Towards a genotypic adaptation strategy for Indian groundnut using

model ensembles

Julian Ramirez-VillegasAndy Challinor

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

• 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

AdaptationChanges 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 adaptationInvolves 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)

Timing of transformational adaptation in sub-Saharan African agriculture

Rippke, U; Ramirez-Villegas, J. et al. 2016. Nature Climate Change, doi:10.1038/nclimate2947

The role of adaptation• Gains from adaptation ~7-15 %, least effective

for maize

Challinor et al. (2014) NCC

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

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 groundnutTraits: improved water use efficiency, improved partitioning, heat tolerance, duration

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 benefit5. Understand robustness and uncertainty in

model projections

Errors and uncertainty in regional scale simulations

Ramirez-Villegas et al. (2015) Eur. J. Agron., doi: 10.1016/j.eja.2015.11.021

DELTA NUDGING LOCI

HIST

ORI

CAL

CHAN

GE (2

030s

, RCP

4.5)

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

Impacts without adaptation

Yield impacts without adaptation

Yield change to 2030Yes! 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?

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

Benefits of genotypic adaptation– Mean yield

Drought management

Duration Extremes

Benefits of genotypic adaptation– Yield variability

Drought management

Duration Extremes

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

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

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.