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The avoided impacts of climate change on crop production. Tom Osborne Thanks to: Simon Gosling, Gillian Fraser, Helen Greatrex , Tim Wheeler, Nigel Arnell. Emissions scenarios. A1B, and AVOID mitigation scenarios. CO 2 concentration. MAGICC @ 2030,2040,2050,…. Global warming.
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The avoided impacts of climate change on crop production Tom Osborne Thanks to: Simon Gosling, Gillian Fraser, Helen Greatrex, Tim Wheeler, Nigel Arnell
Emissions scenarios A1B, and AVOID mitigation scenarios CO2 concentration MAGICC @ 2030,2040,2050,… Global warming Local change in climate Pattern-scaling based upon GCMs echam5, hadcm3, ipsl, cgcm31,ccsm30 Future crop yields GLAM crop model at 0.5 resolution Maize, soybean, wheat Regional crop production ∑(YieldGLAM x Areaobserved)
Global-GLAM crop model • Original GLAM developed by Challinor et al (2004). Process-based crop growth and development with daily timestep. • Crops: soybean, maize, wheat. • Climate: 0.5 resolution using CRU based climate data. Daily weather generator. • Extent: suitable grid cells and sowing date determined by separate algorithm. • Varieties: 3 for maize and soybean, 1 for wheat • Rain-fed simulation only. No irrigation.
Business-as-usual impact of climate change by 2050 on wheat production
Percentage of 2050 “business-as-usual” impact avoided with scenario: A1B-2016-5-L
CO2 fertilisation alters effectiveness (relative and absolute) of AVOID scenarios
Conclusions • Wheat • Effect of mitigation varies regionally • CO2 fertilisation influences effectiveness of mitigation • Interaction with climate change • Maize • Relative effectiveness of scenarios unaffected by GCM or adaptation (as represented here) • Soybean • Mitigation effectiveness insensitive to CO2 and adaptation • Positive CC impacts (S Asia) reduced by mitigation
1961-90 baseline: maize Spatial variability Inter-annual variability