towards innovative cropping systems development of high throughput phenotyping of plant biotic inter

Christophe SALON1,2

Christian JEUDY1

Céline BERNARD2

1UMR Legumes Genetics and Ecophysiology (LEG), INRA

17 rue Sully, BP86510 Dijon Cedex, France

2Experimental Unit Greenhouse and Phenotyping platform,

INRA, 17 rue Sully, BP86510 Dijon Cedex, France

Towards innovative cropping systems :

High Throughput Phenotyping of plant

biotic interactions.

Goals

PPHD Dijon, Phenodays, Slide 2/18

Large Research Unit « Agroecology »

Research units gathered around knowledge for conception & evaluation of the performance of innovative cropping systems

EcolDur

Ecology of communities and

durability of agricultural

systems

GEAPSI

Genetic environmental

determinisms of plant adaptation to

innovative cropping systems

IPM

Mechanisms management of plant-micro

organisms interactions,

MERS

Microbiological and

environmental sanitary risks

Various thematics

Goals

Legumes, Legumes/Cereals Associations

Legumes, Weeds, Cereals, Brassica

Legumes, Arabidopsis, Wine, Tobacco, Tomato

Listeria and root tissues

GEAPSI EcolDur IPM MERS

A variety of research objects

Large Research Unit « Agroecology »


Available:

- large genetic var.

- « omic’s »

Goals

Pheno and Geno … mix

Automated plateforms!

Combine similar phenotyping and genotypage rates

Filling the gap


Phenotyping plant architecture, flowering, senescence, …

Phenotyping pods and seeds

Fluorescence: GFP

Seeds : Number, size and shape

Kroj et al. Development (2003) 130, 6065-6073

• Mutants (Tilling..)

• RILs

• Ecotypes collections

CONTROLED

CONDITIONS

[NO3 - ] [NO3 - ] [NO3 - ]

ENVIRONMENTAL

In vitro kinetic development P. sativum M. truncatula

Phenotyping roots and interactions with micro organisms (nodules, µR), …

Example : case studies of legume plants


Germination date % viable seeds, dormancy…

Gardarin et al. (2011),Ecol. Modelling 222: 626-636 Gardarin et al. (2010), Weed Res. 50: 91-101

Gardarin et al. (2010),Seed Sci. Res.20: 243-256 Sester et al. (2007) Ecol. Modelling 204: 47-58 Sester et al. (2006) Eur. J. Agron. 24: 19-25

“Dynamic” analysis of weed germination under various environmental conditions

Model parameters

Establishing traits/functional relations

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80

Cu

mu

late

d p

rop

ort

ion

of g

erm

ina

ted

se

ed

s

Days since water addition

3°C

6°C

20°C

0

0.2

0.4

0.6

0.8

1

0 50 100 150 200 250 300

Pro

po

rtio

n o

f g

erm

ina

ted

se

ed

s

Cumulated degree-days (°C) since water addition

0

-0.05

-0.95

Soil water

Measuring « manually » phenotypes (1/3)



Model

Moreau et aL. (2006), Plant, Cell and Envir., 29:1087-1098. Moreau et aL. (2007), Plant, Cell and Envir., 30:213-224. Moreau et al. (2008), J. Exp. Bot., 59:3509–3522.

Thèse Delphine Moreau – 13 avril 2007

QUANTITE

TOTALE D’AZOTE

SURFACE FOLIAIRE

PROJETEE

BIOMASSE

SOUTERRAINE

BIOMASSE

TOTALE

Coefficients

d’ajustement

Efficience biologique

Date de début de fixation de N2

1,0

1,5

2,0

2,5

3,0

3,5

0 10 20 30 40

PAR (mol m-2 jour-1)

0

1

2

3

4

0 10 20 30 40

0,0

0,5

1,0

1,5

0 10 20 30 40

(

1500

2250

3000

3750

4500

0 10 20 30 40

0,0

0,1

0,2

0,3

0 2 4 6 8 10

0,0

0,1

0,2

0,3

0,4

0 2 4 6 8 10

PAR (mol m-2 jour-1)PAR (mol m-2 jour-1)PAR (mol m-2 jour-1)

[NO3-] (mM)

[NO3-] (mM) [NO3

-] (mM)

0

400

800

1200

0 2 4 6 8 10

Efficience de conversion de N

en surface projetée

Prélèvement de N

spécifique AVANT

la date de début de

fixation de N2

Prélèvement de N

spécifique APRES


fixation de N2

[NO3-][NO3-]

Dynamic Leaf area measurement

Analytical approaches + model: leaf area is a relevant phenotypic target for detecting any contrasted N nutrition among various genotypes.

Genotypes of Medicago RIL

ranked for ability to uptake N!


Roots Dry Weight (g)

0,04 0,06 0,08 0,10 0,12 0,14 0,16 0,18

No

du

les D

ry W

eig

ht

(g)

0,004

0,006

0,008

0,010

0,012

0,014

0,016

0,018

0,020

Number of nodules counted on the scan

0 200 400 600 800

Rea

l n

um

ber

of

no

du

les

0

200

400

600

800

1000

Surface area

20 30 40 50 60 70

Ro

ot

Dry

Weig

ht

(g r

oo

t/p

lan

t)

0,04

0,06

0,08

0,10

0,12

0,14

0,16

0,18

y = 0.0106 ln(x) + 0.0369, R² = 0.78

y = 0,000041x2 - 0,001530x + 0,087432, R² = 0,839

y = 1.1645 x - 9.45, R² = 0.90

Nodules number and size, appearance

NH4NO3

10 mM -N KNO3

0.5 mM

14 days after inoculation

Split roots

Ruffel et al. (2008), Plant Physiol. 146: 2020-2035. Salon et al. (2009), CRAS, 332 :1022-1033. Jeudy et al. (2010), New Phytol, New Phytol., 185:817-828. Bertin et al. (2010) J. Exp. Bot., 61: 955 - 967.

Nodule number and size, isotopic flux measurements to understand adaptative strategy of plants to N deficiency.

Morphometry versus

functional strategy

identifcation



[Co2] •…under different environmental conditions…

PPHD’s Objectives

High throughput innovative technics of

morphometry for:

• characterizing a large number of biological units, their interactions…

… at various organizational levels (plant/plant, plant /organ)


Nb Units

Unit Surface (m2)

Total surface

(m2) Greenhouses 16 19 304 988 m2 4 21 84 3 40 120 1 84 84 1 100 100 1 128 128 1 168 168

Climatic chambers 4 9 36

75 m2 5 6 30 3 3 9

Infrastructures and equipments: S1 Greenhouses and

climatic chambers

27 greenhouse units (total surface = 1000 m2) 12 climatic chambers (total surface = 75m2)

Organisation


PPHD : S2 building, greenhouses, climatic

chambers (1/3)

A building,

Lemnatec© Phenotyping equipments

greenhouses (240+110m2),

And climatic chambers (80m2)

Organisation


« Scan 3D » system

«Large» biological units

… and rhizotrons (1000)

InoviaFlow.

Cameras: RVB Fluo NIR

Organisation PPHD : S2 building, greenhouses, climatic

chambers (2/3)


« HTS » system

«Small» biological units : petri dishes, seeds, plantlets

Cameras:

RVB

Fluo

NIR

Organisation PPHD : S2 building, greenhouses, climatic

chambers (3/3)


European Funds (30%)

INRA (31%)

Burgundy Region(31%)

French Minister (8%)

2011/2012

«Investments of future»

Phénome (2800 K€)

Budget


Local : Agrosup (partership for image analysis, data mining)

Vitagora

National: Réseau EFOR (partage pour plantes modèle)

« Investissement d’avenir » : Phenome

LETI network

International EU FP7 European Plant Phenomic Network (EPPN)

EU FP7 ABSTRESS project (WP leader, 450K€)

Positioning


NO3-NO3-

N2

CO2

CO2

N

Thèse Delphine Moreau – 13 avril 2007

QUANTITE

TOTALE D’AZOTE

SURFACE FOLIAIRE

PROJETEE

BIOMASSE

SOUTERRAINE

BIOMASSE

TOTALE

Coefficients

d’ajustement

Efficience biologique

Date de début de fixation de N2

1,0

1,5

2,0

2,5

3,0

3,5

0 10 20 30 40

PAR (mol m-2 jour-1)

0

1

2

3

4

0 10 20 30 40

0,0

0,5

1,0

1,5

0 10 20 30 40

(

1500

2250

3000

3750

4500

0 10 20 30 40

0,0

0,1

0,2

0,3

0 2 4 6 8 10

0,0

0,1

0,2

0,3

0,4

0 2 4 6 8 10

PAR (mol m-2 jour-1)PAR (mol m-2 jour-1)PAR (mol m-2 jour-1)

[NO3-] (mM)

[NO3-] (mM) [NO3

-] (mM)

0

400

800

1200

0 2 4 6 8 10

Efficience de conversion de N

en surface projetée

Prélèvement de N

spécifique AVANT


fixation de N2

Prélèvement de N

spécifique APRES


fixation de N2

[NO3-][NO3-]

Analytical

approach

Modelisation Phenotyping

Approach

Do not forget:

- to validate in the field to tackle G *E *M interactions

Identifying differences

among genotypes

Interpreting the

detected difference

Food for thoughts

+ +

- to combine approaches


Food for thoughts

Thanks for your attention….

PPHD Dijon, Phendays, The end

towards innovative cropping systems development of high throughput phenotyping of plant biotic inter

Documents

goals pphd dijon

gap pphd dijon

phenotyping plant architecture

no3 environmental

par mol

bp86510 dijon cedex

phenotyping pods

phenotyping platform