Exp erimenter et caract eriser les vari et es pour pr ...€¦ · Exp erimenter et caract eriser les vari et es pour pr edire leurs performances dans une large gamme d’environnements

Experimenter et caracteriser les varietes pourpredire leurs performances dans une large gamme

d’environnements

Pierre Casadebaig1, Philippe Debaeke1,Emmanuelle Mestries2, Nicolas Langlade3

1. INRA, UMR ”Agroecologies, Innovation, Ruralites”, Toulouse

2. Terres Inovia, Toulouse

3. INRA, UMR ”Laboratoire Interaction Plantes - Microorganismes”, Toulouse

mailto:[email protected]

Contexte Modele Applications References Contexte Demarche

L’experimentation au coeur de l’evaluation varietale

OfficialRegistration

Year n-2 Year n-1 Year n Year n+1

Extensionadvices

Crop variety evaluation program Agri. extension evaluation program

Breeders

Breeding,

Positionning

GEVES, INRA Terres Inovia,

Co-op and industrial sector

trier : ecarter le materiel moins performant

caracteriser : performance, qualite, sensibilite aux principalesmaladies

Journees d’echanges Tournesol 2016-06-29, Toulouse 2 / 20


Beaucoup de donnees, peu de reutilisationReseau Terres Inovia 2008-2013

42

44

46

48

50

−5 0 5Longitude (°)

Latti

tude

(°)

0

20000

40000

60000

80000area (ha)

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2

3

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015Years

Yie

ld (

t/ha)

level●

●

●

breeding

technical

farm

I Informations precieuses mais complexes (effets confondus)I Faible variabilite climatique (2-3 ans, peu de diagnostic)I Biais de representativite (effectif, homogeneite) [1]



Le probleme des interactions G × ELes varietes s’adaptent a leur environnement de croissance !

1

2

3

4

5

T09

OLE

0303

4

T09

OLE

1808

8

T09

OLE

6303

1

T09

OLE

8904

2

T09

OLE

3608

6

T09

OLE

4109

2

T09

OLE

1708

5

T09

OLE

4509

1

T09

OLE

1605

9

T09

OLE

7210

0

T09

OLE

4702

8

T09

OLE

3708

9

T09

OLE

8605

1

T09

OLE

8605

6

T09

OLE

3709

9

T09

OLE

3802

9

T09

OLE

3101

9

T09

OLE

8508

2

T09

OLE

3202

4

T09

OLE

4703

1

T09

OLE

6902

3

T09

OLE

8400

9

T09

OLE

8400

7

T09

OLE

3102

3

T09

OLE

3001

6

Location (increasing water deficit)

Ran

k (f

or o

bser

ved

grai

n yi

eld)

I Les classements sont affectes et le conseil est difficile



Tirer parti des interactions pour mieux utiliser la genetiqueEt si l’on pouvait choisir la meilleure variete pour chaque parcelle ?

OfficialRegistration

Year n-2 Year n-1 Year n Year n+1

Phenotype newlyreleased varieties

Characterize growingenvironments

Build recommendationsfrom simulation outputs

Extensionadvices

Crop variety evaluation program Agri. extension evaluation program

Cu

rre

nt

vari

ety

asse

ssm

en

t ch

ain

Mo

de

l-b

ase

d a

pp

roac

h

to

ass

ist

var

iety

te

stin

g

1.

2.

3.

I Generaliser la connaissance des varietes pour une large gammed’environnements et de modes de production [2]



Approche systemique de l’amelioration des cultures [3, 4]Utiliser la simulation pour analyser et predire les performances des cultures

1. Modeliser (2005-2011)• experimenter pour comprendre et

simplifier• predire la variabilite de traits

complexes

2. Explorer (> 2005)• collecter de donnees de reference• experimentation numerique

3. Concevoir (> 2012)• extrapoler le conseil technique• amelioration varietale

Crop model

phenotypic traits

complex traits

ResponsePhenologyMorphology

Management

Plant

Canopy Soil

f(t, P, E, θ)

Climate

Environment

Genotype

f(G) Quantitative genetics

geneticmarkers

Gene

Gen

e-to-p

hen

otype m

odelin

g

Ideotyp

ing

DM = Ri x RIE x RUE


Contexte Modele Applications References Modele Parametres Evaluation

SUNFLO [5, 6]: representer des processus interdependantsLes boucles de regulation permettent de generer des interactions G × E

Environment

Management Climate Soil Initialization

Genetics

SpeciesCultivar

PhenologyLeaf Area

Light Interception

Biomass

Performance

Water Stress Nitrogen StressThermal Stress Radiation Stress



Developpement de la planteDuree des phases de developpement (champ, potentiel)

●

●

750

800

850

900

vegetative fillingPhenologic phase

Dur

atio

n (°

C.d

)



Architecture de la planteDistribution spatiale de la surface foliaire (champ, potentiel)

area

profile



Reponses de la plante au deficit hydriqueAjustement de la transpiration au deficit hydrique (pots, deficit) [7]



Evaluation du modeleCapacite de prediction quantitative et qualitative (reseau Terres Inovia 2009, n=611)

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rmse = 0.19 ; bias = 0.05

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rmse = 0.17 ; bias = −0.1

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rmse = 0.23 ; bias = 0.01

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rmse = 0.22 ; bias = 0.09

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AURASOL ES BIBA

ES ETHIC EXTRASOL

1.0

1.5

2.0

2.5

1.0

1.5

2.0

2.5

1.0

1.5

2.0

2.5

1.0

1.5

2.0

2.5

1.0 1.5 2.0 2.5 1.0 1.5 2.0 2.5

1.0 1.5 2.0 2.5 1.0 1.5 2.0 2.5Observed Oil Yield (t ha−1)

Sim

ulat

ed O

il Y

ield

(t h

a−1)

25

7.1

3.6

7.1

14.3

10.7

3.6

10.7

17.9

C

B

A

C B AActual

Pre

dict

ed

I Erreur moyenne de prediction : 16.3% (5 q/ha); [11, 18]%

I 57.2% : varietes dans la bonne classe, 35.6% : une erreur

I Un effet de la distance site-station climatique sur la prediction


Contexte Modele Applications References Caracterisation Classification Exploration

Suivi du deficit hydrique au niveau de la planteProfils de deficit hydrique sur 83 essais du reseau Terres Inovia 2009

0

20

40

60

0.0

0.3

0.6

0.9

Rain (m

m)

Water deficit

Apr May Jun Jul Aug Sep Oct

I La simulation permet de passer de l’etat du milieu a la plante



La simulation decrit correctement l’etat de la planteL’indicateur de stress simule explique bien le rendement (MET FUI Oleosol)

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r =−0.01

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r =−0.86

Climatic water deficit (mm) Simple water balance (mm) Simulated water stress (d)

2.5

3.0

3.5

4.0

200 300 400 0 50 100 150 200 2500 10 20 30 40Water deficit index

Obs

erve

d gr

ain

yiel

d (t

ha−1

)

year●

●

●

2008

2009

2010

I 17 sites, 3 genotypes temoins (2008-2010)

I Correlation croissante avec le rendement observe



Une combinaison de stress explique mieux le rendementLe fonctionnement de la culture est decrit avec 92 combinaisons de variables × periodes

0.0

0.4

0.8

1.2

400 800 1200 1600ThermalTime (°Cd)

Abi

otic

str

ess

impa

ct o

n ph

otos

ynth

esis

stresstemperature

nitrogen

water

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r =0.91

combined stresses

2.5

3.0

3.5

4.0

2.5 3.0 3.5 4.0

Fitted grain yield (t ha−1)O

bser

ved

grai

n yi

eld

(t h

a−1)

I Prendre en compte une combinaison de stress ameliore encorela comprehension du systeme



Groupement des env. agronomiquement proches83 essais sont regroupes en 4 grands types d’environnements (MET TI 2009)

●

●

●

0

10

20

30

low water cold highEnvironment types

Str

ess

leve

l (da

ys)

Stress factorTemperature

Nitrogen

Water

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Type

●

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●

low

water

cold

high

I 4 types bases sur eau, T◦, azote (hclust, distance Euclidienne)

I Diagnostiquer la diversite reelle des essais



Description des conditions de culturesQuelles donnees pour extrapoler raisonnablement les resultats des reseaux ?

2000000

2250000

2500000

2750000

3000000

3200000 3600000 4000000Longitude (m)

Latti

tude

(m

)

0

100

200

300

AWC

42.5

45.0

47.5

50.0

−5 0 5 10Longitude (°)

Latti

tude

(°)

0.5

1.0

1.5

PET:P

42.5

45.0

47.5

50.0

−5 0 5 10Longitude (°)

Latti

tude

(°)

Sol JRC, European Soil Database (ESDB) [8]

Climat JRC, 25km Gridded Climate Database (Agri4Cast)

Pratiques Terres Inovia, Enquetes postales, Reseau CAR [9]

Performances Terres Inovia, GEVES, Reseau de post-inscription


http://esdac.jrc.ec.europa.eu/content/esdb-derived-data

http://agri4cast.jrc.ec.europa.eu/DataPortal/RequestDataResource.aspx?idResource=7&o=&r=n


Adaptation locale du choix varietalExtrapolation de 83 essais reels vers 2100 essais numeriques [2]

●

●

●●

0.5

0.6

0.7

0.8

0.9

1.0

ET

:PE

T r

atio

61 %

1.2 t ha−1

100 mm

Avignon

68 %

1.3 t ha−1

100 mm

Toulouse

73 %

1.3 t ha−1

100 mm

Poitiers

74 %

1.7 t ha−1

200 mm

Avignon

77 %

1.5 t ha−1

100 mm

Dijon

80 %

1.5 t ha−1

100 mm

Reims

82 %

1.7 t ha−1

200 mm

Toulouse

87 %

1.9 t ha−1

200 mm

Poitiers

89 %

1.9 t ha−1

200 mm

Dijon

92 %

1.9 t ha−1

200 mm

Reims

117.5

115.5

112.3

111.9

110.1

116.4

113.6

113.3

110.8

110.8

115.9

114.8

113.4

111.2

110.7

116.9

115.1

114.2

111.9

111.4

115.3

115.2

112.3

111.4

110.5

115.6

115

112

111.7

110

117.5

114.1

113.7

111.1

109.3

117.4

113.6

113.6

110.9

109.4

117.9

113.8

113.3

111.1

109.1

117.7

114

113.4

110.4

108.8

12

34

5

genotype

VELLOX

EXTRASOL

NK KONDI

SY LISTEO

ES ETHIC

ES BIBA

NK SINFONI

PEGASOL



Challenges a venir

Articuler experimentation in-vivo et experimentation numerique

Utiliser conjointement deux sources de donnees (discussions,assimilation de donnees, metamodelisation, machine-learning)

Utiliser une methodologie commune pour produire desconnaissances pour differents acteurs (instituts techniques etsemenciers)

Mobiliser la simulation pour l’evaluation varietale mais aussi pourla selection (optimisation numerique, gene-based modeling,selection genomique)


Contexte Modele Applications References

References[1] Claire Barbet-Massin. Quelle representativite des reseaux d’homologation varietale et de developpement ? cas du

tournesol. Master’s thesis, Ecole d’Ingenieurs Purpan, 2011.

[2] Pierre Casadebaig, Emmanuelle Mestries, and Philippe Debaeke. A model-based approach to assist varietyassessment in sunflower crop. accepted in European Journal of Agronomy, 2016.

[3] Graeme Hammer, Mark Cooper, Francois Tardieu, Stephen Welch, Bruce Walsh, Fred van Eeuwijk, Scott Chapman,and Dean Podlich. Models for navigating biological complexity in breeding improved crop plants. Trends in PlantScience, 11(12):587–593, December 2006.

[4] Xinyou Yin, Paul C. Struik, and Martin J. Kropff. Role of crop physiology in predicting gene-to-phenotyperelationships. Trends in Plant Science, 9(9):426–432, September 2004.

[5] Pierre Casadebaig, Lydie Guilioni, Jeremie Lecoeur, Angelique Christophe, Luc Champolivier, and Philippe Debaeke.SUNFLO, a model to simulate genotype-specific performance of the sunflower crop in contrasting environments.Agricultural and Forest Meteorology, 151:163–178, 2011. ISSN 0168-1923. doi:10.1016/j.agrformet.2010.09.012.

[6] Jeremie Lecoeur, Richard Poire-Lassus, Angelique Christophe, Benoit Pallas, Pierre Casadebaig, Philippe Debaeke,Felicity Vear, and Lydie Guilioni. Quantifying physiological determinants of genetic variation for yield potential insunflower. SUNFLO: a model-based analysis. Functional Plant Biology, 38(3):246–259, 2011. ISSN 1445-4416. doi:10.1071/fp09189.

[7] Pierre Casadebaig, Philippe Debaeke, and Jeremie Lecoeur. Thresholds for leaf expansion and transpiration responseto soil water deficit in a range of sunflower genotypes. European Journal of Agronomy, 28:646–654, 2008. doi:10.1016/j.eja.2008.02.001.

[8] Roland Hiederer. Mapping soil properties for europe: Spatial representation of soil database attributes. Technicalreport, JRC, Luxembourg: Publications Office of the European Union, EUR26082EN Scientific and TechnicalResearch series, ISSN 1831-9424, 2013.

[9] Julien Sarron. Diagnostic agronomique sur les causes de ralentissement de la progression des rendements dutournesol en france. Master’s thesis, 2016.


Contexte Modele Applications References

Merci !

CASDAR, FUI, PROMOSOL, Region

Midi-Pyrenees, ANR, Terres Inovia et ses

equipes techniques !

Equipes de recherche

AGIR AGroecologies, Innovations et Ruralites (UMR1248), INRAEA, Toulouse

MIAT Mathematiques et Informatique Appliquees de Toulouse,(UR875), INRA MIA, Toulouse

LIPM Laboratoire Interaction Plantes - Microorganismes,(UMR441), INRA SPE, Toulouse


http://www6.toulouse.inra.fr/agir_eng

http://mia.toulouse.inra.fr

http://www6.toulouse.inra.fr/lipm

Documents

Exp erimenter et caract eriser les vari et es pour pr ...€¦ · Exp erimenter et caract eriser les vari et es pour pr edire leurs performances dans une large gamme d’environnements