potential under short durationMorphology‐ Architecture limiting stress exposure and maximizing resource effiency‐ Environment responsive morphogenesisPhysiology‐ Effective and rapidly inducible tolerance; Hardening?‐ Protection of reproductive processes (e.g., cooling of spikelets)


Implications of phenotypic plasticity

• Increase of G x EIncrease of G x E

• Intelligent use of G x E through management & forecasts

•Avoid or overcome counter‐productive plasticity trade‐off on yield (Nicotra et al. 2010)

• Trade offs among multiple yield objectives• Trade‐offs among multiple yield objectives e.g. sweet sorghum for ‘FFF’ (Gutjahr et al. 2010)


Challenge:Challenge:

Conceive plants having ‘productive’ plasticityConceive plants having productive plasticity

Lesson from the past: start from available genetic diversity, t i hf l h i l i l thi kinot wishful physiological thinking

Understand physiological and genetic architecture of complex traits

Reduce complex traits to component traits recombineReduce complex traits to component traits, recombine intelligently

Give room to discovery, and build it in


Plant modeling to supportPlant modeling to support phenotyping & ideotyping


Adapting the Ideotype conceptMorphology & Phenology• Green revolution for favorable conditions

D fi > Hi h till i & HI l l d i > N i– Dwarfing => High tillering & HI, less lodging => N responsive• Make traditional systems more productive

– Combine PP‐sensitivity with green revolution traits (African sorghums)

Morphology, phenology and biochemistry• Multi‐purpose, new purposes

– Grain/forage cowpea, peanut… (FF)/b /f h ( )– Sweet grain/bioEtOH/forage sorghum (FFF)

– Biomass 2nd generation energy Annuals/Trees– C‐sequestering food/forage crops

Most difficult: Change ecophysiological adaptation (T, drought, CO2)– Combine multiple adaptations with desired plant type– Transformmetabolic type (C4 rice)

T f h bl d (‘ i ’ h ?)– Transform harvestable product (‘rice ’‐sorghum?)


3 steps in Ideotype development where crop d l h lmodels can help

• Characterization of Target Populations of Environments (TPE), incl. CC scenarios=> Use simple agronomic crop model as “lens”=> Use simple agronomic crop model as lens

• Identification of target trait combinations & plant types for TPETPE=> Use crop model with GxExM skills to simulate trait expression &

adaptive value

• Phenotyping process => Association studies => MarkersHeuristics: Extraction of trait parameters from observed variables

=> Specialized process models with small parameter nb=> Specialized process models with small parameter nb.


Role of plant physiology and modelling in plant breeding: phenotypingplant breeding: phenotyping

• Novel tools (imagery, remote sensing): maximize data acquisition on large number of plantslarge number of plants

Rapid fluorescenceOJIP Handy PEA

High resolution Thermography

OJIP Handy PEA

Still need to decorrelate G and E effects (modelling)

g g p y

• Models needed that analyze & predict G response curve to E throughgenotypic parameters


Example of role of modelling in phenotyping

Raymond et al. (2003, 2004)Leaf expansion rate response to drought variables (maize)

Welcker et al. 2007

• LER model QTLs colocate with that of direct measurements (leaf width)

• More stable across E : QTLxE overcome (modeling the cause of QTL instability)

13

• Validated in contrasting genetic background (temperate to tropical)

• LER model QTLs colocate with silk expansion QTLs (ASI): 2 crucial traits in 1! 13


From relevant QTLs to ideotype: integrative process

•QTL validation = evaluation at plant/pop. scale (crop performance):When expressed? When relevant?performance): When expressed? When relevant?

•Modelling must predict accurate G x E x M interactions d d ffand trade‐offs (Hammer et al. 2010)

• Even more challenging when addressing CCV g g g(extrapolation)


Example of role of modelling in phenotyping (cont )

Simulated effect of LER QTL i ld

Incorporation of LER model in APSIM (maize)

phenotyping (cont.)

LER‐QTLs on yield in APSIM (maize)

From Chenu et al 2009 ; Genetics

First real proof of concept for ideotype simulation using crop models driven by genetic parameters

From Chenu et al. 2009 ; Genetics

Doing this for traits for phenotypic plasticity requires models with greater detail of trait interactions (morpho/pheno/physio)


Can plant modeling assist molecularCan plant modeling assist molecular breeding by analyzing genetic &

physiological architecturephysiological architecture of complex traits?

Ongoing work


Vision: Massive use of molecular markers forMassive use of molecular markers for

agronomic traits and agroecological adaptation

Rice & sorghum are sequenced

Mass sequencing of rice genomes plannedMass sequencing of rice genomes planned

Sorghum is a major source of genes in C4 rice project

Mass application of MAS in private seed sector

GRiSP plan for global phenotyping & gene discovery & molecular breeding networks

CC&FS plan for ideotype strategies for 2030 CC horizon


SenegalWARDA

Cold Hot

MontpellierMontpellier

WARDA

C ld H t

Sowing dates

ll

Base TemperatureRoot vigour& architecturephenotyping

Cold HeatMontpellier

PhenotypingN k

Drought

Network

ColdHeat

Madagascar

Philippines

Heat

PhilippinesIRRI

ColombiaCIAT

Drought


Dealing with traits regulatingh l l t h iwhole plant morphogenesis

• Body plan construction

/• +/‐ plastic in response to E depending on G

• Many processes related to meristem activityy p y

tillering, leaf initiation, size, expansion…

Important: Simulation of Outcomes vs. Forcing! Example: Partitioning handled differently ifor agronomic or genetic objectives


Genesis of an organ in the plant system:Plastochron

MeristemInitiation

Productive periodCell division SenescenceX

Expansion

Si k Source phaseSink

commitment

Sinkimplementation

Source phase(case of leaf)

Consequences:• Demand determined before growth• Demand regulated to match supplyBody plan ‐ phenotype • Supply feedbacks on meristem behaviour

Physiological linkage (trade‐off) among traits (Rebolledo et al 2010; Granier & Tardieu 2009)

Resource acquisitionC & water status

(Rebolledo et al 2010; Granier & Tardieu 2009)

Also genetic linkage (organ cell vs. organ n°)(W ter Steege et al. 2005; Tisné et al. 2008)

Rebolledo : phenotyping for absence of linkage?


Development rate DR (1/phyllochron) impact on rice early vigourp y g

CIRAD, greenhouse (2009), 203 japonica cvs.; pot, well watered and stressed

⇒ DR main trait explaining vigor (RGR) d ll t d d d ht ditiunder well watered and drought conditions

⇒ Holds up at constant tillering & leaf size ⇔ Direct effect of DR⇔ Direct effect of DR⇔ trait genetic independence?


Issue of signalingSi k dj t t b th/d l t f db kSink adjustment by growth/development process feedbacks

Meristem Development response:‐Cell division ‐Development rate‐Organ size?

Morphogenesis

l

‐Organ size?

Glu + Fru

SucroseCINOsCINx

Assimilation,Mobilization

Transport &

discharge

Hormonal Sugar

to apoplastStress

signals signal

Stress


EcoMeristem, model of phenotypic plasticity

Ic = index of internal competition = proxy for sugar availability = internal signal


10 20 30 40

tota

l CH

O co

once

ntra

tion

of S

DW)

(g p

er g

TD

W (g

) de

ad le

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umbe

r

0.3

0.2

0.1

0.0

10

4

4

8

6

2

0

6

5

3

2

0

1

DR = 0.025 DR= 0.02 DR = 0.017 DR= 0.014 DR = 0.013 DR = 0.011

(a)

(c)

(e)

10 20 30 40

Cro

p gr

owth

r (g

of s

hoot

per

rate

vi

sual

phyll

ochr

on (°

C.d)

da

y)

tille

r num

ber

100

0.6

0.5

0 .4

0.3

0.2

0.1

0.0

40

80

60

20 15

12

9

6

3

0

(b)

(d)

(f)

International Conference on Crop Improvement, Ideotyping and Modeling for African Cropping Systems under Climate Change (CIMAC), 2011/02/07-09, Stuttgart-Hohenheim , Germany

Simulation experiment with Ecomeristem Source‐sink processes vs. DR

6 DR values, else parameters constant 40 day simulations

Rapid DR increases... => growth rate => transitory reserve depletion => tillering

But can also cause « trophic crisis » => delayed leaf appearance => smaller leaves => accelerated leaf senescence

During drought: ‐ Stress more severe

( because of greater water use ) ... Followed by faster recovery

Days after germination Days after germination

300

250

200

150

100

50

0

-50 0.010 0.012 0.014 0.016 0.018 0.020 0.022 0.024 0.008 0.012 0.016 0.020 0.024 0.028

d-')

RG

R (g

.g'.°

C

1.022

1.020

1.018

1 .016

1.014

1.012

1.010

1.008

1.006

y = 1 + 2.6x-54.8x2 ; r 2=0.67

y = 1+1.55x-34x2 ; r2=0.74

urce

leav

es

dw)

in m

ain

stem

so

(g pe

r g of

leaf

st

arch

y = 401.4-3.3.104x + 6.7.1 07

r 2 = 0.2

International Conference on Crop Improvement, Ideotyping and Modeling for African Cropping Systems under Climate Change (CIMAC), 2011/02/07 -09, Stuttgart-Hohenheim, Germany

Natural genetic diversity cs. potential (in‐silico) diversity

Natural vs. in ‐silico population (performance of model parameter combinations)

Natural relationship of Reserves vs. DR

Developmental rate (°C . d- 1

)

Contribution of parameters MGR Ict Epsib plasto Adjusted R2

1 variable X 0,17 to DR in natural po p ulation 2variables X X 0 ,28

3 variables X X X 0,52 4 variables X X X X 0,55

lc (S

uppl

y/de

man

d)

=> Fast development provides for earlier tillering => But causes tiller abortion due to competition


Dry

mat

ter (

kg/h

a)

n s

7 Phyllo Phyllo Phyllo

55 °C.d LAI green and dead 40 °C.d 70 °C.d 6

5

4

Al

L

3

2

1

0 0 20 40 60 80 100 120 140 160 180

8 4 Phyllo Phyllo Phyllo

55 °C.d 40 ° C.d 70 ° C.d

Tiller number & Ic (Supply /demand)

6 3

mi

Dlë

-s/

1 4 2

lei

Til

2 1

0 0 0 20 40 60 80 100 120 140 160 180

14000 Phyllo 55 °C.d Ag biomass & grain yield Phyllo 40 °C.d

° Phyllo 70 C .d 12000

10000

8000

6000

4000

2000

0 0 20 40 60 80 100 120 140 160 180

Days after sowing

SAMARA (Risocas product): Predicting GxExM of process

traits in an agronomic context

=> Fast development increases LAI => But leads to early leaf senescence

=> Fast development affects biomass yield little => But reduces grain yield (small panicles, poor sink)

International Conference on Crop Improvement, Ideot yping and Modeling for African Croppin g Systems under Climate Chan ge (CIMAC ) , 2011/02/07-09, Stuttgart-Hohenheim, Germany

Outlook

Analytical modeling:

Reductionist vs integrative (complex) process models

Reduce error and calibration effort for complex models

Phenotyping:

Methodologies to capture regulation of key processes

Phenotype specifically, think systemically

Eagerly awaiting the momen t of truth :

Phenotyping done (w/ & w/o models): internatl. Network

Genotyping awaits 600- K SNPs chip

Major loci/alleles? Co - location for different stresses/traits?

Physiologists, be ready for unexpected eye openers!

Merci

Documents

vs. ideotyping: Opportunities and of model assisted crop design …agritrop.cirad.fr/561006/1/document_561006.pdf · 2015-05-29 · Phenotyping vs. ideotyping: Opportunities and Limitations