Xavier Draye, Université Catholique de Louvain, Belgium
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EPSO: The European Plant Science Organisation EPSO Workshop on Plant Phenotyping November 02-03, 2009 Forschungszentrum Jülich, Germany Forschungszentrum Jülich, Germany ICG-3: Phytosphere Jülich Plant Phenotyping Centre (JPPC) Website: http://www.jppc.de http://www.plantphenomics.com/phenotyping2009 Xavier Draye, Université Catholique de Louvain, Belgium Model-assisted phenotyping of root system construction and function
Transcript of Xavier Draye, Université Catholique de Louvain, Belgium
EPSO: The European Plant Science OrganisationEPSO Workshop on Plant PhenotypingNovember 02-03, 2009Forschungszentrum Jülich, Germany
Forschungszentrum Jülich, GermanyICG-3: PhytosphereJülich Plant Phenotyping Centre (JPPC)Website: http://www.jppc.de
http://www.plantphenomics.com/phenotyping2009
Xavier Draye, Université Catholique de Louvain, Belgium
Model-assisted phenotyping of root system construction and function
Model-assisted phenotyping of root system architecture and function
Xavier DrayeGuillaume LobetMathieu Javaux
Crop Physiology and Plant BreedingSoil and Water ResourcesUniversité catholique de Louvain, Belgium
Juelich, 2 november 2008
Outline
Part 1. Phenotyping of root system architecture
Trends: throughput, dynamic, modeling
Part 2. Functional phenotyping (water capture)
Getting plant biology and soil hydrodynamics together
Part 1. RSA phenotyping platforms
Evolution in the level of phenotyping details
-2000:Root mass, deep root mass, root volume
2000-today:Number and length of root axesLateral root length and densitySurrogates for growth rate
Root axes: LAUZLateral roots: conical aperture
Root anglesDirect growth rate measurements
future:3D entering the sceneMore dynamics
Current demand: capturing genetic variability with high throughput
Which level of detail?
Maximum leaf length (mm) Maximum axis length (mm) Axis diameter (mm)
Number of tillers Number of axes LAUZ (mm)
Leaf dry weight (g) Total length of axes (mm) LR density (mm-1)
HT [#10, 6-19%]NT [#17, 4-43%]SDM [#16, 4-24%]
MAxL [#8, 7-15%]NAx [#12, 6-37%]TAxL [#11, 6-19%]
AxØ [#1, 15%]LAUZ [#3, 10-22%]LaL [#2, 14-17%]
LaØ [#1, 12%]LaDens [#1, 10%]LaLR [#2, 13-29%]
Detailed phenotyping is worth the effort
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LengthGrowth
Dreaming of throughput and dynamics (Arabidopsis… again)
X. Draye & G. Beemster, unpublished
Root imaging in rhizotrons
Soil-grown plants, transparent tubes
High resolution images
Length & area → diameter
(e.g. Lemnatec©, Traitmill©)
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© Lemnatec
New in the field: statistical modeling approach
Flat filter-paper substrate
Root scan (every 2 days)
Length-diameter histograms (WinRHIZO)
Statistical model
Growth parameters estimates
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Another one: explicit architectural modeling
RootTyp (PlantSoil 258:103)
Another one: explicit architectural modeling
Strategy:
1. Time-dense phenotyping2. Target a small set of
parameters (distribution)3. Simulate realistic root systems (many)4. Calculate… calculate… calculate
- Age-specific information- Hydraulic architecture- …
Towards automated phenotyping
BenefitsHigh resolutionTime-lapseRoot-background separation
IssuesRoot joiningOverlap in dense regionsRegistration not possible
StrategyFocus on few rootsSkeletonize → Vector-based[SmartRoot software]
ImprovementsBetter separation from the topInteractive image mining
Part 2. Functional phenotyping
Water demand
Distribution ofroot uptake activity
Distribution ofsoil resources
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RSA
Working hypotheses in root water capture
Spatial 1D approach
Root length density (RLD) and root depthBasis for irrigation scheduling, crop growth models
Typical working hypotheses (observations):
Water uptake usually proportional to RLD in wet soilsDeeper roots lead to improved access to water under drought
Motivations for a 3D approach
RSA is closer to underlying biological processes than RLDRSA is the backbone of hydraulic architectureOpportunities to address fine-scale phenomenonsUnderstanding/sorting of key parameters or mechanismsCan be ultimately reduced to lower dimensions
Modeling approach of the soil/root hydraulic architecture
θ(x, y, z, t)ψ(x, y, z, t)Ks(x, y, z, t)
{ node: (x, y, z, d, age, parent) }ψx(node, t)Kr(node, t)Ka(node, t)
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ψs ψx ψx
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Hypotheses: Negligible osmotic gradientNegligible capacity
Boundary conditions:Flux or water potential at the collarWater potential of the deep soil
Modeling approach of the soil/root hydraulic architecture
Light transmission experiments
NIR estimation of surface θ
0h 2h 4h 6h 8h
© Lemnatec
Experimental dataset
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Transpiration
Soil hydraulics (θ - ψ)3D structure
Time series of soil water content maps
Root hydraulics (Kr, Kx)
Typical output of the model: from ∆θ to sink term
Root properties
Soil structureand properties
Inverse modeling: Estimating unknown parameters
( )ji
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Inverse modeling provides estimates of biological parameters
Thank you for your attention...
QTL analysis : Sophie de Dorlodot, René Civava, Pierre Faux, Jean-François Dumasy,Charlotte de Mey, Bill Thomas, Brian Forster
Aeroponics : Tristan Lavigne, Aurélie babé, Beata Orman, Imaging : Eric Aussems, Olivier MonnartModeling : Loïc PagèsSupporters : Achim Walter, François Tardieu, Tom Beeckman
Gerrit Beemster, Malcolm Bennett,…
Funding Fonds National de la Recherche ScientifiqueUniversité catholique de LouvainCommunauté française de BelgiqueBelgian Scientific Policy
