1. Development of a combined crop and climate forecasting system Tim Wheeler and Andrew Challinor t.r.wheeler@rdg.ac.uk Crops and Climate Group

2. A combined crop and climate forecasting system Report from: ‘Food Crops in a Changing Climate’

3. Linking climate information to crop models general circulation model crop model At what scale should information pass between crop and climate models?

4. Find spatial scale of weather-crop relationships Crop modelling at the working spatial scale Hindcasts with observed weather data Ensemble methods Climate change Challinor et. al. (2003) Development of a combined crop / climate forecasting system Challinor et. al. (2004) (Challinor et al, 2004) and reanalysis (Challinor et al, 2005a) (Challinor et al, 2005b,c) (2005c,d) Fully coupled crop- climate simulation Osborne (2004)

5. Simple correlations between rainfall and crop yield Seasonal rainfall and groundnut yields for all India. Time trend removed.rainfallyield

6. Patterns of seasonal rainfall and yield of groundnut in India District level groundnut yields (kg ha -1 ) Mean of 1966 - 1990 Data source: ICRISAT

7. Patterns of seasonal rainfall and yield of groundnut in India Sub-divisional level seasonal rainfall (JJAS, cm) Mean of 1966 - 1990 Data source: IITM

8. Aims to combine: –the benefits of more empirical approaches (low input data requirements, validity over large spatial scales) with –the benefits of a process-based approach (e.g. the potential to capture intra-seasonal variability, and so cope with changing climates) Uses a Yield Gap Parameter to account for the impact of differing nutrient levels, pests, diseases, non-optimal management to simulate farm yields General Large Area Model for Annual Crops (GLAM) Challinor et. al. (2004)

9. Hindcasts of groundnut yield for all India using GLAM

10. Capturing the effects of intra-seasonal variability 1975 Total rainfall: 394mm Model: 1059 kg/ha Obs: 1360 kg/ha 1981 Total rainfall 389mm Model: 844 kg/ha Obs: 901 kg/ha

11. Using ERA40 reanalysis data Andhra Pradesh Gujarat Gujarat: bias correction of climatological mean rainfall works well - Correlation with observed yields 0.49  0.60 Andhra Pradesh: simulated mean yield > observed - Incorrect seasonal cycle (both mean and variability) though Jun and Sept good. This is harder to correct.

12. Using probabilistic climate forecasts Use of DEMETER multi-model ensemble for groundnut yield in Gujarat, 1998 from Challinor et al (2005) Model average63 ensemble members Observed 775 kg ha -1 713 kg ha -1

13. Probabilistic forecasting of crop failure The number of ensemble members predicting yield below a given threshold is an indication of probability of occurrence Found predictability in crop failure

14. Current risk is dominated by water stress; in the future climate run temperature stress dominates in the north. The impact of water and temperature stress at flowering under climate change 1960-1990 1 = no impact 0 = max. impact 2071-2100 Hadley Centre PRECIS model, A2 (high emission) scenario Groundnut

15. Variety response to temperature stress alone under climate change Hadley Centre PRECIS model, A2 (high emission) scenario 2071-2100 Number of years when the total number of pods setting is below 50%. Sensitive varietyTolerant variety

16. An integrated approach to climate impact assessments Crops can modify their own environment –The water cycle and surface temperatures vary according to land use Integrate biological and physical modelling –By working on common spatial scale –By fully coupling the models

17. Fully coupled crop-climate simulation Crops ‘growing’ in HadAM3

18. All-India groundnut yield (red) with simulated mean yield (black) and spatial standard deviation (grey shading). Fully coupled crop-climate simulation

19. Using satellite estimates of rainfall TAMSAT Teo Chee-Kiat David Grimes

20. Conclusions A combined crop and climate modelling system has been developed and tested for the current climate. –It shows skill in seasonal hindcasts and with climate ensembles –It has been used to study crop responses to climate change –Can be fully coupled to a GCM, and driven by satellite data