Modeling ApproachScenario source selection
– Quality check: simulations with hindcasts vs historical climate– Present, 2030 pessimistic, 2030 optimistic?=> Establishment of operational scenario database
Global study– Simple model (GLAM)– Spatialized gridded approach– No detail of varietal differences=> Global mapping of crop response to CC
Zoom-ins: virtual experiments– GxExM model (SAMARA, RIDEV, CROPGRO…)– Model calibration for key varietal types– Identification of trait (crop parameter) ranges– Zooming in on TPEs for each crop (Total of 10-12?)– Sensitivity analyses: trait variation vs environmentÞ Ideotype composition for adapted crops
Need 3 types of crop modelsConsensus tools to translate environment scenarios– Accurate impact prediction to guide policy– Seasonal forecasting, robust standards for yield insurance – Set rational long-term priorities in research
GxExM models to assist in technology generation– TPE characterization– Ideotype concepts for breeding strategies– Extrapolation of technologies
Heuristic model application in phenotyping– Intelligent phenotyping: Extract G from GxExMxN(oise)– Extract « hidden traits » from simple plant observations– Genotypic reaction norms (behavioural traits)
Crop model skills
Crop Type RIDEV ORYZA EcoMeristem SAMARA
Rice Flooded-irrigated
Rainfed-lowland
Upland
Sorghum Grain
Bio-EtOH (FF)
Feature Trait
Phenology Photoperiodism
Thermal response
Microclimate resp.
Architecture Phyllochron
Organ size & Nb
Tillering
Yield GY
GYC
Stem sugar
Biomass
Water stresses Drought
Water logging
Submergence
Thermal stresses Cold sterility
Heat sterility
Avoidance: TC
Avoidance: TOF
Salinity Salt tolerance
CO2 response TE, Amax
Canopy heating
Resource use WUE
NUE
RUE
Green = availableOrange = coming
Saint-Louis
Rosso
Matam
Tillaberi
Sorghum varietal Zoning for W Africa
Irrigated rice cropping calendars in the Sahel
Environmental challenges (1)
Migration of agro-climatic zones• Latitudinal & altitudinal migration• Cropping calendars, crop phenology
– Change in comparative advantage of crop/system
– Change in comparative advantage of different land uses
– Change in pressure on agro-ecologies and natural resource base
=> Trust in adaptation capacity of markets and stake holders
=> Anticipate, inform, assist Þ Let policies ease the transition
Effect of tallness + lateness• Biomass + 44%• Grain yield – 45%• more tillers, more mortality• LAI 3 => 7• Sugar reserves much smaller
High yielding, dwarf, early, sweet typePlant Height 2.0 m LAI
Tillers
Sugars
GY
2
1
0
15
10
5
0
Virtual varieties (sorghum):Impact of trait modification
2 traits changed:plant height & photoperiodismincreased(4.8 m, + 40 d)
7
6
5
4
3
2
1
0
20
15
10
5
0
Ic
90d
130d
7060
50
50607050
60
70
506070
Culms/hill
agDM
LAI
GY
706050
InternodeNSC
SAMARA: Short phyllochron improves vigor but not GY
SAHEL108 in WS 2010 at AfricaRice, Senegal (source limited situation)Phyllochron 50 °Cd: fast-DRPhyllochron 60 °Cd: ‘normal’Phyllochron 70 °Cd: slow-DR
FPI MS
Confirmed by
phenotyping r
esults
Merci