JRV presents on a study about genotypic adaptation at the crop modelling symposium (iCropM) in March 2016.

1. Towards a genotypic adaptation
strategy for Indian groundnut using
model ensembles
Julian Ramirez-Villegas
Andy Challinor
Ramirez-Villegas and Challinor, Climatic Change (in revision)

2. • Introduction
– Key concepts
– Climate change impacts on agriculture
– The importance of adaptation and of genotypic
adaptation
• An ensemble approach to designing
genotypic adaptation strategies
Outline

3. Adaptation
Changes in social-ecological systems in response
to actual and expected impacts of climate
change in the context of interacting nonclimatic
changes (Moser and Ekstrom, 2010 PNAS)
Genotypic adaptation
Involves the incorporation of novel traits in crop
varieties so as to enhance food productivity and
stability and, more broadly, also the design of
crop ideotypes (i.e. crop plants with ideal traits)
for future climates (Ramirez-Villegas et al. 2015
J. Exp. Bot)

4. Timing of transformational adaptation in sub-
Saharan African agriculture
Rippke, U; Ramirez-Villegas, J. et al. 2016. Nature Climate Change, doi:10.1038/nclimate2947

5. The role of adaptation
• Gains from adaptation ~7-15 %, least effective
for maize
Challinor et al. (2014) NCC

6. The importance of genotypic adaptation
Ramirez-Villegas et al. (2015) JXB, doi: 10.1093/jxb/erv014
Model-based estimates
of potential benefit
from crop improvement

7. An ensemble approach to designing
genotypic adaptation strategies
• General Large Area Model
for annual crops (GLAM)
• Projections as ensemble of:
– Parameters
– Climate models (GCMs)
– GCM bias correction
methods
– CO2 response
• One forcing scenario
(RCP4.5) and time period
(2030s)
Focus on Indian
groundnut
Traits: improved water use
efficiency, improved
partitioning, heat tolerance,
duration

8. Methodology steps
1. Calibrate and evaluate model in a historical
period.
2. Model historical and future yields (2030s,
RCP4.5) to quantify climate change impacts
3. Review and map traits onto GLAM parameter
space
4. Quantify genotypic improvement benefit
5. Understand robustness and uncertainty in
model projections

9. Errors and uncertainty in regional
scale simulations
Ramirez-Villegas et al. (2015) Eur. J. Agron., doi: 10.1016/j.eja.2015.11.021

10. DELTA NUDGING LOCI
HISTORICALCHANGE(2030s,RCP4.5)
Ramirez-Villegas and Challinor, Climatic Change (in revision)
Impacts without adaptation

11. Yield impacts without adaptation
Yield change to 2030
Yes! We know there is uncertainty:
but how much, and where does it
matter?
Lower Q
Mean
Upper Q
Reduction in terminal drought + potential to
capitalise with improved WUE genotypes
?? Uncertainty driven by rainfall signal.
Heat stress during reproduction relevant
to a number in simulations -models
don’t hold all answers!!
A frequent decrease in crop duration and
available water (simultaneously). Higher
partitioning? Dec. veg. + inc. grain filling
duration?

12. Ramirez-Villegas and Challinor, Climatic Change (in revision)

13. Benefits of genotypic adaptation
– Mean yield
Drought
management
Duration Extremes

14. Benefits of genotypic adaptation
– Yield variability
Drought
management
Duration Extremes

15. Robustness and uncertainties in
genotypic adaptation options
• R>0.5: moderately robust projections
• R>0.8: very robust projections
Low GLAM skill –
model
improvement
Very low cropping
intensity

16. Robustness and uncertainties in
genotypic adaptation options
• Climate (54 %) and crop (46 %)
contribute similarly to total uncertainty
• GCM structure and GLAM parameters
are main sources of variation
• CO2 a minor source
• Interactions between factors could be
important

17. Key messages
• Uncertainty analysis revealed robust model
outcomes in many situations.
• Heat stress NOT a major stressor. First
breeding cycle should keep focus on
drought. Duration traits seem key, and also
max. assimilation rate.
• Future work to focus on improving links
between simulated physiology and genetic
information.