Andy Challinor A.J.Challinor@leeds.ac.uk

1. Institute for Climate and Atmospheric Science Forecasting food in China: the influence of climate, composition and socio-economics Andy Challinor A.J.Challinor@leeds.ac.uk Co-authors: Evan Fraser, Steve Arnold, Sanai Li, Elisabeth Simelton

2. South Asia China Lobell et al. (2008). Prioritizing Climate Change Adaptation Needs for Food Security in 2030. Science 319.

3. Turner, B. et al. (2003) A framework for vulnerability analysis in sustainability science. PNAS. 100,4, 8074-8079.

4. Progress in modelling food crop production Simulating a range of impacts based on socio-economic scenarios Adaptation, e.g. through choice of crop genotype Quantifying biophysical uncertainty: climate and crop yield ensembles Linking crop yield and climate prediction models to assess impact Agrometeorology

5. Qualitative approach to food systems research Attempts to generalize field studies and link with global drivers. DFID’s “sustainable livelihoods approach” that looks at how different types of capital are used to obtain food. Focus on ways people obtain food.

6. ? Qualitative approach to food systems research Biophysical Modelling

7. Climate impacts and adaptation in China Can wheat yield be simulated using a crop model driven by regional climate model (PRECIS) output? What are the drivers of current and future yields? Is adaptation needed?

8. Tests at two locations showed better model-observation agreement for rainfed simulations than irrigated Wheat cultivation in China Winter wheat is partially irrigated in some regions of China (no quantitative data available)

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

10. Current climate: simulated wheat yield as a function of seasonal total rainfall in China

11. Baseline Grain-filling occurs after flowering Climate change: temperature limitations on yield of winter wheat

12. Increase in simulated wheat yield (%) in response to a doubling of CO2 from 350 to 700 ppm in China (No associated climate change) Two plausible responses to a doubling of CO2

13. The ‘net’ effect of climate change in the North China Plain: Results qualitatively similar for A2 and B2 scenarios Mean yield from: Interannual variability of yield: CV up by ~10-20% across NCP. Winter wheat

14. Baseline Causes of north/south difference: • Increase in the amount of seasonal precipitation in the north • associated decrease in soil water stress • Lengthening of period between flowering and harvest in the north, decrease in much of the south • Super-optimal temperatures • Earlier flowering whilst temperatures are increasing => cooler (sub-optimal) post-flowering temperatures

15. Genotypic adaptation to climate change Which genotypic properties are needed to adapt to climate change? Do these properties exist in the current germplasm?

16. 0% Increase in thermal time requirement 10% 20% Ensemble methods: genotypic adaptation to changes in mean temperature, using QUMP Response to climate change, from over 180,000 crop simulations for one location • Graph suggests 20% increase in TTR is needed • Further simulations and analysis of crop cardinal temperatures suggest a 30% increase may be needed • Simple analysis of field experiments suggests the potential for a 14 to 40% increase within current germplasm Simulation count Challinor et al., 2008b Percentage change in yield

17. ? 0% - ?% • Potential for a 14 to 40% increase within current germplasm Looking across India: what is the adaptive capacity contained within current germplasm? Yield reduction > 50% 20-30% < 10% Area affected Upper estimate Challinor (2008): GECAFS proceedings

18. Looking across India: what is the adaptive capacity contained within current germplasm? Yield reduction > 50% 20-30% < 10% Area affected Upper estimate ? Challinor (2008): GECAFS proceedings

19. What ‘new’ processes will limit yield in the future?

20. Industrial emissions resulting in increased surface ozone are predicted to rise. • Predictions for China particularly high. • Ozone lowers the photosynthetic rate and accelerates leaf senescence • ~5% yield reductions currently; 30% in 2050? • Few crop field studies with O3 carried out in the tropics See e.g. Long et al. (2005); Slingo et al. (2005) Crops and atmospheric composition: O3

21. Future air quality and climate closely linked Probability of max 8-h O3 > 84 ppbv vs. daily max. T (USA) Lin et al. (Atm. Env., 2001) • Correlation of high ozone with • increasing temperature is driven by: • Stagnation in the boundary layer, • biogenic hydrocarbon emissions, • chemical reaction rates, • deposition How will these processes interact to determine future air quality in China?

22. Atmospheric composition modelling at Leeds TOMCAT surface ozone (23 June 2008) • TOMCAT • - State-of-the-art 3D global chemistry-transport model • - Offline, so ideal for process studies, comparison with observations, parameterisation development. • UKCA • - Collaboration between universities and Met Office • - Coupled climate-chemistry-aerosols • - Ozone photochemistry coupled to climate and land-surface • - Coupled ozone deposition fluxes and climatic drivers for future

23. Composition-climate-crop strategy TOMCAT ozone fields Offline studies (no climate-chemistry coupling) for evaluation of parameterisations GLAM with O3 flux parameterisation Stomatal deposition parameterisation for vegetation/crop type Yield Climate drivers (analyses)

24. Composition-climate-crop strategy Coupled (climate-chemistry) studies for prediction UKCA Stomatal ozone flux Surface ozone GLAM with O3 flux parameterisation Yield Land surface scheme Climate drivers From Oct 2008: PhD student joint with Met Office – will work on ozone-vegetation interactions using TOMCAT and UKCA

25. Composition-climate-crop strategy Coupled (climate-chemistry-crop) studies: importance of land use and patterns of deposition UKCA Stomatal ozone flux Surface ozone GLAM with O3 flux parameterisation Yield Land surface scheme Climate drivers From Oct 2008: PhD student joint with Met Office – will work on ozone-vegetation interactions using TOMCAT and UKCA

26. Qualitative approach to food systems research Attempts to generalize field studies and link with global drivers. DFID’s “sustainable livelihoods approach” that looks at how different types of capital are used to obtain food. Focus on ways people obtain food.

27. Analyses of socio-economic drivers of crop productivity • Will farmers have access to the genotypes needed for adaptation? • What characteristics make a food production system vulnerable or resilient to environmental change?

28. Small problem big impact - did not adapt Health impacts Vulnerable Impact of environmental change Economic Impacts Big problem small impact - managed to adapt Resilient Harvest Impacts Exposure (e.g. to droughts of different severity)

29. Invest in other agr activities Vulnerable Doublecropping Fertiliser, Machinery Agr production capital, Invest in agr, GDP share of agr Rural population Increasing impact Infrastructure RESILIENT Electricity Wheat Increasing exposure

30. Conclusions

31. South Asia China Lobell et al. (2008). Prioritizing Climate Change Adaptation Needs for Food Security in 2030. Science 319.

32. Sustainability Science approach to food systems Adaptation More processes (ozone) Uncertainty Impacts Processes Correlations • Starting to happen: • NERC QUEST • ESRC Centre for Climate Change Economics and Policy Increasingly quantitative Decreasingly site-specify Increasingly relevant Qualitative approach to food systems research Biophysical Modelling

34. Summary of observed and modeled increase in wheat yield in response to elevated CO2

35. Vulnerability trend 1960s-2001 No increase in double cropping (only land increase) Low rural labour - inefficient land use. Land conversion projects: from wheat to rice Lowest per capita investments in agriculture (highest double cropping). Guangxi highest mean VI (wheat) of all. Wheat

36. “Food prices are rising on a mix of strong demand from developing countries; a rising global population; more frequent floods and droughts caused by climate change; and the biofuel industry’s appetite for grains, analysts say.” Also: rising input prices (oil, fertiliser) and speculation (e.g. based on expected demand for biofuel)