JRV – Towards a groundnut genotypic adaptation strategy
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Transcript of JRV – Towards a groundnut genotypic adaptation strategy
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Towards a genotypic adaptation strategy for Indian groundnut using
model ensembles
Julian Ramirez-VillegasAndy Challinor
Ramirez-Villegas and Challinor, Climatic Change (in revision)
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• 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
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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)
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Timing of transformational adaptation in sub-Saharan African agriculture
Rippke, U; Ramirez-Villegas, J. et al. 2016. Nature Climate Change, doi:10.1038/nclimate2947
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The role of adaptation• Gains from adaptation ~7-15 %, least effective
for maize
Challinor et al. (2014) NCC
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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
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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
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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
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Errors and uncertainty in regional scale simulations
Ramirez-Villegas et al. (2015) Eur. J. Agron., doi: 10.1016/j.eja.2015.11.021
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DELTA NUDGING LOCI
HIST
ORI
CAL
CHAN
GE (2
030s
, RCP
4.5)
Ramirez-Villegas and Challinor, Climatic Change (in revision)
Impacts without adaptation
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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?
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Ramirez-Villegas and Challinor, Climatic Change (in revision)
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Benefits of genotypic adaptation– Mean yield
Drought management
Duration Extremes
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Benefits of genotypic adaptation– Yield variability
Drought management
Duration Extremes
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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
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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
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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.