1. GLAM-wheat modelling in China Sanai Li Supervisors: Prof. Tim Wheeler, Dr Andrew Challinor Prof. Julia Slingo, Crops and Climate Group

2. Outline Background GLAM-Wheat model evaluation Impact of temperature on wheat Impact of future climate change on wheat

3. Background 1 Assessment of regional crop production is critical, especially for China

4. Background 2 Crop model at the field level Complexity of incorporating the spatial variability of input High input data requirement Climate model output is coarse compared with input to the dynamic crop model (Yang.P). This method is difficult to apply at a regional level

5. Development of a large area wheat model- GLAM-Wheat GLAM-General Large-Area Model for annual crops \  Defining the wheat parameter sets  Quantifying the impact of temperature on crop  Parameterising the CO 2 fertilisation effect

6. Hadley Centre RCM system-Providing Regional Climates for Impacts Studies - PRECIS From the first China-UK Collaboration Project Observed Annual Tmean ( o C), 1961-90Simulated Annual Tmean ( o C), 1961-90 Observed Annual Precipitation (mm/day), 1961-90Simulated Annual Precipitation (mm/day), 1961-90

7. Comparison of wheat yield between observations and simulations at the county level

8. Correlation between observed and simulated yield at the county/city (70-129km) level and field level Significant level Spring wheat Winter wheat

9. Comparison of simulated and observed wheat yield (kg/ha) at 0.5 o scale across China (a) Observations (b) Simulations

10. Difference between observed and simulated mean wheat yield (%) (correlation r= 0.83,p<0.001)

11. Cardinal temperature values for selected annual crops under conditions in which temperature is the only limiting variable

12. The observed impact of temperature on wheat Tendency of temperature from 1951 to 2001(Ren et al, 2003)

13. The simulated response of wheat yield (%) to an increase in temperature ( o C) in China T+1 T+2

14. Wheat yield change (%) for per 1 o C rise in temperature MethodsLocationYield change(%) Observations Global (1961-2002)-3.2 to -8.4 Lobell and Field, 2007 CERES-Wheat model South Asia Central America Brazil -2.6 ± 0.7 -5.1 ±1.8 -7.1 ±2.4 Lobell et al., 2008 GLAM-Wheat model China-4.6 to -5.7

15. Changes in average precipitation (mm day -1 ) from PRECIS for 2071 to 2099 under the A2 scenario relative to the baseline (1961-1990) (a) A2 Annual (b) A2 DJF (c) A2 MAM (d) A2 JJA(e) A2 SON

16. (a) A2 Annual (c) A2 JJA(b) B2 Annual (d) B2 JJA Changes in the annual and summer mean temperature ( o C) (relative to 1961-1990) for the A2 and B2 scenarios during 2071 to 2099.

17. The predicted change in the average winter yield during 2072 to 2100 for the A2 and B2 scenarios, relative to the baseline (1961-1990) A2 without the CO 2 effectA2 with the CO 2 effect B2 without the CO 2 effectB2 with the CO 2 effect

18. Predicted changes (relative to baseline) in the coefficient of the variation of winter wheat yield for 2072 to 2100 in the North China Plain

19. Conclusions The GLAM model is suitable to simulate crop yield at large scales (approximately 100 km) for regional area, county and global studies of the impacts of climate change Across China, the simulated average wheat yield would reduce by 4.6-5.7% for each 1 o C rise in mean temperature. Without the CO 2 effect, wheat yield by 2100 is expected to increase by 20-50% in the north of the North China plain, and reduce by 10- 40% in the south. The CO 2 effect tends to offset the negative impact. The variability of wheat yield is expected to increase due to an increase in climate variability.

20. Thanks